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      Reply to the reviewers

      Reviewer #1

      In this article, Amico et al. explore how Spindly self-regulates its interaction with Dynein-Dynactin. They propose that Spindly adopts an auto-inhibited, closed conformation that blocks the CC1 box and Spindly motif, preventing its interaction with dynein-dynactin. The authors used a combination of X-ray crystallography, biochemistry, and structure predictions to detail the intramolecular interactions in Spindly that mediate this closed state. They then use analytical SEC to test their proposed auto-inhibition mechanism by monitoring Spindly binding to the pointed end complex. They suggest that auto-inhibited Spindly is unable to bind Dynein-Dynactin regardless of the presence or absence of Spindly's cargo, the RZZ complex. In contrast, by using mutagenesis to prevent this auto-inhibition, the authors show that uninhibited Spindly can interact with members of the Dynein-Dynactin complex. Finally, they use cellular experiments to show that relieving autoinhibition prevents the proper localization of Spindly and Dynein-Dynactin to kinetochores during mitosis, likely due to the formation of ectopic Spindly-Dynein-Dynactin complexes in these cells.

      This is an interesting paper that provides important insights into the mechanism of Spindly regulation and its associations with its interacting partners. However, additional work is necessary to support some of their conclusions. In addition, the text is at times quite dense and harder to follow, which prevents their findings as being impactful as could be possible for the bigger picture paradigms of kinetochore function.

      We thank the reviewer for a supportive assessment and for raising some concerns that we have now fully addressed in our revision.

      Major Points:

      The crosslinking and mass photometry experiments are done at highly differing concentrations (5 μM vs. 10 nM). The mass photometry should be performed at the same concentration as the crosslinking experiments to determine if Spindly forms a higher order oligomer at the higher concentration. These results will aid in the interpretation of the crosslinking mass spectrometry experiments, as the observed interactions could be intermolecular contacts rather than intramolecular contacts if Spindly is tetrameric at these concentrations, as is suggested in figure 4E for specific Spindly constructs.

      We thank the reviewer for raising this point. Mass photometry (MP) requires very low sample concentrations as it is essentially a single molecule technique, and therefore the particle density cannot be increased arbitrarily. To assess whether the Spindly construct is prevalently tetrameric at the concentration of the crosslinking experiment, we performed the crosslinking experiments at the standard concentration, and only then diluted the samples and performed MP measurements. The results, displayed in two new panels (Figure 1 – Supplement 1M-N), show that crosslinked samples are primarily dimeric, providing further evidence that we are looking at bona fide intra-dimer contacts.

      In figure 2, more conclusive evidence is needed to show that full length Spindly does not form a complex with Dynein-Dynactin. My interpretation of the gels in figure 2D suggests that full length Spindly does form a complex with Dynein-Dynactin, as in the final gel (red outline) it looks as if full length Spindly is indeed peaking with the rest of the Dynein-Dynactin proteins, albeit with excess Spindly eluting later. Figure legends containing protein concentrations used in SEC assays would aid in the interpretation of this data.To conclusively show that full length Spindly doesn't form a complex with Dynein-Dynactin, additional assays will be necessary, such as pull-down assays, or mass photometry.

      We have now added concentrations of binding species at the relevant points of the figure legends.

      The essence of the reviewer’s concern is that full length Spindly, like BicD2, binds the DD, which would invalidate our model that Spindly is auto-inhibited in absence of a second trigger (other than DD), or alternatively showing that auto-inhibition can be easily overcome. Our conclusion that Spindly remains auto-inhibited, however, is strongly supported by the gels in Figures 2D-G. There, the peak containing DD and BicD2 and eluting around 6.2 ml (panel D) is not visible when BicD2 is replaced with Spindly (panel E), and RZZ does not change this (panels F-G). Note that the peak at 6.6 ml appears to be a contaminant, possibly DNA, and it is visible also with individual Dynein and Dynactin samples. These experiments strongly support our point and we have tried to improve the presentation of the results by boxing relevant fractions of the displayed SDS-PAGEs.

      We have now also repeated these experiments with recombinant human Dynactin. The new results are displayed in Figure 2 – Supplement 2. Also in this case, we see minimal complex formation with Spindly and complex formation with BicD2, even if the trailing of Dynein, Dynactin, and Spindly in the earlier elution fractions (already in the absence of complex formation) makes the gels harder to interpret. We also note that these experiments are consistent with those with the isolated PE complex.

      Regretfully, we cannot gather additional information by mass photometry because even our positive control dissociates at the extremely low concentrations required to image this very large complex.

      In figure 3C, 3E, and figure 5C, there is a shift in the PE peaks in the presence of Spindly, but it isn't clear why doesn't the complex doesn't elute earlier than Spindly alone. If the complex is dissociating on the column, additional assays are necessary to confirm that these Spindly constructs stably interact with PE. If this shift is also accompanied by a major change in shape, thus allowing Spindly to elute later than it does alone, this needs to be explored or explained further.

      Elution from a size exclusion chromatography column is dominated by the hydrodynamic radius of the macromolecule. In this particular case, Spindly is highly elongated and essentially sets an upper limit for the elution volume of both the un-complexed and complexed protein. We have described this behavior in many other cases of highly elongated proteins (e.g. Huis in ‘t Veld et al. eLife 2019). We are aware that the binding affinity for the interaction of Spindly and the PE complex is low, and therefore are not surprised to observe dissociation of the complex during the SEC run, i.e. upon dilution of the sample after incubation. In these experiments, we have tried to focus on the shift in elution volume of the PE complex from its elution position in isolation.

      The authors should provide better a rationale for why the pointed-end complex is used in figure 3 in lieu of the complex used in figure 2.

      We now write that the Spindly motif of adaptors binds the pointed end complex with measurable affinity also in absence of Dynein (near line 294). We then clarify that “As the Spindly motif is predicted to sit within the autoinhibited portion of the protein, we hypothesized that the PE-Spindly motif interaction could be used as a proxy to measure the autoinhibition status of Spindly, bypassing the need to form the entire Dynein-Dynactin-Spindly complex.”

      In Figure 5I, WT Spindly also binds to LIC, although less WT Spindly is bound to LIC than Spindly CC2* or Spindly deltaRV. This should be addressed in the text.

      Thank you for pointing this out. We have now clarified this in the text near line 440.

      The authors claim that the mechanism they describe may be a paradigm for dynein activation by other adaptors at various cellular locations, but they aren't able to identify a mechanism for how Spindly converts from its auto-inhibited state to its permissive state. A more thorough examination of this mechanism is necessary to claim that this mechanism could be paradigmatic, or a revision of the text is needed.

      Following an additional concern by reviewer 3, we have now revised the text to meet this concern. So, both in the last sentence of the abstract, and in the last paragraph of the discussion, we do not any longer discuss our results as paradigmatic, although we have reasons to believe that they might be eventually recognized as such, after additional examples will have been analyzed.

      Minor Points:

      1) The manuscript could benefit from careful review of the text, captions, and figures, as a few minor typos and inconsistencies in the figures and text were present.

      We have now re-reviewed the text and figures to try eliminate residual inconsistencies.

      2) The list of common structural and functional features of Dynein-Dynactin adaptors could be indicated more clearly.

      We have re-written this part of the Introduction, where we now indicate more clearly the features of the DD complex

      3) Several times the authors use alpha fold predictions to confirm their data. Although the predictions support several of their conclusions, saying that predictions can confirm the data is an overstatement.

      We thank the reviewer for pointing this out. We now replaced “confirmed” with “also supported” on line 190, where we explicitly referred to AF2 predictions as “confirmatory”. We also re-wrote a statement in the Discussion where we had commented on the power of AF2 and indicate that it “became available in the late phases of our work as a guiding and validation tool” (line 524).

      4) Figure 1H would be improved by the addition of the amino acid numbers in the domain diagram.

      Fixed – we also added amino acid numbers in 1G for consistency.

      5) Concentrations used for each protein for the analytical SEC experiments should be listed in the figure or caption.

      Thank you for suggesting this. We have now added the protein concentrations for these experiments directly in the legends.

      6) In addition to the caption, it would be helpful to the reader to indicate which experiments use farnesylated Spindly.

      Done in legends wherever applicable.

      7) Error bars are missing from the WT sample in figure 5J. This figure would benefit from statistical analysis.

      Done – see also point 4, Reviewer 2.

      Significance:

      This paper builds on recent work from the Mussachio lab and others exploring the nature of the fibrous corona at kinetochores and the molecular basis for dynein recruitment. This paper is focused on the structural nature of the interactions that underlie Spindly recruitment to kinetochores and its interactions with dynein and other factors. Although reductionist in its approach, this paper has the potential to have broad implications for thinking about the control of corona assembly and dynein recruitment with an elegant auto-regulation of Spindly. Researchers interested in cell division, chromosome segregation, kinetochore function, dynein regulation, and the structural basis for core cellular processes should be interested in this paper.

      Reviewer #2

      The study by d'Amico et al. presents an in-depth analysis of how intramolecular folding of the coiled-coil adaptor Spindly regulates its interaction with the motor dynein and its obligatory co-factor dynactin. Using biochemical reconstitution and diverse biophysical approaches (including cross-linking mass spectrometry, X-ray crystallography, AF2-based structure prediction, size exclusion chromatography, and analytical ultracentrifugation), the authors uncover and dissect an intricate Spindly autoinhibition mechanism. At kinetochores Spindly is known to co-oligomerize into filaments with the RZZ complex (its kinetochore receptor/cargo), which drives expansion of the outermost kinetochore region (the corona). Here the authors show that Spindly is a dimer in solution and that successive coiled-coil segments interact with each other in an asymmetric 'closed' conformation that is unable to form a complex with dynein and dynactin. Specifically, a 2-residue insertion in the middle of Spindly's first coiled-coil (CC1) creates a kink that allows CC1 to fold back on itself, which has two important structural consequences: it brings a key segment in CC2 (residues 276-309) in contact with a CC1 region called the CC1 box (previously shown to bind dynein light intermediate chain), and it blocks a motif at the beginning of CC2, called the Spindly motif, from accessing the pointed end complex that caps dynactin's minifilament. Mutations in either the CC1 box, the CC1 2-residue insertion, or the CC2(276-309) segment, 'open up' full-length Spindly and promote its interaction with the dynactin pointed end complex and, in case of the latter two types of mutants, with dynein light intermediate chain. CC1 box-deficient Spindly and the CC2 segment mutant (which corresponds to two charge-inverting point mutations) also support complex formation of Spindly and intact dynein-dynactin. Interestingly, while the CC2 mutant can bind to RZZ, the interaction between RZZ and wild-type Spindly is insufficient to make Spindly competent for dynein-dynactin binding (even when RZZ-Spindly are phosphorylated by mitotic kinases). The authors therefore propose that releasing Spindly from autoinhibition requires an additional trigger at the kinetochore, which likely involves an interaction between the Spindly CC2(276-309) segment and an as yet unidentified kinetochore component. The CC2 mutant is also shown to be defective in kinetochore recruitment and in Spindly-RZZ filament formation in vitro, suggesting kinetochore recruitment of Spindly is coupled to kinetochore expansion through a mechanism involving CC2(276-309).

      The experiments are of excellent technical quality and the results are presented in a logical and concise manner. There is clarity in the writing (the introduction deserves particular praise), and the authors' conclusions are fully supported by the data. Although there is no direct structural evidence for Spindly's closed conformation, as the authors themselves are careful to point out, the numerous Spindly mutants that are characterized (only some of which are mentioned in the summary above) in aggregate make a convincing case for the proposed autoinhibition mechanism.

      We are very grateful to the reviewer for supporting our work

      Minor comments:

      • Page 5: "605-residue adaptor Spindly". State that "605-residue" refers to the human protein.

      We have added this clarification

      • Page 8: "The region of Spindly downstream of the Spindly box (residues 281-322) is very conserved among Spindly orthologues, but not among other members of the BICD adaptor family (Figure 1 - Supplement 1L)." This is not very obvious from the alignment shown in the figure.

      We agree with the reviewer that the text, as written, was confusing. We have now rephrased it and write “Downstream of the Spindly box, sequences of Spindly orthologues and BICD family adaptors diverge”

      • Page 13: "...(A23V-A24V) mutant, which has been previously shown to inhibit the interaction with the LIC2 in a similar assay (Gama et al., 2017)." The LIC isoform used in the referenced study was LIC1.

      Thank you for identifying this error. We have corrected the text accordingly.

      -Figure 5J: Information about statistical significance should be added.

      Done. See also Minor point 7, Reviewer 1.

      -Figure 7B - D: Red on black is not an ideal color choice for these graphs.

      We now replaced red with yellow

      -Page 15: When discussing the recently discovered interphase functions of Spindly, also cite Clemente et al. (2018; doi:10.3390/jdb6020009) and Conte et al. (2018; doi:10.1242/bio.033233).

      We apologize for the involuntary omission of these two references, which have now been included in the revised manuscript.

      -Page 17: "Evidence supporting this idea is that mutations in the 276-306 region, including the deletion of this entire fragment or the introduction of charge-inverting point mutations at residues 295 and 297 respectively abolish or largely decrease the kinetochore recruitment of Spindly ((Raisch et al., 2021) and this study),...". Sacristan et al. (2018) should also be cited in this context, as this study established the importance of residues 274-287 for Spindly recruitment to kinetochores.

      We agree and apologize for the inadvertent omission. We have now included the Sacristan et al. reference in this context.

      • Page 17: "In vitro, the 276-306 region is also required for the assembly of RZZ-Spindly filaments (this study and (Raisch et al., 2021))." It could also be mentioned here that residues 274-287 of Spindly are necessary for RZZ-Spindly filament formation in cells, as shown by Sacristan et al. (2018).

      We have now reported this fact on lines 560-561.

      • Page 17: "Plausibly, the solution to this conundrum will require biochemical reconstitutions addressing the spectrum of interactions that this protein establishes at the kinetochore." Presumably, "this protein" refers to Spindly, but this is not clear since the subject of the preceding sentence is RZZ.

      Done – line 565

      Significance

      Cargo transport by cytoskeletal motors must be tightly regulated to establish and maintain intracellular organization and for faithful execution of development, including cell division. Much of this regulation occurs at the motor-cargo interface but remains poorly understood at the molecular level. In recent years it has become clear that adaptor proteins not only provide a physical link between motors and their cargo but also participate in motor activation. Adaptor-coupled activation is particularly important for dynein, because adaptors promote dynein's interaction with its essential co-factor dynactin.

      BICD2 (along with other Bicaudal D proteins) is the most intensely studied dynein adaptor and has long been known to be subject to autoinhibition with regard to dynein-dynactin binding, which is relieved by cargo binding to the BICD2 C-terminal region. A important question has been whether the same regulatory logic applies to other dynein adaptors. The study by d'Amico et al. presents the first evidence that conformational inhibition extends to adaptors other than Bicaudal D proteins. The study also reveals that Spindly's autoinhibition mechanism is more complex than that of BICD2. This likely reflects Spindly's dual function in dynein-dynactin recruitment and kinetochore expansion. The results of d'Amico et al. suggest that the Spindly autoinhibition mechanism has evolved to coordinate the two processes, and this idea is further supported by a recent study on the RZZ-Spindly interaction from the same group (Raisch et al. 2021; doi:10.1101/2021.12.03.471119). One of the most important insights from d'Amico et al. is that there must be another binding partner of Spindly at kinetochores besides the RZZ complex that participates in the relief of Spindly autoinhibition. The study has therefore identified an important future research direction. It will be interesting to investigate whether additional adaptors follow the multi-step activation model proposed here for Spindly.

      Regarding the technical aspects, the study illustrates that AF2-based structure prediction is a powerful tool for investigating conformational regulation, and it introduces an important innovation: the ability to generate recombinant human dynactin opens the door to the engineering of dynactin mutants, which promises to accelerate mechanistic dissection of this essential dynein co-factor. In conclusion, the study represents a significant step forward in our understanding of how dynein-cargo interactions are regulated by adaptor proteins and is therefore of general interest for researchers studying the molecular mechanisms of chromosome segregation as well as intracellular transport.

      Reviewer #3

      The Dynein-Dynactin (DD) complex interacts with different activating adaptors to assemble functional motor complexes capable of moving along microtubules while transporting various cargoes. However, it remains poorly understood how DD activation is precisely controlled so that Dynein-mediated transport is only stimulated at the appropriate time and place. DD adaptor regulation is likely a crucial piece of this puzzle. In this manuscript, the authors show that Spindly, a mitotic adaptor of DD complex, undergoes a series of conformational rearrangements that result in efficient Spindly autoinhibition and affect its ability to bind DD. The work from d'Amico et al includes an impressive amount of biochemical and biophysical data, supported by well-designed experiments that are carefully documented. Resorting to crosslinking experiments and protein structural modelling, the authors find that several intramolecular contacts occur between specialized domains within Spindly N-terminus. The resulting compact conformation occludes important DD-binding motifs in Spindly and, thus, limits the access of DD to the adaptor. By utilizing different Spindly mutants predicted to render the adaptor more elongated, the authors bypass Spindly autoinhibition and rescue binding to DD in vitro. Surprisingly, unlike other DD adaptors, Spindly autoinhibition is not relieve upon binding to its cargo (the RZZ complex) arguing that the interaction with an additional binding partner is require to fully unleash the potential of Spindly to bind DD. In line with this, the authors identify a Spindly mutant that is unable to localize to kinetochores from human cells, despite its open conformation. Collectively, this work provides significant advances in the understanding of Spindly regulation and brings a new perspective to the mechanism of DD adaptor activation and therefore should be of interest for a wide audience.

      We are very grateful to the reviewer for the support and for the thorough and constructive evaluation of our work.

      Major concerns:

      • The authors show that Spindly 33-605 is able to form a complex with DD which eventually enables the recruitment of Dynactin to kinetochores from Spindly 33-605-expressing cells. This result is unexpected since this Spindly mutant lacks CC1 box, which has been previously shown to be required for the kinetochore localization of Dynactin (Sacristan et al 2018). A more comprehensive discussion about this discrepancy would enrich the article and benefit the audience.

      We thank the reviewer for pointing this out. We now write (line 492): “This result was unexpected, because the CC1 box has been previously shown to be required for kinetochore localization of Dynactin (Sacristan et al., 2018)”

      • In Fig.7, the authors show that two Spindly mutants (Spindly CC2* and Spindly chimera) are unable to fully decorate the kinetochores from human cells. The same is true for Spindly AA/VV mutant. Do the authors know whether these mutants are expressed as stable proteins in cells for example by performing a western blot analysis?

      In this revised version of our manuscript, we have explained more clearly that in this experiment we electroporate recombinant proteins. These are essentially the same proteins that we use for the experiments in vitro. This provides an internal test in these experiments, because we can verify, through their successful expression and purification, that the proteins are stable. We cannot exclude, however, that the proteins are “treated differently” in cells, for instance because they interact differently with certain binding partners in ways that modifies their stability. As the proteins are not expressed continuously, but rather introduced in the cells in a single electroporation event several hours before imaging, the overall levels of these proteins may differ. We have now included a representative western blot (Figure 7 – Supplement 1C) that demonstrates the levels of electroporated proteins in the experiments in Figure 7. SpindlyCC2*appears to be present at somewhat lower levels than the other constructs. mChSpindly33-605 and Spindlychimera, on the other hand, were present at very similar levels, supporting our conclusion that a kinetochore-binding region is impaired in the latter. We now refer in the main text to the uncertainty created by the comparatively lower cellular levels of SpindlyCC2*. We have also chosen more representative kinetochores for the insets of CC2* and Chimera in Figure 7A.

      • In line with the previous point, could the authors tether each Spindly mutant to the kinetochore for example by fusing the construct to known kinetochores proteinssuch as Mis12 and test whether these fusion constructs are now able to recruit Dynactin to kinetochores?

      This would be a potentially interesting experiment. However, reasoning that Spindly is a strong dimer that needs to interact with another strong hexamer like the RZZ complex, discouraged us as these stoichiometries would almost certainly complicate the interpretation of these experiments. It is clear that further work will be required to define the complete picture for this complex system.

      • The authors conclude that the 2-step or multistep mechanism involved in the regulation of Spindly activation may be a common mechanism to different DD adaptors. However, the authors point out to existing differences between the conformational arrangement of Spindly and another DD adaptor, BICD2, arguing against a common mode of regulation for all adaptors. This needs to be clarified.

      The reviewer has a good point and we have indeed tuned this down. We have re-written the last sentence of the abstract and replaced it with “Thus, our work illustrates how Dynein can be specifically activated at a defined cellular locale.” We also write (line 592): Whether a similar 2-step or multistep mechanism applies to additional cargo-adaptor systems is an important question for future studies.

      Minor concerns:

      • In Fig.2D, full length Spindly does not bind DD in vitro. This is most likely to occur because Spindly N-terminus adopts a compacted conformation and hinders the access to DD-binding motifs. In Fig.2B, the authors show a structural prediction for Spindly 1-275 which should adopt a more elongated shape. According to prevailing model, this construct should now be able to bind DD in a similar biochemical assay.

      We agree with the reviewer that Spindly1-275 (and Spindly∆276-306) might be expected to be strong DD binders based on our model. Indeed, these proteins bind to the PE, albeit apparently weakly. Nevertheless, as explained in lines 350 and following, these mutants appear to form higher oligomers and we have not been able to show convincingly that they are fully open and available to bind DD.

      • In Gama et al 2017, LIC1 was able to pull down a wild-type N-terminal Spindly construct. How do the authors reconcile this with the data presented in this manuecript?

      We have expanded the discussion, also to answer major point 5, reviewer 1, on line 446 and following, where we also refer to the observation of Gama et al. 2017.

      • The section where the authors test point mutations to open Spindly ("Opening up Spindly with point mutations") should be better contextualized. The transition is difficult to follow as it is.

      We have now rephrased this part of the text to make are thoughts clearer.

      • In the text, it is not clear whether Mps1 kinase is required to promote RZZ oligomerization in the presence of Spindly chimera, an uninhibited Spindly mutant. According to the model, this mutant construct should drive oligomerization independently of Mps1 (as the N-terminal deletion construct from Sacristan et al 2018).

      The reviewer is correct and we have rephrased this part of the text to clarify

      • The nomenclature the authors adopt for the CC1 second conserved motif (SCM) and for the Spindly motif (SM) can be confusing at some point when identifying each mutant in the text and figures. Nomenclature should be standardized.

      We agree with the reviewer and have now adopted a different nomenclature for the CC2 box or second conserved motif, namely HBS1, for Heavy Chain Binding Site 1. This functional annotation derives from work of one of our laboratories (Carter) and has been discussed with, and approved by, Geert Kops, whose laboratory had originally proposed the name “CC2 box”, as well as Reto Gassmann, Erika Holzbaur, Roberto Dominguez, Sam Reck Peterson, Rick McKenney, and Ahmet Yildiz.

      • In Fig.6A, mCh-Spindly 33-605 and mCh-Spindly chimera lines have the same color.

      Thank you for spotting this subtle mistake. We have corrected the color line.

      Significance

      The work represents a significant advance in the field and it would be of interest for a wide range of audiences.

    2. 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

      The Dynein-Dynactin (DD) complex interacts with different activating adaptors to assemble functional motor complexes capable of moving along microtubules while transporting various cargoes. However, it remains poorly understood how DD activation is precisely controlled so that Dynein-mediated transport is only stimulated at the appropriate time and place. DD adaptor regulation is likely a crucial piece of this puzzle. In this manuscript, the authors show that Spindly, a mitotic adaptor of DD complex, undergoes a series of conformational rearrangements that result in efficient Spindly autoinhibition and affect its ability to bind DD. The work from d'Amico et al includes an impressive amount of biochemical and biophysical data, supported by well-designed experiments that are carefully documented. Resorting to crosslinking experiments and protein structural modelling, the authors find that several intramolecular contacts occur between specialized domains within Spindly N-terminus. The resulting compact conformation occludes important DD-binding motifs in Spindly and, thus, limits the access of DD to the adaptor. By utilizing different Spindly mutants predicted to render the adaptor more elongated, the authors bypass Spindly autoinhibition and rescue binding to DD in vitro. Surprisingly, unlike other DD adaptors, Spindly autoinhibition is not relieve upon binding to its cargo (the RZZ complex) arguing that the interaction with an additional binding partner is require to fully unleash the potential of Spindly to bind DD. In line with this, the authors identify a Spindly mutant that is unable to localize to kinetochores from human cells, despite its open conformation. Collectively, this work provides significant advances in the understanding of Spindly regulation and brings a new perspective to the mechanism of DD adaptor activation and therefore should be of interest for a wide audience.

      Major concerns:

      • The authors show that Spindly 33-605 is able to form a complex with DD which eventually enables the recruitment of Dynactin to kinetochores from Spindly 33-605-expressing cells. This result is unexpected since this Spindly mutant lacks CC1 box, which has been previously shown to be required for the kinetochore localization of Dynactin (Sacristan et al 2018). A more comprehensive discussion about this discrepancy would enrich the article and benefit the audience.
      • In Fig.7, the authors show that two Spindly mutants (Spindly CC2* and Spindly chimera) are unable to fully decorate the kinetochores from human cells. The same is true for Spindly AA/VV mutant. Do the authors know whether these mutants are expressed as stable proteins in cells for example by performing a western blot analysis?
      • In line with the previous point, could the authors tether each Spindly mutant to the kinetochore for example by fusing the construct to known kinetochores proteinssuch as Mis12 and test whether these fusion constructs are now able to recruit Dynactin to kinetochores?
      • The authors conclude that the 2-step or multistep mechanism involved in the regulation of Spindly activation may be a common mechanism to different DD adaptors. However, the authors point out to existing differences between the conformational arrangement of Spindly and another DD adaptor, BICD2, arguing against a common mode of regulation for all adaptors. This needs to be clarified.

      Minor concerns:

      • In Fig.2D, full length Spindly does not bind DD in vitro. This is most likely to occur because Spindly N-terminus adopts a compacted conformation and hinders the access to DD-binding motifs. In Fig.2B, the authors show a structural prediction for Spindly 1-275 which should adopt a more elongated shape. According to prevailing model, this construct should now be able to bind DD in a similar biochemical assay.
      • In Gama et al 2017, LIC1 was able to pull down a wild-type N-terminal Spindly construct. How do the authors reconcile this with the data presented in this manuecript?
      • The section where the authors test point mutations to open Spindly ("Opening up Spindly with point mutations") should be better contextualized. The transition is difficult to follow as it is.
      • In the text, it is not clear whether Mps1 kinase is required to promote RZZ oligomerization in the presence of Spindly chimera, an uninhibited Spindly mutant. According to the model, this mutant construct should drive oligomerization independently of Mps1 (as the N-terminal deletion construct from Sacristan et al 2018).
      • The nomenclature the authors adopt for the CC1 second conserved motif (SCM) and for the Spindly motif (SM) can be confusing at some point when identifying each mutant in the text and figures. Nomenclature should be standardized.
      • In Fig.6A, mCh-Spindly 33-605 and mCh-Spindly chimera lines have the same color.

      Significance

      The work represente a significant advance in the field and it would be of interest for a wide range of audiences.

    3. 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

      The study by d'Amico et al. presents an in-depth analysis of how intramolecular folding of the coiled-coil adaptor Spindly regulates its interaction with the motor dynein and its obligatory co-factor dynactin. Using biochemical reconstitution and diverse biophysical approaches (including cross-linking mass spectrometry, X-ray crystallography, AF2-based structure prediction, size exclusion chromatography, and analytical ultracentrifugation), the authors uncover and dissect an intricate Spindly autoinhibition mechanism. At kinetochores Spindly is known to co-oligomerize into filaments with the RZZ complex (its kinetochore receptor/cargo), which drives expansion of the outermost kinetochore region (the corona). Here the authors show that Spindly is a dimer in solution and that successive coiled-coil segments interact with each other in an asymmetric 'closed' conformation that is unable to form a complex with dynein and dynactin. Specifically, a 2-residue insertion in the middle of Spindly's first coiled-coil (CC1) creates a kink that allows CC1 to fold back on itself, which has two important structural consequences: it brings a key segment in CC2 (residues 276-309) in contact with a CC1 region called the CC1 box (previously shown to bind dynein light intermediate chain), and it blocks a motif at the beginning of CC2, called the Spindly motif, from accessing the pointed end complex that caps dynactin's minifilament. Mutations in either the CC1 box, the CC1 2-residue insertion, or the CC2(276-309) segment, 'open up' full-length Spindly and promote its interaction with the dynactin pointed end complex and, in case of the latter two types of mutants, with dynein light intermediate chain. CC1 box-deficient Spindly and the CC2 segment mutant (which corresponds to two charge-inverting point mutations) also support complex formation of Spindly and intact dynein-dynactin. Interestingly, while the CC2 mutant can bind to RZZ, the interaction between RZZ and wild-type Spindly is insufficient to make Spindly competent for dynein-dynactin binding (even when RZZ-Spindly are phosphorylated by mitotic kinases). The authors therefore propose that releasing Spindly from autoinhibition requires an additional trigger at the kinetochore, which likely involves an interaction between the Spindly CC2(276-309) segment and an as yet unidentified kinetochore component. The CC2 mutant is also shown to be defective in kinetochore recruitment and in Spindly-RZZ filament formation in vitro, suggesting kinetochore recruitment of Spindly is coupled to kinetochore expansion through a mechanism involving CC2(276-309).

      The experiments are of excellent technical quality and the results are presented in a logical and concise manner. There is clarity in the writing (the introduction deserves particular praise), and the authors' conclusions are fully supported by the data. Although there is no direct structural evidence for Spindly's closed conformation, as the authors themselves are careful to point out, the numerous Spindly mutants that are characterized (only some of which are mentioned in the summary above) in aggregate make a convincing case for the proposed autoinhibition mechanism.

      Minor comments:

      • Page 5: "605-residue adaptor Spindly". State that "605-residue" refers to the human protein.
      • Page 88: "The region of Spindly downstream of the Spindly box (residues 281-322) is very conserved among Spindly orthologues, but not among other members of the BICD adaptor family (Figure 1 - Supplement 1L)." This is not very obvious from the alignment shown in the figure.
      • Page 13: "...(A23V-A24V) mutant, which has been previously shown to inhibit the interaction with the LIC2 in a similar assay (Gama et al., 2017)." The LIC isoform used in the referenced study was LIC1.
      • Figure 5J: Information about statistical significance should be added.
      • Figure 7B - D: Red on black is not an ideal color choice for these graphs.
      • Page 15: When discussing the recently discovered interphase functions of Spindly, also cite Clemente et al. (2018; doi:10.3390/jdb6020009) and Conte et al. (2018; doi:10.1242/bio.033233).
      • Page 17: "Evidence supporting this idea is that mutations in the 276-306 region, including the deletion of this entire fragment or the introduction of charge-inverting point mutations at residues 295 and 297 respectively abolish or largely decrease the kinetochore recruitment of Spindly ((Raisch et al., 2021) and this study),...". Sacristan et al. (2018) should also be cited in this context, as this study established the importance of residues 274-287 for Spindly recruitment to kinetochores.
      • Page 17: "In vitro, the 276-306 region is also required for the assembly of RZZ-Spindly filaments (this study and (Raisch et al., 2021))." It could also be mentioned here that residues 274-287 of Spindly are necessary for RZZ-Spindly filament formation in cells, as shown by Sacristan et al. (2018).
      • Page 17: "Plausibly, the solution to this conundrum will require biochemical reconstitutions addressing the spectrum of interactions that this protein establishes at the kinetochore." Presumably, "this protein" refers to Spindly, but this is not clear since the subject of the preceding sentence is RZZ.

      Significance

      Cargo transport by cytoskeletal motors must be tightly regulated to establish and maintain intracellular organization and for faithful execution of development, including cell division. Much of this regulation occurs at the motor-cargo interface but remains poorly understood at the molecular level. In recent years it has become clear that adaptor proteins not only provide a physical link between motors and their cargo but also participate in motor activation. Adaptor-coupled activation is particularly important for dynein, because adaptors promote dynein's interaction with its essential co-factor dynactin.

      BICD2 (along with other Bicaudal D proteins) is the most intensely studied dynein adaptor and has long been known to be subject to autoinhibition with regard to dynein-dynactin binding, which is relieved by cargo binding to the BICD2 C-terminal region. A important question has been whether the same regulatory logic applies to other dynein adaptors. The study by d'Amico et al. presents the first evidence that conformational inhibition extends to adaptors other than Bicaudal D proteins. The study also reveals that Spindly's autoinhibition mechanism is more complex than that of BICD2. This likely reflects Spindly's dual function in dynein-dynactin recruitment and kinetochore expansion. The results of d'Amico et al. suggest that the Spindly autoinhibition mechanism has evolved to coordinate the two processes, and this idea is further supported by a recent study on the RZZ-Spindly interaction from the same group (Raisch et al. 2021; doi:10.1101/2021.12.03.471119). One of the most important insights from d'Amico et al. is that there must be another binding partner of Spindly at kinetochores besides the RZZ complex that participates in the relief of Spindly autoinhibition. The study has therefore identified an important future research direction. It will be interesting to investigate whether additional adaptors follow the multi-step activation model proposed here for Spindly.

      Regarding the technical aspects, the study illustrates that AF2-based structure prediction is a powerful tool for investigating conformational regulation, and it introduces an important innovation: the ability to generate recombinant human dynactin opens the door to the engineering of dynactin mutants, which promises to accelerate mechanistic dissection of this essential dynein co-factor.

      In conclusion, the study represents a significant step forward in our understanding of how dynein-cargo interactions are regulated by adaptor proteins and is therefore of general interest for researchers studying the molecular mechanisms of chromosome segregation as well as intracellular transport.

      Reviewer expertise keywords: same as the keywords of this manuscript.

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      Referee #1

      Evidence, reproducibility and clarity

      In this article, Amico et al. explore how Spindly self-regulates its interaction with Dynein-Dynactin. They propose that Spindly adopts an auto-inhibited, closed conformation that blocks the CC1 box and Spindly motif, preventing its interaction with dynein-dynactin. The authors used a combination of X-ray crystallography, biochemistry, and structure predictions to detail the intramolecular interactions in Spindly that mediate this closed state. They then use analytical SEC to test their proposed auto-inhibition mechanism by monitoring Spindly binding to the pointed end complex. They suggest that auto-inhibited Spindly is unable to bind Dynein-Dynactin regardless of the presence or absence of Spindly's cargo, the RZZ complex. In contrast, by using mutagenesis to prevent this auto-inhibition, the authors show that uninhibited Spindly can interact with members of the Dynein-Dynactin complex. Finally, they use cellular experiments to show that relieving autoinhibition prevents the proper localization of Spindly and Dynein-Dynactin to kinetochores during mitosis, likely due to the formation of ectopic Spindly-Dynein-Dynactin complexes in these cells.

      This is an interesting paper that provides important insights into the mechanism of Spindly regulation and its associations with its interacting partners. However, additional work is necessary to support some of their conclusions. In addition, the text is at times quite dense and harder to follow, which prevents their findings as being impactful as could be possible for the bigger picture paradigms of kinetochore function.

      Major Points:

      1. The crosslinking and mass photometry experiments are done at highly differing concentrations (5 μM vs. 10 nM). The mass photometry should be performed at the same concentration as the crosslinking experiments to determine if Spindly forms a higher order oligomer at the higher concentration. These results will aid in the interpretation of the crosslinking mass spectrometry experiments, as the observed interactions could be intermolecular contacts rather than intramolecular contacts if Spindly is tetrameric at these concentrations, as is suggested in figure 4E for specific Spindly constructs.
      2. In figure 2, more conclusive evidence is needed to show that full length Spindly does not form a complex with Dynein-Dynactin. My interpretation of the gels in figure 2D suggests that full length Spindly does form a complex with Dynein-Dynactin, as in the final gel (red outline) it looks as if full length Spindly is indeed peaking with the rest of the Dynein-Dynactin proteins, albeit with excess Spindly eluting later. Figure legends containing protein concentrations used in SEC assays would aid in the interpretation of this data. To conclusively show that full length Spindly doesn't form a complex with Dynein-Dynactin, additional assays will be necessary, such as pull-down assays, or mass photometry.
      3. In figure 3C, 3E, and figure 5C, there is a shift in the PE peaks in the presence of Spindly, but it isn't clear why doesn't the complex doesn't elute earlier than Spindly alone. If the complex is dissociating on the column, additional assays are necessary to confirm that these Spindly constructs stably interact with PE. If this shift is also accompanied by a major change in shape, thus allowing Spindly to elute later than it does alone, this needs to be explored or explained further.
      4. The authors should provide better a rationale for why the pointed-end complex is used in figure 3 in lieu of the complex used in figure 2.
      5. In Figure 5I, WT Spindly also binds to LIC, although less WT Spindly is bound to LIC than Spindly CC2* or Spindly deltaRV. This should be addressed in the text.
      6. The authors claim that the mechanism they describe may be a paradigm for dynein activation by other adaptors at various cellular locations, but they aren't able to identify a mechanism for how Spindly converts from its auto-inhibited state to its permissive state. A more thorough examination of this mechanism is necessary to claim that this mechanism could be paradigmatic, or a revision of the text is needed.

      Minor Points:

      1. The manuscript could benefit from careful review of the text, captions, and figures, as a few minor typos and inconsistencies in the figures and text were present.
      2. The list of common structural and functional features of Dynein-Dynactin adaptors could be indicated more clearly.
      3. Several times the authors use alpha fold predictions to confirm their data. Although the predictions support several of their conclusions, saying that predictions can confirm the data is an overstatement.
      4. Figure 1H would be improved by the addition of the amino acid numbers in the domain diagram.
      5. Concentrations used for each protein for the analytical SEC experiments should be listed in the figure or caption.
      6. In addition to the caption, it would be helpful to the reader to indicate which experiments use farnesylated Spindly.
      7. Error bars are missing from the WT sample in figure 5J. This figure would benefit from statistical analysis.

      Significance

      This paper builds on recent work from the Mussachio lab and others exploring the nature of the fibrous corona at kinetochores and the molecular basis for dynein recruitment. This paper is focused on the structural nature of the interactions that underlie Spindly recruitment to kinetochores and its interactions with dynein and other factors. Although reductionist in its approach, this paper has the potential to have broad implications for thinking about the control of corona assembly and dynein recruitment with an elegant auto-regulation of Spindly. Researchers interested in cell division, chromosome segregation, kinetochore function, dynein regulation, and the structural basis for core cellular processes should be interested in this paper.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)): **Summary:** Techniques to probe the local environment of membrane proteins are sparse, although the influence of lipids on the membrane protein's function are known since many years. Therefore, the paper by Umebayashi et al. is important. The environment-sensitive dye Nile red (NR) coupled to a membrane protein is an appropriate sensor for monitoring the local membrane fluidity. Linking of Nile red to the receptor via a flexible tether was achieved with the acyl carrier protein (ACP)-tag method. Experiments showed that depending on the ACP site a certain linker length is required to have NR inserted in the membrane and thus be an effective sensor for lipid disorder. This technology could be of general usability to study the environment of membrane proteins in the context of their function. As an example, the technique allowed insulin induced membrane disorder in the close insulin receptor vicinity to be observed. Further, results suggested that tyrosine activity is required for this disorder to happen. The experimental results appear to be complete and controls were made.

      **Major comments:** 1) Sometimes technical terms are used without explanation: What is the GP value? What is ACP-IR? The spectrum was measured in number of rois? The reader can find those abbreveations out, but it would be nice to have them defined.

      We have made a list of abbreviations.

      2) Fig. 1d) is confusing. The ACP-IR labelling is evident in 3 panels, but there is no difference in the color (emission spectra of 1992-ACP-IR vs 2031-ACP-IR should be visible??). The DAPI staining is very different. When doing the latter, how difficult is it to get the staining equal?

      The differences in spectra cannot be seen because we used pseudo colors for display of the DAPI and CoA-PEG-NR staining. The reviewer’s comments about the unequal DAPI staining is correct. The reason for this is most likely that the cell membrane is unequally permeabilized by PFA treatment. As the point of this figure is just to show that the plasma membrane is labeled, dependent upon the expression of the ACP-tagged insulin receptor, we don’t think that the variable intensities of the DAPI staining is important. DAPI is simply used to indicate the position of the cells.

      3) How can one interpret Fig. 4: a) Control goes over 4 frames, at 240" insulin is added, and 10 frames should show a fluctuation difference?

      We showed 4 frames after control treatment that showed no significant change was observed by control treatment. We expected that clear changes would be invoked by insulin treatment in GP images, however these changes, while visible in the GP images, are difficult to see for the untrained observer. This is the reason why we used the ZNCC method in the subsequent figures to better visualize the changes.

      1. b) A color shift from blue to green is visible after insulin addition. But it is faint - difficult to assess from the pseudo color scheme. What does 1000 pixel top/1000 pixel bottom mean in c). Is it an attempt to better visualize the fluctuation? It is difficult to recognize a difference before and after adding insulin. d) It seems that the kymograph set should show this. What is the color scale? Why is 3 so untypical, i.e., no change? Box 6 is also peculiar: the left side does not show a strong change upon insulin administration, the right side does. Why? We appreciate the helpful comments for improving our manuscript.

      As pointed out, the change of GP value is extremely small before and after insulin addition, so it is difficult to fully visualize the change with normal pseudo-color expression. To deal with this, we adopted the following two methods to visualize minute changes.

      1) Visualization of local changes of the statistical GP value showed by ZNCC throughout the time-lapse images (Fig. 6 and Fig. S2B).

      2) Visualization of the top/bottom 1000 pixels of the sorting ZNCC value in each image (Fig. 7 and Fig. S2C). The top 1000 pixels are the ones that showed the largest changes. The bottom 1000 pixels are the ones that showed the smallest changes.

      Owing to these expressions, we found out that the level of the response against the insulin signal was spatially and temporally heterogeneous in the membrane.

      As for the color scale, in order to clarify the meaning of the difference of color, we have added the description about the relationship between the color and the ZNCC value in the results section.

      4) How is the kymogram calculated? The legend says 'The horizontal dimension represents the averaged ZNCC inside the rectangular area, and the vertical dimension represents time'. The averaged ZNCC is a single value, so it is not clear why the kymogram shows a variation from left to right. May it be the ZNCC was averaged just vertically?

      We apologize that we did not provide information regarding making the kymograph.

      In the yellow rectangular area (Fig. 6B), the ZNCC values of the pixels with the same x coordinate value were vertically averaged, which were represented as the horizontal direction of the kymograph. That is, one horizontal line of the kymograph holds the spatial distribution of the ZNCC value along the horizontal direction of the membrane, and the vertical direction shows their time changes. To make it easier to understand, we refined the description about the kymograph in the legend of Fig. 6.

      5) When calculating cross-correlation values on images, they need to be aligned. What fraction of the total image does the selected 19x19 box represent? As described, I imagine that a rolling CC over 19x19 pixels is calculated over an image from the time lapse series comparing it with the reference Iave(x,y). Compared to the 3x3 median filtered CP image, the ZNCC image should then be much more blurred??

      Below we provide more information regarding the calculation of ZNCC.

      Each local window for ZNCC calculation is set to a 19x19 pixels centered on every single pixel excluding the edges of an image. The ZNCC value calculated in that window is set to a center pixel of that area. After that, a new window centered on the adjacent pixel is set and calculate the new ZNCC. That is, the calculation window is slid throughout the image. Also, the calculated ZNCC value is not set to all the pixels of the window, but is set to only the center pixel of the window, so there is no blur effect like median filtering.

      The figure below shows a schematic view of our ZNCC calculation.

      Schematic view of our ZNCC calculation

      **Minor comment:** On page 16 supplementary is not spelled properly.

      corrected

      Reviewer #1 (Significance (Required)):

      The key point of this paper is convincing and the new technology appears to have a lot of potential. It can be applied to study membrane protein function in the context of its environment, the lipid bilayer.

      Membrane fluidity measurements have been developed (e.g., using fluorescent probes like laurdan). However, the trick to link a probe like nile red by ACP technology to the insulin receptor and to observe its activity is quite new.

      A most recent description of such a technology is in TrAC Trends in Analytical Chemistry Volume 133, December 2020, 116092.

      This is an interesting review, but not directly impacting on our work.

      **Referees cross-commenting**

      All comments are constructive and important. The paper is important but needs to be amended as proposed.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)): **Summary:** In this manuscript, authors generated an ACP-attached Nile Red probe in order to specifically label Insulin receptor in the membrane. Owing to this specificity, one can measure the lipid membrane properties around a specific protein in the membrane. **Major comments:**

      For the conclusions in the manuscript to be convincing, in my opinion, these additional data need to be added. Some of these are new experiments, and some are detailed analysis of existing data. The new experiments are not for new line of investigation, instead it is to confirm their statements and conclusions. The major point is the reliability of spectral shift. In usual environment sensitive probes, it is certain that they are in the membrane whatever is done to the membrane. However, when the probe is attached to a protein, it is not trivial to have the same confidence that the probe is always inside the membrane, and it is in the same plane of the membrane. 1992-ACP-IR is a good example; authors state that it binds to the protein outside the membrane, but when there is cholesterol addition and -maybe more interestingly- cholesterol removal, the dye still reacts and changes its emission (even PreCT changes its emission quite a bit at the 570 nm region). This is a clear indication of a change in localization of the probe upon some changes in the membrane. This implies that observed spectral shifts may not be due to lipid packing differences, but due to localization of the probes. For this reason, it is crucial to know where any environment sensitive probe localize in the membrane with respect to membrane normal, and this knowledge is more important for this probe. Related to this, the spectral difference upon insulin treatment and activation of insulin receptor could be due to changes in probe's localization in the membrane. Especially because authors show in Fig1e, the spectra can change depending on the probe localization. Relatedly, quantum yield of NR should be significantly different when it is inside vs outside membrane. Authors should show QY for 1992-ACP-NR and 2031-ACP-NR with different PEG lengths and upon insulin treatment.

      We understand the logic of the request to measure the QY, since the QY of Nile red is much higher in organic solvents than in aqueous solutions, so it might be predicted that the QY of Nile red is higher in a lipid bilayer than when covalently bound to the protein in an aqueous environment. However, this argument depends upon the mechanism for the increase in quantum yield when going from aqueous to a non-polar solution. One possible explanation is based on the intrinsic properties of the dye under the two conditions. The alternative explanation would be that the dye would aggregate (be insoluble) in aqueous solution and therefore either not fluoresce or self-quench. In this case, we believe that the latter is the explanation because we and others have previously shown the turn-on properties of the probe when binding to proteins (SNAP-tag and others). It is not simple to measure QY in the cell under a microscope, but we have done something similar shown in supplementary figure 4. We labeled the three ACP-receptor complexes with PEG11-Nile red and co-stained with antibody to the Insulin Receptor. We then calculated a relative quantum yield. There were very little differences at all between the relative quantum yields, so we conclude that it is not the environment of the probe, which affects the quantum yield under these conditions, but the fact that it is covalently attached to a protein and incapable of forming aggregates. What distinguishes these constructs is the emission spectrum, not the quantum yield. In supplementary Table 2 we also did QY measurements in vitro and we could reproduce the increase of quantum yield by association with liposomes or in organic solvents. We tested whether non-covalent association with a protein would increase the QY by incubation with the lipid binding protein, BSA, in PBS. This was not the case, strongly pointing to the conclusion that it is the covalent association with the protein that increases the QY, not association with a protein. We believe that our demonstration of changes in fluorescent spectra with changes in cholesterol, large changes in fluorescent spectra with linker length for the 1992 construct and voltage sensitivity using patch-clamp prove that the Nile red is reporting on the membrane environment under the conditions we propose.

      **Minor comments:** - Fig 1d requires quantification We do not agree on this. This is simply to show that the labeling is dependent upon expression of the relevant ACP-IR constructs. There is no detectable labeling of the control.

      • Voltage sensitivity of different PEG length of 2031-ACP probe should be added. We have added this data in figure 2 panel E.

      • Fig 3a graph should show all data points, not only bar graphs. Also, the band in 3a for +CoA-PEG-NR is dimmer than other bands, is it specific to this particular gel since quantification does not show any difference?

      There is no significant difference- Fig 4d, colour code is needed.

      Done

      • Fig 5b and Fig3d are basically the same experiments in terms of control measurement, why is the difference in 3b is 0.04 GP unit while it is 0.007 GP unit?

      We explain in the MS, but have improved the title of Y-axis in Fig.5 b graph so that the difference in what is plotted is clear. - Why is inhibitor data so noisy? We should discuss.

      We don’t know the exact reason why inhibitor data is noisy, but we speculate that the actin cytoskeleton and phosphoinositide-dependent signaling could affect the membrane stability, and the membrane environment would be fluctuated in the presence of latrunculin B or PI3K inhibitor.

      Reviewer #2 (Significance (Required)): Overall, this is a very useful approach, and this line of research will yield very useful tools to shed light on how lipids surrounding proteins can change their function. Major advance of the paper is the new chemical biology tool. There is also biological data on how insulin can change the insulin receptor's membrane environment which is contradictory to some old literature claiming that InsR becomes more "rafty" upon insulin treatment (e.g., PMID: 11751579).

      If this type of tagging proves robust and reproducible (limitations and concerns listed above and below), it could be used by other researchers to tag their protein of interest and investigate the lipid environment around those proteins.

      The downside of this method is that the probe requires ACP tag, a relatively less used tag than others in biology, therefore researchers interested in using this probe should have their proteins with ACP tag. Moreover, the linker length and ACP-tag position are quite crucial parameters (and probably should be optimized for each protein). Longer PEG lengths cannot report on changes efficiently (Fig3b), while shorter lengths are prone to artefacts as they can go out of membrane (Fig1 and Fig2). This might limit its widespread use.

      The reason for using the ACP tag is that neither the SNAP tap nor the HALO tag working. The tethered Nile Red preferred to bind to the tqg rather than inserting into the membrane.

      **Referees cross-commenting** I agree with all comments and concerns of other reviewers. I see the usability and potential of this new technology along with its limitations as all three reviewers pointed out.

      Reviewer #3 (Evidence, reproducibility and clarity (Required)): See below. No concerns on any of these issues.

      Reviewer #3 (Significance (Required)): **Critique:** This MS reports a proof-of-principle for using site-directed environmentally sensitive probe technology to assess the local membrane environment of a receptor tyrosine kinase (IR) upon activation. This technology addresses a major gap in our arsenal of tools to study the mechanisms of membrane signaling as the parameters of interest are biophysical parameters rather than purely biochemical ones. How to do this with spatial and temporal resolution is a major challenge. This study builds on previous work by the Riezman group that develops an extrinsic labeling system to tether Nile Red to specific sites on the ectodomain of a signaling receptor and then probe local membrane environments as a function of receptor activity. This is a carefully done study is well-controlled, is clever in design and is well-described. Although the major issues to which such a general technology could contribute involve intracellular (and not extracellular) event, the advances described will be of general interest -- particularly that local membrane order decreases when IR becomes activated. Specific comments for the authors' consideration follow:

      **Specific Comments:** (i) As a general comment, the authors are measuring extracellular plasma membrane leaflet properties that may or may not translate to what is happening in the local inner leaflet environment. A general reader may well miss the significance of this. This point needs to be more explicitly emphasized in the Discussion.

      This has been discussed in the revised version.

      (ii) Why not treat cells with a PLC inhibitor to block PIP2 hydrolysis and ask if that inhibits membrane disorder. It is PIP2 hydrolysis/resynthesis that regulates the actin cytoskeleton at signaling receptors and this seems an attractive candidate for study.

      There is a long list of attractive post-signaling events of the insulin receptor and how this works in different cell types that could be tested. We believe that this is beyond the scope of this study and we encourage others to do this.

      (iii) The data acquisition time is at least 4 min which is long enough for activated receptors to be recruited to sites of endocytosis. Can the authors exclude the possibility that what they are measuring isn't reflective of such spatial reorganization? Does a clathrin inhibitor block the observed change in local membrane order for activated IR? We determined localization to AP2 adaptor containing clathrin coated pits at the cell surface and showed that during the time-course of the experiment that there is no significant change in co-localization or evidence for endocytosis (new figure 9). Therefore, we decided not to do the clathrin inhibitor blocking experiment because we believe that it could only lead to indirect effects.

      (iv) Receptor activation is accompanied by other transitions such as dimerization, etc. Can the authors exclude the possibility that what they are measuring is related to changes in depth of insertion of the NR probe into the plasma membrane outer leaflet that is a consequence of IR conformational transitions associated with activation? This is highly unlikely given the fact that fluidification of the membrane environment is found with all length linkers. Given the intervals in increases in linker length on the 2031 construct, which is the closest to the membrane, it is very difficult to conceive that any of the ones larger than 5 PEGs restrict significantly the membrane insertion of the dye. **Referees cross-commenting**

      I think we have a consensus opinion

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      Referee #3

      Evidence, reproducibility and clarity

      See below. No concerns on any of these issues.

      Significance

      Critique:

      This MS reports a proof-of-principle for using site-directed environmentally sensitive probe technology to assess the local membrane environment of a receptor tyrosine kinase (IR) upon activation. This technology addresses a major gap in our arsenal of tools to study the mechanisms of membrane signaling as the parameters of interest are biophysical parameters rather than purely biochemical ones. How to do this with spatial and temporal resolution is a major challenge. This study builds on previous work by the Riezman group that develops an extrinsic labeling system to tether Nile Red to specific sites on the ectodomain of a signaling receptor and then probe local membrane environments as a function of receptor activity.

      This is a carefully done study is well-controlled, is clever in design and is well-described. Although the major issues to which such a general technology could contribute involve intracellular (and not extracellular) event, the advances described will be of general interest -- particularly that local membrane order decreases when IR becomes activated. Specific comments for the authors' consideration follow:

      Specific Comments:

      (i) As a general comment, the authors are measuring extracellular plasma membrane leaflet properties that may or may not translate to what is happening in the local inner leaflet environment. A general reader may well miss the significance of this. This point needs to be more explicitly emphasized in the Discussion.

      (ii) Why not treat cells with a PLC inhibitor to block PIP2 hydrolysis and ask if that inhibits membrane disorder. It is PIP2 hydrolysis/resynthesis that regulates the actin cytoskeleton at signaling receptors and this seems an attractive candidate for study.

      (iii) The data acquisition time is at least 4 min which is long enough for activated receptors to be recruited to sites of endocytosis. Can the authors exclude the possibility that what they are measuring isn't reflective of such spatial reorganization? Does a clathrin inhibitor block the observed change in local membrane order for activated IR?

      (iv) Receptor activation is accompanied by other transitions such as dimerization, etc. Can the authors exclude the possibility that what they are measuring is related to changes in depth of insertion of the NR probe into the plasma membrane outer leaflet that is a consequence of IR conformational transitions associated with activation?

      Referees cross-commenting

      I think we have a consensus opinion

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this manuscript, authors generated an ACP-attached Nile Red probe in order to specifically label Insulin receptor in the membrane. Owing to this specificity, one can measure the lipid membrane properties around a specific protein in the membrane.

      Major comments:

      For the conclusions in the manuscript to be convincing, in my opinion, these additional data need to be added. Some of these are new experiments, and some are detailed analysis of existing data. The new experiments are not for new line of investigation, instead it is to confirm their statements and conclusions. The major point is the reliability of spectral shift. In usual environment sensitive probes, it is certain that they are in the membrane whatever is done to the membrane. However, when the probe is attached to a protein, it is not trivial to have the same confidence that the probe is always inside the membrane, and it is in the same plane of the membrane. 1992-ACP-IR is a good example; authors state that it binds to the protein outside the membrane, but when there is cholesterol addition and -maybe more interestingly- cholesterol removal, the dye still reacts and changes its emission (even PreCT changes its emission quite a bit at the 570 nm region). This is a clear indication of a change in localization of the probe upon some changes in the membrane. This implies that observed spectral shifts may not be due to lipid packing differences, but due to localization of the probes. For this reason, it is crucial to know where any environment sensitive probe localize in the membrane with respect to membrane normal, and this knowledge is more important for this probe. Related to this, the spectral difference upon insulin treatment and activation of insulin receptor could be due to changes in probe's localization in the membrane. Especially because authors show in Fig1e, the spectra can change depending on the probe localization. Relatedly, quantum yield of NR should be significantly different when it is inside vs outside membrane. Authors should show QY for 1992-ACP-NR and 2031-ACP-NR with different PEG lengths and upon insulin treatment.

      Minor comments:

      • Fig 1d requires quantification
      • Voltage sensitivity of different PEG length of 2031-ACP probe should be added.
      • Fig 3a graph should show all data points, not only bar graphs. Also, the band in 3a for +CoA-PEG-NR is dimmer than other bands, is it specific to this particular gel since quantification does not show any difference?
      • Fig 4d, colour code is needed.
      • Fig 5b and Fig3d are basically the same experiments in terms of control measurement, why is the difference in 3b is 0.04 GP unit while it is 0.007 GP unit?
      • Why is inhibitor data so noisy?

      Significance

      Overall, this is a very useful approach, and this line of research will yield very useful tools to shed light on how lipids surrounding proteins can change their function. Major advance of the paper is the new chemical biology tool. There is also biological data on how insulin can change the insulin receptor's membrane environment which is contradictory to some old literature claiming that InsR becomes more "rafty" upon insulin treatment (e.g., PMID: 11751579).

      If this type of tagging proves robust and reproducible (limitations and concerns listed above and below), it could be used by other researchers to tag their protein of interest and investigate the lipid environment around those proteins.

      The downside of this method is that the probe requires ACP tag, a relatively less used tag than others in biology, therefore researchers interested in using this probe should have their proteins with ACP tag. Moreover, the linker length and ACP-tag position are quite crucial parameters (and probably should be optimized for each protein). Longer PEG lengths cannot report on changes efficiently (Fig3b), while shorter lengths are prone to artefacts as they can go out of membrane (Fig1 and Fig2). This might limit its widespread use.

      Referees cross-commenting

      I agree with all comments and concerns of other reviewers. I see the usability and potential of this new technology along with its limitations as all three reviewers pointed out.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      Techniques to probe the local environment of membrane proteins are sparse, although the influence of lipids on the membrane protein's function are known since many years. Therefore, the paper by Umebayashi et al. is important. The environment-sensitive dye Nile red (NR) coupled to a membrane protein is an appropriate sensor for monitoring the local membrane fluidity. Linking of Nile red to the receptor via a flexible tether was achieved with the acyl carrier protein (ACP)-tag method. Experiments showed that depending on the ACP site a certain linker length is required to have NR inserted in the membrane and thus be an effective sensor for lipid disorder. This technology could be of general usability to study the environment of membrane proteins in the context of their function. As an example, the technique allowed insulin induced membrane disorder in the close insulin receptor vicinity to be observed. Further, results suggested that tyrosine activity is required for this disorder to happen. The experimental results appear to be complete and controls were made.

      Major comments:

      1) Sometimes technical terms are used without explanation: What is the GP value? What is ACP-IR? The spectrum was measured in number of rois? The reader can find those abbreveations out, but it would be nice to have them defined.

      2) Fig. 1d) is confusing. The ACP-IR labelling is evident in 3 panels, but there is no difference in the color (emission spectra of 1992-ACP-IR vs 2031-ACP-IR should be visible??). The DAPI staining is very different. When doing the latter, how difficult is it to get the staining equal?

      3) How can one interpret Fig. 4: a) Control goes over 4 frames, at 240" insulin is added, and 10 frames should show a fluctuation difference? b) A color shift from blue to green is visible after insulin addition. But it is faint - difficult to assess from the pseudo color scheme. What does 1000 pixel top/1000 pixel bottom mean in c). Is it an attempt to better visualize the fluctuation? It is difficult to recognize a difference before and after adding insulin. d) It seems that the kymograph set should show this. What is the color scale? Why is 3 so untypical, i.e., no change? Box 6 is also peculiar: the left side does not show a strong change upon insulin administration, the right side does. Why?

      4) How is the kymogram calculated? The legend says 'The horizontal dimension represents the averaged ZNCC inside the rectangular area, and the vertical dimension represents time'. The averaged ZNCC is a single value, so it is not clear why the kymogram shows a variation from left to right. May it be the ZNCC was averaged just vertically?

      5) When calculating cross-correlation values on images, they need to be aligned. What fraction of the total image does the selected 19x19 box represent? As described, I imagine that a rolling CC over 19x19 pixels is calculated over an image from the time lapse series comparing it with the reference Iave(x,y). Compared to the 3x3 median filtered CP image, the ZNCC image should then be much more blurred??

      Minor comment:

      On page 16 supplementary is not spelled properly.

      Significance

      The key point of this paper is convincing and the new technology appears to have a lot of potential. It can be applied to study membrane protein function in the context of its environment, the lipid bilayer.

      Membrane fluidity measurements have been developed (e.g., using fluorescent probes like laurdan). However, the trick to link a probe like nile red by ACP technology to the insulin receptor and to observe its activity is quite new.

      A most recent description of such a technology is in TrAC Trends in Analytical Chemistry Volume 133, December 2020, 116092.

      Referees cross-commenting

      All comments are constructive and important. The paper is important but needs to be amended as proposed.

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      Reply to the reviewers

      First we would like to express our deep gratitude to the reviewers for thoroughly and fairly reviewing our work.


      Reviewer #1:

      Major Concerns

      1. A major concern I have is with the use of DAPT to modulate Notch signaling, and investigate the impact on integrins, Yap, cadherins, etc. Gamma-secretase, the target of DAPT, cleaves not only Notch receptors, but also IntegrinB1, Nectins, Cadherins, Ephrins and more. This recent review lists 149 substrates (Guner & Lichtenthaler Seminars in Cell & Developmental Biology 2020). The risk that some of the results reflect DAPT impact on IntegrinB1, Cadherins etc themselves is significant. The authors should validate their findings with more specific modulation of Notch activity, for example with a Notch blocking antibody, with siRNA, or with SAHM1. We agree with the reviewer´s comment and will add additional key experiments using SAHM1 as alternative inhibitor of Notch activity.

      Furthermore, EGTA was used to "acutely destabilize VE-Cadherin". But EGTA chelates Calcium, which is essential for Notch structure, and EGTA is thus a well-known activator of Notch signaling (see eg Rand MD et al. (2000) Calcium depletion dissociates and activates heterodimeric notch receptors. Mol Cell Biol). The authors rightfully describe and cite this paper, but the use of EGTA nonetheless confounds interpretation. The authors check for NICD levels (at what timepoint?) but the staining is cytoplasmic (also not labelled in the figure per se, but described in the figure legend? - please label the staining in the panel). And in any case, NICD is very short-lived and nuclear staining cannot be taken as a hallmark of signaling activity. In particular if staining is performed at a time point at which the receptor and NICD may have been exhausted/depleted. The authors should validate these observations/conclusions with the Notch reporter to conclusively demonstrate whether EGTA does not activate Notch in their system.

      To test whether transient treatment with EGTA causes Notch activation we will repeat this experiment with Notch reporter activity as readout.

      Trans-endocytosis of NECD on different substrates: the authors suggest that trans-endocytosis of NECD by Dll4 increases on softer substrates. But the authors also show that soft substrates lead to spreading out of cells, which could confound interpretation (is overlapping membranes, not internalization). The authors could validate trans-endocytosis by FACS: check if red Dll4+ cells contain more NECD. It is also not clear to me in this experiment whether the authors are looking at green NECD, or Notch1 full length, since they write "overlap of Notch1 and Dll4", which would not reflect trans-endocytosis but interactions at the cell surface for both cells. Please also define "overlay intensity", or explain further.

      We will validate the trans-endocytosis by flow cytometry. In addition, we describe the procedure for microscopic analysis more clearly (methods section, p 4; results section, p 17-19)

      The authors conclude their introduction with a statement that mechanosensitivity of Notch is linked to endocytosis, but their conclusion from Fig 6C was that Notch stiffness-dependence was independent of endocytosis, using the rhDll4..?

      We have now rephrased this sentence.

      • *

      Minor concerns

      1. In the introduction, the authors describe Dll3 as a Notch ligand that activates Notch signaling in trans. To my knowledge, Dll3 has only been described as a cis-inhibitor of Notch signaling. (I think this may have arisen during repeated edits of the manuscript!) This has now been corrected in the current version.

      In the introduction, the authors state that Notch1, Dll4 and Jag1 control angiogenesis, but then they only describe what Notch1/Dll4 do in the next few sentences. Perhaps one sentence to describe the role of Jag1 would help avoid the feeling of being "left hanging".

      This has now been corrected in the current version.

      Data presentation: please show all bar graphs with the individual replicates (dotplots).

      We have now changed all bar graphs into scatter plots.

      Data analysis/normalization: many graphs represent normalization of values in multiple steps which are not described in the methods/legends/results. For example, Notch reporter gene activity (Fig 1A) is Firefly divided by Renilla, and presumably normalized to the control condition at 1 (or an average of 1 for the three controls?). This is not explained. Also, it is not clear whether the data reported for the Control condition are Huvec on rhDll4 compared (normalized) to Huvec on control substrate (and similar for each other condition). What controls are included in this experiment? Please provide the full data to provide insight into the magnitude of activation by Dll4 itself. Perhaps "Control" is without rhDll4? But the bar underneath A/B implies this rhDll4 was used in all conditions.

      We have edited our manuscript accordingly to avoid these ambiguities.

      Statistics: data should be presented as means +/- standard deviation, not standard error of the mean (see for example Barde & Barde Perspect Clin Res. 2012): "SEM quantifies uncertainty in estimate of the mean whereas SD indicates dispersion of the data from mean. As readers are generally interested in knowing the variability within sample, descriptive data should be precisely summarized with SD."

      We now use SD instead of SEM.

      Statistics: In the Methods section, the authors state that one-way ANOVA was followed by Dunnett's multiple comparison test, and two-way ANOVA was followed by Tukey's multiple comparison test. Dunnett is used to compare every mean to a control mean, while Tukey is used to compare every mean with every other mean. Fig 1 describes using Dunnett for Fig 1B, but the end of the legend days Tukey was used. However Fig 1A,C show internal pairwise comparisons to plastic. Please be sure to explain which statistics were used where, and why, and if plastic was set as the comparator, please be explicit about this. Fig 3 uses "Sidak's corrected two-way ANOVA" and "Sidak's multiple comparison test"? I think Sidak is a method to correct alpha or p for multiple comparisons, as stated in the first instance, but it is described why this was used here, and not in other analyses, and whether the authors then applied Tukey's post-hoc test as described in the methods section? Similar comments for Fig 6. It is counter-intuitive that the plastic -1.5kPa PDMS difference with no error-bar overlap in 1A would be 1-star significance, while the plastic-70kPa difference with almost overlapping error bars in 1B would be 4-star significance. Please check/show values. In Fig 1B Figure legend, the authors write "Data is presented in a bar plot and compared with the integrin β____1 intensities without DAPT treatment", but this is not the statistical comparison presented. Fig 3B shows a very minor difference with overlapping error bars as 3-star significance? Is this correct?

      We have checked all statistical issues and corrected where necessary. Since the sample size and variance were homogenous in all comparisons we now uniformly use ANOVA and Tukey´s multiple comparison test as post hoc to keep things simple.

      How much nuclear NICD (NICD intensity) is there in control conditions? (Control missing from Fig 1B, D).

      We will repeat the experiment and compare the NICD levels with those in non-activated cells on plastic.

      A DAPI counterstaining for 1B/D right panels would facilitate evaluation of whether NICD nuclear intensity is increased. The same applies for nuclear YAP assessment in Fig 3B. I assume a nuclear counter-stain was done for quantification of nuclear NICD intensity, and nuclear YAP intensity, but this is not described in the Materials and Methods, please add a description of how intensity was quantified, and provide nuclear counterstain images. (Also, what is the unit on the y-axis of "intensity" graphs? Arbitrary units (a.u.)?

      The counterstaining method with Hoechst as well as the use of the nuclear staining for quantitative analysis of images are now described in the Methods section and where needed in the figure legends. The y-axis of the intensity graphs now has a dimension (a.u.). We decided against overlay of the nuclear staining with the NICD or YAP images for graphical reasons (visibility of the respective staining).

      How much "overall" integrin B1 is there in DAPT-treated conditions in Fig 2C? (related to the concept that DAPT could be cleaving integrin B1, it could be depleted at 24 hours..?)

      We will additionally add this experiment and validate the effect of Noch inhibition on the overall intergrin level by the alternative inhibitor SAHM1

      More details regarding the analysis procedure need to be added to the Methods Section. Were cells segmented and then mean intensity estimated for the whole cell? Was this done by means of Intensity Ratio Nuclei Cytoplasm Tool plugin for Fiji alone? Were images background corrected, corrected for inhomogeneous illumination, normalized? In the case of Integrin beta 1 active, the expression seems to be patterned, was intensity expressed as mean intensity of every pixel corresponding to cytoplasm? For VE Cadherin staining, how was intensity estimated (only pixels corresponding to membrane were considered or every pixel of the cell)? Many figures are originated from a confocal microscope: were z-stacks acquired and then maximum projections done? Were z-stacks acquired and then fluorescence quantified in 3D images? Was a single plane acquired or analyzed, and if that is the case, how was this plane chosen?

      The requested information has now been inserted in the respective results and method sections.

      In Fig 4A, how is VE-Cadherin intensity quantified? As an average per field of view? Or per cell? And if per cell, how was each cell delineated? And if not per cell, how were equal cell numbers ensured? In FRAP experiment, how was intensity quantified? Was it per cell, per field of view or per region? Was each bleached region analyzed separately, or each cell? The datapoints should be either added to Figure 4C or as supplementary to assess the fitting. How many bleached regions per cell were done and how many cells were analyzed? In FRAP experiment, was bleaching done with an increased pixel dwell time? Was laser intensity increased? Do you have an estimation of laser power (not percentage) or flux?

      These issues are now described in more detail in the respective figure legend.

      Figure S2 is not referenced in the manuscript - I think a reference to "Figure S3" in the NECD transendocytosis section (no page numbers or line numbering) should be to Fig S2 instead?

      Sorry for this mistake! We corrected this now.

      In Figure 5A NICD nuclear intensity normalized somehow (normalization not explained), and stiffness no longer appears to regulate NICD levels as shown in Figure 1B.

      We have now described the normalization better in the figure legend. The difference to the results in Fig. 1B is that in Fig. 5A the cells were not activated by Dll4 sender cells or rhDll4 (endogenous Notch activity). This is now stated more clearly.

      Fig 6B: From the immuno at right there is a clear stiffness-dependent difference in Transferrin uptake. How were "single cell uptake" and "number of particles" quantified? (How were cell bodies identified?) Uptake could also be verified with FACS.

      In this point, we disagree with the reviewer: we really do not see a systematic difference in intensities between the different substrates. The process of image analysis is now better described in the figure legend. The result was so clear that we did not use FACS as complementary approach.

      Fig 6C: there appear to be very different numbers of cells in the brightfield image at right. Are the 70, 1.5, and 0.5 kPa Notch reporter activities different from one another or only different from plastic? Might these results reflect cell density/increased Notch signaling due to more cell-cell contacts?

      Unfortunately, with decreasing stiffness the PDMS gels become optically more and more cloudy, giving the false impression of a higher cell number. We tried to circumvent this by changing contrast and brightness of the images, but to no satisfying effect. We now mention this issue in the figure legend.

      How was the Dll4 coating of the different substrates done?

      The coating of the substrates is now described under a specific subheading in the Methods section.

      It would be helpful to describe the composition of Collagen G (Collagen I) in the text (it is a risk to expect vendor information to remain available indefinitely).

      The role and composition of the Collagen G coatings was included in the text (p 7). Further information on the manufacturer of the product used is included in the methods section.

      Please list catalog numbers for all reagents, and dilutions used for antibodies.

      We have added this information wherever possible.

      Instead of using red and green for images, maybe cyan, yellow and/or magenta could be used to help the reader see what is being shown (especially if the reader might be color blind).

      We will of course adhere to the respective policy of the publishing journal, once the manuscript is accepted.

      Packages and tools such as Intensity Ratio Nuclei Cytoplasm Tool plugin for FIJI should be referenced.

      We have now referenced respective tools.

      Reviewer #2:

      *Major comments: *

      Is there difference on a growth rate of cells on softer vrs stiffer gels that could affect cell morphology/signaling pathways?

      This is an important point and we will perform additional respective experiments.

      Nuclear localization of NICD and YAP would be good to validate with western blot.

      Quantification of Western Blots (especially after nuclear isolation) is – at least in our hands – much less sensitive and reliable then quantitative imaging. We do not think that this experiment would strengthen our study.

      In Figure 3 and Figure 5, siRNA experiments would strengthen the data. DAPT is not only an inhibitor of Notch but affects to other proteins as well. This should be stated.

      A similar point was raised by Reviewer#1 with the suggestion to use SAHM1 as an alternative to DAPT. As suggested we will add these experiments.

      How was the mean VE-cadherin branch length determined? This term often refers to angiogenesis assay/sprout formation and maybe another one should be considered here to describe VE-cadherin junction morphology.

      Add to all figure texts how many cells were used for the analyses*. *

      The cell number is now added wherever appropriate.

      In Fig. 6C the cell morphology of HUVECs look abnormal in comparison to other images and should be re-done.

      In contrast to all other experiments the cells where not confluent in this case. The different morphology is a sign of the lack of neighbours, not of some problem with the cells.

      Was all the data normally distributed and thus ANOVA was used? Please add more details on the statistics part. Did you remove outliers?

      Like also suggested by Reviewer #1 we have added more information on statistics and streamlined this. The data are normally distributed, outliers wer not removed.

      MTT assay of DAPT would need to be presented as it can be cytotoxic. Cells are not well visible in Fig 2C with DAPT. DAPI and F-actin staining would help to see the cell morphology.

      We will add respective data on cell viability after DAPT (and SAHM1) treatment in a revised version of the manuscript.

      Minor comments:

      Please clarify how coating with rhDDL4 is done as this was unclear at least for this reviewer.

      The coating of the substrates is now described under a specific subheading in the Methods section.

      HUVECs are known to be hard to transfect. Please provide data on transfection efficiencies of all transiently transfected cells.

      We did not systematically monitor transfection efficiencies in this context, since there was always an internal control (e.g. co-reporter in the reporter gene assay) or the data were obtained on a single cell based quantification. Generally, we yield transfection efficiencies around 30% with HUVECs.

      Reviewer #3:

      Major comments:

      • *

      1) The authors use recombinant Dll4 or Dll4-expressing ("sender") cells to activate Notch in co-cultured cells. This is per se fine however, one might over-estimate all other observed downstream effects as endogenous Notch activity is lower. It would be important to see how naïve HUVEC or other primary endothelial cells respond to changes in stiffness. qPCR of Notch target genes such as Hey1, Hey2, Hes5, Dll4 is frequently used as a readout of Notch activity in this context. Also. the Notch transcriptional reporter assay might be a suitable read-out-

      In Fig.5A we show data on endogenous Notch activity (- EGTA) on substrates with different stiffness. In this case NICD levels in the nucleus do not differ. It will definitely be interesting to repeat this experiment based on the reporter gene assay.

      2) As the authors mention in the Discussion, cell density could be of utmost importance given the fact that Notch signaling usually is assumed as an in trans signaling event between adjacent cell membranes. However, also other signaling modes (in cis, cis inhibition, JAG1 vs DLL4 ratio) might be important. As such, the authors should carefully document an report on cell density in all experiments. Secondly, the authors should use other conditions such as sparse cell density and thirdly the authors should measure transcriptional effects of stiffness on Notch ligand expression.

      In all experiments (with the exception of Fig. 6C) we used confluent cells. With the sparse cells (Fig. 6C) we also observe stiffness dependency. Investigating Notch ligand expression is definitely a good idea and will be investigated in the revised manuscript.

      3) The authors need to compare stiffness in their model with physiological conditions in developing tissues and ideally also in tumor which often have increased tissue stiffness.

      *Good point! We have now integrated such comparisons in the Discussion. *

      4) Is Notch activation due to changes in stiffness dependent on the presence of ligands or could it be that (unspecific) binding of Notch receptors to ECM could trigger cleavage just by conformational change?

      Since there is no stiffness dependent response on collagen (Fig. 6C, left panel), an effect of unspecific binding is highly unlikely.

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      Referee #3

      Evidence, reproducibility and clarity

      The authors use different cell culture conditions to alter stiffness (DPMS model) and to measure the effect on Notch signaling and potential upstream and downstream factors. The experiments suggest that softer stiffness leads to higher Notch signaling activity in cultured endothelial cells which had been further stimulated by the Notch ligand DLL4. The data suggest that beta1 integrin activity is promoted by Notch which supports previous findings by others. Also, there is a bidirectional interaction with VE-Cadherin also supporting previous findings. This is a solid study using cultured cells only. The topic is of interest for researches investigating vascular biology, potentially also tumor vascular biology, ECM stiffness and its effect on signaling and Notch signaling per se.

      Major comments:

      • Are the key conclusions convincing? YES
      • Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? NO
      • Would additional experiments be essential to support the claims of the paper? YES, SEE BELOW-
      • Are the suggested experiments realistic in terms of time and resources? YES within about a six months' time period.
      • Are the data and the methods presented in such a way that they can be reproduced? YES, however, more information is needed about cell density on the plates and the DLL4 expression level on the sender cells.
      • Are the experiments adequately replicated and statistical analysis adequate? YES, however showing data points within the bar graphs would improve this study.

      • The authors use recombinant Dll4 or Dll4-expressing ("sender") cells to activate Notch in co-cultured cells. This is per se fine however, one might over-estimate all other observed downstream effects as endogenous Notch activity is lower. It would be important to see how naïve HUVEC or other primary endothelial cells respond to changes in stiffness. qPCR of Notch target genes such as Hey1, Hey2, Hes5, Dll4 is frequently used as a readout of Notch activity in this context. Also. the Notch transcriptional reporter assay might be a suitable read-out-

      • As the authors mention in the Discussion, cell density could be of utmost importance given the fact that Notch signaling usually is assumed as an in trans signaling event between adjacent cell membranes. However, also other signaling modes (in cis, cis inhibition, JAG1 vs DLL4 ratio) might be important. As such, the authors should carefully document an report on cell density in all experiments. Secondly, the authors should use other conditions such as sparse cell density and thirdly the authors should measure transcriptional effects of stiffness on Notch ligand expression.
      • The authors need to compare stiffness in their model with physiological conditions in developing tissues and ideally also in tumor which often have increased tissue stiffness.
      • Is Notch activation due to changes in stiffness dependent on the presence of ligands or could it be that (unspecific) binding of Notch receptors to ECM could trigger cleavage just by conformational change?

      Significance

      It was shown that Notch1 acts as a mechanosensor in endothelial cells. However, it is unclear how blood flow activates Notch1. Also, it is clear that stiffness influences blood vessel formation, which is under genetic control of Notch signaling. The importance of this study is to show that stiffness has a strong effect on Notch1 activation (maybe by increasing pulling force of ligands and subsequent endocytosis).

      The major limitations of this study are:

      1. work was only performed in cell culture, unclear whether there is any relevance in vivo
      2. there is an artificial (over)-activation of endothelial Notch signaling by Dll4 expressing cells. Unclear whether this reflects physiological Notch signaling activity.
      3. The mechanism how Notch1 gets activated remained elusive.
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      Referee #2

      Evidence, reproducibility and clarity

      Kretchmer et al. investigates the role of substrate stiffness on Notch signalling pathway. They show increased Notch activity on softer substrates. Transendocytosis of NECD is suggested to be regulated by the substrate stiffness. They also conclude that the softer the substrate the more integrin beta 1 is activated.

      Major comments:

      Is there difference on a growth rate of cells on softer vrs stiffer gels that could affect cell morphology/signaling pathways?

      Nuclear localization of NICD and YAP would be good to validate with western blot.

      In Figure 3 and Figure 5, siRNA experiments would strengthen the data. DAPT is not only an inhibitor of Notch but affects to other proteins as well. This should be stated.

      How was the mean VE-cadherin branch length determined? This term often refers to angiogenesis assay/sprout formation and maybe another one should be considered here to describe VE-cadherin junction morphology.

      Add to all figure texts how many cells were used for the analyses.

      In Fig. 6C the cell morphology of HUVECs look abnormal in comparison to other images and should be re-done.

      Was all the data normally distributed and thus ANOVA was used? Please add more details on the statistics part. Did you remove outliers?

      MTT assay of DAPT would need to be presented as it can be cytotoxic. Cells are not well visible in Fig 2C with DAPT. DAPI and F-actin staining would help to see the cell morphology.

      Minor comments:

      Please clarify how coating with rhDDL4 is done as this was unclear at least for this reviewer. HUVECs are known to be hard to transfect. Please provide data on transfection efficiencies of all transiently transfected cells.

      Significance

      The paper is interesting for the researchers studying angiogenesis and also cancer as the matrix stiffness regulates cancer progression.

      My expertise lies in understanding mechanisms of angiogenesis, endothelial cell function and crosstalk with other cell types of the vessel wall. My group also studies Hippo signaling and has vast experience on isolation, culturing and doing experiments on HUVECs and other types of endothelial cells.

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      Referee #1

      Evidence, reproducibility and clarity

      In this manuscript, Kretschmer and colleagues investigate the role of matrix stiffness in Notch signaling using a series of gain and loss of function experiments (over-expression and inhibitors). As read-outs they use Notch reporter assays, FRAP, transferrin uptake, and immunofluorescence analyses. The authors conclude that softer substrates potentiate Notch signaling. While the questions are interesting and important, I am concerned with the use of inhibitors with off-target or unintended effects, as listed below. There is also some information missing from Materials and Methods which makes it difficult to assess the methodology and resulting conclusions.

      Major Concerns

      1. A major concern I have is with the use of DAPT to modulate Notch signaling, and investigate the impact on integrins, Yap, cadherins, etc. Gamma-secretase, the target of DAPT, cleaves not only Notch receptors, but also IntegrinB1, Nectins, Cadherins, Ephrins and more. This recent review lists 149 substrates (Guner & Lichtenthaler Seminars in Cell & Developmental Biology 2020). The risk that some of the results reflect DAPT impact on IntegrinB1, Cadherins etc themselves is significant. The authors should validate their findings with more specific modulation of Notch activity, for example with a Notch blocking antibody, with siRNA, or with SAHM1.
      2. Furthermore, EGTA was used to "acutely destabilize VE-Cadherin". But EGTA chelates Calcium, which is essential for Notch structure, and EGTA is thus a well-known activator of Notch signaling (see eg Rand MD et al. (2000) Calcium depletion dissociates and activates heterodimeric notch receptors. Mol Cell Biol). The authors rightfully describe and cite this paper, but the use of EGTA nonetheless confounds interpretation. The authors check for NICD levels (at what timepoint?) but the staining is cytoplasmic (also not labelled in the figure per se, but described in the figure legend? - please label the staining in the panel). And in any case, NICD is very short-lived and nuclear staining cannot be taken as a hallmark of signaling activity. In particular if staining is performed at a time point at which the receptor and NICD may have been exhausted/depleted. The authors should validate these observations/conclusions with the Notch reporter to conclusively demonstrate whether EGTA does not activate Notch in their system.
      3. Trans-endocytosis of NECD on different substrates: the authors suggest that trans-endocytosis of NECD by Dll4 increases on softer substrates. But the authors also show that soft substrates lead to spreading out of cells, which could confound interpretation (is overlapping membranes, not internalization). The authors could validate trans-endocytosis by FACS: check if red Dll4+ cells contain more NECD. It is also not clear to me in this experiment whether the authors are looking at green NECD, or Notch1 full length, since they write "overlap of Notch1 and Dll4", which would not reflect trans-endocytosis but interactions at the cell surface for both cells. Please also define "overlay intensity", or explain further.
      4. The authors conclude their introduction with a statement that mechanosensitivity of Notch is linked to endocytosis, but their conclusion from Fig 6C was that Notch stiffness-dependence was independent of endocytosis, using the rhDll4..?

      Minor concerns

      1. In the introduction, the authors describe Dll3 as a Notch ligand that activates Notch signaling in trans. To my knowledge, Dll3 has only been described as a cis-inhibitor of Notch signaling. (I think this may have arisen during repeated edits of the manuscript!)
      2. In the introduction, the authors state that Notch1, Dll4 and Jag1 control angiogenesis, but then they only describe what Notch1/Dll4 do in the next few sentences. Perhaps one sentence to describe the role of Jag1 would help avoid the feeling of being "left hanging".
      3. Data presentation: please show all bar graphs with the individual replicates (dotplots).
      4. Data analysis/normalization: many graphs represent normalization of values in multiple steps which are not described in the methods/legends/results. For example, Notch reporter gene activity (Fig 1A) is Firefly divided by Renilla, and presumably normalized to the control condition at 1 (or an average of 1 for the three controls?). This is not explained. Also, it is not clear whether the data reported for the Control condition are Huvec on rhDll4 compared (normalized) to Huvec on control substrate (and similar for each other condition). What controls are included in this experiment? Please provide the full data to provide insight into the magnitude of activation by Dll4 itself. Perhaps "Control" is without rhDll4? But the bar underneath A/B implies this rhDll4 was used in all conditions.
      5. Statistics: data should be presented as means +/- standard deviation, not standard error of the mean (see for example Barde & Barde Perspect Clin Res. 2012): "SEM quantifies uncertainty in estimate of the mean whereas SD indicates dispersion of the data from mean. As readers are generally interested in knowing the variability within sample, descriptive data should be precisely summarized with SD."
      6. Statistics:
        • a. In the Methods section, the authors state that one-way ANOVA was followed by Dunnett's multiple comparison test, and two-way ANOVA was followed by Tukey's multiple comparison test. Dunnett is used to compare every mean to a control mean, while Tukey is used to compare every mean with every other mean. Fig 1 describes using Dunnett for Fig 1B, but the end of the legend days Tukey was used. However Fig 1A,C show internal pairwise comparisons to plastic. Please be sure to explain which statistics were used where, and why, and if plastic was set as the comparator, please be explicit about this.
        • b. Fig 3 uses "Sidak's corrected two-way ANOVA" and "Sidak's multiple comparison test"? I think Sidak is a method to correct alpha or p for multiple comparisons, as stated in the first instance, but it is described why this was used here, and not in other analyses, and whether the authors then applied Tukey's post-hoc test as described in the methods section? Similar comments for Fig 6.
        • c. It is counter-intuitive that the plastic -1.5kPa PDMS difference with no error-bar overlap in 1A would be 1-star significance, while the plastic-70kPa difference with almost overlapping error bars in 1B would be 4-star significance. Please check/show values.
        • d. In Fig 1B Figure legend, the authors write "Data is presented in a bar plot and compared with the integrin β1 intensities without DAPT treatment", but this is not the statistical comparison presented.
        • e. Fig 3B shows a very minor difference with overlapping error bars as 3-star significance? Is this correct?
      7. How much nuclear NICD (NICD intensity) is there in control conditions? (Control missing from Fig 1B, D).
      8. A DAPI counterstaining for 1B/D right panels would facilitate evaluation of whether NICD nuclear intensity is increased. The same applies for nuclear YAP assessment in Fig 3B. I assume a nuclear counter-stain was done for quantification of nuclear NICD intensity, and nuclear YAP intensity, but this is not described in the Materials and Methods, please add a description of how intensity was quantified, and provide nuclear counterstain images. (Also, what is the unit on the y-axis of "intensity" graphs? Arbitrary units (a.u.)?
      9. How much "overall" integrin B1 is there in DAPT-treated conditions in Fig 2C? (related to the concept that DAPT could be cleaving integrin B1, it could be depleted at 24 hours..?)
      10. More details regarding the analysis procedure need to be added to the Methods Section. Were cells segmented and then mean intensity estimated for the whole cell? Was this done by means of Intensity Ratio Nuclei Cytoplasm Tool plugin for Fiji alone? Were images background corrected, corrected for inhomogeneous illumination, normalized? In the case of Integrin beta 1 active, the expression seems to be patterned, was intensity expressed as mean intensity of every pixel corresponding to cytoplasm? For VE Cadherin staining, how was intensity estimated (only pixels corresponding to membrane were considered or every pixel of the cell)? Many figures are originated from a confocal microscope: were z-stacks acquired and then maximum projections done? Were z-stacks acquired and then fluorescence quantified in 3D images? Was a single plane acquired or analyzed, and if that is the case, how was this plane chosen?
      11. In Fig 4A, how is VE-Cadherin intensity quantified? As an average per field of view? Or per cell? And if per cell, how was each cell delineated? And if not per cell, how were equal cell numbers ensured?
      12. In FRAP experiment, how was intensity quantified? Was it per cell, per field of view or per region? Was each bleached region analyzed separately, or each cell? The datapoints should be either added to Figure 4C or as supplementary to assess the fitting. How many bleached regions per cell were done and how many cells were analyzed?
      13. In FRAP experiment, was bleaching done with an increased pixel dwell time? Was laser intensity increased? Do you have an estimation of laser power (not percentage) or flux?
      14. Figure S2 is not referenced in the manuscript - I think a reference to "Figure S3" in the NECD transendocytosis section (no page numbers or line numbering) should be to Fig S2 instead?
      15. In Figure 5A NICD nuclear intensity normalized somehow (normalization not explained), and stiffness no longer appears to regulate NICD levels as shown in Figure 1B.
      16. Fig 6B: From the immuno at right there is a clear stiffness-dependent difference in Transferrin uptake. How were "single cell uptake" and "number of particles" quantified? (How were cell bodies identified?) Uptake could also be verified with FACS.
      17. Fig 6C: there appear to be very different numbers of cells in the brightfield image at right. Are the 70, 1.5, and 0.5 kPa Notch reporter activities different from one another or only different from plastic? Might these results reflect cell density/increased Notch signaling due to more cell-cell contacts?
      18. How was the Dll4 coating of the different substrates done?
      19. It would be helpful to describe the composition of Collagen G (Collagen I) in the text (it is a risk to expect vendor information to remain available indefinitely).
      20. Please list catalog numbers for all reagents, and dilutions used for antibodies.
      21. Instead of using red and green for images, maybe cyan, yellow and/or magenta could be used to help the reader see what is being shown (especially if the reader might be color blind).
      22. Packages and tools such as Intensity Ratio Nuclei Cytoplasm Tool plugin for FIJI should be referenced. https://github.com/MontpellierRessourcesImagerie/imagej_macros_and_scripts/wiki/Intensity-Ratio-Nuclei-Cytoplasm-Tool#how-to-cite-the-tool

      Significance

      The concept of how stiffness regulates Notch signaling is of timely interest. While the mechanobiology of Notch has attracted a fair amount of attention (publications), less is known of how stiffness impacts Notch signaling.

      The work could be of interest to the Notch field, biomechanics, cell biology/adhesion experts. It could be relevant for designing cellular scaffolds for biological or medical applications.

      The expertise of this reviewer is Notch and imaging.

  2. Jun 2022
    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      Response to Reviewers

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

      This is a well-executed and interesting study addressing a still controversial issue in clathrin-mediated endocytosis, namely the nature of curvature generation during formation of endocytic clathrin coated vesicles. The authors have applied new techniques to this old question, including state-of-the-art high resolution 3D single-molecule localization microscopy (SMLM, i.e. Super-resolution microscopy), a new maximum-likelihood based fitting framework to fit complex geometric models into localized point clouds (Wu et al., 2020, BioRxix) and mathematical modeling leading to a new cooperative curvature model of clathrin coat remodeling and temporal reconstruction of CCP structural dynamics based on the distribution of static super-resolution images. This is an important contribution, but will it resolve the controversy of constant curvature vs constant area for CCP invagination? I doubt it. In some ways the controversy is somewhat contrived and, as this paper shows the answer is unlikely to be either or. Below are some specific comments, in somewhat random order, from someone (a curmudgeon?) who has reviewed and/or carefully read these papers since 1980. Points that the authors should address are in bold. All can be addressed with modifications to the text, as the one experiment I asked for (quantification of clathrin recruitment) is impossible with this approach).

      • I wonder how many people who cite Heuser's 1980 paper have ever read it carefully. Indeed, many of the observations made here were also made by Heuser. Below, for example, is a summary I wrote, but then removed from a review as it was too lengthy "While Heuser favored the model that CCPs assemble first as flat structures and then rearrange during invagination, he was also careful to note several caveats. First, he observed that the edges of CCPs were 'ragged', likely reflecting sites of assembly of new polygons and that pentagons were more abundant at the edges. Thus, he argued that 'if even a few of these edge pentagons were destined to become completely surrounded with hexagons, it would be necessary to conclude that some degree of curvature can be built into coats as soon as they form". Second, by examining tilted sections he observed that "even the flattest baskets have a small degree of inward curvature, and many were complete hemispheres". Finally, he cautioned that his images were snap-shots and a precursor-product relationship could not, therefore, be unambiguously established and that the very large flat lattices he observed might well be 'prove to be some sort of dead end'. We now know that fibroblasts, in particular, have large numbers of static flat clathrin plagues."

      Thus, many of the author's conclusions, i.e. that 'completely flat clathrin coats are rare (pg 12, although they're not numbered), and that curved structures can be seen to emerge from the edges of flat lattices (see Supplemental Figure 1a, 3 examples on the right) are indeed consistent with Heuser's observations. In many ways, Heuser's 1980 paper is used as a straw man argument for the constant area model. The authors should more accurately cite and acknowledge this seminal paper.

      Response: __We thank the reviewer for this insightful and constructive input on the interpretation of the constant area model (CAM). We have revised the discussion (Page 14, Lines 397-402), citing Heuser’s observations more carefully and in similarity of what was already suggested eloquently by the reviewer. We agree that the strict interpretation of the CAM is misleading, and early evidence already suggests its flawed approximation of the endocytic mechanism (further mentioned now on __Page 15, Lines 429-431).

      • As Heuser did in his 1980 classic, the authors here would do well to note several caveats related to their analyses. These include:

      +

      Like Heuser they have assembled static imaged to create a pseudotemporal model, albeit using a much more quantitative approach. Nonetheless, it seems that this assumes only a single, stereotypic pathway for CCV formation. How good is this assumption? We know from dynamic imaging that there exists significant heterogeneity in both the kinetics and the molecular composition of CCPs. The authors should acknowledge this limitation.

      __Response: __We agree with the reviewer that the lack of direct temporal information is a clear limitation of our approach.

      We now introduce this limitation on Page 16, Lines 474-484, where we discuss the disadvantage of reconstructing an average trajectory based on static images. Here, the assumption of a single, stereotypic pathway of endocytosis is addressed. We cannot exclude the possibility of slight mechanistic variations being averaged out using our approach. However, we want to highlight the fact that our approach seems sensitive enough to distinguish between structures that originate via endocytosis, and structures that derived from a different pathway, potentially from the Golgi.

      We further address the kinetic variability in terms of abortive events on Page 14, Lines 405-411, __and discuss their effect on the mechanistic interpretation of our results. Generally speaking, abortive events are characterized as dim and short-lived structures in live-cell acquisitions. As the earliest structures in our data set already contain half the final coat area, we are most likely not capturing these abortive events in the first place (potential technical reasons for not capturing earlier structures are discussed on __Page 14, Lines 385-395).

      • The method, which required that they 'optimized the sample preparation to densely label clathrin at endocytic sites' involves labeling cells to near saturation with rabbit polyclonal antibodies to both clathrin light chains and clathrin heavy chains followed by detection with a second polyclonal donkey anti-rabbit. This gives 20 nm of additional and presumably flexible linker on the label. How might this effect the measurements and modeling? The Wu et al paper, which BTW has not been peer-reviewed, shows high precision fitting of the nuclear pore structure, but using endogenously tagged NUP-95, not two-layers of antibodies. The authors will need to discuss this limitation, it is my biggest concern regarding the analysis shown.

      Response: __We acknowledge the limitations imposed by indirect immunolabelling and formulated a hypothesis on how this could affect our model fit (mentioned on __Page 13, Line 363, illustrated in Supplementary Figure 6). A larger linkage error between label and target molecule would increase the distribution of localizations around the true underlying structure. As LocMoFit fits our spherical model directly to the localization coordinates, it is able to take this distribution into account, and will weigh the fit results based on the uncertainty of the localization estimation. A uniform distribution of labels around the true underlying structure should therefore be fitted accurately also at larger linkage error. A non-uniform labeling could occur should e.g. the densely crowded space between the coat and the plasma membrane not allow for the diffusion of the antibody to the clathrin epitopes. In that case, labeling would be one-sided, and instead of the true underlying structure, LocMoFit would optimize the spherical model to the highest probability density of label around + 10 nm from the true clathrin coat. This would result in an overestimation of the radius by the model, which we could correct by substracting 10 nm from the experimentally determined radius. This was done in Supplementary Figure 6 for the hypotheses of (1) uniform displacement by the antibodies; (2) biased displacement of the antibodies towards the cytosol; and (3) biased displacement of the antibodies towards the plasma membrane. Whilst we see that the fitting parameters scale with the corrected radii, the mechanistic interpretation of partial flat pre-assembly on the membrane, and subsequent bending and surface area growth still holds true.

      • One reason for continued controversy in this field is the lack of rany attempt to resolve findings obtained using different methods. Can a parsimonious explanation be found, or are their artifacts or misinterpretations of previous findings that can explain the discrepancies? Any valid model should fit all of the valid data. For example, the authors fail to cite a recent paper by Willy et al in Dev Cell (PMID 34774130), which has been on BioRxiv since 2019 (doi: https://doi.org/10.1101/715219). Here, similar to this present study, the authors used high resolution SIM-TIR to analyze ~1000 CCPs in 3 different cells lines (sadly non-overlapping with the cells used herein) and in Drosophila embryos to quantitatively test the two models. They conclude that their findings unambiguously support a constant curvature model. The authors would do the field a favor if they carefully read this paper and identified areas of commonality (i.e. that curvature is detected at early stages in both cases) and possible explanations for the discrepancies. Certainly, they should not ignore it.

      Response: __We agree with the reviewer on the importance of consolidating findings from different studies to converge to a generally accepted mechanism of clathrin coat formation. We had indeed cited Willy et al in the introduction, but agree that further discussion of their findings should be included. We therefore discuss their findings in more detail, also in comparison to our work, on __Page 17, Lines 502-511. We agree that we reach contradictory conclusions, which we think lies at least in part with the way that Willy et al. analyze their data. Willy et al. acquire 2D projections of the endocytic clathrin structures, whose size is just at the limit of their image resolution. They then compare their projected sizes to a purist constant area model, which assumes that a coat has to grow to its entire surface as an entirely flat structure and then instantaneously snaps to an increased curvature, resulting in a sudden drop of the projected area (footprint). As we and others (e.g. Bucher et al 2011, Heuser, 1980) have observed, completely flat lattices are rare, and curvature is initiated before final surface area is acquired. We do not agree that the absence of a purist constant area model implies that clathrin mediated endocytosis follows a constant curvature trajectory. Instead, we imagine that our cooperative curvature model is likely to fit well with the observations of Willy and colleagues.

      • An important body of evidence that is not considered in their model or discussion is that derived from live cell imaging. In addition to the heterogeneity mentioned above, studies have shown that the clathrin addition to CCPs is complete (i.e. the growth phase) occurs within the first ~20-30s, followed by a variable length (0->100s) plateau phase (Loerke et al, PMID 21447041). Both the current study and the Willy et al study admit that they may not be able to detect the earliest intermediates in CCP assembly. Indeed, in this study the smallest surface area CCPs are only 2-fold smaller than the largest CCPs, suggesting that over half of the triskelions have been recruited before a CCP can be distinguished from the background of clustered, nonspecifically-bound antibodies. Could the authors be monitoring events during the plateau phase and not the earliest events? Regardless, the findings are important as they address the nature of curvature generation during this plateau phase. While monitoring curvature generation during early events in CME, a recent study (Wang et al., eLife, PMID 32352376) showed that the acquisition of curvature within the first 20s of CCP assembly was a distinguishing feature between abortive and productive events. The authors might discuss how these studies on CCP dynamics might (or might not) inform their models.

      __Response: __We thank the reviewer for this very insightful comment and discuss this hypothesis on __Page 16-17, Lines 485-511. __We suggest that part of the initiating/growth phase observed in live-cell dynamics falls into the fast, flat assembly that we are unable to capture with our approach. It is challenging to clearly identify at which point in real-time we are detecting our earliest sites. We would however argue that the plateau phase in real-time could coincide with curvature generation and final addition of triskelia at the lattice rim. The variability in the duration of this plateau phase could therefore result from variable recruitment speed of triskelia and other factors during the finalizing of the vesicle neck.

      • The authors advertise 'quantitative' description of clathrin coated structure and indeed their measurements and models are quantitative; but there is no measure of intensity/numbers of triskelions and CCP growth: an important piece of quantitative data. I expect this is impossible with indirect immunofluorescence but should be considered as a limitation of the approach. Indeed, to my knowledge no one has yet quantitatively measured curvature generation in parallel to clathrin addition at CCPs (closest is Saffarian and Kirchhausen, PMID 17993495), but they don't discuss the relationship.

      Response: __We agree with the reviewer that quantifying the number of triskelia would be an essential piece of information to correlate area growth and curvature generation with dynamic information retrieved from fluorescence intensity in live-cell studies. Unfortunately, the indirect immunolabelling approach used in this work complicated this quantification, and direct comparison between number of localizations and fluorescence intensity cannot be made. However, we do observe a correlation between coat surface area and number of localizations in our data and show this in the newly added __Supplementary Figure 7. This allows us to formulate the hypothesis on Page 16-17, Lines 485-511, which suggests that the plateauing of fluorescence intensity coincides with curvature generation and final triskelia addition to the coat rim. We further highlight the necessity of capturing both high spatial and temporal resolution simultaneously, to ultimately overcome this limitation.

      • On page 7 equation 1, you assume a constant growth rate for addition of triskelia, but later describe that the rate might be cooperative (as the number of edges increases). How would this affect your modeling?

      Response: __We formulate the __surface area growth rate of the clathrin coat to be proportional to the rim length with a constant____ rate. The cooperativity between clathrin molecules we consider to affect the rate of curvature generation. The more molecules are present, the more the entire coat is inclined to bent. We rephrased that section to emphasize this distinction (Page 8, Line 217).

      Minor points:

      • Can you indicate in the first paragraph of the results that you are using indirect immunofluorescence with rabbit anti-CLCA, anti-CHC and detection with donkey anti-rabbit for labeling, to augment the rather vague statement 'we optimized the sample preparation to densely label clathrin at endocytic sites'.

      Response: __We added a clear indication on the labelling strategy used in this work on __Page 4, Lines 109-110.

      • I'm not comfortable with the conclusioin on page 5 that your data 'indicates that at the time point of scission, the clathrin coat of nascent vesicles is still incomplete'. Other explanations might be the relative kinetics of scission vs CCP growth (i.e. these structures are too transient to detect), or that deeply invaginated pits are sheered-off the membrane during sample preparation (there is evidence that most biochemically isolated CCVs are derived from sheered CCPs).

      Response: __We extended the explanation for the absence of fully closed vesicles with the hypotheses mentioned by the reviewer on __Page 5, Lines 159-161.

      • Bottom of page 5, can you briefly mention what data is shown in Supplemental Figure 2 (ie. Figure 2D and examples of likely non-endocytic CCPs shown in Supplemental Figure 2). When I read this, I questioned your speculation.

      Response: __We clarified the cross reference to (now) Supplementary Figure 3 accordingly on __Page 6, Lines 184-185.

      • Can you indicate N CCPs from N cells in the data in Tables 2-3 for fibroblasts and U2OS cells? Do you observe and have to ignore a larger number of flat/clustered CCPs in the fibroblasts?

      Response: __We indicated the number of cells and sites per data set in the Table captions on __Page 36, Lines 51; 959; and 967. We did not quantify the number of flat/clustered, plaque like structures in our data sets. During data acquisition, we would specifically select cells with minimal number of these structures present, and even within this cell chose an area in the periphery exhibiting low number of plaques. Our data is therefore not ideal to reliably quantify plaque density between different cell lines. Qualitative observations showed that whilst we had to disregard a few cells from the U2OS and SK-MEL-2 cell-lines due to high plaque formation, the 3T3 fibroblasts were relatively straight forward to image, as few cells showed high plaque density. A recent study by Hakanpää et al., 2022 (bioRxiv) showed the decreased formation of plaques when cells were seeded on fibronectin. The fact that fibroblasts excrete their own fibronectin agrees well with our observations of relatively few 3T3 cells exhibiting extensive plaque formation.

      • The last 3 paragraphs of the Introduction are results. The Introduction might best be used to review literature in more detail, discuss the reasons why uncertainty still exists and perhaps indicate how the methods applied here will help.

      Response: __We re-wrote the last 3 paragraphs of the introduction, now clearly stating the knowledge gap in the field, and what methods would be required to bridge it (Page 3, Lines 80-102).__

      Reviewer #1 (Significance (Required)):

      This is another excellent addition to a growing list of papers seeking to define the process of curvature generation at endocytic clathrin coated pits. In my opinion, its impact would be increased by better integrating the results presented here with other studies and methods, including the recent paper by Willy et al and the large body of literature on coated pit dynamics, some of which might be relevant in interpreting results, or at least placing them in a real vs pseudo-temporal perspective. The methods introduced and the quality of imaging, modeling and quantification further increase the study's significance. The finds will be of interest to those in the CME field, those studying membrane curvature generation in other contexts, those modeling CME, vesicle formation and curvature generation and those using SMLM to discern the structure of macromolecular assemblies.

      Reviewer expertise: Clathrin-mediated endocytosis (Sandra Schmid)

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

      Summary In this article, the authors aimed to investigate the dynamic of clathrin lattice during clathrin-mediated endocytosis (CME). Overall, they successfully achieved the goal by observing a large number of clathrin spots from several cell lines with 3D single-molecule localization microscopy (SMLM). With the help of this high-resolution imaging technique, they were able to describe the physical properties of each spot and reconstruct the assembly and remodeling of the clathrin coat. Moreover, by comparing the constant area/curvature model with their own data, the authors highlighted that neither of the prevailing models perfectly explained what they observed and proposed 'cooperative curvature model'. With the novel model, the authors were able to reconstruct the clathrin coat remodeling in different cell lines and concluded that the simultaneously bending and assembly of the clathrin coat is a homogenous property of endocytosis.

      The experiments and analytical procedures are well-designed and performed, and the manuscript is well-organized. The conclusion 'cooperative curvature model' was deduced from a large amount of data analysis and clearly stated in the text. I would like to recommend its publication if the following issues will be clarified.

      Major comments:

      1. The authors compared the morphological dynamics of clathrin-coated pit among three different cell lines (SK-MEL-2, U2OS, and 3T3) and found slight differences. As U2OS cells was derived from bone tissues, it has different mechanical properties (membrane tension, elasticity of cortical layer, etc..). It would be interesting to consider those mechanical properties in understanding the morphology (Figure 2) and progress (Figure 4) of the CME. Considering the fact that the bending energy of the plasma membrane is dependent on the membrane tension, they may be able to find some relationships between mechanical properties of the cell cortex and CME.

      __Response: __We thank the reviewer for this comment and very much agree that the relationship between mechanical properties structural adaptation of the endocytic machinery is a highly interesting question. We came to the same conclusion and are therefore exploring this relationship at the moment. This is however not a straightforward task, and the complex nature of plasma membrane mechanics necessitates careful experimental design. It is therefore outside the scope of this publication. We do think this point further highlights the potential of the method presented here, as it allows the investigation of additional principles in clathrin-mediated endocytosis mechanics. We do hope to share our insights on this topic soon.

      In Figure 4, the authors estimated the progression of the CME using the frequency distribution of theta. However, I wonder how they handled the events which were aborted in the middle of the CME. It had been suggested that some CME are aborted during the initial step of the CME. The authors should consider (at least discuss) those abortive events, which can disturb the analysis.

      Response: __Generally speaking, abortive events (now discussed on __Page 14, Lines 405-411) are characterized as dim and short-lived structures in live-cell acquisitions. As the earliest structures in our data set already contain half the final coat area, we are most likely not capturing these abortive events in the first place (potential technical reasons for not capturing earlier structures are discussed on Page 14, Lines 385-395).

      Abortive events throughout the later process of endocytosis would, according to our data, still follow the same mechanistic trajectory as other sites. They could potentially slightly skew our pseudotime analysis, as they would result in an overestimation of specific endocytic stages. The overall mechanistic insight of our work would not be greatly affected, as curvature generation would still occur according to the same trajectory. Due to the low impact on our overall results we do not discuss these late abortive events further.

      Minor comments:

      1. Page5, result section 2. The author should further explain why vesicles from trans Golgi could responsible for the small disconnected set of data points corresponding to the vesicles with larger curvatures.

      Response: __We extended our explanation for the presence of non-endocytically derived structures in our data set on __Page 6, Lines 184-189. We further extended the supplementary information with an additional experiment (Supplementary Figure 4), highlighting the absence of AP2-positive structures within the disconnected population. As AP2 is a specific marker for CME, these results further solidify our hypothesis. Further experiments would be required to determine their exact origin, and are outside of the scope of this publication.

      Page7, line 6. The author assumed that the clathrin coat starts growing on a flat membrane. However, as is mentioned in the discussion, clathrin has been proved to have curvature sensing ability which could be further amplified by adapter proteins by several times (Zeno et al., 2021). So, it seems that clathrin preferred a highly curved membrane instead of a flat one. Is it still reasonable to make this assumption?

      Response: __Whilst our assumption states the growing of clathrin coat on flat membranes, we do not restrict our model to an intercept through 0, and it would therefore still hold true even in the case of growth starting on slightly bent membranes. The impact of the preference of clathrin for curvature is considered as a potential mechanistic explanation for the positive feedback in curvature generation described by our model. We therefore already cite the reference mentioned by the reviewer on __Page 8, Line 224.

      As we do observe flat structures in our data set (discussed more in detail now on Page 14, Lines 396-404), we still think the assumption of early flat growth holds true.

      Page 9, result section 4. In the sentence: "we effectively generated the average trajectories of how curvature, surface area, projected area and lattice edge change during endocytosis in SK-MEL-2 cells (Figure 4B-E)." Here I think the authors are describing Figure 4C-F.

      __Response: __That is correct, an oversight on our part. We changed the cross-reference.

      Page 11, discussion. In the sentence: "A deviation of the cross-sectional profile from a circle is nevertheless preserved in the averaging (Supplementary Figure 5)." I didn't see supplementary figure 5 in the article.

      Response: __We changed the cross-reference. We were addressing a subsection of __Supplementary Figure 8.

      Reviewer #2 (Significance (Required)):

      From a vast amount of microscopic images and data analysis, the manuscript gives a clear model on the progress of the CME, which integrates two opposing models; constant area and constant curvature models. This is a big progress in our understanding of the molecular mechanism of CME, and will attract many researchers in the field of cell biology. From a viewpoint of my expertise (molecular imaging of plasma membrane and endocytic processes), this manuscript has significant impact on the related research fields.

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

      Summary: The authors used single-molecule localization microscopy of clathrin in fixed cells (2 human cell lines, one mouse) to capture snapshots of a clathrin-mediated endocytosis (CME), fitted these localizations to a geometric model of a forming vesicle, and used these fitted measurements to test existing models of clathrin-mediated vesicle formation before refining their own. Specifically, the closing angle, a measure of vesicle completeness, was used as a proxy for growth-stage of the vesicle such that the many captured snapshots could reconstruct a pseudo-timeline with an unknown parameterization of time on closing angle. Two standard models of CME vesicle formation, where the surface area is kept constant or where the curvature is kept constant, were examined and determined to be incommensurate with the pseudo timelines of curvatures and surface area. The authors then describe their own model for CME vesicle formation, in which neither surface area nor curvature are constant in evolution of the vesicle, and cooperative forces are hypothesized to non-linearly modulate the curvature-growth as a function of closing angle. Additionally, by binning snapshots and then aligning, scaling, and azimuthally smoothing each bin, they reconstruct representations of distinct endocytic stages.

      Major comments:

      Most results are quite convincing, and the authors do a nice job of displaying examples of SMLM data, both with fit results as well as example clathrin assemblies that are too far removed from their budding-vesicle model to be included for analysis, for example. It is also worth noting that the clathrin images themselves appear to be very high-quality - clearly, as detailed in the methods, attention was given to each step of the imaging and reconstruction process.

      While the presented cooperative curvature model seems reasonable and surely fits the curvature-, surface area-, and rim length-vs. closing angle data better than the simplistic constant surface-area and constant curvature models, it also has more parameters, namely: gamma (the initial rate of curvature change with closing angle) and H_0 (the final preferred curvature). It would be appropriate to calculate an information criterion (e.g. Bayesian), using an assumption of Gaussian-distributed errors (presumably the data fitting in R was least squares, so this would match) to justify the additional parameters.

      Response: __This is an important observation by the reviewer. Indeed, our model uses one more parameter compared to the models we compare it with. To justify this, we performed the calculation as suggested by the reviewer, and found that the cooperative curvature model (CoopCM) indeed results in the lowest BIC (__Supplementary Notes). We therefore are confident that out of the three models tested in this work, our CoopCM fits best to the underlying experimental data (Page 8, Lines 232-235).

      A related issue relates to the error in the extracted value of the closing angle from a single 3D reconstruction - the error distribution should be quantified for this very important parameter. The errors in the other parameters extracted from the fits are less important, but would enhance the paper.

      Response: __We thank the reviewer for pointing out the importance of the estimation error of the key parameter closing angle. To address this point, based on the geometrical model, we simulated clathrin-coated structures with closing angles evenly distributed across the entire range (0-180°). This realistic simulation represents the data quality (e.g., localization precision and labeling efficiency) of the experimental data (corresponding methods are included in __Pages 22- 23, Lines 679-706). The result of fitting these structures using LocMoFit shows an unbiased estimation with small spread of the error (overall STD = 2.82°; see the newly included Supplementary Figure 2a).

      Pseudo-temporal sorting on closing angle makes sense and I appreciate the authors mentioning potential caveats to the monotonicity, etc. However, a comment about the impact of closing angle errors on the pseudo-time determinations would be helpful. The agreement of theta-rank plots with the hypothesized sqrt(t) scaling is reassuring.

      I additionally appreciate the robustness of fitting a geometric structure from localizations rather than relying on pseudo-temporal sorting on clathrin count extracted from localization-merging of multi-blinking emitters.

      Response: __The pseudo-temporal sorting is based on the precisely estimated closing angle, and therefore is also precise, as the distribution of the fitted closing angle has no significant distortion compared to the expectation (__Supplementary Figure 2b).

      The authors did a nice job of qualifying their more speculative claims, in particular I appreciated their mentioning the possibility that smaller clathrin coats could be below their detection limit.

      The authors state a set of data points in suppl. figure 2D (and suppl. Fig 3A-C) are "likely" small clathrin-coated vesicles from the trans Golgi. I appreciate the examples rendered in that figure so a reader can appraise, but if they have my background they might not know how reasonable exclusion of this data is from model testing. This claim could be rephrased or the rationale expanded upon to justify the Golgi hypothesis.

      Response: __We agree with the reviewer and further expanded on our hypothesis on the origin of the structures within the disconnected cloud of data points (Page 6, Lines 184-189). We further performed an additional experiment (Supplementary Figure 4)__, where we simultaneously imaged the clathrin coat at high resolution, and the CME specific AP2 complex tagged with GFP at diffraction limited resolution. We observed that there were no AP2-GFP positive structures present in the disconnected cloud of our data set, and conclude that these structures indeed must originate via a different pathway.

      The data and methods are presented such that they could be reproduced, and replicating their experiment in multiple cell lines, across multiple species, would seem to be adequate replication. As mentioned above, the statistical analysis of whether the model complexity is justified by improved goodness of fit is currently missing but can readily be checked and added.

      Minor comments:

      Last paragraph of the introduction, positive feedback is mentioned but not the slowing down as preferred curvature is realized (inclusion of which might help foster a clearer understanding of the model early on).

      Response: __We now mention the slowing down towards a preferred curvature in our introduction on __Page 3, Lines 100-102.

      In Fig. 1, please state in the figure caption what is being displayed in the two large panels and what is the color map. Is this the 3D data from the overlapping elliptical Gaussians projected on the plane in a "hot" map? Further, in the top right small panels, are the x-y images projections of all z, or measured at a specific z?

      Response: __We adjusted Figure 1 and the figure caption to clearly explain what is mentioned in each superresolution panel. The exact details for image rendering, including the color map and gaussian blurring of the localization coordinates are now described in the methods on __Page 21, Lines 625-627. Ultimately, the x-y images represent an enlarged view of the projections as visible in the previous two panels. We hope that rephrasing of Figure 1 legend clarifies this accordingly.

      In Eqn. (1), epsilon is not defined.

      Response: __The definition is mentioned on __Page 8, Line 210, right before the equation, same as for kon.

      For the theta-rank plots (Fig4 B, SFig D-F ii) moving the theta(t)=sqrt(t) red curves behind sorted theta data would make the data easier to see.

      __Response: __We adjusted the Figures according to the reviewer's suggestion.

      "Laser" in sentence about the speckle reducer should probably be plural.

      Response: __We corrected this grammar mistake, and changed “laser” to “lasers” on __Page 20, Line 586.

      I would like to see the "custom" algorithm based on redundant cross-correlation for drift correction briefly described.

      Response: __We added an explanation on the algorithm used for the drift correction on __Pages 20-21, Lines 611-617.

      A legend for supplemental figure 3 A-C would be nice.

      Response: __We added a legend for the various models in (now) __Supplementary Figure 5, and further made some clarifications in the figure caption.

      If the definition of the abbreviation flat-to-curved-transition as FTC was explicit I missed it.

      Response: __As we do not use this abbreviation anywhere else in the manuscript, we removed it from the __Supplementary Note to avoid confusion.

      Resolution of 20 and 30 nm (laterally and axially, respectively) was quoted once towards the beginning of the manuscript as being an improvement resulting from the localization method described in Li et al., 2018. Resolution can be difficult to speak about precisely, but the methods section would seem to indicate that localizations are filtered at 20 nm lateral localization precision (potentially 30 nm axially?), and I think the authors could consider rephrasing to depict this unless I am missing elsewhere a description of the resolution metric being used.

      Response: __The original 20 and 30 nm resolution (laterally and axially) was calculated based on the median localization precision values in x-y and z for a representative image, using the FWHM approach (described in Methods __Page 21, Lines 621-624). After consideration of the reviewer's question, we found the modal value to be a better quantity to calculate the resolution, and changed this in the text accordingly (Page 4, Lines 113-115, and Methods Page 21, Lines 621-624).

      Reviewer #3 (Significance (Required)):

      Proteins involved with inducing curvature in membranes are in general very exciting targets for localization microscopy, yet still for many systems questions remain unanswered. The authors tackle one such question in this manuscript. In other, unresolved, discussions, the posed hypotheses are quite similar to the simplistic models surpassed in this work (e.g. that curvature scales linearly with local protein copy number, or that surface area scales linearly with local protein copy number). The idea of cooperativity may be useful for others to consider, and the authors additionally demonstrate a seemingly smooth workflow using their separately described tools (primarily LoMoFit; Wu et al. 2021).

      I myself am not an expert on CME or vesicle trafficking. My background is primarily in SMLM method development and SMLM / fluorescence image analysis. From my perspective, the novelty of the biological conclusions appears to be the authors' specific cooperative model and the presence of two structural states which are enriched (closing angle 70{degree sign} and 130{degree sign}). As referenced, and authors F. Frey and U. S. Schwarz nicely present in Bucher et al. 2018, the constant curvature and constant surface area models are known to be inaccurate descriptions of CME evolution, and further it is also known that clathrin first assembles small flat structures before beginning to curve the membrane. However, the 3D super-resolution imaging and direct evaluation of a 3D model geometry in this work is a nice extension of the 2D super-resolution imaging and projection evaluation in the authors' previous work studying endocytosis through ensemble averaging in yeast (Mund et al. 2018) as well as the analysis on projections in Bucher et al. 2018. Fully 3D treatment of the clathrin structures allows the authors to orient asymmetric assemblies such that they are averaged out in their ensemble reconstruction, and as they point out the molecular specificity afforded by a fluorescence-based technique ensures unbiased segmentation of clathrin-involved endocytic sites. In other words, while this work does not describe a technical advance not already described elsewhere, it sets a nice example for those researching protein-membrane interactions of how to leverage the right tools to clearly and directly answer their questions. With their additional work to make these tools extensible to other geometries, multiple color channels, etc., I expect their work to inspire quality studies in other systems. That significance is complementary to their proposal of a reasonable model for the geometric evolution of CME.

      References:

      Maximum-likelihood model fitting for quantitative analysis of SMLM data, Yu-Le Wu, Philipp Hoess, Aline Tschanz, Ulf Matti, Markus Mund, Jonas Ries, bioRxiv 2021.08.30.456756; doi: https://doi.org/10.1101/2021.08.30.456756

      Bucher, D., Frey, F., Sochacki, K.A. et al. Clathrin-adaptor ratio and membrane tension regulate the flat-to-curved transition of the clathrin coat during endocytosis. Nat Commun 9, 1109 (2018). https://doi.org/10.1038/s41467-018-03533-0

      Markus Mund, Johannes Albertus van der Beek, Joran Deschamps, Serge Dmitrieff, Philipp Hoess, Jooske Louise Monster, Andrea Picco, François Nédélec, Marko Kaksonen, Jonas Ries, Systematic Nanoscale Analysis of Endocytosis Links Efficient Vesicle Formation to Patterned Actin Nucleation, Cell, 174, 4, (2018). https://doi.org/10.1016/j.cell.2018.06.032.

      s

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      The authors used single-molecule localization microscopy of clathrin in fixed cells (2 human cell lines, one mouse) to capture snapshots of a clathrin-mediated endocytosis (CME), fitted these localizations to a geometric model of a forming vesicle, and used these fitted measurements to test existing models of clathrin-mediated vesicle formation before refining their own. Specifically, the closing angle, a measure of vesicle completeness, was used as a proxy for growth-stage of the vesicle such that the many captured snapshots could reconstruct a pseudo-timeline with an unknown parameterization of time on closing angle. Two standard models of CME vesicle formation, where the surface area is kept constant or where the curvature is kept constant, were examined and determined to be incommensurate with the pseudo timelines of curvatures and surface area. The authors then describe their own model for CME vesicle formation, in which neither surface area nor curvature are constant in evolution of the vesicle, and cooperative forces are hypothesized to non-linearly modulate the curvature-growth as a function of closing angle. Additionally, by binning snapshots and then aligning, scaling, and azimuthally smoothing each bin, they reconstruct representations of distinct endocytic stages.

      Major comments:

      Most results are quite convincing, and the authors do a nice job of displaying examples of SMLM data, both with fit results as well as example clathrin assemblies that are too far removed from their budding-vesicle model to be included for analysis, for example. It is also worth noting that the clathrin images themselves appear to be very high-quality - clearly, as detailed in the methods, attention was given to each step of the imaging and reconstruction process.

      While the presented cooperative curvature model seems reasonable and surely fits the curvature-, surface area-, and rim length-vs. closing angle data better than the simplistic constant surface-area and constant curvature models, it also has more parameters, namely: gamma (the initial rate of curvature change with closing angle) and H_0 (the final preferred curvature). It would be appropriate to calculate an information criterion (e.g. Bayesian), using an assumption of Gaussian-distributed errors (presumably the data fitting in R was least squares, so this would match) to justify the additional parameters.

      A related issue relates to the error in the extracted value of the closing angle from a single 3D reconstruction - the error distribution should be quantified for this very important parameter. The errors in the other parameters extracted from the fits are less important, but would enhance the paper.

      Pseudo-temporal sorting on closing angle makes sense and I appreciate the authors mentioning potential caveats to the monotonicity, etc. However, a comment about the impact of closing angle errors on the pseudo-time determinations would be helpful. The agreement of theta-rank plots with the hypothesized sqrt(t) scaling is reassuring. I additionally appreciate the robustness of fitting a geometric structure from localizations rather than relying on pseudo-temporal sorting on clathrin count extracted from localization-merging of multi-blinking emitters.

      The authors did a nice job of qualifying their more speculative claims, in particular I appreciated their mentioning the possibility that smaller clathrin coats could be below their detection limit.

      The authors state a set of data points in suppl. figure 2D (and suppl. Fig 3A-C) are "likely" small clathrin-coated vesicles from the trans Golgi. I appreciate the examples rendered in that figure so a reader can appraise, but if they have my background they might not know how reasonable exclusion of this data is from model testing. This claim could be rephrased or the rationale expanded upon to justify the Golgi hypothesis.

      The data and methods are presented such that they could be reproduced, and replicating their experiment in multiple cell lines, across multiple species, would seem to be adequate replication. As mentioned above, the statistical analysis of whether the model complexity is justified by improved goodness of fit is currently missing but can readily be checked and added.

      Minor comments:

      Last paragraph of the introduction, positive feedback is mentioned but not the slowing down as preferred curvature is realized (inclusion of which might help foster a clearer understanding of the model early on).

      In Fig. 1, please state in the figure caption what is being displayed in the two large panels and what is the color map. Is this the 3D data from the overlapping elliptical Gaussians projected on the plane in a "hot" map? Further, in the top right small panels, are the x-y images projections of all z, or measured at a specific z?

      In Eqn. (1), epsilon is not defined.

      For the theta-rank plots (Fig4 B, SFig D-F ii) moving the theta(t)=sqrt(t) red curves behind sorted theta data would make the data easier to see.

      "Laser" in sentence about the speckle reducer should probably be plural.

      I would like to see the "custom" algorithm based on redundant cross-correlation for drift correction briefly described.

      A legend for supplemental figure 3 A-C would be nice.

      I would enjoy hearing the authors' thoughts on why resting points at closing angle 70{degree sign} and 130{degree sign} are present. If these thoughts can be readily rationalized/referenced some speculation might even be warranted in the text.

      If the definition of the abbreviation flat-to-curved-transition as FTC was explicit I missed it.

      Resolution of 20 and 30 nm (laterally and axially, respectively) was quoted once towards the beginning of the manuscript as being an improvement resulting from the localization method described in Li et al., 2018. Resolution can be difficult to speak about precisely, but the methods section would seem to indicate that localizations are filtered at 20 nm lateral localization precision (potentially 30 nm axially?), and I think the authors could consider rephrasing to depict this unless I am missing elsewhere a description of the resolution metric being used.

      Significance

      Proteins involved with inducing curvature in membranes are in general very exciting targets for localization microscopy, yet still for many systems questions remain unanswered. The authors tackle one such question in this manuscript. In other, unresolved, discussions, the posed hypotheses are quite similar to the simplistic models surpassed in this work (e.g. that curvature scales linearly with local protein copy number, or that surface area scales linearly with local protein copy number). The idea of cooperativity may be useful for others to consider, and the authors additionally demonstrate a seemingly smooth workflow using their separately described tools (primarily LoMoFit; Wu et al. 2021).

      I myself am not an expert on CME or vesicle trafficking. My background is primarily in SMLM method development and SMLM / fluorescence image analysis. From my perspective, the novelty of the biological conclusions appears to be the authors' specific cooperative model and the presence of two structural states which are enriched (closing angle 70{degree sign} and 130{degree sign}). As referenced, and authors F. Frey and U. S. Schwarz nicely present in Bucher et al. 2018, the constant curvature and constant surface area models are known to be inaccurate descriptions of CME evolution, and further it is also known that clathrin first assembles small flat structures before beginning to curve the membrane. However, the 3D super-resolution imaging and direct evaluation of a 3D model geometry in this work is a nice extension of the 2D super-resolution imaging and projection evaluation in the authors' previous work studying endocytosis through ensemble averaging in yeast (Mund et al. 2018) as well as the analysis on projections in Bucher et al. 2018. Fully 3D treatment of the clathrin structures allows the authors to orient asymmetric assemblies such that they are averaged out in their ensemble reconstruction, and as they point out the molecular specificity afforded by a fluorescence-based technique ensures unbiased segmentation of clathrin-involved endocytic sites. In other words, while this work does not describe a technical advance not already described elsewhere, it sets a nice example for those researching protein-membrane interactions of how to leverage the right tools to clearly and directly answer their questions. With their additional work to make these tools extensible to other geometries, multiple color channels, etc., I expect their work to inspire quality studies in other systems. That significance is complementary to their proposal of a reasonable model for the geometric evolution of CME.

      References:

      Maximum-likelihood model fitting for quantitative analysis of SMLM data Yu-Le Wu, Philipp Hoess, Aline Tschanz, Ulf Matti, Markus Mund, Jonas Ries bioRxiv 2021.08.30.456756; doi: https://doi.org/10.1101/2021.08.30.456756

      Bucher, D., Frey, F., Sochacki, K.A. et al. Clathrin-adaptor ratio and membrane tension regulate the flat-to-curved transition of the clathrin coat during endocytosis. Nat Commun 9, 1109 (2018). https://doi.org/10.1038/s41467-018-03533-0

      Markus Mund, Johannes Albertus van der Beek, Joran Deschamps, Serge Dmitrieff, Philipp Hoess, Jooske Louise Monster, Andrea Picco, François Nédélec, Marko Kaksonen, Jonas Ries, Systematic Nanoscale Analysis of Endocytosis Links Efficient Vesicle Formation to Patterned Actin Nucleation, Cell, 174, 4, (2018). https://doi.org/10.1016/j.cell.2018.06.032.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      In this article, the authors aimed to investigate the dynamic of clathrin lattice during clathrin-mediated endocytosis (CME). Overall, they successfully achieved the goal by observing a large number of clathrin spots from several cell lines with 3D single-molecule localization microscopy (SMLM). With the help of this high-resolution imaging technique, they were able to describe the physical properties of each spot and reconstruct the assembly and remodeling of the clathrin coat. Moreover, by comparing the constant area/curvature model with their own data, the authors highlighted that neither of the prevailing models perfectly explained what they observed and proposed 'cooperative curvature model'. With the novel model, the authors were able to reconstruct the clathrin coat remodeling in different cell lines and concluded that the simultaneously bending and assembly of the clathrin coat is a homogenous property of endocytosis. The experiments and analytical procedures are well-designed and performed, and the manuscript is well-organized. The conclusion 'cooperative curvature model' was deduced from a large amount of data analysis and clearly stated in the text. I would like to recommend its publication if the following issues will be clarified.

      Major comments:

      1. The authors compared the morphological dynamics of clathrin-coated pit among three different cell lines (SK-MEL-2, U2OS, and 3T3) and found slight differences. As U2OS cells was derived from bone tissues, it has different mechanical properties (membrane tension, elasticity of cortical layer, etc..). It would be interesting to consider those mechanical properties in understanding the morphology (Figure 2) and progress (Figure 4) of the CME. Considering the fact that the bending energy of the plasma membrane is dependent on the membrane tension, they may be able to find some relationships between mechanical properties of the cell cortex and CME.
      2. In Figure 4, the authors estimated the progression of the CME using the frequency distribution of theta. However, I wonder how they handled the events which were aborted in the middle of the CME. It had been suggested that some CME are aborted during the initial step of the CME. The authors should consider (at least discuss) those abortive events, which can disturb the analysis.

      Minor comments:

      1. Page5, result section 2. The author should further explain why vesicles from trans Golgi could responsible for the small disconnected set of data points corresponding to the vesicles with larger curvatures.
      2. Page7, line 6. The author assumed that the clathrin coat starts growing on a flat membrane. However, as is mentioned in the discussion, clathrin has been proved to have curvature sensing ability which could be further amplified by adapter proteins by several times (Zeno et al., 2021). So, it seems that clathrin preferred a highly curved membrane instead of a flat one. Is it still reasonable to make this assumption?
      3. Page 9, result section 4. In the sentence: "we effectively generated the average trajectories of how curvature, surface area, projected area and lattice edge change during endocytosis in SK-MEL-2 cells (Figure 4B-E)." Here I think the authors are describing Figure 4C-F.
      4. Page 11, discussion. In the sentence: "A deviation of the cross-sectional profile from a circle is nevertheless preserved in the averaging (Supplementary Figure 5)." I didn't see supplementary figure 5 in the article.

      Significance

      From a vast amount of microscopic images and data analysis, the manuscript gives a clear model on the progress of the CME, which integrates two opposing models; constant area and constant curvature models. This is a big progress in our understanding of the molecular mechanism of CME, and will attract many researchers in the field of cell biology. From a viewpoint of my expertise (molecular imaging of plasma membrane and endocytic processes), this manuscript has significant impact on the related research fields.

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      Referee #1

      Evidence, reproducibility and clarity

      This is a well-executed and interesting study addressing a still controversial issue in clathrin-mediated endocytosis, namely the nature of curvature generation during formation of endocytic clathrin coated vesicles. The authors have applied new techniques to this old question, including state-of-the-art high resolution 3D single-molecule localization microscopy (SMLM, i.e. Super-resolution microscopy), a new maximum-likelihood based fitting framework to fit complex geometric models into localized point clouds (Wu et al., 2020, BioRxix) and mathematical modeling leading to a new cooperative curvature model of clathrin coat remodeling and temporal reconstruction of CCP structural dynamics based on the distribution of static super-resolution images. This is an important contribution, but will it resolve the controversy of constant curvature vs constant area for CCP invagination? I doubt it. In some ways the controversy is somewhat contrived and, as this paper shows the answer is unlikely to be either or. Below are some specific comments, in somewhat random order, from someone (a curmudgeon?) who has reviewed and/or carefully read these papers since 1980. Points that the authors should address are in bold. All can be addressed with modifications to the text, as the one experiment I asked for (quantification of clathrin recruitment) is impossible with this approach).

      1. I wonder how many people who cite Heuser's 1980 paper have ever read it carefully. Indeed, many of the observations made here were also made by Heuser. Below, for example, is a summary I wrote, but then removed from a review as it was too lengthy

      "While Heuser favored the model that CCPs assemble first as flat structures and then rearrange during invagination, he was also careful to note several caveats. First, he observed that the edges of CCPs were 'ragged', likely reflecting sites of assembly of new polygons and that pentagons were more abundant at the edges. Thus, he argued that 'if even a few of these edge pentagons were destined to become completely surrounded with hexagons, it would be necessary to conclude that some degree of curvature can be built into coats as soon as they form". Second, by examining tilted sections he observed that "even the flattest baskets have a small degree of inward curvature, and many were complete hemispheres". Finally, he cautioned that his images were snap-shots and a precursor-product relationship could not, therefore, be unambiguously established and that the very large flat lattices he observed might well be 'prove to be some sort of dead end'. We now know that fibroblasts, in particular, have large numbers of static flat clathrin plagues."

      Thus, many of the author's conclusions, i.e. that 'completely flat clathrin coats are rare (pg 12, although they're not numbered), and that curved structures can be seen to emerge from the edges of flat lattices (see Supplemental Figure 1a, 3 examples on the right) are indeed consistent with Heuser's observations. In many ways, Heuser's 1980 paper is used as a straw man argument for the constant area model. The authors should more accurately cite and acknowledge this seminal paper. 2. As Heuser did in his 1980 classic, the authors here would do well to note several caveats related to their analyses. These include: - a. Like Heuser they have assembled static imaged to create a pseudotemporal model, albeit using a much more quantitative approach. Nonetheless, it seems that this assumes only a single, stereotypic pathway for CCV formation. How good is this assumption? We know from dynamic imaging that there exists significant heterogeneity in both the kinetics and the molecular composition of CCPs. The authors should acknowledge this limitation. - b. The method, which required that they 'optimized the sample preparation to densely label clathrin at endocytic sites' involves labeling cells to near saturation with rabbit polyclonal antibodies to both clathrin light chains and clathrin heavy chains followed by detection with a second polyclonal donkey anti-rabbit. This gives 20 nm of additional and presumably flexible linker on the label. How might this effect the measurements and modeling? The Wu et al paper, which BTW has not been peer-reviewed, shows high precision fitting of the nuclear pore structure, but using endogenously tagged NUP-95, not two-layers of antibodies. The authors will need to discuss this limitation, it is my biggest concern regarding the analysis shown.<br /> 3. One reason for continued controversy in this field is the lack of rany attempt to resolve findings obtained using different methods. Can a parsimonious explanation be found, or are their artifacts or misinterpretations of previous findings that can explain the discrepancies? Any valid model should fit all of the valid data. For example, the authors fail to cite a recent paper by Willy et al in Dev Cell (PMID 34774130), which has been on BioRxiv since 2019 (doi: https://doi.org/10.1101/715219). Here, similar to this present study, the authors used high resolution SIM-TIR to analyze ~1000 CCPs in 3 different cells lines (sadly non-overlapping with the cells used herein) and in Drosophila embryos to quantitatively test the two models. They conclude that their findings unambiguously support a constant curvature model. The authors would do the field a favor if they carefully read this paper and identified areas of commonality (i.e. that curvature is detected at early stages in both cases) and possible explanations for the discrepancies. Certainly, they should not ignore it. 4. An important body of evidence that is not considered in their model or discussion is that derived from live cell imaging. In addition to the heterogeneity mentioned above, studies have shown that the clathrin addition to CCPs is complete (i.e. the growth phase) occurs within the first ~20-30s, followed by a variable length (0->100s) plateau phase (Loerke et al, PMID 21447041) . Both the current study and the Willy et al study admit that they may not be able to detect the earliest intermediates in CCP assembly. Indeed, in this study the smallest surface area CCPs are only 2-fold smaller than the largest CCPs, suggesting that over half of the triskelions have been recruited before a CCP can be distinguished from the background of clustered, nonspecifically-bound antibodies. Could the authors be monitoring events during the plateau phase and not the earliest events? Regardless, the findings are important as they address the nature of curvature generation during this plateau phase. While monitoring curvature generation during early events in CME, a recent study (Wang et al., eLife, PMID 32352376) showed that the acquisition of curvature within the first 20s of CCP assembly was a distinguishing feature between abortive and productive events. The authors might discuss how these studies on CCP dynamics might (or might not) inform their models. 5. The authors advertise 'quantitative' description of clathrin coated structure and indeed their measurements and models are quantitative; but there is no measure of intensity/numbers of triskelions and CCP growth: an important piece of quantitative data. I expect this is impossible with indirect immunofluorescence but should be considered as a limitation of the approach. Indeed, to my knowledge no one has yet quantitatively measured curvature generation in parallel to clathrin addition at CCPs (closest is Saffarian and Kirchhausen, PMID 17993495), but they don't discuss the relationship. 6. On page 7 equation 1, you assume a constant growth rate for addition of triskelia, but later describe that the rate might be cooperative (as the number of edges increases). How would this affect your modeling?

      Minor points:

      • Can you indicate in the first paragraph of the results that you are using indirect immunofluorescence with rabbit anti-CLCA, anti-CHC and detection with donkey anti-rabbit for labeling, to augment the rather vague statement 'we optimized the sample preparation to densely label clathrin at endocytic sites'.
      • I'm not comfortable with the conclusioin on page 5 that your data 'indicates that at the time point of scission, the clathrin coat of nascent vesicles is still incomplete'. Other explanations might be the relative kinetics of scission vs CCP growth (i.e. these structures are too transient to detect), or that deeply invaginated pits are sheered-off the membrane during sample preparation (there is evidence that most biochemically isolated CCVs are derived from sheered CCPs).
      • Bottom of page 5, can you briefly mention what data is shown in Supplemental Figure 2 (ie. Figure 2D and examples of likely non-endocytic CCPs shown in Supplemental Figure 2). When I read this, I questioned your speculation.
      • Can you indicate N CCPs from N cells in the data in Tables 2-3 for fibroblasts and U2OS cells? Do you observe and have to ignore a larger number of flat/clustered CCPs in the fibroblasts?
      • The last 3 paragraphs of the Introduction are results. The Introduction might best be used to review literature in more detail, discuss the reasons why uncertainty still exists and perhaps indicate how the methods applied here will help.

      Significance

      This is another excellent addition to a growing list of papers seeking to define the process of curvature generation at endocytic clathrin coated pits. In my opinion, its impact would be increased by better integrating the results presented here with other studies and methods, including the recent paper by Willy et al and the large body of literature on coated pit dynamics, some of which might be relevant in interpreting results, or at least placing them in a real vs pseudo-temporal perspective. The methods introduced and the quality of imaging, modeling and quantification further increase the study's significance. The finds will be of interest to those in the CME field, those studying membrane curvature generation in other contexts, those modeling CME, vesicle formation and curvature generation and those using SMLM to discern the structure of macromolecular assemblies.

      Reviewer expertise: Clathrin-mediated endocytosis (Sandra Schmid)

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      Reply to the reviewers

      We thank the reviewers for their constructive comments and are pleased that all reviewers share our opinion, that the present study “makes an important contribution to the molecular architecture of mitochondria”, is in addition “an important advancement in our understanding of the mechanism by which Cqd1 regulates CoQ distribution” and will “thereby appealing to the broad readership of the journals”. We are convinced that addressing the important points raised by the reviewers will further strengthen the manuscript and result in additional significant insights in the molecular function of Cqd1.

      Reviewer #1:

      The major concerns affecting the conclusions are: 1) Experimental evidence is lacking on the contribution of contact site formation by Cqd1 to the effects on mitochondrial architecture and respiration-dependent growth. Determining the effects of the overexpression of the kinase-dead mutant on mitochondrial morphology and contact site formation with Por1-Om14 can address that.

      We thank reviewer #1 for raising these important points. Indeed, the various functions of Cqd1 might be independent from each other and so far we cannot distinguish between them. As suggested by the reviewer we will analyze the effect of overexpression of CQD1 in the Dups1 deletion mutant and make use of the point mutant in the conserved ATP binding domain which cannot complement the phenotype of the Dups1 Dcqd1 double deletion mutant. We generated a yeast mutant strain expressing Om14-3xHA in the absence of wild type Cqd1. Expression of the cqd1(E330A) mutant in the Om14-3xHA background and subsequent immunoprecipitation will allow us to test whether ATP binding is also essential for contact site formation. Preliminary experiments showed that the overexpression of cqd1(E330A) in the Dcqd1 deletion background results in a growth defect comparable to that caused by overexpression of CQD1 WT. Therefore, we think it might be more promising to analyze the interaction of Om14 and Cqd1 E330A at wild type level in order to avoid pleiotropic effects.

      In addition, we will further characterize the cqd1(E330A) mutant by analyzing the effect of its overexpression on mitochondrial morphology, cell growth and assembly of MICOS and F1FO ATP synthase in the Dcqd1 deletion background.

      2) Related to point #1, Cqd1 overexpression in deltaUsp1 cells could have addressed whether the role of Cqd1 in contact sites and mitochondrial architecture is independent of its role on CoQ distribution and phospholipid metabolism. Further characterization of the kinase-dead Cqd1 mutant on CoQ distribution, contact sites, mitochondrial archictecture and phsophsolipid metabolism might help discerning how these activities can be separated.

      We agree that the related points 1) and 2) raised by reviewer #1 are important and addressed our plans in the response on point 1).

      3) It is unclear how both Cqd1 overexpression and deletion induce mitochondrial fragmentation. Performing live cell imaging with a mitochondrial-phoactivatable GFP to measure mitochondrial fusion rates could help discerning the causes for fragmentation. It is a possibility that overexpression induced fragmentation by activating fission without changing fusion, while deletion induced fragmentation by blocking fusion.

      We thank reviewer #1 for bringing up this point. Perhaps our explanation in this respect was too short. Fig. 4E shows that deletion of CQD1 does not result in altered mitochondrial morphology, however, deletion of CQD1 in the Dups1 background leads to virtual complete fragmentation of the mitochondrial network. This is likely due to inhibition of mitochondrial fusion through disturbed processing of the fusion protein Mgm1 (see Fig. 4D). In contrast, overexpression of CQD1 does NOT result in formation of small mitochondrial fragments, but in formation of huge mitochondrial clusters which in addition contain a large proportion of ER membranes. So, we don’t think that this phenotype is related to either enhanced fission or reduced fusion. We will clarify this point in text of the revised manuscript.

      Minor comment:

      1) Figure 4 claims that mitochondrial function is impaired by ups1 deletion, which Cqd1 deletion exacerbates. However, no respiration data is shown in figure 1, only measurements of mitochondrial architecture are shown. Thus, oxygen consumption measurements are needed to claim effects on mitochondrial function.

      We did not want to claim that mitochondria lose respiratory competence upon simultaneous deletion of CQD1 and UPS1. Actually, our results indicate that the Dups1 Dcqd1 double deletion mutant grows like wild type on complete medium containing glycerol. Therefore, respiration is not impaired in this mutant. However, mitochondrial function is not restricted to ATP production by oxidative phosphorylation. The reviewer probably refers to Figure 4 where we show that mitochondrial biogenesis and dynamics are impaired in the Dups1 Dcqd1 double deletion mutant – the heading of the legend summarizes this as "mitochondrial function". We will be more precise in the revised version on this point and add a panel showing growth of the mutant strain on non-fermentable carbon source to avoid any further confusion.

      2) Some Western blots lack quantifications and statistical analyses of independent experiments.

      It is correct that some quantification and the respective statistics were missing in the initially submitted manuscript. We will add the requested information in the revised version of the manuscript.

      Reviewer #2:

      I have the following concerns for the authors to consider. (1) Although biochemical evidence shows that Cqd1 is likely a factor that forms CS structures in mitochondria, it would make the manuscript stronger if the authors can observe uneven distribution of Cqd1 in the mitochondrial membranes (assessed by fluorescent microscopy or ideally high-resolution microscopy) and the presence of Cqd1 in the region of close apposition of the OM and IM by immunogold labeling for electron microscopy.

      Two independent lines of evidence show that Cqd1 is a novel contact site protein: (i) it is found in the contact site fraction in density gradients (Fig. 6A), and (ii) it can be co-immunoprecipitated with outer membrane proteins (Fig. 6G, H, I). Furthermore, the co-IP is supported by cross-links of expected size (Fig. 6F). In sum, we feel that this is solid evidence to support our claim that Cqd1 is present in mitochondrial contact sites. However, it still might be interesting to check an uneven distribution of Cqd1 in mitochondria, as suggested by the reviewer. We will do this by 3D deconvolution fluorescence microscopy.

      (2) Since the structural characterization of Cqd1 is important to understand its interactions with the OM proteins and other UbiB protein kinase-like family proteins, Coq8 and Cqd2, take different orientations, the membrane topology of Cqd1 should be experimentally analyzed. The authors state, "two hydrophobic stretches can be identified in the Cqd1 sequence, of which the first one (amino acids 125-142) might be a bona fide transmembrane segment" (lines 97-100); then is Cqd1 a single membrane spanning protein or two-membrane spanning protein?  

      Unfortunately, it was not possible to test the location of the N terminus experimentally because an N-terminally tagged variant of Cqd1 (tag inserted between presequence and mature part) turned out to be unstable. We consider it very unlikely that the second hydrophobic stretch is a transmembrane domain as it is rather short (only 11 amino acids). Furthermore, several Cqd1 homologs in other fungi, including Yarrowia lipolytica, Aspergillus niger and Schizosaccharomyces pombe, are lacking the second hydrophobic stretch. Therefore, we propose that the major part of Cqd1 including the protein kinase-like domain is exposed to the intermembrane space. We will point out this more clearly in the revised manuscript.

      (3) The authors state, "conserved GxxxG dimerization motif (amino acids 504‐508)" (Fig. 1A caption), but this description needs a reference. The GxxxG motif was proposed to mediate transmembrane helix-helix association (https://doi.org/10.1006/jmbi.1999.3489), which is not consistent with the membrane topology proposed by the authors.

      We thank reviewer #2 for this comment. It is correct that GxxxG motifs are usually present in transmembrane a-helices. However, there is information available indicating that these motifs may also be present in soluble proteins and are stabilizing dimeric interactions for instance in the homodimeric Holliday-junction protein resolvase (Kleiger et al., 2002; doi: 10.1021/bi0200763.). However, as this point is not critical for our conclusions we will remove the discussion of the GxxxG motif from the revised manuscript.

      (4) What is the role of the kinase activity of Cqd1 in the CS formation? The effects of overexpression of Cqd1 (Fig. 7) should be tested for its E330A mutant.

      We also thank reviewer #2 for raising this important point similar to reviewer #1. Please see our response to point 1) of reviewer #1.

      (5) Is there stoichiometric as well as quantitative information on the 400 kD complex consisting of Cqd1, Por1 and Om14? Does the stoichiometry and amount of the complex depend on the growth condition? Does the complex contain other Por1 interacting IM proteins like Mdm31?

      We appreciate that reviewer #2 points out this important aspect. It might well be that the amount of the Cqd1 containing complex depends on growth conditions since its presence might be important for phospholipid homeostasis, CoQ distribution and mitochondrial architecture and morphology which for sure strongly depend on growth conditions. Therefore, we will try to analyze the amount of the Cqd1 complex present in mitochondria isolated from yeast cells grown on different media by BN-PAGE. So far we do not have any information on the stoichiometry of this complex and we feel that an analysis would go beyond the scope of this study. We agree with reviewer #2 that Mdm31 is an obvious candidate for an interaction partner of Cqd1. We actually tested this by co-immunoprecipitation using Cqd1-3xHA or Mdm31-3xHA. However, none of these approaches resulted in successful co-isolation of the potential interaction partner. We will mention this result in the revised manuscript.

      (6) For Fig. 7E, the authors state, "consistently, we observed dramatically increased mitochondria‐ER interactions Cqd1 overexpression", but this observation could be due to secondary effects because overexpression of Cqd1 itself already caused abnormal morphology of mitochondria.

      We thank reviewer #2 for bringing up this important point. To check whether the increased mitochondria‐ER interactions are a secondary effect due to altered mitochondrial morphology we will analyze the mitochondria‐ER interactions in other mitochondrial morphology mutants by fluorescence microscopy. This will reveal whether abnormal mitochondrial morphology generally leads to disturbed ER structure.

      (7) Since the antagonistic role of Cqd2 to Cqd1 was proposed, the results of the experiments for Cqd1 can be compared with those for Cqd2. For example, what will become of overexpression of Cqd2 instead of Cqd1 for Fig. 7? What is the lipid composition of the cqd1Dcqd2D double deletion mutant cells (the decreased PA level is recovered?)? Lines 424-425: In summary, overexpression of Cqd1 causes severe phenotypes on growth, formation of mitochondrial structural elements, and mitochondrial architecture and morphology. Is this phenotype affected by overexpression of Cqd2?

      This point raised by reviewer #2 is very interesting. Our preliminary experiments and previously published data (Tan et al., 2013) indicate that overexpression of Cqd2 is also toxic and results in the formation of huge mitochondrial clusters. Therefore, we will extend our study and analyze the effect of overexpression of CQD2, either alone or in combination with overexpression of CQD1.

      Reviewer #3:

      1) The central point of the paper is that Cqd1 is part of a novel contact site between the inner and the outer membrane. Om14 and Por1 were identified as outer membrane components of this contact site by immunoprecipitation. The data look convincing but they were generated from targeted experiments to test the involvement of suspected proteins. Ideally, one would like to see a cross-linking mass spectrometry (XL-MS) experiment that identifies the physical interactions of Cqd1 without bias.

      We thank reviewer #3 for acknowledging the presented data as convincing. Considering the significant amount of experiments planned for the revised version of the manuscript, we hope that reviewer #3 agrees that this point is not essential.

      2) Could an analogous blot of the MICOS complex be added to Figure 6D?

      Of course, we are happy to include BN-PAGE analysis showing the running behavior of MICOS next to the Cqd1 containing complex in Fig. 6D.

      3) In the Introduction, a host of contact sites is mentioned, which are partly from older papers. I'm not sure whether this is the accepted view of the field. Also, newer data suggest that the permeability transition pore is derived from complex V rather than ANT, CK, and VDAC. The authors should double check in order to represent the current state of the art

      We thank reviewer #3 for this comment. We will update this part according to the more recent literature.

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      Referee #3

      Evidence, reproducibility and clarity

      Khosravi et al present a comprehensive characterization of the mitochondrial protein Cqd1. They show that Cqd1 is an integral inner membrane protein that affects the mitochondrial lipid composition and that Cqd1 deletion exacerbates the delta-ups phenotype, which is also related to abnormalities in lipids. Importantly, Cqd1 is part of a large protein complex that behaves like an inner-outer membrane contact site upon sucrose density gradient centrifugation. The outer membrane proteins Por1 and Om14 were identified as likely interaction partners of Cqd1. The authors demonstrate clearly that the complex is distinct from MICOS. The data are logically presented and the paper is well organized. The results are interesting and offer a new prospective on the function of Cqd1. Although the potential involvement in lipid metabolism is not developed from the mechanistic point of view, the discovery of a new contact site between the two mitochondrial membranes is important.

      Minor critique

      1. The central point of the paper is that Cqd1 is part of a novel contact site between the inner and the outer membrane. Om14 and Por1 were identified as outer membrane components of this contact site by immunoprecipitation. The data look convincing but they were generated from targeted experiments to test the involvement of suspected proteins. Ideally, one would like to see a cross-linking mass spectrometry (XL-MS) experiment that identifies the physical interactions of Cqd1 without bias.
      2. Could an analogous blot of the MICOS complex be added to Figure 6D?
      3. In the Introduction, a host of contact sites is mentioned, which are partly from older papers. I'm not sure whether this is the accepted view of the field. Also, newer data suggest that the permeability transition pore is derived from complex V rather than ANT, CK, and VDAC. The authors should double check in order to represent the current state of the art.

      Significance

      The paper makes an important contribution to the molecular architecture of mitochondria.

      My expertise is mainly in mitochondrial lipids

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      Referee #2

      Evidence, reproducibility and clarity

      I have the following concerns for the authors to consider.

      1. Although biochemical evidence shows that Cqd1 is likely a factor that forms CS structures in mitochondria, it would make the manuscript stronger if the authors can observe uneven distribution of Cqd1 in the mitochondrial membranes (assessed by fluorescent microscopy or ideally high-resolution microscopy) and the presence of Cqd1 in the region of close apposition of the OM and IM by immunogold labeling for electron microscopy.
      2. Since the structural characterization of Cqd1 is important to understand its interactions with the OM proteins and other UbiB protein kinase-like family proteins, Coq8 and Cqd2, take different orientations, the membrane topology of Cqd1 should be experimentally analyzed. The authors state, "two hydrophobic stretches can be identified in the Cqd1 sequence, of which the first one (amino acids 125-142) might be a bona fide transmembrane segment" (lines 97-100); then is Cqd1 a single membrane spanning protein or two-membrane spanning protein?  
      3. The authors state, "conserved GxxxG dimerization motif (amino acids 504‐508)" (Fig. 1A caption), but this description needs a reference. The GxxxG motif was proposed to mediate transmembrane helix-helix association (https://doi.org/10.1006/jmbi.1999.3489), which is not consistent with the membrane topology proposed by the authors.
      4. What is the role of the kinase activity of Cqd1 in the CS formation? The effects of overexpression of Cqd1 (Fig. 7) should be tested for its E330A mutant.
      5. Is there stoichiometric as well as quantitative information on the 400 kD complex consisting of Cqd1, Por1 and Om14? Does the stoichiometry and amount of the complex depend on the growth condition? Does the complex contain other Por1 interacting IM proteins like Mdm31?
      6. For Fig. 7E, the authors state, "consistently, we observed dramatically increased mitochondria‐ER interactions Cqd1 overexpression", but this observation could be due to secondary effects because overexpression of Cqd1 itself already caused abnormal morphology of mitochondria.
      7. Since the antagonistic role of Cqd2 to Cqd1 was proposed, the results of the experiments for Cqd1 can be compared with those for Cqd2. For example, what will become of overexpression of Cqd2 instead of Cqd1 for Fig. 7? What is the lipid composition of the cqd1Dcqd2D double deletion mutant cells (the decreased PA level is recovered?) ? Lines 424-425: In summary, overexpression of Cqd1 causes severe phenotypes on growth, formation of mitochondrial structural elements, and mitochondrial architecture and morphology. Is this phenotype affected by overexpression of Cqd2?

      Significance

      Mitochondrial functions rely on the formation of intramitochondrial contact sites (CS) between the outer membrane (OM) and inner membrane (IM). It is established that MICOS, involved in cristae junction formation, contributes to the formation of the CS through its interactions with the OM proteins including the SAM complex, TOM complex, Por1 etc. However, it is also recognized that CS can be formed independently of MICOS. Here Khosravi et al. report that Cqd1 in the IM could interact with Por1 and Om14 in the OM to form MICOS-independent CS. Cqd1 was previously reported to be involved in normal cellular CoQ distribution. Now Cqd1 was shown to be genetically and functionally related to the mitochondrial lipid biosynthetic pathway involving Ups1 and Crd1. Deletion of the CQD1 gene causes PA (phosphatidic acid) to decrease and overexpression of Cqd1 causes abnormal IM morphology. Most of the experiments were carefully performed and the results are properly interpreted. The present findings will extend our understanding of the mitochondria membrane architecture significantly, thereby appealing to the broad readership of the journals.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      Khosravi et al show that the protein cqd1, which was shown to export CoQ outside the mitochondria, forms a contact site by interacting with por1-om14. They conclude that the main role of this complex is to control mitochondrial architecture and phospholipid metabolism. The data shown to draw this conclusion are the effects of cqd1 overexpression altering mitochondrial morphology, as well as the exacerbation of the effects of usp1 deletion by Cqd1 deletion.

      The major concerns affecting the conclusions are:

      1. Experimental evidence is lacking on the contribution of contact site formation by Cqd1 to the effects on mitochondrial architecture and respiration-dependent growth. Determining the effects of the overexpression of the kinase-dead mutant on mitochondrial morphology and contact site formation with Por1-Om14 can address that.
      2. Related to point #1, Cqd1 overexpression in deltaUsp1 cells could have addressed whether the role of Cqd1 in contact sites and mitochondrial architecture is independent of its role on CoQ distribution and phospholipid metabolism. Further characterization of the kinase-dead Cqd1 mutant on CoQ distribution, contact sites, mitochondrial archictecture and phsophsolipid metabolism might help discerning how these activities can be separated.
      3. It is unclear how both Cqd1 overexpression and deletion induce mitochondrial fragmentation. Performing live cell imaging with a mitochondrial-phoactivatable GFP to measure mitochondrial fusion rates could help discerning the causes for fragmentation. It is a possibility that overexpression induced fragmentation by activating fission without changing fusion, while deletion induced fragmentation by blocking fusion.

      Minor comment:

      1. Figure 4 claims that mitochondrial function is impaired by ups1 deletion, which Cqd1 deletion exacerbates. However, no respiration data is shown in figure 1, only measurements of mitochondrial architecture are shown. Thus, oxygen consumption measurements are needed to claim effects on mitochondrial function.
      2. Some Western blots lack quantifications and statistical analyses of independent experiments.

      Significance

      The finding that Cqd1 forms new contact sites and interacts with Usp1 is significant and is an important advancement in our understanding of the mechanism by which Cqd1 regulates CoQ distribution. This work will be of high interest to researchers on the mitochondria field, CoQ biogenesis, and inter and intra-organellar communication.

      However, it is still unclear whether the effects observed on mitochondrial architecture are just secondary to disturbed CoQ distribution or whether they are a primary consequence of Cqd1 forming these contact sites (effects independent of CoQ distribution and lipid metabolism as concluded by the authors).

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      Reply to the reviewers

      Manuscript number: RC-2022-01288R

      Corresponding author(s): Florence Naillat, Seppo Vainio, Dagmar Iber

      1. General Statements [optional]

      This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

      We would like to thank the reviewers for their positive and constructive reviews. We have already addressed their major concerns by including additional data, especially in the new Figure 3. We also detail the planned experiments that we propose to perform to address their remaining comments. Some points are mentioned in both section 2 and 3 of the revision plan.

      2. Description of the planned revisions

      Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

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

      Major Comments:

      1. Fig 3, I don't understand why the control lost Six2 expression (Fig 3A). Fig 3E is not consistent withFig 3d, which showed that the treatment of Fgf8 antibody significantly decreases the number of live cells. Lastly, the 3D culture matrix experiments did not provide evidence on the role of Fgf8 for NPC condensation.
      2. *

      The figure 3A showed a reduced expression of SIX2 expression (Red) in the NPC population in the kidney. When the NPC population is cultured without any FGF ligands, the SIX2 expression in the NPCs disappeared. Our results are similar to Dapkunas et al 2019 (Arvydas Dapkunas, Ville Rantanen, Yujuan Gui, Maciej Lalowski, Kirsi Sainio, Satu Kuure, and Hannu Sariola. Simple 3d culture of dissociated kidney mesenchyme mimics nephron progenitor niche and facilitates nephrogenesis wnt-independently. Scientific reports, 9(1):1–10, 201).

      • *

      The figure 3D showed that the antibody against FGF8 prevented the SIX2 expression in the NPC population from the nephrosphere experiment. We have modified the legend of figure 3D.

      • *

      We agreed with the reviewer that this result might confuse the reader. We are carrying another set of experiment for the Figure 3E where nephrospheres will be treated with and without FGF8 ligand instead of culturing a full kidney with and without FGF8 ligand for 24 hours as mentioned in figure 3E.

      We would like to mention that Dapkunas et al. 2019 demonstrated that dissociated kidney mesenchyme which contain the NPC population formed spontaneously self-organized spheres with the addition of FGF2 ligand and PP2 a Src inhibitor. By staining with Pax2 antibody which is a marker for progenitors and early nephron precursors we could show similarly as in Dapkunas et al that ectopic FGF8 ligand induced PAX2 expression whereas the antibody against FGF8 did not induce PAX2 expression in the cultured nephrosphere (Figure 3E-G)

      • *

      At the end of "A model based on Fgf8-induced motility leads to robust condensation of NPC", there is not a conclusive sentence.

      We have modified the text. We have written “We conclude from these simulation results that the chemokinetic effect of FGF8 enables the niche-wide distribution of NPCs. This allows them to reach the vicinity of the UB and also to enter the sphere of influence of epithelial factors that support the immobilization of NPCs. The corresponding motility gradient that appeared in the simulations (Supplementary Fig. Sup3) is in agreement with experimental observations Combes et al. 2016 (Alexander N. Combes, James G. Lefevre, Sean Wilson, Nicholas A. Hamilton, and Melissa H. Little. Cap mesenchyme cell swarming during kidney development is influenced by attraction, repulsion, and adhesion to the ureteric tip. (2016) Developmental Biology, 418(2):297–306. The simulations also show that excess FGF8 can override the guidance of epithelial signaling and prevent mesenchymal condensation.”

      Whole kidney qPCR results are not enough to support the claim of incomplete deletion of Fgf8 in mouse models. Protein staining or mRNA detection in section is required to support the claim. In addition, clear explanation is required on how the phenotypes of Fgf8 KO mice are associated the function of Fgf8 for NPC condensation.

      The qPCR results of the figure 3I and J have been carried out on the nephrospehere assays. In figure 3I, the nephrosphere assay which consist of culturing the kidney mesenchyme with ectopic FGF8 ligand for 24 hours. This showed that the NPCs markers were sustained due to FGF8 ligand. This is further confirmed with the staining of PAX2 a marker for progenitors and early nephron precursors which stained the aggregated NPC cells expressing SIX2 marker as in Dapkunas et al. 2019 (Figure 3E-G).

      • *

      I can not understand the last sentence well "Further work is required to reveal how Fgf8 along with its receptors and inhibiting factors orchestrates NPC condensation, its ......"

      We have modified the text. We have written “It is known that FGF8 ligand interacts with several FGF receptors and such interaction can also be modulated by heparan sulfate proteoglycan which will consequently regulate the gradient of FGF8 concentration (Harish et al. 2022 bioRXiv). Towards this goal a detailed ligand/receptor interactions in \textit{in vivo} is required to fully understand how FGF8 imparts its function.”

      • *

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

      Major concerns:

      • Parametric tests are not appropriate for a small sample size; non-parametric tests should be applied to examine if data are robust. Specifically, in Figures 3F and 3G, where n=3.

      *We would like to thank the reviewer as we did not write the correct statistical test that we have carried out. It is the 2-way Anova Sidak multiple comparison that is a non-parametric test. We have corrected the legend of the figure 3. *

      • *

      • In Figure 2E it is unclear how many kidneys were analyzed since the graph shows an n=5 per genotype while the figure legend indicates an n=6. Similarly, please indicate the number of independent biological samples analyzed in panel 2G rather than only the total number of cells, and specify the statistical test used for data analysis.

      We would like to thank the reviewer for catching our written mistake for the figure 2E. We have analysed 5 kidneys and we have corrected the legend. We have added the number of independent biological samples analyzed in the figure 2G and the statistical test (Wilcoxon signed-rank test) used in the legend.

      • *

      • Can the authors clarify how the experiment described in Figure 3E was performed? It is unclear if NPCs were treated with FGF8 ligand (as indicated in the chart legend) or with an anti-FGF8b antibody (as described in the figure legend). Moreover, the authors stated that "the loss of Six2 expression as a result of the absence of FGF8 was not completely due to cells death as more live cells were observed", however, the number of live cells seems similar between control and FGF8-treated nephrospheres. Can the authors comment on that?

      *The figure was mislabeled the NPC were treated with the ectopic FGF8 ligand (mistake has been corrected in manuscript). *

      To detect the number of cells (dead/live), the flow cytometry was utilized on a full cultured kidney for 24 hours with or without ectopic FGF8 ligand. This method requires several washing steps which can remove the dead cells during the procedure. However, we are planning to repeat the same experiment using the nephrosphere assay where the nephrosphere will be cultured with or without ectopic FGF8 ligand for 24 hours before being sorted to check the live and dead cells.

      • *

      • The authors argued that "the NPCs, the Six2+ cells, accumulate around the UB tip and that NPC induction is interrupted failing the PTAs formation". Please include quantification of Lhx1-positive structures to assess the number of PTA structures in wildtype, as well as, in Pax8Cre;Fgf8n/c and Wnt4Cre;Fgf8n/c mutant kidneys.

      As we never have worked with Lhx1 antibody before, we are optimizing the protocol for the staining of the Lhx1 antibody combined with Troma (epithelial marker) and Six2 (NPC population marker) antibodies to highlight the NPC population from the ureteric bud in the WT kidney slides. We have few sample slides for Pax8Cre;Fgf8n/c and Wnt4Cre;Fgf8n/c mutant kidneys and we would like a working protocol before any staining.

      • It is unclear if both male and female offspring were collected. If so, did the authors observe sex-related differences in outcomes?

      *We have use male and female embryos. We did not genotype for the sex of the embryos in any of the experiments. In such way the sample collection was unbiased regarding the sex of the embryos. *

      Minor concerns:

      • Abbreviations should be defined at first mention in the text (e.g. "MET" in the second paragraph of the Introduction) and in each figure/table legend (e.g. UB, CM, tNPCs, PTA in Figure 1).

      We appreciate the suggestion and include all the abbreviation in the text and in the legend of Fig1.

      • In Figure 7, please label the structures (kidney; ureters, and bladder) in the urogenital system of control and mutant mice to facilitate the reader's understanding.

      We appreciate the suggestion and have labelled the structures in the figure 7.

      • For consistency, please include an inset showing a higher magnification image for Figure 8C. We followed the reviewer’s suggestion and have included a higher magnification image for the figure 8C.

      • Please revise the following sentence for clarity: "Primary antibody incubation duration and temperature was."

      We clarified the sentence and have added the temperature and the time for the staining for each staining in the table 2.

      • The legend of Supplementary Figure 1 needs to be improved, as it does not contain all information required to fully understand the presented results.

      We have rewritten the legend of the supplementary figure 1 as the reviewer suggested.

      • In Supplementary Figure 2, please include the phospho-GSK3β relative expression normalized to GSK3β expression.

      We have calculated the ratio of phospho Gsk3 to GSK3 and no statistical difference was found.

      • *

      3. Description of the revisions that have already been incorporated in the transferred manuscript

      Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

      • *

      Several minor comments regarding typos and simple errors have already been incorporated in the transferred manuscript. The changes are highlighted in blue in the revised submission.

      *We have addressed all the minor comments that the reviewers have kindly highlighted to us. We feel these were straightforward to do and feasible in a short time, so do not require a detailed listed plan. *

      As mentioned above we are planning to stain the samples WT, Pax8Cre;Fgf8n/c and Wnt4Cre;Fgf8n/c mutant kidneys with Lhx1 antibody counterstained with Troma and Six2 markers and quantify the number of observed PTA structures


      Description of analyses that authors prefer not to carry out

      Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

      • *
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      Referee #2

      Evidence, reproducibility and clarity

      This is a well-written and organized manuscript that investigated the role of FGF8 in chemokinesis and condensation of nephron progenitor cells to the ureteric bud during metanephric kidney development. The results described in this present study are scientifically relevant, and the figures clearly support the content and authors' conclusions. However, there are some major and minor concerns that should be addressed by the authors.

      Major concerns:

      • Parametric tests are not appropriate for a small sample size; non-parametric tests should be applied to examine if data are robust. Specifically, in Figures 3F and 3G, where n=3.
      • In Figure 2E it is unclear how many kidneys were analyzed since the graph shows an n=5 per genotype while the figure legend indicates an n=6. Similarly, please indicate the number of independent biological samples analyzed in panel 2G rather than only the total number of cells, and specify the statistical test used for data analysis.
      • Can the authors clarify how the experiment described in Figure 3E was performed? It is unclear if NPCs were treated with FGF8 ligand (as indicated in the chart legend) or with an anti-FGF8b antibody (as described in the figure legend). Moreover, the authors stated that "the loss of Six2 expression as a result of the absence of FGF8 was not completely due to cells death as more live cells were observed", however, the number of live cells seems similar between control and FGF8-treated nephrospheres. Can the authors comment on that?
      • The authors argued that "the NPCs, the Six2+ cells, accumulate around the UB tip and that NPC induction is interrupted failing the PTAs formation". Please include quantification of Lhx1-positive structures to assess the number of PTA structures in wildtype, as well as, in Pax8Cre;Fgf8n/c and Wnt4Cre;Fgf8n/c mutant kidneys.
      • It is unclear if both male and female offspring were collected. If so, did the authors observe sex-related differences in outcomes?

      Minor concerns:

      • Abbreviations should be defined at first mention in the text (e.g. "MET" in the second paragraph of the Introduction) and in each figure/table legend (e.g. UB, CM, tNPCs, PTA in Figure 1).
      • In Figure 7, please label the structures (kidney; ureters, and bladder) in the urogenital system of control and mutant mice to facilitate the reader's understanding.
      • For consistency, please include an inset showing a higher magnification image for Figure 8C.
      • Please revise the following sentence for clarity: "Primary antibody incubation duration and temperature was."
      • The legend of Supplementary Figure 1 needs to be improved, as it does not contain all information required to fully understand the presented results.
      • In Supplementary Figure 2, please include the phospho-GSK3β relative expression normalized to GSK3β expression.

      Significance

      This study provides provide conceptual and methodological insights relevant to the field and it will be of considerable interest to the readers.

      My field of expertise: kidney development and disease (mouse models, kidney explants, cell culture). I do not have sufficient expertise to evaluate the 2D simulations of NPC condensation to the ureteric epithelium.

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      Referee #1

      Evidence, reproducibility and clarity

      NPC condensation is essential for nephron formation, but underlying regulation mechanisms remain elusive. Previous studies have demonstrated the important role of Fgf8 for the survival of NPCs. In this manuscript, Sharma et al reveal a novel function of Fgf8. By using mouse models, quantitative imaging assays, and data-driven computational modeling, they demonstrated the crucial role of Fgf8 signaling for the coordination of NPCs behaviors to the UB, especially for NPC condensation. Generally speaking, the manuscript was well organized and written. The experiments and analysis were well done. However, I have following concerns:

      1. Fig 3, I don't understand why the control lost Six2 expression (Fig 3A). Fig 3E is not consistent withFig 3d, which showed that the treatment of Fgf8 antibody significantly decreases the number of live cells. Lastly, the 3D culture matrix experiments did not provide evidence on the role of Fgf8 for NPC condensation.
      2. At the end of "A model based on Fgf8-induced motility leads to robust condensation of NPC", there is not a conclusive sentence.
      3. Whole kidney qPCR results are not enough to support the claim of incomplete deletion of Fgf8 in mouse models. Protein staining or mRNA detection in section is required to support the claim. In addition, clear explanation is required on how the phenotypes of Fgf8 KO mice are associated the function of Fgf8 for NPC condensation.
      4. I can not understand the last sentence well "Further work is required to reveal how Fgf8 along with its receptors and inhibiting factors orchestrates NPC condensation, its ......"

      Significance

      It is novel to study the role of Fgf8 for NPC condensation as a chemokine.

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      Reply to the reviewers

      We thank all reviewers for their very helpful comments. We feel that the comments pointed to a few main issues that we could remedy. First, we found that many comments and concerns could be addressed with work from our previous paper (doi.org/10.1101/2020.11.24.396002). To fix this, we added additional descriptions of experiments done previously and additional citations. We discussed more in depth an experiment that shows that ciliary membrane and membrane proteins can indeed come from the cell body plasma membrane, we talked more about how we determined that the actin puncta are representative of membrane remodeling functions like endocytosis, and we discussed some of the mechanistic insights provided by our previous work that are applicable here. We hope that this helps to answer several of the reviewer questions. Second, there were a few experiments we thought would be useful to add. These are represented in bold in our responses below. Briefly, we added a measure of internalization or endocytosis in the drp3 mutant, we added some images of cilia to the phalloidin figure to orient readers’ views of the cell, we added some additional mechanistic insight (supplemental figure 3), and we added an axoneme stain to confirm that the axoneme was extending (supplemental figure 4). Finally, we fixed some of our wording in the paper to represent our findings more accurately. Together, we hope that these revisions will address the reviewer concerns.

      Additionally, we added some data that we collected while waiting on reviews. We investigated the requirement for myosin in this pathway and include this data in the supplement.

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

      The current manuscript by Bigge et al. demonstrated that the chemical inhibition of GSk3 causes ciliary elongation in Chlamydomonas reinhardtii. They show that lithium induced ciliary lengthening is majorly due to GSK3 inhibition. Consistent with earlier reports, they show that new protein synthesis is not required for lithium induced ciliary elongation. The authors report that targeting endocytosis either by using chemical inhibitors (dynasore and CK-666) or genetic mutants (dpr3 and Arpc4) does not cause lithium induced ciliary elongation. They further reveal enhanced actin dynamics in lithium treated cells and such activity is lost in Arpc4 mutants. Based on these results, the authors concluded that endocytic pathways may be involved in lithium induced ciliary lengthening. The results are interesting, and this work is important in understanding more about ciliary length regulation. However, more experimental evidence addressing the current interpretation that endocytic pathways may be involved in lithium induced ciliary lengthening is required.

      Major comments: 1 The authors use chemical inhibitors as major tools for their study. However, the specificity of these inhibitors is a concern. How specific are these GSK3 inhibitors such as LiCl? Can authors show that LiCl mediated ciliary lengthening is due to inhibition of GSK3? Authors used BFA and Dynasore to show that not the Golgi, but the endocytosis derived membrane is required for ciliary lengthening. Again, here the specificity of these inhibitors is a concern. Especially as Dynasore has been shown to have non-specific effects.

      We agree that the specificity of chemical inhibitors can be a concern. This is why we used 4 separate inhibitors of GSK3, each showing elongation of cilia and an increase in actin puncta (suggesting an increase in actin dynamics at the membrane). While these different inhibitors may have different off-target effects. Their intended target, GSK3, is the same, suggesting that the shared phenotype from each inhibitor is conserved. The ability of LiCl to affect GSK3 activity in Chlamydomonas was also investigated in depth with a kinase assay and a western blot in Wilson, 2004 (doi: 10.1128/EC.3.5.1307-1319.2004). To address the off-target effects of Dynasore, we employed the drp3 mutant to confirm genetically what we saw from the chemical inhibition. We also show in our previous paper that Dynasore and PitStop2 have similar effects in Chlamydomonas, both of them inhibiting the internalization of a dye-labelled membrane, suggesting that they both function to block endocytosis (doi.org/10.1101/2020.11.24.396002). While no mutant or alternative inhibitor is available to look at the effects of BFA, this inhibitor and its effects on cilia have been well-characterized in Dentler, 2013 (doi.org/10.1371/journal.pone.0053366).

      Does inducing/enhancing endocytosis independent of GSK3 by other means has any effect on ciliary length regulation?

      Our concern with the proposed experiment is that even if elongation requires endocytosis, all endocytosis might not lead to ciliary elongation when endocytosis is for other purposes. For example, endocytosis could occur for other purposes, like nutrient uptake, that will have no effect on cilia. The plasma membrane to cilium pathway may be a targeted pathway triggered by specific disruptions. Therefore, we don’t feel that the proposed experiments will add to our model.

      The major claim of this paper is that LiCl mediated ciliary lengthening is due to enhanced endocytosis. Although authors showed that inhibition of endocytosis results in reduced ciliary length, it is important to show if GSK3 inhibition by LiCl (or any other inhibitor) causes any increased cellular endocytosis? Similarly, what is the effect of GSK3 mutants on endocytosis?

      *We show an increase in actin dynamics at the membrane and actin puncta following treatment with LiCl and the other GSK3 inhibitors. We show here and in our previous paper (doi.org/10.1101/2020.11.24.396002), that these puncta are likely endocytic based on the timing of their appearance and the proteins required for puncta formation (including the Arp2/3 complex and Clathrin) (Figure 7, previous paper). We updated our latest version to reflect the data we have already collected and presented as follows: *

      “Further, they rely on proteins typically thought to be involved in endocytosis including the Arp2/3 complex and clathrin, and they form at times when it makes sense for endocytosis to be occurring, like immediately following deciliation when membrane and protein must be recruited to cilia in a timeframe too short for new protein and membrane synthesis, sorting, and trafficking (Bigge et al. 2020). Thus, we stained cells with phalloidin to visualize filamentous actin and these endocytosis-like punctate structures when cells are treated with GSK3 inhibitors.”

      A phenotypic mutant of GSK3 does not currently exist in Chlamydomonas, and methods of reliably introducing mutants in Chlamydomonas do not currently exist. Thus, we used the array of GSK3 inhibitors.

      Are these endocytic processes enhanced specifically at/or around the cilium during the ciliary lengthening process?

      *Based on our phalloidin staining data, these processes are primarily enhanced near the cilium, but puncta also exist throughout the cell. To more clearly show this and in response to a comment from reviewer 2, we added a set of images with brightfield to demonstrate where the dots are in relation to cilia. We also added arrows to the images in the figure to point out the apex of the cell as determined by the filamentous actin structures in the cells. *

      Authors claim that drp3 is a target of GSK3 and, similar to the canonical dynamin, functions in endocytosis. While, it is an important observation, experiments are required to show the role of drp3 in endocytosis and also to show that it is indeed a target of GSK3.

      To address this comment, we are employing an experiment that was designed in our previous paper (doi.org/10.1101/2020.11.24.396002, Figure 5B-E). This experiment uses a lipophilic membrane dye, FM4-46FX. The dye binds to the membrane but is unable to enter the cell alone. It is quickly endocytosed and results in vesicular-like structures within the cell. We added a panel to Figure 3 where we do this experiment in wild-type and ____drp3 mutant cells. This shows that endocytosis is affected by the mutation in DRP3. The discussion of this new data is summarized in the text as follows:

      “Additionally, we showed that this DRP is required for internalization of a lipophilic membrane dye, FM4-46FX through endocytosis. This dye binds to the membrane but is unable to enter the cells on its own and must be endocytosed. In wild-type cells it is quickly endocytosed and visible as puncta within the cell (Figure 3F, H) (Bigge et al. 2020). However, in drp3 mutants the amount of dye endocytosed is significantly lower (Figure 3G-H), suggesting that DRP3 is required for optimal endocytosis in these cells.”

      Mechanistic insights into how endocytosis/actin dynamics regulate ciliary lengthening would be interesting to see. Further, it is interesting to see if the ciliary signaling defects caused by abnormal ciliary length can be rescued by inhibition of endocytosis.

      *In our previous paper (doi.org/10.1101/2020.11.24.396002), we dive into the mechanisms tying together actin dynamics, endocytosis, and cilia. We find that Arp2/3 complex-nucleated actin networks are required for endocytosis to reclaim ciliary membrane and membrane proteins from a pool in the plasma membrane for the rapid early stages of ciliary assembly. We believe that this is a similar mechanism to what is occurring when cells elongate following lithium treatment. This is because there are several parallels in phenotypes: *

      -The Arp2/3 complex is required for both ciliary assembly (Figure 1, previous paper) and ciliary elongation resulting from lithium treatment. In the case of ciliary assembly, treating with cycloheximide to block the synthesis of new protein fully eliminates regrowth in the absence of the Arp2/3 complex, suggesting this Arp2/3 complex dependent mechanism in early ciliary assembly does not involve new protein synthesis (Figure 2, previous paper). Similarly, the process of ciliary elongation in response to lithium does not require new protein synthesis.

      *-A burst in actin dynamics/actin puncta occurs immediately following deciliation during early regrowth and during growth initiated by lithium treatment. We know these puncta are Arp2/3 complex and clathrin dependent (Figures 4 and 7, previous paper). *

      *-Both initial ciliary assembly or ciliary maintenance and elongation of cilia due to lithium treatment require endocytosis (Figures 5, 7-8, previous paper) but not require Golgi-derived membrane (Figure 3, previous paper). *

      *-Also in the previous paper, we find that this mechanism is required for the internalization and relocalization of a ciliary membrane protein for mating (Figure 6, previous paper). We also find that ciliary membrane proteins move from the plasma membrane to the cilia during ciliary assembly (Figure 7-8, previous paper). *

      *This is summarized in the text as follows: *

      *In the introduction we added: *

      “Previous data from our lab suggest that the Arp2/3 complex and actin are involved in reclaiming material from the cell body plasma membrane that is required for normal ciliary assembly (Bigge et al. 2020). We show that the Arp2/3 complex is required for the normal assembly of cilia and for endocytosis of both plasma membrane and plasma membrane proteins in various contexts. Further, we find that deciliation triggers Arp2/3 complex-dependent endocytosis by observing an increase in actin puncta immediately following deciliation (Bigge et al. 2020).”

      And in the discussion we added:

      “Previous work has shown that while the Golgi is required for ciliary maintenance and assembly (Dentler 2013), it is not the only source of membrane. Instead, we found that membrane reclaimed through actin and Arp2/3-complex dependent endocytosis is required for ciliary assembly or growth from zero length (Bigge et al. 2020). More specifically, we found that the Arp2/3 complex is required for normal ciliary maintenance and ciliary assembly, especially in the early stages when membrane and protein are needed quickly. The Arp2/3 complex is also required for the internalization of membrane and a specific ciliary membrane protein required for mating. Further, we show that endocytosis-like actin puncta form immediately following deciliation in an Arp2/3 complex and clathrin-dependent manner, and that membrane from the cell body plasma membrane can be reclaimed and incorporated into cilia (Bigge et al. 2020). This led us to question whether that same mechanism might be required for ciliary elongation from steady state length induced by lithium treatment.”

      Minor comments: 1. The paper needs a thorough proof reading as it harbors many spelling mistakes, grammatical errors, and poor sentence formation in multiple instances.

      *The paper was thoroughly read, and spelling mistakes and grammar were fixed. *

      Supplemental Figure S2A and S2B should be quoted separately from S2C and S2D.

      *This was updated in the latest version of the paper. *

      In Page 6 paragraph 2 - "authors wrote "To determine if GSK3 could be a potential kinase for this protein, we employed ScanSite4.0, which confirmed that of the 9 DRPs of Chlamydomonas, the only one with a traditional GSK3 target sequence was DRPs (Supplemental Figure 2)." No data is shown in S2 with regard to this. Either data needs to be shown or change the text in a way to avoid confusion.

      *The text was changed in a way to avoid confusion. *

      It would be nice to see if GSK3 can actually phosphorylate DRP3.

      *This would be interesting, however there is not currently a simple way to test this. There is not an antibody for DRP3 that shares enough of its immunogen sequence with the Chlamydomonas DRP3 sequence to use for a western blot. *

      The authors observe that arpc4 mutants do not form actin puncta upon LiCl treatment. Could this phenotype be rescued by complementing with WT ARPC4.

      *We showed in our previous paper (doi.org/10.1101/2020.11.24.396002) that the actin puncta could be rescued by re-expression of wild-type ARPC4 (Figure 4). *

      The concentration of inhibitors is described differently in the text and figure legends (for example Fig. 4A)

      *In the figure legend of figure 4, the concentration of 6-BIO was accidentally reported as 100 µM instead of the correct value (100 nM) as it was throughout the rest of the paper. This was addressed in the latest version. *

      The p values are not significant in some of the figures. (Fig. 4D &Fig. 5C)

      P values were provided for all comparisons in an effort to be transparent and so that readers could draw their own conclusions about the data.

      Reviewer #1 (Significance (Required)):

      The current manuscript by Bigge et al. demonstrates that endocytosis is required for GSK3 inhibition mediated ciliary lengthening. Maintenance of proper length of cilia is crucial and its dysregulation results in pathogenesis. This work takes the field forward and helps in our understanding of how ciliary length is regulated. This work is of interest to researchers working in the field of ciliary biology as well as to those working on endocytosis.

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

      Summary: The authors show in this study that Lithium and other GSK3-beta inhibitors induce cilia elongation in Chlamydomonas. They further demonstrate that inhibition of endocytosis by Dynasore prevents the induced elongation of cilia. They speculate that a Dynamin-related protein might be involved in this process, and determine 9 Dynamin related proteins (DRPs) in Chlamydomonas of which DRP3 shows the highest sequence similarity. Lithium-induced ciliary elongation is prevented in DRP3 mutants supporting the author's hypothesis and indicating that DRP3 might be a GSK3-beta target, similar to some animal Dynamins. Since Dynamins interact with the F-actin regulator ARP3/3-complex, and because F-actin reorganization is observed in cells after GSK3-beta inhibition, they test the induction of ciliary elongation in arpc4 mutants and after blocking the ARP-complex by CK-666. Indeed, F-actin remodeling and cilia elongation were prevented after loss of ARP-complex function. The induction of ciliary elongation and F-actin remodeling also correlates with the emergence of strong F-actin punctae in cells, and the authors interpret that as induction of Dynamin-dependent endocytosis (also addressed in a current preprint from the group). From that, the conclude that endocytosis is required for delivering membrane to the growing cilium and that this is required for the observed effects. While this claim is somewhat supported by a lack of cilia elongation inhibition after treatment to prevent protein synthesis or Golgi function, direct evidence for membrane delivery to the cilium, the need for membrane delivery for ciliary elongation, and presence of bona fide endocytotic vesicles is sadly missing. Therefore, this study sheds new light on an important process in ciliary functional regulation and also furthers our understanding on why GSK3-beta inhibition induces elongated cilia in many cell systems, but I am not convinced that the conclusions are actually supported by the data, as the two key points in question were not experimentally addressed at this point.

      Main points: 1. The authors need to demonstrate that new membrane is delivered in the process to the growing cilium. E.g. this could be done by membrane stains (pulse) and static or live-cell imaging analysis in untreated, GSK3-beta inhibitor treated and in mutants.

      *In our previous paper (doi.org/10.1101/2020.11.24.396002), we do an experiment similar to the one described here (Figure 8, previous paper). We biotinylated all surface proteins, then removed the cilia (and therefore all labelled ciliary surface proteins) and allowed them to regrow. We then isolated the new cilia and probed for biotinylated proteins because any biotinylated proteins must have come from the surface of the cell. We found that the cilia did contain membrane proteins from the surface of the cell. This experiment shows that membrane and membrane proteins derived from the plasma membrane are entering growing cilia during regeneration. We added a description of this experiment to the text as follows: *

      “Conversely, when treated with Dynasore to inhibit endocytosis, cilia could not elongate to the same degree as untreated cells (Figure 3A-B), implying endocytosis is required for lithium-induced elongation and that endocytosis requires dynamin. This is consistent with results from our previous studies which show that ciliary membrane and membrane proteins are delivered from the cell body plasma membrane to the cilia. In an experiment first performed in Dentler 2013 and then later in Bigge et al. 2020, we biotinylated all cell surface proteins. Then, deciliated cells and allowed cilia to regrow. We then isolated cilia and probed for biotinylated proteins. Any biotinylated proteins present must have come from the cell body plasma membrane, and we found that indeed biotinylated proteins exist in the newly grown cilia, suggesting that ciliary membrane and membrane proteins can be recruited from the cell body plasma membrane (Dentler 2013; Bigge et al. 2020).”

      However, this experiment cannot be done in the case of lithium because cilia are not removed meaning they already will contain labelled surface proteins. Additionally, cells do not regrow cilia in the presence of lithium, meaning that we cannot add a regeneration. Regardless, work from our previous paper described above does establish that ciliary membrane and membrane proteins are able to come from the cell body plasma membrane as the reviewer requested.

      Along the same line, the authors need to demonstrate that the punctae are truly endocytotic vesicles. For that uptake assays/stains could be used and additional markers. Furthermore, there are multiple modes of endocytosis (e.g. Clathrin) besides Dynamin. The authors should determine if blocking other modes of endocytosis has similar or divergent effects on cilia elongation.

      *In our previous paper (doi.org/10.1101/2020.11.24.396002) we supplement the actin puncta data with membrane labelling to show that the puncta are likely endocytic pits (doi.org/10.1101/2020.11.24.396002, Figure 5). We also show that the puncta require both the Arp2/3 complex and active clathrin to form, further suggesting that they are endocytic (Figure 7, previous paper). We added this to the paper as follows: *

      “Further, they rely on proteins typically thought to be involved in endocytosis including the Arp2/3 complex and clathrin, and they form at times when it makes sense for endocytosis to be occurring, like immediately following deciliation when membrane and protein must be recruited to cilia in a timeframe too short for new protein and membrane synthesis, sorting, and trafficking (Bigge et al. 2020). To provide additional evidence that these are endocytic puncta, we also showed that a corresponding increase in membrane internalization occurs during this same timeframe using a fluorescent membrane dye that is endocytosed in wild-type cells (Bigge et al. 2020).”

      Additionally, Dynamin is required for most forms of endocytosis, including clathrin mediated endocytosis. In the previous paper (doi.org/10.1101/2020.11.24.396002), which we cite here, we do a deep dive into which endocytic proteins are present in Chlamydomonas. We found that clathrin mediated endocytosis is the most highly conserved on the endocytic processes we looked at (Figure 5, previous paper).

      We did add a new figure to this paper (Figure 4) using a dye that labels membrane in lithium treated cells. This dye binds to the plasma membrane but is unable to enter cells by itself and must be endocytosed. We found that during the first 30 minutes of lithium treatment there is increased membrane dye internalization.

      No cilia are actually shown in the study. I personally, would like to see how these cilia look like, especially in relation to the sites of F-actin remodeling and punctae formation. What comes first? Please also provide a axoneme staining to confirm elongation of the ciliary core and what happens to the tubulin pool when cilia cannot elongate any more? Is it accumulating at the ciliary base?

      We added a panel demonstrating where the puncta are in relation to cilia in Figure 4 with a brightfield overlay.* We also look at the appearance and timing of these puncta more in depth in our previous paper (doi.org/10.1101/2020.11.24.396002, Figure 7). We find that puncta form immediately following deciliation and start to return to normal following about 10 minutes of regrowth. We think that this mechanism of ciliary elongation in lithium is similar to what occurs during those early steps of ciliary assembly suggesting that the dots likely form very early on. *

      We also included axoneme staining in Supplemental figure 4*. We show that the axoneme does continue to elongate with the cilia. After about 90 minutes, the cilia actually stop growing and detach from the cells (doi: 10.1128/EC.3.5.1307-1319.2004, doi: doi.org/10.1247/csf.12.369). However, we are interested in the more acute mechanisms that result in ciliary elongation. *

      The authors also claim that the method of GSK3 inhibition is not important. It would be more correct to say that the mode/drug of GSK3 inhibition is not important, but discuss how some of the minor variance between treatments could be explained (incl. the timeline and temporal dynamics of the diverging effects; and the dose-dependency as low concentrations of BIO seem to induce shortening but high doses induce elongation of cilia).

      *We further discussed this in the text as follows: *

      “The minor variances between the drugs could be explained by the timeline in which we tested cilia (90 minutes) or the exact dosages we used. An example of this is 6-BIO where treatment with a low dose of 100 nM caused ciliary lengthening, but treatment with a higher concentration of 2 µM reportedly caused ciliary shortening (Kong et al. 2015). Together, the data suggest that the mode of inhibition by chemical targets of GSK3 is not important for ciliary lengthening. Whether GSK3 was inhibited via competition for ATP binding or phosphorylation, cilia were able to elongate.”

      They propose here a positive effect of F-actin build up in cilia length regulation, while most studies to date report ciliary shortening to correlate with increased F-actin at the ciliary base. I believe that this is not highlighted and discussed enough, which I find reduces the overall quality of the paper (but is easy to improve). It might be also interesting to test if other F-actin inducers/stabiliziers have the same effect?

      *This is addressed in the discussion in the latest version in depth as follows: *

      “One important detail to point out is that Chlamydomonas differ from mammalian cells in that they have a cell wall. The stability awarded by the cell wall means that Chlamydomonas does not require a cortical actin network as mammalian cells do. Thus, in Chlamydomonas, we are able to investigate actin dynamics and functions without the interference of the cortical actin network. This also means that some of the effects we see might be masked in mammalian cells by the presence of the cortical actin network and the effect that it has on ciliary assembly and maintenance.”

      *We also added a section to the introduction to address this concern early on so that readers will have this difference in mind as they read the paper: *

      “Additionally, unlike mammalian cells, Chlamydomonas lacks a cortical actin network which simplifies the relationship between cilia and actin and makes this an ideal model to study such interactions.”

      Also, F-actin inducers/stabilizers do not typically have the same effect because the filamentous actin needed for these processes must be dynamic, or able to undergo rapid depolymerization and repolymerization as needed during this fairly quick timeframe. This is demonstrated in Avasthi, 2014 (*doi.org/10.1016/j.cub.2014.07.038). Cells were treated with several actin targeting inhibitors including LatB which results in depolymerization of filaments and Jasplakinolide which results in stabilization of filaments. In both cases, ciliary regeneration is impaired suggesting that actin must be dynamic for its functions related to cilia. *

      Minor points: 1. In many Figures, the x-axis is labeled "Number of values", but I think that maybe number of observations might be more appropriate.

      We discussed this point and decided to change the axis titles to “Number of cilia”.

      The author often use the word "normally" elongating, but in all cases the elongation is induced = abnormal situation. Maybe the authors could use a different term.

      We originally used “normally” because there are times when we get defective elongation but not no elongation. In the latest version we changed this to “elongation consistent with untreated wild-type cells” or something along those lines.

      It is puzzling as to why DRP3 was chosen, while DRP2 actually is most similar in terms of domain composition. Maybe they could discuss that. They also could explain a bit better how the mutants were generated in which a "cassette was inserted early in the gene". What kind of disruption is expected?

      DRP3 was chosen because it has the highest sequence identity (and similarity). DRP2 while containing all domains, has low overall sequence conservation. DRP3 is also the only DRP that showed a potential GSK3 target site when investigated with ScanSite4.0. This was all made clearer in the text as follows:

      “Chlamydomonas contains 9 DRPs with similarity to a canonical dynamin (DRP1-9). Despite lacking 2 of the canonical dynamin domains, the DRP with the highest sequence similarity and identity to canonical dynamin is DRP3 (Supplemental Figure 2C-D). To determine if GSK3 could be a potential kinase for this protein, we employed ScanSite4.0, which confirmed that of the 9 DRPs of Chlamydomonas, the only one with a traditional GSK3 target sequence was DRP3.”

      The representative images in Figure 4A do not really seem to match the quantifications.

      *The quantitative data suggest that these different treatments have increased dots, which we believe the representative images do show. LiCl and CHIR99021 have the most dots, while 6-BIO and Tideglusib have more dots, but less than LiCl and CHIR99021. *

      line 109: "of-targets" should be off-targets

      Fixed in the latest version, thanks for pointing this out.

      line 141: "delivery form the Golgi" should be FROM the Golgi

      Fixed in the latest version, thanks for pointing this out.

      line 160: "was DRPs" should be was DRP3

      Fixed in the latest version, thanks for pointing this out.

      line 204/205: the sentence starting "Thus, we phalloidin..." should be rephrased. It sounds not quite correct

      Fixed in the latest version, thanks for pointing this out.

      line 209: Figure 4A should refer to Figure 4B

      Fixed in the latest version, thanks for pointing this out.

      line 211: "times or rapid ciliary" should be of rapid ciliary...

      Fixed in the latest version, thanks for pointing this out.

      line 257: "in lithium." Should be in lithium treated cells Fixed in the latest version, thanks for pointing this out.

      Reviewer #2 (Significance (Required)):

      This study sheds new light on an important process in ciliary functional regulation and also furthers our understanding on why GSK3-beta inhibition induces elongated cilia in many cell systems, but I am not convinced that the conclusions are actually supported by the data, as the two key points in question were not experimentally addressed at this point.

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

      Chlamydomonas maintains relatively regular length of cilia (flagella). However, when the cell is exposed to high concentration of lithium ions, it elongates cilia further. In this work, Bigge and Avasthi made experiments to build a potential hypothesis of molecular mechanism of this unusual cilia elongation. Their hypothesis is (1) cilia elongation is triggered, depending on supply of extra membrane (not proteins), (2) membrane is supplied from plasma membrane by clathrin-dependent endocytosis (not from Golgi), (3) this endocytosis contains Arp2/3 complex, (4) GSK3 downregulates Arp2/3 dependent endocytosis and (5) GSK3 is suppressed by lithium. They conducted well-organized experiments to prove each step. While some of them are indirect, their hypotheses were supported experimentally in outline.

      (1) is undoubted, since the authors demonstrated that inhibition of protein production by cycloheximide did not influence cilia elongation.

      (2) The authors clearly demonstrated that source of ciliary membrane for elongation is plasma membrane and not Golgi by examining specific inhibitors' effect. They also showed protein transfer from plasma membrane to cilia, by biotinylaing surface proteins in the cell, deciliating and growing cilia and detecting biotinylated proteins in cilia. This part rather characterizes initial growth of cilia, not elongation. Therefore this result must be properly described in the context of this work (which is elongation of cilia).

      This comment was particularly helpful as it also helps us address some of the comments from the other reviewers. We updated the description of this experiment in the context of this work in the latest version as follows:

      “Further, they rely on proteins typically thought to be involved in endocytosis including the Arp2/3 complex and clathrin, and they form at times when it makes sense for endocytosis to be occurring, like immediately following deciliation when membrane and protein must be recruited to cilia in a timeframe too short for new protein and membrane synthesis, sorting, and trafficking (Bigge et al. 2020). To provide additional evidence that these are endocytic puncta, we also showed that a corresponding increase in membrane internalization occurs during this same timeframe using a fluorescent membrane dye that is endocytosed in wild-type cells (Bigge et al. 2020).”

      For (3)-(4), they visualized Arp2/3 localization, showing highly condensed Arp2/3. They interpreted these particles as sign of clathrin endocytosis. Since so far such an endocytosis particle has not been reported in Chlamydomonas, the authors confirmed that DRPs are target of GSK3 to indirectly show GSK3 influences formation of endocytosis. This reviewer thinks the author should be able to directly confirm endocytosis for example by electron microscopy (of traditional epon-embedded and stained cells).

      We visualized Arp2/3 complex-dependent filamentous actin localization. We provide DRP3 as a potential target of GSK3, but do not report that it is the target that results in increased endocytosis or increased ciliary length. We agree that electron microscopy would be ideal to visualize endocytosis in these cells. However, we feel this is outside the scope of this current work. But, we do have plans to look at endocytosis in Chlamydomonas *using electron microscopy in the future and hope that the increased context from the previous data are sufficient at this time. *

      (5) was elegantly proved by multiple drugs (all known as inhibitor of GSK3), including lithium.

      After fixing these points, this manuscript will be ready for publication.

      Minor points: Line188-191: not clear. What are *** and ****?

      Fixed in the latest version, thanks for pointing this out.

      Line262-264: It would be helpful how the initial cilia growth of the arpc4 cell.

      We agree that this would be helpful information, and included more of a description of how ciliary growth is affected by loss of Arp2/3 complex function in the latest version: “Specifically, we found that the Arp2/3 complex is required for reclamation of membrane from a pool in the plasma membrane during the rapid growth that occurs during early ciliary assembly”.

      Line321: it should read as follows. Cang 2014; carlsson and Bayly 2014). While we...

      Fixed in the latest version, thanks for pointing this out.

      Line329: were -> where

      Fixed in the latest version, thanks for pointing this out.

      Line365-366: Lithium-treated cells are not motile. Any thought why? Maybe protein production is not necessary for apparent cilia elongation, but necessary for elongation of functional cilia.

      *This is an interesting idea. However, even when protein production is allowed to proceed, Lithium-treated cells are not motile. This is a ciliary dysfunction, and in fact, after about 90 minutes incubation with lithium, the cilia of these cells start to crash out or fall off, demonstrating that these are not healthy cells or healthy cilia. *

      Reviewer #3 (Significance (Required)):

      This work is an important step toward the understanding of cilia elongation and thus growth mechanism. It will attract wide audience who have interest in cell biology and motility. My expertise is about motile cilia and their 3D structure.

    2. 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

      Chlamydomonas maintains relatively regular length of cilia (flagella). However, when the cell is exposed to high concentration of lithium ions, it elongates cilia further. In this work, Bigge and Avasthi made experiments to build a potential hypothesis of molecular mechanism of this unusual cilia elongation. Their hypothesis is (1) cilia elongation is triggered, depending on supply of extra membrane (not proteins), (2) membrane is supplied from plasma membrane by clathrin-dependent endocytosis (not from Golgi), (3) this endocytosis contains Arp2/3 complex, (4) GSK3 downregulates Arp2/3 dependent endocytosis and (5) GSK3 is suppressed by lithium. They conducted well-organized experiments to prove each step. While some of them are indirect, their hypotheses were supported experimentally in outline.

      (1) is undoubted, since the authors demonstrated that inhibition of protein production by cycloheximide did not influence cilia elongation.

      (2) The authors clearly demonstrated that source of ciliary membrane for elongation is plasma membrane and not Golgi by examining specific inhibitors' effect. They also showed protein transfer from plasma membrane to cilia, by biotinylaing surface proteins in the cell, deciliating and growing cilia and detecting biotinylated proteins in cilia. This part rather characterizes initial growth of cilia, not elongation. Therefore this result must be properly described in the context of this work (which is elongation of cilia).

      For (3)-(4), they visualized Arp2/3 localization, showing highly condensed Arp2/3. They interpreted these particles as sign of clathrin endocytosis. Since so far such an endocytosis particle has not been reported in Chlamydomonas, the authors confirmed that DRPs are target of GSK3 to indirectly show GSK3 influences formation of endocytosis. This reviewer thinks the author should be able to directly confirm endocytosis for example by electron microscopy (of traditional epon-embedded and stained cells). (5) was elegantly proved by multiple drugs (all known as inhibitor of GSK3), including lithium. After fixing these points, this manuscript will be ready for publication.

      Minor points:

      Line188-191: not clear. What are and *?

      Line262-264: It would be helpful how the initial cilia growth of the arpc4 cell.

      Line321: it should read as follows.

      Cang 2014; carlsson and Bayly 2014). While we...

      Line329: were -> where

      Line365-366: Lithium-treated cells are not motile. Any thought why? Maybe protein production is not necessary for apparent cilia elongation, but necessary for elongation of functional cilia.

      Significance

      This work is an important step toward the understanding of cilia elongation and thus growth mechanism. It will attract wide audience who have interest in cell biology and motility. My expertise is about motile cilia and their 3D structure.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      The authors show in this study that Lithium and other GSK3-beta inhibitors induce cilia elongation in Chlamydomonas. They further demonstrate that inhibition of endocytosis by Dynasore prevents the induced elongation of cilia. They speculate that a Dynamin-related protein might be involved in this process, and determine 9 Dynamin related proteins (DRPs) in Chlamydomonas of which DRP3 shows the highest sequence similarity. Lithium-induced ciliary elongation is prevented in DRP3 mutants supporting the author's hypothesis and indicating that DRP3 might be a GSK3-beta target, similar to some animal Dynamins. Since Dynamins interact with the F-actin regulator ARP3/3-complex, and because F-actin reorganization is observed in cells after GSK3-beta inhibition, they test the induction of ciliary elongation in arpc4 mutants and after blocking the ARP-complex by CK-666. Indeed, F-actin remodeling and cilia elongation were prevented after loss of ARP-complex function. The induction of ciliary elongation and F-actin remodeling also correlates with the emergence of strong F-actin punctae in cells, and the authors interpret that as induction of Dynamin-dependent endocytosis (also addressed in a current preprint from the group). From that, the conclude that endocytosis is required for delivering membrane to the growing cilium and that this is required for the observed effects. While this claim is somewhat supported by a lack of cilia elongation inhibition after treatment to prevent protein synthesis or Golgi function, direct evidence for membrane delivery to the cilium, the need for membrane delivery for ciliary elongation, and presence of bona fide endocytotic vesicles is sadly missing. Therefore, this study sheds new light on an important process in ciliary functional regulation and also furthers our understanding on why GSK3-beta inhibition induces elongated cilia in many cell systems, but I am not convinced that the conclusions are actually supported by the data, as the two key points in question were not experimentally addressed at this point.

      Main points:

      1. The authors need to demonstrate that new membrane is delivered in the process to the growing cilium. E.g. this could be done by membrane stains (pulse) and static or live-cell imaging analysis in untreated, GSK3-beta inhibitor treated and in mutants.
      2. Along the same line, the authors need to demonstrate that the punctae are truly endocytotic vesicles. For that uptake assays/stains could be used and additional markers. Furthermore, there are multiple modes of endocytosis (e.g. Clathrin) besides Dynamin. The authors should determine if blocking other modes of endocytosis has similar or divergent effects on cilia elongation.
      3. No cilia are actually shown in the study. I personally, would like to see how these cilia look like, especially in relation to the sites of F-actin remodeling and punctae formation. What comes first? Please also provide a axoneme staining to confirm elongation of the ciliary core and what happens to the tubulin pool when cilia cannot elongate any more? Is it accumulating at the ciliary base?
      4. The authors also claim that the method of GSK3 inhibition is not important. It would be more correct to say that the mode/drug of GSK3 inhibition is not important, but discuss how some of the minor variance between treatments could be explained (incl. the timeline and temporal dynamics of the diverging effects; and the dose-dependency as low concentrations of BIO seem to induce shortening but high doses induce elongation of cilia).
      5. They propose here a positive effect of F-actin build up in cilia length regulation, while most studies to date report ciliary shortening to correlate with increased F-actin at the ciliary base. I believe that this is not highlighted and discussed enough, which I find reduces the overall quality of the paper (but is easy to improve). It might be also interesting to test if other F-actin inducers/stabiliziers have the same effect?

      Minor points:

      1. In many Figures, the x-axis is labeled "Number of values", but I think that maybe number of observations might be more appropriate.
      2. The author often use the word "normally" elongating, but in all cases the elongation is induced = abnormal situation. Maybe the authors could use a different term.
      3. It is puzzling as to why DRP3 was chosen, while DRP2 actually is most similar in terms of domain composition. Maybe they could discuss that. They also could explain a bit better how the mutats were generated in which a "cassette was inserted early in the gene". What kind of disruption is expected?
      4. The representative images in Figure 4A do not really seem to match the quantifications.
      5. line 109: "of-targets" should be off-targets
      6. line 141: "delivery form the Golgi" should be FROM the Golgi
      7. line 160: "was DRPs" should be was DRP3
      8. line 204/205: the sentence starting "Thus, we phalloidin..." should be rephrased. It sounds not quite correct
      9. line 209: Figure 4A should refer to Figure 4B
      10. line 211: "times or rapid ciliary" should be of rapid ciliary...
      11. line 257: "in lithium." Should be in lithium treated cells

      Significance

      This study sheds new light on an important process in ciliary functional regulation and also furthers our understanding on why GSK3-beta inhibition induces elongated cilia in many cell systems, but I am not convinced that the conclusions are actually supported by the data, as the two key points in question were not experimentally addressed at this point.

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      Referee #1

      Evidence, reproducibility and clarity

      The current manuscript by Bigge et al. demonstrated that the chemical inhibition of GSk3 causes ciliary elongation in Chlamydomonas reinhardtii. They show that lithium induced ciliary lengthening is majorly due to GSK3 inhibition. Consistent with earlier reports, they show that new protein synthesis is not required for lithium induced ciliary elongation. The authors report that targeting endocytosis either by using chemical inhibitors (dynasore and CK-666) or genetic mutants (dpr3 and Arpc4) does not cause lithium induced ciliary elongation. They further reveal enhanced actin dynamics in lithium treated cells and such activity is lost in Arpc4 mutants. Based on these results, the authors concluded that endocytic pathways may be involved in lithium induced ciliary lengthening. The results are interesting, and this work is important in understanding more about ciliary length regulation. However, more experimental evidence addressing the current interpretation that endocytic pathways may be involved in lithium induced ciliary lengthening is required.

      Major comments:

      1. The authors use chemical inhibitors as major tools for their study. However, the specificity of these inhibitors is a concern. How specific are these GSK3 inhibitors such as LiCl? Can authors show that LiCl mediated ciliary lengthening is due to inhibition of GSK3? Authors used BFA and Dynasore to show that not the Golgi, but the endocytosis derived membrane is required for ciliary lengthening. Again, here the specificity of these inhibitors is a concern. Especially as Dynasore has been shown to have non-specific effects.
      2. Does inducing/enhancing endocytosis independent of GSK3 by other means has any effect on ciliary length regulation?
      3. The major claim of this paper is that LiCl mediated ciliary lengthening is due to enhanced endocytosis. Although authors showed that inhibition of endocytosis results in reduced ciliary length, it is important to show if GSK3 inhibition by LiCl (or any other inhibitor) causes any increased cellular endocytosis? Similarly, what is the effect of GSK3 mutants on endocytosis?
      4. Are these endocytic processes enhanced specifically at/or around the cilium during the ciliary lengthening process?
      5. Authors claim that drp3 is a target of GSK3 and, similar to the canonical dynamin, functions in endocytosis. While, it is an important observation, experiments are required to show the role of drp3 in endocytosis and also to show that it is indeed a target of GSK3.
      6. Mechanistic insights into how endocytosis/actin dynamics regulate ciliary lengthening would be interesting to see. Further, it is interesting to see if the ciliary signaling defects caused by abnormal ciliary length can be rescued by inhibition of endocytosis.

      Minor comments:

      1. The paper needs a thorough proof reading as it harbors many spelling mistakes, grammatical errors, and poor sentence formation in multiple instances.
      2. Supplemental Figure S2A and S2B should be quoted separately from S2C and S2D.
      3. In Page 6 paragraph 2 - "authors wrote "To determine if GSK3 could be a potential kinase for this protein, we employed ScanSite4.0, which confirmed that of the 9 DRPs of Chlamydomonas, the only one with a traditional GSK3 target sequence was DRPs (Supplemental Figure 2)." No data is shown in S2 with regard to this. Either data needs to be shown or change the text in a way to avoid confusion.
      4. It would be nice to see if GSK3 can actually phosphorylate DRP3.
      5. The authors observe that arpc4 mutants do not form actin puncta upon LiCl treatment. Could this phenotype be rescued by complementing with WT ARPC4.
      6. The concentration of inhibitors is described differently in the text and figure legends (for example Fig. 4A)
      7. The p values are not significant in some of the figures. (Fig. 4D &Fig. 5C)

      Significance

      The current manuscript by Bigge et al. demonstrates that endocytosis is required for GSK3 inhibition mediated ciliary lengthening. Maintenance of proper length of cilia is crucial and its dysregulation results in pathogenesis. This work takes the field forward and helps in our understanding of how ciliary length is regulated. This work is of interest to researchers working in the field of ciliary biology as well as to those working on endocytosis.

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      Reply to the reviewers

      Point-by-point response

      We thank the reviewers for their constructive comments. We have addressed all of them to the best of our knowledge. Our responses are shown below in bold and all changes in the text are highlighted in yellow.

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

      This study of Rizk, Bekiaris and colleagues is well written, carefully edited, and nicely placed into the trending context of the juvenile immune system development.

      They suggest that cIAP ubiquitin ligases cIAP1 and cIAP2 sustain type 3 γδ T after 4-5 weeks of age in mice. As a mechanism they show that these ubiquitin ligases are required in a cell-intrinsic manner to maintain cMAF and RORγt levels, and that this depends likely on overt activation of the non-canonical NF-κB pathway.

      Extrinsic factors such as microbiota did not seem to play a major role in this context.

      **Major comments:**

      It is absolutely crucial to directly and stringently control the efficiency of cIAP depletion via RORgt-cre, which may take some time and thus perhaps only reaches relevant (exponential) penetrance at early adulthood?

      Fig 4C is nice, however the Birc2 loxP sites may be far less efficient than those in the ROSA26-LSL-RFP system.

      - We thank the reviewer for this comment. In this regard, we sorted day 1 old γδT17 cells from the thymus of Cre+ and Cre- mice and screened for Birc2 mRNA (cIAP1) expression. We additionally compared expression to CD27+ γδ T cells, from the same thymi as RORγt-neg controls. Please see new Fig S5A and text line 208-209.

      However, the pre-puberty timing aspect is surprising, but without this aspect the conclusions would be similarly exciting.

      - The fact that Birc2 is indeed deleted in newborn thymocytes, supports our conclusions that its impact is seen progressively while mice are aging

      **Minor comments:**

      • To understand the general impact of cIAP on gdT17 homeostasis, the authors should consider investigating them in additional organs, as these gdT17 are quite tissue-resident and differentially adapt to their environment, where they use specific anti-apoptotic strategies to persist, including expression of Bcl2a1 family proteins.

      - We have investigated lung from adult ΔIAP1/2 and found significantly reduced γδT17 cells, in accordance with our data in the LN, gut and skin. Please see new Fig S1E and text lines 134-136.

      • Fig. 3: Has the presence of gdT17 in the graft been analyzed or enumerated? Experiments shown in Fig 3 AB and FG might collectively suggest that co-transferred gdT17 from the 45.1 BM graft could have reconstituted the regenerated gdT17 compartment in competition with the radioresistant 45.1/2 host gdT17 cells. This would actually not compromise the results, as the cIAP deficient cells did not persist.

      - We are not entirely sure what the reviewer means with this comment. We believe that the data in Fig 3 clearly shows that ΔIAP1/2 cells cannot compete with WT cells. This is also reinforced in Fig S3 where the host is ΔIAP1/2.

      Reviewer #1 (Significance (Required)):

      **Significance**

      This work is very original and might be of pharmacological interest for approaches targeting cIAP, e.g. in order to enhance anti-viral therapies.

      **Referee Cross-commenting**

      No further comments.

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

      The authors studied the effect of the inactivation of cIAP1 and 2 on the development and evolution of γδ T cells and in type 3 innate lymphoid cells (ILC3) using RORc-Cre induced inatiction of cIAP1 in combination or not with cIAP2 whole body KO. The authors showed that these two E3 ubiquitin ligases that regulate the NF-κB pathway, are important to maintain a population of IL-17 producers γδ T and ILC3 in adult animals. This lack of maintenance is correlated with a loss of c-MAF and RORγt expression in the two cell types and may be related to a deficiency in entering cell cycle in response to various cytokines. The authors also established that the mechanism is independent of the TNFR1 pathway. The article is well written, clear and most of the conclusions are well supported by the data showed. The results presented are novel and interesting for the field. However, I would suggest some major changes to make the story suitable for publication.

      1- The study of 2 different cell types brings some confusion to the story, even if I understand it makes some sense to pool these two parts in the same article. The γδ T cell part is more complete than the ILC3 part, which brings some frustration for the reader, as nothing indicates that the mechanisms leading to the loss of maintenance are similar in the 2 cell types. I would suggest to simply remove the ILC3 part and keep it for another article. If the authors wish to keep it in this article, they must perform a similar set of experiments already done for the γδ T cell part, especially the lineage tracing performed in figure 5 as c-Maf is known to be important in ILC plasticity for ILC3 and ILC1. They would also need to confirm the mechanisms involved in the process leading to ILC3 decrease.

      - We thank the reviewer for this comment. We do realize that the ILC3 part of the story may seem incomplete. For this reason, we have taken into consideration the reviewer’s advice and performed lineage tracing in ILC3 cells. In adult ΔIAP1/2 mice that were reporting RFP in RORγt+ cells, we found that within the ILC population, there was 10-fold reduction in RFP+ cells, suggesting that it is unlikely they convert to a non-ILC3 population. Please see new Fig S9C and text lines 297-302. In accordance KLRG1+ ILC2 numbers were not affected (Fig S9C).

      Next, we isolated sLP lymphocytes from 4-week old mice and treated them with cytokines that are known to induce ILC3 proliferation including IL-7, IL-1β and IL-23. We also chose these cytokines to concur with our γδT17 findings. However, we could not induce cell cycle in either WT or ΔIAP1/2 cells. We contacted experts in the field, namely Dr David Withers at the University of Birmingham, who contacted further experts (Dr Matt Hepworth), in order to ask for advice of how to induce gut ILC3 proliferation. We quote David Withers “we have never had any joy making ILC3 proliferate much in vitro”, and Matt Hepworth “have been looking at this and have struggled to make them proliferate in vivo or in vitro”. So, unfortunately, we cannot test ILC3 proliferation in the same way we did for γδT17 cells.

      2- Although the authors nicely excluded the TNFR1 pathway from the mechanisms leading to γδ T cell loss in adult, the overt activation of the cRel pathway is not enough established as far as I am concerned. It would at least require a more thorough quantification of the immunofluorescent staining done. Showing only one cell is not enough. If possible, using another approach to confirm these data would also be needed.

      - We have now quantified RelB nuclear translocation over 4 experiments and found a significant increase in ΔIAP1/2 cells. Please see new panel in Fig 5F and text lines 244-250. Furthermore, there was a significant increase in Relb mRNA in ΔIAP1/2 newborn thymic γδT17 cells, which is consistent with activation of the non-canonical NF-kB pathway. Please see new Fig S6C and text lines 244-246.

      3- The expression level/quantity of protein of cIAP1/2 in γδ T cells from WT animal at the various stages of development has not been analyzed. Does it remain constant? Does it vary throughout development of γδ T cells? This information is important to further enforce and understand the role of these protein in the development of γδ T cells.

      - Unfortunately, we cannot quantify cIAP1/2 protein levels in these cells for technical reasons. There is no Ab for flow and only a cIAP1 Ab for western blots, which is of course impossible when dealing with such low cell numbers.____ However, we have contacted Dr Dominic Grün who had done a single-cell RNA-seq profiling of γδ T cells throughout different developmental stages, and asked to analyze expression levels of Birc2 and Birc3. We found that both Birc2 and Birc3 were expressed across all subsets of fetal and adult thymic γδ T cells with no specific enrichment and no apparent up- or down-regulation between the two time points. Please see attached Figure 1.

      Attached Figure 1: expression patterns of Birc2 and Birc3 at a single cell level in the different populations of fetal and adult thymic γδ T cells.

      4- In Figure 4D-E, the authors showed that in vitro, γδ T cells fail to progress through cell cycle in response to IL-7 or IL1b+ IL-23. Is a similar block detectable directly ex-vivo? Furthermore, it appears that Imiquimod treatment restore at least partially the deficiency in γδ T cells in the double KO mice. It would mean other cytokines or TCR triggering is rescuing this phenotype. Could the author test in vitro other stimuli and test whether γδ T cells are reactive to some stimuli but not others? It would bring some lights on the signaling regulated by cIAP1/2.

      - There is little if any detectable active cell cycling of these cells directly ex vivo, as shown by near absence of Ki67+7AAD+ cells (see below). We can still pick up small differences in Ki67+ cells but this is not sufficient to conclude whether there is more or less cell division. Please see attached figure 2.

      Attached Figure 2: ex-vivo cell cycle analysis of γδT17 cells form 4 week old- ΔIAP2 or ΔIAP1/2 mice.

      - We have now tested how 4-week old γδT17 cells from ____Δ____IAP1/2 mice respond to IL-2 and TCR stimulation. We found that similar to IL-7, IL-1b and IL-23, cells lacking IAPs proliferate less under both conditions. See new Fig S5C and lines 227-228.

      **Minor point:**

      although the authors cite a reference in the result section, could they show a dot plot confirming that the CD44hi CCR6+ or CCR6+ are the only population producing IL-17 among, γδ T cells?

      - We now show this in Fig S1D and lines 129-130

      Reviewer #2 (Significance (Required)):

      This article describes a new role for the cIAP1 and 2 in the maintenance of γδ T cells and ILC3s. In line with their previous work (Rizk, 2019), they show that this effect is correlated with a loss in c-MAF expression, which is a major transcription factor for these 2 cell types. These discoveries are of interest for specialists in the field, including myself. I am an expert in T cells and ILCs, with an interest in c-MAF function in these cell types.

      **Referee Cross-commenting**

      No further comments.

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      Referee #2

      Evidence, reproducibility and clarity

      The authors studied the effect of the inactivation of cIAP1 and 2 on the development and evolution of T cells and in type 3 innate lymphoid cells (ILC3) using RORc-Cre induced inatiction of cIAP1 in combination or not with cIAP2 whole body KO. The authors showed that these two E3 ubiquitin ligases that regulate the NF-B pathway, are important to maintain a population of IL-17 producers T and ILC3 in adult animals. This lack of maintenance is correlated with a loss of c-MAF and RORγt expression in the two cell types and may be related to a deficiency in entering cell cycle in response to various cytokines. The authors also established that the mechanism is independent of the TNFR1 pathway. The article is well written, clear and most of the conclusions are well supported by the data showed. The results presented are novel and interesting for the field. However, I would suggest some major changes to make the story suitable for publication.

      1- The study of 2 different cell types brings some confusion to the story, even if I understand it makes some sense to pool these two parts in the same article. The T cell part is more complete than the ILC3 part, which brings some frustration for the reader, as nothing indicates that the mechanisms leading to the loss of maintenance are similar in the 2 cell types. I would suggest to simply remove the ILC3 part and keep it for another article. If the authors wish to keep it in this article, they must perform a similar set of experiments already done for the T cell part, especially the lineage tracing performed in figure 5 as c-Maf is known to be important in ILC plasticity for ILC3 and ILC1. They would also need to confirm the mechanisms involved in the process leading to ILC3 decrease.

      2- Although the authors nicely excluded the TNFR1 pathway from the mechanisms leading to T cell loss in adult, the overt activation of the cRel pathway is not enough established as far as I am concerned. It would at least require a more thorough quantification of the immunofluorescent staining done. Showing only one cell is not enough. If possible, using another approach to confirm these data would also be needed.

      3- The expression level/quantity of protein of cIAP1/2 in T cells from WT animal at the various stages of development has not been analyzed. Does it remain constant? Does it vary throughout development of T cells? This information is important to further enforce and understand the role of these protein in the development of T cells.

      4- In Figure 4D-E, the authors showed that in vitro, T cells fail to progress through cell cycle in response to IL-7 or IL1b+ IL-23. Is a similar block detectable directly ex-vivo? Furthermore, it appears that Imiquimod treatment restore at least partially the deficiency in T cells in the double KO mice. It would mean other cytokines or TCR triggering is rescuing this phenotype. Could the author test in vitro other stimuli and test whether T cells are reactive to some stimuli but not others? It would bring some lights on the signaling regulated by cIAP1/2.

      Minor point:

      although the authors cite a reference in the result section, could they show a dot plot confirming that the CD44hi CCR6+ or CCR6+ are the only population producing IL-17 among , T cells?

      Significance

      This article describes a new role for the cIAP1 and 2 in the maintenance of T cells and ILC3s. In line with their previous work (Rizk, 2019), they show that this effect is correlated with a loss in c-MAF expression, which is a major transcription factor for these 2 cell types. These discoveries are of interest for specialists in the field, including myself. I am an expert in T cells and ILCs, with an interest in c-MAF function in these cell types.

      Referee Cross-commenting

      No further comments.

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      Referee #1

      Evidence, reproducibility and clarity

      This study of Rizk, Bekiaris and colleagues is well written, carefully edited, and nicely placed into the trending context of the juvenile immune system development.

      They suggest that cIAP ubiquitin ligases cIAP1 and cIAP2 sustain type 3 γδ T after 4-5 weeks of age in mice. As a mechanism they show that these ubiquitin ligases are required in a cell-intrinsic manner to maintain cMAF and RORγt levels, and that this depends likely on overt activation of the non-canonical NF-κB pathway. Extrinsic factors such as microbiota did not seem to play a major role in this context.

      Major comments:

      It is absolutely crucial to directly and stringently control the efficiency of cIAP depletion via RORgt-cre, which may take some time and thus perhaps only reaches relevant (exponential) penetrance at early adulthood? Fig 4C is nice, however the Birc2 loxP sites may be far less efficient than those in the ROSA26-LSL-RFP system.

      However, the pre-puberty timing aspect is surprising, but without this aspect the conclusions would be similarly exciting.

      Minor comments:

      • To understand the general impact of cIAP on gdT17 homeostasis, the authors should consider investigating them in additional organs, as these gdT17 are quite tissue-resident and differentially adapt to their environment, where they use specific anti-apoptotic strategies to persist, including expression of Bcl2a1 family proteins.
      • Fig. 3: Has the presence of gdT17 in the graft been analyzed or enumerated? Experiments shown in Fig 3 AB and FG might collectively suggest that co-transferred gdT17 from the 45.1 BM graft could have reconstituted the regenerated gdT17 compartment in competition with the radioresistant 45.1/2 host gdT17 cells. This would actually not compromise the results, as the cIAP deficient cells did not persist.

      Significance

      Significance

      This work is very original and might be of pharmacological interest for approaches targeting cIAP, e.g. in order to enhance anti-viral therapies.

      Referee Cross-commenting

      No further comments.

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      Reply to the reviewers

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

      The manuscript by Sasaki et al titled "Conditional GWAS of non-CG transposon methylation in Arabidopsis thaliana reveals major polymorphisms in five genes" employed conditional GWAS to identify trans-regulators of mCHG levels in Arabidopsis natural accessions, after controlling for mCHH. Using loss of function mutants for couple of these genes, the authors also tested their effects on mCHG levels.

      Overall, this manuscript makes a nice contribution. I suggest the following improvements to enhance the quality of this manuscript.

      Comments:

      1. MSI1 has been shown to be copurified with TCX5, a component of DREAM Complex. The DREAM complex transcriptional regulates CMT3, MET1, DDM1 in a cell cycle dependent manner (ref: Yong-Qiang Ning, 2020 nature plants). Tcx5/6 double mutants have ectopic gain of TE and genic mCHG. It would be nice to refer this paper and add to the MSI1 part accordingly. Absolutely: thanks for suggesting this!

      Multifaceted regulation of mCHG levels seems to be evident from this and previous studies. Why would such complex pathways evolv to regulate mCHG? Bewick et al 2016 and Wendte et al 2019 showed lack of CMT3 or ectopic expression of CMT3 can influence CG gene body methylation (gbM). One possibility is that these five factors regulate CHG to maintain it at a level that is just enough to target TE. Irrespective of the functional relevance of gbM, differences in the levels of these five factors might result in erroneous gbM. It would be interesting to look for the rates of gbM and number of gbM genes in the natural accession carrying 1 to 4 number of mCHG-decreasing alleles. Also, in the one line from Iberian peninsula carrying polymorphisms in all five genes.

      Yes, the connection between CHG and gbM is very interesting and deserves more attention. We looked for the effect of cumulative mCHG-decreasing alleles on gbM, but there was no association with gbM — but this is really not expected given the stable epigenetic inheritance of gbM. The Iberian peninsula line carrying all decreasing alleles did slightly lower gbM levels, but it is impossible to exclude the effects of population structure. Since we have nothing to add beyond speculation, we prefer not to go into this topic.

      The authors mentioned a significant peak for mCHG|mCHH on RdDM-targeted transposons was located 196 bp downstream of MIR823a and not on mature miRNA. Therefore, this cannot directly impair miR823 base pairing with CMT3 mRNA transcripts and its cleavage. Moreover, natural accessions carrying alternative MIRNA823 allele show reduced CMT3 and mCHG levels, meaning more miR823 levels? Does this 196 downstream region contain any regulatory feature that effects miR823 transcription? Or this region still falls in the primary miRNA hairpin region? A single nucleotide change in pri-miRNA can have a significant impact on its secondary structure that can impede DICER processivity and effectively levels of mature miR823 molecules? It will be beyond the scope of this paper to pin down the exact mechanism. But a simple stem loop RT-PCR for miR823 levels in reference and alternative accessions would be informative (on accessions that grow at the same speed). Perhaps, the authors can at least model SNP induced pri-miRNA secondary structure variations using Vienna RNAFold (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi) and present MEF values (maximum free energy) for representative accessions.

      Stem-loop qRT-PCR for MIR823a expression would indeed be helpful to confirm allelic effects. However, comparing lines with wildly different genetic backgrounds is fraught with difficulty due to trans-effects. Furthermore, MIR823a is expressed specifically during embryogenesis, and the expression quickly decreases after the early heart stage (Papareddy et al., 2021). Thus, we would need to extract microRNA from embryos at exactly the same developmental stage, from lines that may develop at different speeds.. Most likely, time-series data would be required, and generating such data is a massive undertaking. As noted in the paper, we did measure MIR823a expression by stem-loop qRT-PCR for several lines carrying reference and alternative alleles but the results were inconclusive. A proper study of this is beyond the scope of this paper.

      Testing predicted effects on RNA secondary structure, on the other hand, is eminently feasible. As suggested, we used Vienna RNAFold for the region, including the GWAS peak. Since the SNP is linked to a 35 bp deletion (shown in S4A), it is closer to the MIR823A coding region than 196 bp. However, the results indicate that the SNP (Chr3:4496626) is not within the stem-loop. It remains possible that this SNP tags multiple SNPs in the annotated stem regions. This is now mentioned.

      Figure 1A can be made more reader friendly. Perhaps this can be broken down into correlation plots for individual conditions or tissue types. In addition, it might be good to add individual r-square values for each of them instead of compound r-square.

      We respectfully disagree, since the main point of the figure is the overall correlation and heterogeneity, rather than the correlation within sub-sets. Instead of splitting the plot, we changed color contrasts to make it easier to read.

      Page 3, Paragraph 1 from line 3 to end of paragraph. The authors wrote "Much of this variation is due to differences in the environment (including tissue, which can be viewed as a cellular environment)". A possible explanation is these two tissues have different mitotic indices (fraction of cells diving and non-diving; flowers have more dividing cell, leaves have more non dividing and endoreduplicated cells) that explains non-CG variation. I would suggest authors to change the text to this and refer to Filipe Borges et al 2021 Current biology paper.

      This is certainly a possibility, although higher mCHG levels in flower buds presumably also reflect higher CMT3 expression during embryogenesis (Feng et al. 2020; Gutzat et al. 2020; Papareddy et al. 2021). We now mention both explanations and cite Borges et al. (2021).

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

      Summary:

      Sasaki et al. carried out a conditional GWAS analysis of TE-CHG methylation in Arabidopsis thaliana natural accessions. They revealed multiple associations with SNPs in known DNA methylation genes. A new finding is the association found proximal to JMJ26, which had no previously described role in the maintenance/establishment of RdDM-targeted transposons. The authors validate the JMJ26 association using a loss-of-function mutant of JMJ26, which essentially recapitulates the GWAS effect, suggesting that JMJ26 is likely causal. An important point of the study is that the associations detected with conditional GWAS have not been seen in previous univariate (i.e. unconditional) GWAS, probably due to to a lack of power. At the sub-genome-wide threshold the authors discovered further, albeit weaker, associations that were also highly enriched for known DNA methylation genes.

      Overall impression:

      The manuscript is clearly written, and the functional validation of the JMJ26 GWAS signal is commendable and certainly goes beyond the typical GWA study. Beyond this validated association however, the GWAS results are mainly confirmatory. They essentially highlight that methylation genes previously identified by way of mutant screens are variable in natural populations, and (probably) causative of non-CG methylation variation in TEs. What I personally found very distracting throughout the manuscript was the strong emphasis on the methodological aspect; that is, the conditional GWAS, which is really not new. Furthermore, the conceptual/philosophical discussion about what is a complex trait or what can be called polygenic was slightly pedantic and distracted from the biological message.

      There are three points here. First, we disagree that the GWAS results are confirmatory. Sure, only one of our associations is connected with a novel gene, but the fact that the four other genes apparently harbor major polymorphism is a new finding that contributes to our understanding of the function of this trait (and, possibly, these genes). Second, while it is possible that we emphasize statistical methodology too much, we do this for clarity, not to claim that what we are doing is novel. Third, we are similarly not interested in defining what is polygenic and what isn’t, but rather put the results in the context of other studies. We have changed the writing in various places to make it clearer (and hopefully less distracting/pedantic).

      A conceptual comment:

      • The conditional GWAS presented here is conceptually very similar to conditional QTL mapping approaches where candidate loci are included, a priori, as covariates in the model, and a scan is performed to search for additional modifiers. It is known that this approach increases power because the scan is performed on the residual trait variation (having accounting for effect of candidate loci). This is also the idea behind MQM mapping, although in the latter the inclusion is not restricted to candidate loci. Instead of including candidate SNPs as covariate the authors include TE-CHH methylation levels as a covariate as it is highly correlated with TE-CHG methylation. By doing this, the authors essentially "control" for any SNP affecting the covariance between CHG and CHH, even if these SNPs (and their genetic architecture) remain unknown. Hence, the conditional scan is mainly on the residual variation in TE-CHG methylation that is unique to this context (i.e. independent of CHH). That additional TE-CHG associated loci pop up in this scan is perhaps not so surprising.

      We agree, and have even written papers on this very subject. We were surprised by this comment as we felt we had included lengthy sections (see also comment above) about methodology, emphasizing that multi-trait analysis is a good idea in principle. One of our purposes here is to provide a beautiful example demonstrating this. We have tried to make these points clearer.

      The finding that this conditional GWAS yields again a handful of loci of that explain a considerable part of the trait (now residual trait) variation leads the authors to suggest that the genetic architecture underlying non-CG methylation of TEs is not "polygenic". I think this is semantics. All the authors have done is relegate any causal SNPs underlying the covariance between TE-CHG and TE-CHH to the right hand side of the equation of their GWAS model, and subsumed it under the predictor "TE-CHH methylation levels". That is, the genetic architecture underlying this covariance is still unknown, difficult to identify and probably highly polygenic.

      Again we agree, and fail to see why the reviewer thinks we do not. Nowhere do we claim that the overall covariance has a simple basis, and we explicitly state that it is the conditional mCHG variation that has an oligogenic basis. We did write that “univariate GWAS of mCHG variation failed to detect any significant associations, leading us to conclude, erroneously, that the trait was simply too polygenic”, which was imprecise, and arguably erroneous. The word “erroneously” has been removed in the revision.

      The authors essentially decompose a complex traits into parts and map genetic architectures for each part. Although each part seems less complex and more oligogenic than polygenic, when putting all the parts back together, I would argue we are getting close to a complex trait with a polygenic architecture. The study by Hüther et al, which the authors also cite, is another example of how a complex trait can be decomposed into parts. In reference to one of the authors' GWAS associations, they say "...this association was also recently found by Hüther et al. (2022) using GWAS for unconditional mCHG levels of individual transposons. The MIR823A polymorphism appears to almost exclusively affect mCHG (Figs. S2, S3), primarily targeting the same transposons as a CMT3 knock-out...". In the case of Hüther et al., the complex TE-CHG methylation trait is simplified by selecting specific TEs, a priori, that are differential methylated in CMT3 knock-out lines. One could go on like this, and continue to peel away this complex trait. But, again, this does not mean that the overall TE-CHG methylation trait is not complex nor polygenic. It spirals down into a discussion of what is actually meant by "complex" or "polygenic", which is an interesting discussion, but - in the case - of this manuscript takes away from the biological message. My point is perhaps best reflected in the following statement from the discussion section: "Despite high heritability, univariate GWAS of mCHG variation failed to detect any significant associations, leading us to conclude, erroneously, that the trait was simply too polygenic (Kawakatsu et al., 2016)." But a few lines below the authors seem to realize what they have actually done "We believe that, by controlling for mCHH, we have effectively simplified the trait, revealing genetic factors affecting mCHG only, perhaps by affecting the maintenance of this type of DNA methylation."

      The phrase “seem to realize” is unwarranted and unnecessary sarcasm. Given that we cite the two century-old papers that first demonstrated that it was possible to decompose complex traits into Mendelian ones, it should be obvious that we understand what we have done. That our writing could have been better is another matter. As noted above, the word “erroneously” has been dropped, and we have also changed the second sentence to make it obvious that this is obvious. We suspect that whether one finds this part of the Discussion “distracting” or not depends on training and background — our objective was to explain our results to readers who (unlike us and the reviewer) are not well-versed in quantitative genetics.

      Specific comments

      1. A large part of the manuscript focuses on SNPs that enriched for a priori genes that fall below the genome-wide significance threshold. While I see the reasons for doing this in this particular manuscript, I do not see how this is useful in general (again this approach is partly "sold" on methodological grounds). The approach can obviously not be extended to study traits where a priori gene sets are unavailable or incomplete. Moreover, the "FDR" approach based on the a priori gene set labels GWAS hits that are not within the a priori set "false discoveries", which may or may not be true. Moreover, there is no "natural" stopping point for going below GWAS thresholds. An alternative, to this would be to perform a targeted GWAS for a priori genes (+ a LD window around them). Since this alleviates the multiple testing burden, I would be curious to see what this yields both in terms of conditional and unconditional analysis. Candidates that show a signal could be included as covariates and a conditional scan for unknown genes could then be performed.

      The FDR analysis using a set of a priori genes should be explained in detail in this ms. It is cumbersome to go to another manuscript to see what was done exactly, especially since this information is also difficult to dig up in the Atwell 2010 study. Although I understand the idea behind this approach, I would be concerned that this type of "FDR" analysis assumes that that all methylation genes are known. A novel candidate that was perhaps never identified in mutants screens before would be classified as a false discovery. Similarly, known candidates that carry no functional polymorphisms in nature, perhaps because they are highly constraint, will never become a discovery.

      Comments 1 and 9 largely overlap, and so we moved 9 here for clarity and respond to both at the same time. We agree that the enrichment analysis should be explained in this article as well, so as to save the reader from finding the supplement to an old paper. A new section has been added to Methods. In this section, we also try to preempt some of the misunderstandings in the reviewer's comments.

      First, our approach is indeed generally applicable. Whether it is useful depends on what you want to do, and yes, the utility will depend on the quality of the independent data, but note that the a priori gene set does not have to be genes: you could use this approach to compare coding vs non-coding regions of the genome, for example.

      Second, we are not trying to “sell” our approach (or anything else for that matter).

      Third, the approach does not label GWAS hits that are not within the a priori set as false discoveries: it says nothing about these hits.

      Fourth, we are not sure what is meant by a ‘“natural” stopping point for going below GWAS thresholds’, but our approach does provide a simple way to explore how FDR (in the a priori set!) depends on the threshold used.

      Fifth, the proposed alternative of “targeted GWAS” (non-genomewide association, as it were) is not equivalent, because our approach was not designed to increase power by alleviating the multiple testing burden, but rather to rigorously demonstrate that there is a signal in the data when faced with uncalibrated p-values. That it can also be used to explore sub-significant associations is a nice side-effect that we exploit here.

      Sixth, we do not assume that all methylation genes are known, nor is our goal to find them all.

      With regards to the CMT2 signals (particularly section "Further evidence for allelic heterogeneity at CMT2") it would have been useful/clearer to break down CHH into CWA and non-CWA.

      While this is a sensible suggestion, the focus of this paper is on mCHG, and refining the mCHH measurement would essentially amount to re-doing all analyses.

      I understand that the authors set out to do this conditional analysis because previously no hits could be found for CHG TE methylation. However, have the authors considered going the other way around and performing a CHH|CHG analysis to find additional QTL affecting CHH methylation, partly indepedently of CHG?

      Yes, this was in the paper, but we only mention it in the Discussion (and Fig S13) as the results were only of methodological interest (as expected, they were very similar).

      The authors write: "While both mCHG and mCG showed high heritability, GWAS yielded little in terms of significant associations. This might be because these "traits" are highly polygenic, or because they are at least partly transgenerationally inherited, and hence do not behave like standard phenotypes." Please clarify what they mean by "not behave like standard phenotypes".

      Done.

      The authors write: "Our starting point is the observation that mCHG and mCHH levels on transposons are strongly correlated in the 1001 Epigenomes data set (Kawakatsu et al., 2016), especially for RdDM- targeted transposons (Fig. 1A; see Methods). Much of this variation ....". What is mean by "this variation"?

      The sentence has been changed to make this clearer.

      A few lines below, they write "...huge". Please rephrase.

      Done.

      The authors write: "sample data set ("Leaf SALK ambient temperature"; n=846). Interestingly, the covariance between mCHH and mCHG showed the same pattern in data generated by knocking out known or potential DNA methylation regulators in the same genetic background (Fig. 1B) (Stroud et al., 2013). This demonstrates strong co-regulation of these types of methylation, in particular for RdDM-targeted transposons." It is noticeable that many double mutants are off the diagonal. To me this indicates that they affect one context more than the other (i.e. they break covariance). Second, it suggests that they are probably interacting non-additively. It would be great if the authors could comment on this observation; perhaps also later in the ms, where they make a case for additivity.

      We are not convinced that the double- or triple-mutant show non-additivity. Adding up effects in Figure 1 works pretty well. As for our GWAS results, it is clear that small effects (like the ones in our GWAS) will always tend to look additive for simple mathematical reasons. This does not mean that no interactions exist, and we emphasize this in the paper. We also have an example of non-linearity when it comes to TE activity. This is now also emphasized.

      The authors write: " it is difficult to say what fraction of these factors is genetic and what is environmental, but, regardless of this, we hypothesized that the substantial covariance could reduce power of GWAS for either mCHH or mCHG (when using a standard univariate model), and that an analysis accounting for this covariance might perform better...". The arguments given thus far are not sufficient to understand why a "substantial covariance" between traits would reduce the power to map individual traits. I think more needs to be done here to motivate this.

      The sentence following the one quoted is “In essence, we sought to simplify a complex trait by breaking it into constituent parts”, which is very much part of the motivation. As the reviewer noted above, it is not surprising that a conditional analysis turns out to be more powerful. The comment may have arisen from the statement “This insight is the basis for this paper”, which is misleading — there is no insight here, just a very obvious hypothesis, which turned to be correct. We have changed the writing to make this clearer.

      The authors write" "However, MSI1 is required to control DNA methylation via repression of MET1, and a loss of FAS2 in CAF-1 induces mCHG hypermethylation (Fig 1B) (Stroud et al., 2013; Jullien et al., 2008)...", where is the "FAS2 in CAT-1" result visible in Fig. 1B?

      fas2 induces mCHG hypermethylation in CMT2-targeted TEs, presumably via a complex that also involves MSI1. It is marked in Fig. 1B. We have rephrased the sentence to make this clearer.

      The results presented in "A jmjC gene is a novel modifier of mCHG in RdDM-targeted transposons" could have been showcased better. Only after reading the methods part did I realize that the authors generated CRISPR mutants. It reads as if the authors just picked up some available loss of function mutants and profiled them. But, clearly, much more work was involved here and the authors could have brought that out more. Perhaps more generally, I think all the new functional analysis the authors perform is largely "under-sold" in this manuscript at the expense of unnecessary methodological/concpetual discussion (see point above).

      We actually generated CRISPR/CAS9 mutants only for MIR823A (Table S5). For JMJ26, a t-DNA insertion line was available, and results based on this and rescue lines provided sufficient results. To clarify this, we corrected the subsection titles.

      In section "The power and complexity of conditional GWAS", the authors write "The performance of GWAS relies on using the right model for the relation between genotype and phenotype. As with other statistical methods, using the wrong model may lead to unpredictable results." This seems like a too obvious of a statement.

      Indeed: it is meant ironically. It is obvious, yet people do it.

    2. 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

      Summary:

      Sasaki et al. carried out a conditional GWAS analysis of TE-CHG methylation in Arabidopsis thaliana natural accessions. They revealed multiple associations with SNPs in known DNA methylation genes. A new finding is the association found proximal to JMJ26, which had no previously described role in the maintenance/establishment of RdDM-targeted transposons. The authors validate the JMJ26 association using a loss-of-function mutant of JMJ26, which essentially recapitulates the GWAS effect, suggesting that JMJ26 is likely causal. An important point of the study is that the associations detected with conditional GWAS have not been seen in previous univariate (i.e. unconditional) GWAS, probably due to to a lack of power. At the sub-genome-wide threshold the authors discovered further, albeit weaker, associations that were also highly enriched for known DNA methylation genes.

      Overall impression:

      The manuscript is clearly written, and the functional validation of the JMJ26 GWAS signal is commendable and certainly goes beyond the typical GWA study. Beyond this validated association however, the GWAS results are mainly confirmatory. They essentially highlight that methylation genes previously identified by way of mutant screens are variable in natural populations, and (probably) causative of non-CG methylation variation in TEs. What I personally found very distracting throughout the manuscript was the strong emphasis on the methodological aspect; that is, the conditional GWAS, which is really not new. Furthermore, the conceptual/philosophical discussion about what is a complex trait or what can be called polygenic was slightly pedantic and distracted from the biological message.

      A conceptual comment:

      • The conditional GWAS presented here is conceptually very similar to conditional QTL mapping approaches where candidate loci are included, a priori, as covariates in the model, and a scan is performed to search for additional modifiers. It is known that this approach increases power because the scan is performed on the residual trait variation (having accounting for effect of candidate loci). This is also the idea behind MQM mapping, although in the latter the inclusion is not restricted to candidate loci. Instead of including candidate SNPs as covariate the authors include TE-CHH methylation levels as a covariate as it is highly correlated with TE-CHG methylation. By doing this, the authors essentially "control" for any SNP affecting the covariance between CHG and CHH, even if these SNPs (and their genetic architecture) remain unknown. Hence, the conditional scan is mainly on the residual variation in TE-CHG methylation that is unique to this context (i.e. independent of CHH). That additional TE-CHG associated loci pop up in this scan is perhaps not so surprising.

      The finding that this conditional GWAS yields again a handful of loci of that explain a considerable part of the trait (now residual trait) variation leads the authors to suggest that the genetic architecture underlying non-CG methylation of TEs is not "polygenic". I think this is semantics. All the authors have done is relegate any causal SNPs underlying the covariance between TE-CHG and TE-CHH to the right hand side of the equation of their GWAS model, and subsumed it under the predictor "TE-CHH methylation levels". That is, the genetic architecture underlying this covariance is still unknown, difficult to identify and probably highly polygenic.

      The authors essentially decompose a complex traits into parts and map genetic architectures for each part. Although each part seems less complex and more oligogenic than polygenic, when putting all the parts back together, I would argue we are getting close to a complex trait with a polygenic architecture. The study by Hüther et al, which the authors also cite, is another example of how a complex trait can be decomposed into parts. In reference to one of the authors' GWAS associations, they say "...this association was also recently found by Hüther et al. (2022) using GWAS for unconditional mCHG levels of individual transposons. The MIR823A polymorphism appears to almost exclusively affect mCHG (Figs. S2, S3), primarily targeting the same transposons as a CMT3 knock-out...". In the case of Hüther et al., the complex TE-CHG methylation trait is simplified by selecting specific TEs, a priori, that are differential methylated in CMT3 knock-out lines. One could go on like this, and continue to peel away this complex trait. But, again, this does not mean that the overall TE-CHG methylation trait is not complex nor polygenic. It spirals down into a discussion of what is actually meant by "complex" or "polygenic", which is an interesting discussion, but - in the case - of this manuscript takes away from the biological message. My point is perhaps best reflected in the following statement from the discussion section: "Despite high heritability, univariate GWAS of mCHG variation failed to detect any significant associations, leading us to conclude, erroneously, that the trait was simply too polygenic (Kawakatsu et al., 2016)." But a few lines below the authors seem to realize what they have actually done "We believe that, by controlling for mCHH, we have effectively simplified the trait, revealing genetic factors affecting mCHG only, perhaps by affecting the maintenance of this type of DNA methylation."

      Specific comments

      • A large part of the manuscript focuses on SNPs that enriched for a priori genes that fall below the genome-wide significance threshold. While I see the reasons for doing this in this particular manuscript, I do not see how this is useful in general (again this approach is partly "sold" on methodological grounds). The approach can obviously not be extended to study traits where a priori gene sets are unavailable or incomplete. Moreover, the "FDR" approach based on the a priori gene set labels GWAS hits that are not within the a priori set "false discoveries", which may or may not be true. Moreover, there is no "natural" stopping point for going below GWAS thresholds. An alternative, to this would be to perform a targeted GWAS for a priori genes (+ a LD window around them). Since this alleviates the multiple testing burden, I would be curious to see what this yields both in terms of conditional and unconditional analysis. Candidates that show a signal could be included as covariates and a conditional scan for unknown genes could then be performed.
      • With regards to the CMT2 signals (particularly section "Further evidence for allelic heterogeneity at CMT2") it would have been useful/clearer to break down CHH into CWA and non-CWA.
      • I understand that the authors set out to do this conditional analysis because previously no hits could be found for CHG TE methylation. However, have the authors considered going the other way around and performing a CHH|CHG analysis to find additional QTL affecting CHH methylation, partly indepedently of CHG?
      • The authors write: "While both mCHG and mCG showed high heritability, GWAS yielded little in terms of significant associations. This might be because these "traits" are highly polygenic, or because they are at least partly transgenerationally inherited, and hence do not behave like standard phenotypes." Please clarify what they mean by "not behave like standard phenotypes".
      • The authors write: "Our starting point is the observation that mCHG and mCHH levels on transposons are strongly correlated in the 1001 Epigenomes data set (Kawakatsu et al., 2016), especially for RdDM- targeted transposons (Fig. 1A; see Methods). Much of this variation ....". What is mean by "this variation"?
      • A few lines below, they write "...huge". Please rephrase.
      • The authors write: "sample data set ("Leaf SALK ambient temperature"; n=846). Interestingly, the covariance between mCHH and mCHG showed the same pattern in data generated by knocking out known or potential DNA methylation regulators in the same genetic background (Fig. 1B) (Stroud et al., 2013). This demonstrates strong co-regulation of these types of methylation, in particular for RdDM-targeted transposons." It is noticeable that many double mutants are off the diagonal. To me this indicates that they affect one context more than the other (i.e. they break covariance). Second, it suggests that they are probably interacting non-additively. It would be great if the authors could comment on this observation; perhaps also later in the ms, where they make a case for additivity.
      • The authors write: " it is difficult to say what fraction of these factors is genetic and what is environmental, but, regardless of this, we hypothesized that the substantial covariance could reduce power of GWAS for either mCHH or mCHG (when using a standard univariate model), and that an analysis accounting for this covariance might perform better...". The arguments given thus far are not sufficient to understand why a "substantial covariance" between traits would reduce the power to map individual traits. I think more needs to be done here to motivate this.
      • The FDR analysis using a set of a priori genes should be explained in detail in this ms. It is cumbersome to go to another manuscript to see what was done exactly, especially since this information is also difficult to dig up in the Atwell 2010 study. Although I understand the idea behind this approach, I would be concerned that this type of "FDR" analysis assumes that that all methylation genes are known. A novel candidate that was perhaps never identified in mutants screens before would be classified as a false discovery. Similarly, known candidates that carry no functional polymorphisms in nature, perhaps because they are highly constraint, will never become a discovery.
      • The authors write" "However, MSI1 is required to control DNA methylation via repression of MET1, and a loss of FAS2 in CAF-1 induces mCHG hypermethylation (Fig 1B) (Stroud et al., 2013; Jullien et al., 2008)...", where is the "FAS2 in CAT-1" result visible in Fig. 1B?
      • The results presented in "A jmjC gene is a novel modifier of mCHG in RdDM-targeted transposons" could have been showcased better. Only after reading the methods part did I realize that the authors generated CRISPR mutants. It reads as if the authors just picked up some available loss of function mutants and profiled them. But, clearly, much more work was involved here and the authors could have brought that out more. Perhaps more generally, I think all the new functional analysis the authors perform is largely "under-sold" in this manuscript at the expense of unnecessary methodological/concpetual discussion (see point above).
      • In section "The power and complexity of conditional GWAS", the authors write "The performance of GWAS relies on using the right model for the relation between genotype and phenotype. As with other statistical methods, using the wrong model may lead to unpredictable results." This seems like a too obvious of a statement.

      Significance

      The manuscript is clearly written, and the functional validation of the JMJ26 GWAS signal is commendable and certainly goes beyond the typical GWA study. Beyond this validated association however, the GWAS results are mainly confirmatory. They essentially highlight that methylation genes previously identified by way of mutant screens are variable in natural populations, and (probably) causative of non-CG methylation variation in TEs.

    3. 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

      The manuscript by Sasaki et al titled "Conditional GWAS of non-CG transposon methylation in Arabidopsis thaliana reveals major polymorphisms in five genes" employed conditional GWAS to identify trans-regulators of mCHG levels in Arabidopsis natural accessions, after controlling for mCHH. Using loss of function mutants for couple of these genes, the authors also tested their effects on mCHG levels. Overall, this manuscript makes a nice contribution. I suggest the following improvements to enhance the quality of this manuscript.

      Comments:

      1. MSI1 has been shown to be copurified with TCX5, a component of DREAM Complex. The DREAM complex transcriptional regulates CMT3, MET1, DDM1 in a cell cycle dependent manner (ref: Yong-Qiang Ning, 2020 nature plants). Tcx5/6 double mutants have ectopic gain of TE and genic mCHG. It would be nice to refer this paper and add to the MSI1 part accordingly.
      2. Multifaceted regulation of mCHG levels seems to be evident from this and previous studies. Why would such complex pathways evolv to regulate mCHG? Bewick et al 2016 and Wendte et al 2019 showed lack of CMT3 or ectopic expression of CMT3 can influence CG gene body methylation (gbM). One possibility is that these five factors regulate CHG to maintain it at a level that is just enough to target TE. Irrespective of the functional relevance of gbM, differences in the levels of these five factors might result in erroneous gbM. It would be interesting to look for the rates of gbM and number of gbM genes in the natural accession carrying 1 to 4 number of mCHG-decreasing alleles. Also, in the one line from Iberian peninsula carrying polymorphisms in all five genes.
      3. The authors mentioned a significant peak for mCHG|mCHH on RdDM-targeted transposons was located 196 bp downstream of MIR823a and not on mature miRNA. Therefore, this cannot directly impair miR823 base pairing with CMT3 mRNA transcripts and its cleavage. Moreover, natural accessions carrying alternative MIRNA823 allele show reduced CMT3 and mCHG levels, meaning more miR823 levels? Does this 196 downstream region contain any regulatory feature that effects miR823 transcription? Or this region still falls in the primary miRNA hairpin region? A single nucleotide change in pri-miRNA can have a significant impact on its secondary structure that can impede DICER processivity and effectively levels of mature miR823 molecules? It will be beyond the scope of this paper to pin down the exact mechanism. But a simple stem loop RT-PCR for miR823 levels in reference and alternative accessions would be informative (on accessions that grow at the same speed). Perhaps, the authors can at least model SNP induced pri-miRNA secondary structure variations using Vienna RNAFold (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi) and present MEF values (maximum free energy) for representative accessions.
      4. Figure 1A can be made more reader friendly. Perhaps this can be broken down into correlation plots for individual conditions or tissue types. In addition, it might be good to add individual r-square values for each of them instead of compound r-square.
      5. Page 3, Paragraph 1 from line 3 to end of paragraph. The authors wrote "Much of this variation is due to differences in the environment (including tissue, which can be viewed as a cellular environment)". A possible explanation is these two tissues have different mitotic indices (fraction of cells diving and non-diving; flowers have more dividing cell, leaves have more non dividing and endoreduplicated cells) that explains non-CG variation. I would suggest authors to change the text to this and refer to Filipe Borges et al 2021 Current biology paper.

      Significance

      The manuscript by Sasaki et al titled "Conditional GWAS of non-CG transposon methylation in Arabidopsis thaliana reveals major polymorphisms in five genes" employed conditional GWAS to identify trans-regulators of mCHG levels in Arabidopsis natural accessions, after controlling for mCHH. Using loss of function mutants for couple of these genes, the authors also tested their effects on mCHG levels. Overall, this manuscript makes a nice contribution. I suggest the following improvements to enhance the quality of this manuscript.

      Comments:

      1. MSI1 has been shown to be copurified with TCX5, a component of DREAM Complex. The DREAM complex transcriptional regulates CMT3, MET1, DDM1 in a cell cycle dependent manner (ref: Yong-Qiang Ning, 2020 nature plants). Tcx5/6 double mutants have ectopic gain of TE and genic mCHG. It would be nice to refer this paper and add to the MSI1 part accordingly.
      2. Multifaceted regulation of mCHG levels seems to be evident from this and previous studies. Why would such complex pathways evolv to regulate mCHG? Bewick et al 2016 and Wendte et al 2019 showed lack of CMT3 or ectopic expression of CMT3 can influence CG gene body methylation (gbM). One possibility is that these five factors regulate CHG to maintain it at a level that is just enough to target TE. Irrespective of the functional relevance of gbM, differences in the levels of these five factors might result in erroneous gbM. It would be interesting to look for the rates of gbM and number of gbM genes in the natural accession carrying 1 to 4 number of mCHG-decreasing alleles. Also, in the one line from Iberian peninsula carrying polymorphisms in all five genes.
      3. The authors mentioned a significant peak for mCHG|mCHH on RdDM-targeted transposons was located 196 bp downstream of MIR823a and not on mature miRNA. Therefore, this cannot directly impair miR823 base pairing with CMT3 mRNA transcripts and its cleavage. Moreover, natural accessions carrying alternative MIRNA823 allele show reduced CMT3 and mCHG levels, meaning more miR823 levels? Does this 196 downstream region contain any regulatory feature that effects miR823 transcription? Or this region still falls in the primary miRNA hairpin region? A single nucleotide change in pri-miRNA can have a significant impact on its secondary structure that can impede DICER processivity and effectively levels of mature miR823 molecules? It will be beyond the scope of this paper to pin down the exact mechanism. But a simple stem loop RT-PCR for miR823 levels in reference and alternative accessions would be informative (on accessions that grow at the same speed). Perhaps, the authors can at least model SNP induced pri-miRNA secondary structure variations using Vienna RNAFold (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi) and present MEF values (maximum free energy) for representative accessions.
      4. Figure 1A can be made more reader friendly. Perhaps this can be broken down into correlation plots for individual conditions or tissue types. In addition, it might be good to add individual r-square values for each of them instead of compound r-square.
      5. Page 3, Paragraph 1 from line 3 to end of paragraph. The authors wrote "Much of this variation is due to differences in the environment (including tissue, which can be viewed as a cellular environment)". A possible explanation is these two tissues have different mitotic indices (fraction of cells diving and non-diving; flowers have more dividing cell, leaves have more non dividing and endoreduplicated cells) that explains non-CG variation. I would suggest authors to change the text to this and refer to Filipe Borges et al 2021 Current biology paper.
  3. May 2022
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      Reply to the reviewers

      Manuscript number: RC-2022-01392R

      Corresponding author(s): Ilan Davis

      General Statements

      We thank the reviewers for their constructive and helpful comments on our manuscript. We are delighted to find their consensus that the manuscript represents a useful resource for the Drosophila community in particular, and for the fields of neural development and post-transcriptional gene regulation. The following is our detailed responses and plan for how we will address all the major points raised by the reviewers. We also plan to address all minor points fully and have been through them in great detail one by one, so we are confident this is feasible within a reasonable and expected time frame.

      Description of the planned revisions

      Reviewer #1

      Major 1: For the wildtype CS flies, there is no YFP mRNA signal in neuroblast region and how about YFP mRNA signal in MB, OL VNC and NMJ regions? What is the criterion of setting laser power and gain for the mRNA level of 200 genes? Is it difficult to distinguish background and true signal of the mRNA in different area?

      This is a good point about background intensity levels (from non-specific binding of the YFP smFISH probe) across different tissue regions. We thank the review for raising it. Signal:background decreases with depth in all of the tissues, with superficial cells displaying similarly high signal:background in the CNS and NMJ, while signal:background in neuropil regions of the CNS are slightly lower. To address this point, we plan to include a supplementary figure to show background fluorescence of the smFISH probe across all regions of the CNS and NMJ.

      To address the point about image acquisition settings, we will included the following additional information in the Methods section (Page 17):

      “Consistent image acquisition settings (laser power, pixel dwell time or camera exposure, detector gain) were used for experimental and control experiments. Acquisition settings were optimized to achieve fast acquisition and high signal:background for each instrument.”

      We will add a further explicit explanation to the manuscript referring to previous publications, that the nature of the smFISH method makes it relatively simple to distinguish background from true signal. True punctae have a relatively uniform size, symmetrical shape, and consistent intensity distribution. Whereas background punctae that are either larger than diffraction-limited punctae or have lower intensity can easily be separated from real signal.

      Major 2: Would the insertion of YFP affect gene expression? Comparing to CS in Fig 1K, the dlg1 mRNA signals in dlg1::YFP line (Fig 1F) increases a lot. I do not know if this phenotype happens only in this area. So could you show some other regions for dlg1::YFP flies.

      This is a good point raised by both Reviewer #1 and Reviewer #2 (Major point 1). We agree that a proper quantification of the effect of YFP-insertion will bolster our conclusion, highlighting the utility of protein-trap collections for systematic analysis of post-transcriptional regulations. To address this, we plan to: (i) provide quantifications of dlg1 transcript expression in the CNS and NMJ and compare the levels between dlg1::YFP and wild-type lines, and (ii) provide new figure visuals reflecting our quantification results.

      Major 3: Is the dlg::YFP homozygous available? Among 200 gene trap lines, how many of them can be homozygous?

      This is a good point raised by both Reviewer #1 and Reviewer #3 (Major point 1). The dlg1::YFP (CPTI-000207) line used for the control experiments is homozygous. However, it is a great point that not all of the YFP insertions are homozygous viable. Out of the 200 lines we screened, 131/200 (65.5%) insertions are homozygous viable, whereas 69/200 (34.5%) are homozygous lethal or are unknown. We have addressed this caveat in the Methods section (Page 16) with the following statement:

      The majority of YFP insertion lines are homozygous (65.5%, 131/200), those that are not homozygous viable were kept over balancer chromosomes.”

      Our provisional analysis shows that the number of nervous system compartments expressing YFP-fused protein or mRNA are not affected by homozygous lethality. We plan to include this analysis in the revised manuscript.

      Major 4: Have you tried to investigate the mRNA and protein localization in adult brains?

      Yes, in a related study, we demonstrated that this approach also works in the adult brain (Mitchel et al., 2021, DOI:10.7554/eLife.62770). A systematic analysis of protein and mRNA expression patterns in the adult brain would be highly interesting and is certainly possible, however it is beyond the scope of the manuscript. To address this point, we will cite our related work and emphasise more clearly the wider applicability of our technique.

      Major 5: In Fig 3C, the authors claimed in MB or OL soma regions, some genes are protein expression only but no mRNA present. I wonder how do you explain this phenotype in soma.

      Our favoured explanation is that protein is more stable than mRNA. Therefore, after the mRNA is translated, it could get degraded while the protein is still present in the cell. We will add text in the relevant section to mention potential differential stability of protein/mRNA.

      Major 6: Since sgg mRNA localize to both sides of NMJ, would KCl stimulus affect sgg mRNA amount and localization in muscle?

      That is an interesting question. The data in Fig. 8I-J show that there is no additional Sgg::YFP protein accumulation at the muscle post synaptic density in response to KCl stimulus. It’s been shown elsewhere (Ataman et al., 2008, DOI:10.1016/j.neuron.2008.01.026) that Sgg protein translocates to the muscle nucleus in response to KCl stimulus. Determining whether that mechanism requires translation of new protein would require a complete new study with translational analysis and would distract from the message of the current study.

      Reviewer #2

      Major 1: Although the group is using an established and published set of gene traps, it would be good to confirm protein expression for same gene to increase confidence or provide more details on how is known that the YFP insertions do not affect mRNA stabilization or transcription or protein expression/localization. For example in Figure 1 F' versus K it is unclear why in the DlgYFP insertion there are more Dlg in situ signals than are observed in and around a neuroblast as compared to the wild type control. From the description provided these appear to the maximum intensity images. Is this due to background or an effect of the YFP insertion itself? Because of the increased level of expression is there a feedback loop of the protein regulating the mRNA expression? If had expression of Dlg protein in this figure would also confirm the YFP insertion mirrored the endogenous and it would be easier to discern if there were any changes in the number of Dlg mRNA molecules present. As this was the proof of principle example for the screen this information would increase confidence in the remainder of the data presented. AS an important part of the screen is looking at the potential for post transcriptional regulation this is an important factor to address.

      Thank you for the valuable suggestion. We agree with the reviewer that the comparison of dlg1 transcript levels would provide a valuable control. This point was raised by both Reviewer #1 and Reviewer #2. Please see [Reviewer #1 - Major point 2] for our response.

      Major 2: Will this pipeline capture information on whether is secreted (contain a signal regulatory peptide) or not as then would expect to be discordant. This should be clarified or commented on.

      The reviewer’s comment is correct. Secreted proteins may show discordant distribution of protein and mRNA between cell types even in absence of post-transcriptional regulations. Note that Shaggy (Sgg) is a secreted protein but we observe that most of the protein products are expressed in the same cell as the RNA. We propose to follow the reviewer’s suggestion and revise the text to discuss the limitation of our pipeline in identifying proteins regulated via secretory modes.

      Major 3: General molecular function is listed in supplementary table 1 but will other types of information be able to be correlated from datasets or databases as well.

      This question highlights a major feature of our dataset and associated metadata The analysis in Supplementary Table 1 is used to assess the functional representation of the 200 genes in our screen against the all known genes. We found that ~90% of GOSlim terms are covered by the 200 genes, highlighting the diversity of our list of genes. On the other hand, our Zegami resource (Accompanying data for Zegami) contains a rich collection of metadata (including the full list of GO terms) associated with each gene in the dataset, and extends that information to the entire genome. We anticipate that the Zegami resource will be a valuable platform to query data from our analysis and other databases. To address this, we plan to: (i) revise the legend for the Supplementary Table 1, and (ii) revise the text to clarify what kind of information is available in our Zegami resource.

      Reviewer #3

      Major 1: The approach relies on gene traps that often fail to be made homozygous, presumably due to deleterious function of the YFP insert. This is an obvious limitation of the study, which the authors address, but do so insufficiently by only analyzing a single case Dlg1. The authors should report how many of the 200 YFP-traps can produce viable homozygous animals, whether phenotypes can be observed, and any other relevant information to assess the functional properties of the tagged genes.

      Thank you for requesting further information on homozygous viability of the YFP-trap collection. This point was raised by both Reviewer #1 and Reviewer #3. Please see [Reviewer #1 - Major point 3] for our response.

      Major 2: The term "discordant" is used for non-congruous RNA/Protein levels in soma and distal processes, and sometimes the two are analyzed in the same figure (e.g Fig 3A). When it is stated that 98% of genes are discordant, this is an over-simplification as what the authors describe as "discordant" is expected to occur frequently in the distal process, but less often in the soma (which is what the authors find when presenting the data for individual compartments - Fig 3B-C). This is confusing because the observation means completely different things in the two compartments, though both are interesting to describe. These analyses, and their interpretation, should be kept separate.

      This is a fair point raised by the reviewer. To address this point we plan to: (i) prepare two separate tables summarising our annotation in soma and neurite compartments, and (ii) revise the text accordingly to explain and discuss how the discordant protein and mRNA expression pattern can arise both within different compartments of a cell or between different cell types in a cell lineage

      Major 3: There is not enough emphasis placed on the cell-type specific regulation of RNAs. There are very few studies that have investigated how localization of individual RNAs changes in different cell types or regions of the nervous system, and the authors find that this is quite prevalent. Therefore, the rather superficial analysis of these data fails to take advantage of a major strength of the data. For example, for the discordant genes that differ in neuropil localization between different regions of the CNS, what types of molecules do they encode, what is their function in neurons (if known), and why might they be required locally in one region of the CNS but not the other?

      We appreciate that the Reviewer recognizes the power of comparing RNA localization patterns across different brain regions (Figure 5R). We reported on a common set of synaptic mRNAs that encode nuclear proteins across the different regions of the nervous system. Per the Reviewer’s suggestion, we have begun to look into region-specific patterns of expression. In Figure 5R, two categories with the largest number of genes are ‘protein_MB_syn’ and ‘protein_OL_syn’, which contain proteins that are specific to those regions. However, given the small number of 15-16 genes, gene ontology enrichment analysis has limited power to infer information on the entire genome.

      We plan to revise the manuscript:

      to include tables with lists of genes specific to MB and OL regions. to revise the manuscript to include in the discussion a caveat of the limited power of analysis based on a small number of genes.

      Major 4: The authors conclude that mRNA and protein co-localization in glia processes shows that mRNA localization makes a major contribution of the proteome in processes. However, there is not enough evidence for such conclusion since neither translation of these mRNAs nor lack of protein trafficking from the somas was shown.

      The significant role of RNA localisation in shaping the local proteome and performing proteostatic regulation has been studied in detail (Zappulo et al., 2017, von Kugelgen and Chekulaeva 2022 Giandomenico et al., 2022). However, the reviewer’s comment is correct that we do not show direct evidence of mRNA translation or protein trafficking. Therefore, we propose to: (i) clarify the text by including the citation of these publications, and (ii) qualify our claim that mRNA localization is a major contribution of the proteome in neurite or glial processes.

      Zappulo et al., 2017, DOI: 10.1038/s41467-017-00690-6

      von Kugelgen and Chekulaeva 2022 DOI: 10.1002/wrna.1590

      Giandomenico et al., 2022, DOI: 10.1016/j.tins.2021.08.002

      Major 5: An important caveat of this technique that should be discussed is the lack of knowledge about the translation of these mRNAs, if the mRNA that is being detected is the same as the one that is translated. While the authors emphasize the discordance between mRNA and protein localization, it is not possible to know whether these mRNAs are being translated where they are found, e.g. soma vs neuropil. Moreover, there are many examples (e.g. BDNF) where the isoform influences the subcellular localization of the mRNA. There is no way of studying the isoforms here, and we could be looking for a different mRNA isoform localized to a specific compartment compared to the protein. These points must be discussed.

      We agree with the reviewer that our method does not provide information on whether the detected mRNA is being translated in time and space. Elucidating the relative contribution of localised mRNA in shaping the local proteome is not a trivial task and it is being actively investigated in the field. However, we believe our dataset provides a unique high-resolution map of transcripts that are potentially regulated at post-transcriptional and translational levels. It would be promising to follow up the ‘discordant’ genes identified from our survey using experimental methods that are able to track mRNA-ribosome associations (e.g. TRICK) in future studies. To address this point, we will revise the text to discuss this caveat.

      Thank you for pointing out the matter with mRNA isoforms. Our preliminary analysis indicates that 71% of the screened genes have constitutive YFP-insertions (i.e. YFP-cassette traps all mRNA isoforms). However, we agree that our approach cannot discriminate the case where protein produced from an mRNA isoform is trafficked and co-localises with another mRNA isoform that did not give rise to that protein. We plan to revise the text to discuss this point explicitly.

      Description of the revisions that have already been incorporated in the transferred manuscript

      Several minor comments regarding typos and simple errors have already been incorporated in the transferred manuscript. The changes are highlighted in yellow in the revised submission.

      We plan to address all the useful numerous minor comments that the reviewers have kindly highlighted to us. We feel these are straightforward to do and feasible in a short time, so do not require a detailed listed plan. If the reviewers feel they do afterall need such a list, we will be happy to provide it. However, there is one minor comment that we feel requires a little more explanation:

      Description of analyses that authors prefer not to carry out

      Reviewer #3 - Minor Comment on Figure 8: “...____they should characterize the (khc) mutant NMJs: what is the change in size, synapse number, etc..

      The khc mutants are already known to show synapse morphology phenotypes (Kang et al., 2014), though the khc23/khc27 transheterozygous allele has previously been used to assess localization defects at the larval NMJ (Gardiol and St. Johnston, 2014). Moreover, our manuscript (Figure 8) focuses on post-developmental stimulus-dependent processes, rather than cellular-level synapse developmental parameters with this mutant. The reviewer correctly points out that the khc developmental phenotypes are likely to have other secondary defects as a result of impaired microtubule transport. The purpose of that mutant was to assess the molecular-level question of whether microtubule-based transport is required for sgg mRNA localization at the axon terminal. The consequences and exact mechanism of disrupted transport are beyond the scope of this study. To address this point explicitly, we will:

      Revise the manuscript to quote more explicitly and clearly the developmental khc phenotype. Revise the manuscript to explain the difference between the developmental role of khc and role in the transport of sgg specifically to the axon terminal. Revise the manuscript to explain more explicitly the limitations of this mutant.

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      Referee #3

      Evidence, reproducibility and clarity

      In this manuscript, the authors address the important topic of post-transcriptional gene regulation using the larval nervous system in Drosophila. They utilize a novel approach taking advantage of existing protein trap library, which permits use of the same smFISH probe to detect an array of 200 RNAs and visualize their corresponding protein expression. Furthermore, the authors developed a computational pipeline to visualize and analyze the resulting data, which should enhance the application of this method by other researchers. A major strength of the data comes from the analysis of multiple cell types in distinct compartments of the nervous system, cell types (neuron, glia, neuroblast), and subcellular domains. From the cumulative data, the authors are able to describe several interesting observations relating to cell-specific post-transcriptional regulation, regulation within a central-neuroblast lineage and glial post-transcriptional regulation, among others.

      However, in spite of these strengths, there are several concerns related to the organization and interpretation of the manuscript that the authors should address in order to improve the manuscript:

      General concerns:

      1. The approach relies on gene traps that often fail to be made homozygous, presumably due to deleterious function of the YFP insert. This is an obvious limitation of the study, which the authors address, but do so insufficiently by only analyzing a single case Dlg1. The authors should report how many of the 200 YFP-traps can produce viable homozygous animals, whether phenotypes can be observed, and any other relevant information to assess the functional properties of the tagged genes.
      2. The term "discordant" is used for non-congruous RNA/Protein levels in soma and distal processes, and sometimes the two are analyzed in the same figure (e.g Fig 3A). When it is stated that 98% of genes are discordant, this is an over-simplification as what the authors describe as "discordant" is expected to occur frequently in the distal process, but less often in the soma (which is what the authors find when presenting the data for individual compartments - Fig 3B-C). This is confusing because the observation means completely different things in the two compartments, though both are interesting to describe. These analyses, and their interpretation, should be kept separate.
      3. There is not enough emphasis placed on the cell-type specific regulation of RNAs. There are very few studies that have investigated how localization of individual RNAs changes in different cell types or regions of the nervous system, and the authors find that this is quite prevalent. Therefore, the rather superficial analysis of these data fails to take advantage of a major strength of the data. For example, for the discordant genes that differ in neuropil localization between different regions of the CNS, what types of molecules do they encode, what is their function in neurons (if known), and why might they be required locally in one region of the CNS but not the other?
      4. The authors conclude that mRNA and protein co-localization in glia processes shows that mRNA localization makes a major contribution of the proteome in processes. However, there is not enough evidence for such conclusion since neither translation of these mRNAs nor lack of protein trafficking from the somas was shown.
      5. An important caveat of this technique that should be discussed is the lack of knowledge about the translation of these mRNAs, if the mRNA that is being detected is the same as the one that is translated. While the authors emphasize the discordance between mRNA and protein localization, it is not possible to know whether these mRNAs are being translated where they are found, e.g. soma vs neuropil. Moreover, there are many examples (e.g. BDNF) where the isoform influences the subcellular localization of the mRNA. There is no way of studying the isoforms here, and we could be looking for a different mRNA isoform localized to a specific compartment compared to the protein. These points must be discussed.

      Minor suggestions:

      • The authors should identify GO terms to understand what types of molecules are subjected to RNA regulation. They provide a supplementary table for all genes, but it would be useful to have a chart showing the proportion of different GO terms represented in the overall gene set, genes that show cell-specific regulation, genes that show neuron vs glia specific regulation, etc.
      • "However, post-transcriptional regulation can also manifest itself within a cell, so that a protein is localised to a distinct site from the mRNA that encodes it". While subcellular RNA localization may represent a regulatory layer, I do not agree that proteins that function in the cell at a different location than their translation site represents regulation per se. Many such cases exist for proteins that are trafficked!
      • "The majority of individual puncta appearing in the dlg1::YFP line (51% in the brain, 64% in larval muscles". Why is the agreement between YFP and endogenous FISH so low? Do many individual RNAs fail to hybridize? This should be discussed.
      • "However, one gene, indy, is highly transcribed in neuroblasts and a single ganglion mother cell before it is rapidly shut off (Figure S1A)". This figure does not exist. Where are the data?
      • The authors should be consistent about calling perineurial or perineural glia (both correct) in their images and text.
      • "We only observe a minority of localised axonal mRNAs that lack the protein they encode at the axon extremities, in contrast to our findings in the mushroom body, optic lobe, and ventral nerve cord neuropils" These results are not contrasted, as in all neuropils the minority of localized mRNAs are those lacking their corresponding proteins. For example, 9% in NMJ vs 7.5% in OL neuropil according to Fig. 1B. What is conflicting with the conclusion?
      • "These results suggest that motor axons are more selective than the other neuronal extensions in the mRNAs that are transported over their very long distances from the soma to the neuromuscular synapse" The current literature says that the same mechanism (cis-elements) is used to transport mRNAs to subcellular compartments, which would be inconsistent with the idea of motor axons being "more selective" than other neurons for the same mRNA, but just a result of fewer mRNAs being found in motor neurons: 34.% of the mRNAs are found in motor neurons soma vs 83% in OL soma, 86.5% in VNC soma, and 70.5% in MB soma. To get to this conclusion, the authors should show that mRNAs previously found in the neuronal extensions of other neurons are not found in the axons of motor neurons but are still expressed in thesir somas. They might want to suggest different RBPs involved in the transport or discussing the very long distance they need to travel which can influence their detection in the tips. Figures
      • Figure 1. Experimental approach summary
        • Some colors do not show well and should be changed, e.g: grey in Fig. 1A, and Fig. 1B probe sites indicated in light blue and pink within the introns of dlg1.
        • Fig. 1E': There appears to be a large discrepancy in co-detection % for CNS and muscle in the graph judging by the size of circles, yet in the text, it is stated that there is average of 51% and 64% in the two, respectively. I don't see any green circles with over 25% agreement in the graph. Are the colors correct here?
        • Fig. 1D-I: It's difficult to identify where the zoomed panels come from. E has its own square (indicating zoom in E'). Please make this square dashed or a different color in E so it is clear F and G do not come from there.
        • Comparing Fig. 1F vs K: Why does there appear to be so much more dlg1 mRNA in the YFP-tag condition? If this is due to selection of imaging area, please choose a more similar region to image so the RNA levels are comparable. Otherwise it indicates the YFP-tag line has more RNA expression, which is likely not the case.
      • Figure 2. Analysis pipeline overview
        • The lines for the first two zoomed panels are switched: The optic lobe is going to VNC and vice-versa.
      • Figure 3. Overall summary of results
        • Figure 3A: Soma/Neuropil/muscle should be separate or at least ordered such that they are next to each other to facilitate direct comparison of genes in the same region of the cell in neurons from different CNS areas. Why are glia not included in this summary? A third color should be used to indicate when there is neither mRNA nor protein expression.
        • "Compiling all the information together shows that there are that 196/200 or 98% of the genes show discordance between RNA and protein expression" However, 5 genes shown in Fig. 3A do not show "discordance": CG9650, cup, Lasb, rg, and vsg!!
      • Figure 4. Neuroblast lineage analysis
        • Is clustering around the NB sufficient to determine lineage relationship? There seems to be other neurons around the NB.
        • More examples should be shown for the post-transcriptional category, as it is the most interesting category, and there are many different possible outcomes. Are there cases of transcriptional control and post-transcriptional regulation? Are there cases where the youngest neurons (closer to the NB) in the progeny are expressing the protein while the oldest are not? If not, could this be an artifact from a slow translation and the protein being detected only after building up in the cell? Top1 protein (Fig. 4D) seems to be less expressed in the youngest neurons.
        • "The transcription rate of these genes, as indicated by the relative intensity of smFISH nuclear transcription foci, is similar across the neuroblast lineage, however protein signal is only detectable in a minority of the progeny cells (Figure 4E)". Many nuclei lack clear large spots, but have small spots indicative of RNA; how is this interpreted? Do they lack transcription, or is this due failure of the smFISH to capture all transcription sites? Were transcripts actually counted to assess cell-specific differences? This should be possible with smFISH
      • Figure 5. RNA synaptic localization
        • A have global analysis comparison of all neuropil areas would be welcome in this figure.
        • "Surprisingly, another 59 transcripts are present at synapses without detectable levels of protein (Figure 5E-H)" This text does not correspond to Fig 5E-H but 5I-L. Where is the text about 5E-H?
        • For Fig. 5J and 5N RNA appears scattered regularly throughout the entire panel area. How sure are the authors that this is not due to poor signal/noise? For example, perhaps too much probe being used for these targets.
        • Fig. 5R is not cited in the text.
      • Figure 6. RNA localization in glia
        • For Fig. 6B-G it is hard to tell if there is any overlap of the RNA and Glia. Maybe show multiple zoomed-in merged images and/or highlight the structures with lines that are present in all panels.
        • For Fig. 6L-O: How reproducible is this small amount of RNA puncta in the NMJ glia? Is this possibly biologically important?
        • Why do cartoons labelling subnuclear/perinuclear glia in Fig.6 and Fig.S6 show different localization?
        • The cartoons seem to extrapolate from the data: While in Fig 6B-D, we see neither the big bright spot of transcription in the glial nucleus nor as many transcripts in the neuropil, they are both present in the cartoon. In Fig. 6E-G there is no indication of cortical glia soma nor the transcription spot only in glia nuclei.
        • "To assess glial localisation for the 200 genes of interest, we used a pan-glial gal4 driving a membrane mCherry marker (repo-GAL4>UAS-mcd8-mCherry) to learn the expression pattern of all glial cells, and then classified the pattern in the YFP lines (without the marker) based on knowledge of that expression pattern. We validated this approach by combining the RFP marker" Did the authors use mCherry or RFP for these experiments? Also, the previous sentence is redundant.
      • Figure 7. RNA localization at neuromuscular synapse
        • RNA for these genes seems far too spread throughout the muscle to draw any conclusions
        • Also with so many RNAs distributed in the muscle, specific localization of RNA molecule to the precise PSD would have no conceivable benefit
        • I suggest drawing lines around the protein expression to facilitate visualization of the mRNA localization for panels B, F and J. It is especially hard to conclude anything from panels B and F.
        • Light grey with white dots is hard to see in the cartoons
      • Figure 8. Role of khc and activity in sgg localization
        • Presumably there is a huge number of developmental problems associated with this mutant that could cause decrease in sgg localization
        • If the authors include this, then they should characterize the mutant NMJs: what is the change in size, synapse number, etc..
        • Is there more sgg accumulated in soma as a result of less transport? Is sgg being expressed at the same level?
        • Fig. 8F-H: Why is Dlg1 accumulated in the entire axon, not just the presume synapse?
        • Fig. 8J: Why is sgg signal occurring in circles disconnected from the main axon? The authors should show a different image

      Significance

      This is a significant and complex paper that contributes with novel tools to an important issue

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      Titlow et al present a data resource paper for mRNA localization and protein expression in vivo focusing on the larval nervous system which is an area of high interest currently. They screen a known group of YFP gene trap lines (200 lines) and looked at specific aspects of the nervous system such as expression in neuroblasts, the mushroom bodies, glia or the NMJ. They also present a computational workflow using this set of 200 genes for the investigation of the subcellular localization and potential role of post transcriptional regulation in whole larval tissues. This uses the image data obtained experimentally and then compares with existing datasets to obtain more information.

      Major comments

      The authors results largely support the claims made in the manuscript. Is a clear proof of concept analysis of specific examples and then presentation of examples from different part of the nervous system. Different aspects of the gene trap lines are taken into account. Is a high level analysis of the sub cellular localization of mRNA and protein in different parts of the nervous system. Some interesting new insights which can lead to more in depth analysis of mechanism are presented. Is an interesting idea and presents a method in which to approach a fieId that has many remaining open questions. This manuscript is an important and timely analysis that will be of high interest in the field.<br /> Is a positive that the authors confirmed the YFP mRNA in situs with an endogenous gene in situ. Although the group is using an established and published set of gene traps, it would be good to confirm protein expression for same gene to increase confidence or provide more details on how is known that the YFP insertions do not affect mRNA stabilization or transcription or protein expression/localization. For example in Figure 1 F' versus K it is unclear why in the DlgYFP insertion there are more Dlg in situ signals than are observed in and around a neuroblast as compared to the wild type control. From the description provided these appear to the maximum intensity images. Is this due to background or an effect of the YFP insertion itself? Because of the increased level of expression is there a feedback loop of the protein regulating the mRNA expression? If had expression of Dlg protein in this figure would also confirm the YFP insertion mirrored the endogenous and it would be easier to discern if there were any changes in the number of Dlg mRNA molecules present. As this was the proof of principle example for the screen this information would increase confidence in the remainder of the data presented. AS an important part of the screen is looking at the potential for post transcriptional regulation this is an important factor to address Will this pipeline capture information on whether is secreted (contain a signal regulatory peptide) or not as then would expect to be discordant. This should be clarified or commented on. General molecular function is listed in supplementary table 1 but will other types of information be able to be correlated from datasets or databases as well.

      Minor comments

      On page 9 refer to Figure 6S which I think is supposed to be Figure S6. In text refer to an example of gli but show gs2 in the figure so it is unclear what is being referred to or shown. Could include more description on the generation of the supplementary tables and analysis of the tables. I could not find any description/legend which made analysis of some of the tables more difficult. The data set was trained on a known set of data (analyzed by experts. It would be interesting to see what it could do with a novel set of genes in the context of post transcriptional regulation, but that is beyond the overall scope of this manuscript.

      Significance

      This is an interesting idea and is a useful resource for the genes analyzed. Gives an initial tool to analyze the expression of genes. Allows for systematic analysis of mRNA (smFISH) and protein on a larger scale but with high resolution. Adds new knowledge in terms of the localization of mRNAs and protein in the periphery of neural and glia processes which may inform future analyses of the role of these genes in these tissues.

      Is a useful resource within neurodevelopment in Drosophila and post transcriptional regulation. Would be of interest to a general audience as workflow could be applied to any tissue or set of genes. Covers a very broad set of genes with disparate biological functions again making this of interest to a broader audience.

      Expertise of reviewer Drosophila, neurodevelopment, RNA regulation, post transcriptional regulation, polarity and adhesion.

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      Referee #1

      Evidence, reproducibility and clarity

      This manuscript by Titlow et al. systematically analyzed spatial distribution of 200 gene's mRNA and protein, and found common discordance between them. Moreover, the browsable resource is pretty useful to most fly people. Though the authors did huge amount of experiments and analysis, and got several really interesting findings, there are some basic questions need to be answered.

      Major 1: For the wildtype CS flies, there is no YFP mRNA signal in neuroblast region and how about YFP mRNA signal in MB, OL VNC and NMJ regions? What is the criterion of setting laser power and gain for the mRNA level of 200 genes? Is it difficult to distinguish background and true signal of the mRNA in different area?

      Major 2: Would the insertion of YFP affect gene expression? Comparing to CS in Fig 1K, the dlg1 mRNA signals in dlg1::YFP line (Fig 1F) increases a lot. I do not know if this phenotype happens only in this area. So could you show some other regions for dlg1::YFP flies.

      Major 3: Is the dlg::YFP homozygous available? Among 200 gene trap lines, how many of them can be homozygous?

      Major 4: Have you tried to investigate the mRNA and protein localization in adult brains?

      Major 5: In Fig 3C, the authors claimed in MB or OL soma regions, some genes are protein expression only but no mRNA present. I wonder how do you explain this phenotype in soma.

      Major 6: Since sgg mRNA localize to both sides of NMJ, would KCl stimulus affect sgg mRNA amount and localization in muscle?

      Minor 1: You claimed that Fig 1E shows high magnification image of the inset in D, but the scale bars are the same.

      Minor 2: Figure 1 legend: K-N, are the images individual channels shown in E? Or in J?

      Minor 3: In Fig 2A, optic lobe neuropil and VNC neuropil are mislabeled.

      Minor 4: Only one panel has scale bar in Fig 4.

      Minor 5: What is Fig 5B'and F'? You should describe them in the Figure legends.

      Significance

      The browsable resource is pretty useful to most fly people. The authors did huge amount of experiments and analysis, and got several really interesting and important findings.This work will provide mRNA localization information for post-transcriptional regulation studies.

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      Reply to the reviewers

      General Statements [optional]

      We are grateful for the very kind, thoughtful, and detailed comments of the reviewers, which we have strived to fully integrate into the revised manuscript.

      Of note are the concerns with the data from stages S21 and S22, which we acknowledge do appear to be qualitatively and quantitatively distinct from the other samples. While we are unable to completely disambiguate meaningful biological variation from technical or experimental noise using our data, we hope a few additional analyses and visualization tools we have included can provide greater confidence in the reliability of our findings.

      Additionally, while attempting to evaluate Reviewer #2’s suggestions about examining the distribution of intergenic peaks along the genome, we discovered an error in our code that resulted in the improper assignment of peak categories. The error resulted in the improper assignment of intronic and exonic peaks as intergenic peaks. While the largest group of peaks in our dataset remains distal intergenic peaks (30.2%), and distal intergenic peaks remain a larger proportion of our intergenic peaks than proximal intergenic peaks, many of the peaks originally assigned to the intergenic categories have been reclassified as exonic or intronic peaks. We have updated our code and figures upon reanalysis of our data and have revised our findings and discussion accordingly.

      Description of the planned revisions

      Reviewer #3, Comment #3 of 11_

      “In general, I thought that the bioinformatic methods (i.e., the code or the options used for each program) would have been helpful for my understanding in some cases. The authors say that these will be published on an accompanying GitHub repository, which should be fine if this is sufficient for journal policy.”_

      We are still at work compiling the code for our analyses into a more reader-friendly form and setting up a GitHub repository to enable easy access to more detailed methods for interested readers. Some of the most important settings have been included in the Methods and Supplementary Methods sections, but we hope to include more thorough detailing of our pipelines in the GitHub repository. The raw data for portions of the RNA-Seq and all of the ATAC-Seq data have been uploaded to the Sequence Read Archive, and we are finalizing additional raw data submission. We are also in the process of determining what data to include in our Gene Expression Omnibus submission, which we hope to include all pertinent final data analysis files as well as any intermediate or accompanying datasets which would facilitate downstream analyses. The large size and number of our final analysis files has resulted in some challenges with data transfer and storage, which has delayed the upload and submission process.

      We are also collating several of the data visualization scripts built for this manuscript into a Jupyter notebook. This tool will enable the visualization of ImpulseDE2 models and peak classifications for arbitrary genes and genome regions of a user’s choice, alongside additional functions which are discussed in this revision plan.

      Description of the revisions that have already been incorporated in the transferred manuscript

      We have addressed the following substantive concerns with the manuscript:

      Reviewer #2, Comment #2 of 3:_

      “Authors have repeatedly used S21 and S22 throughout the manuscript to support their claims with clustering etc. May authors shed some light on the differences in replicates for these timepoints. Furthermore, I could not find Fig 3J, perhaps author would like to point out Fig 3H.”_

      Reviewer #3, Cross-comment #2 of 3:_

      “Focus on stages S21/S22: This might indeed be somewhat problematic. The libraries from these two stages (particularly S21) seem to be very different from those from the other stages. In the PCA (Fig. 1C), S21 doesn't cluster well with anything, and the difference between the two replicates is massive compared to other stages. The accessibility pattern (Fig. 1D) also looks odd. The libraries also have the lowest scores for % of mapped reads (Fig. S2B), fragment size distribution (S2E), and Spearman correlation (S2I). All this could be biologically sound and be due to a major developmental transition at this point, but maybe it justifies revisiting the data and testing whether leaving out S21 (and/or S22) makes a big difference for the clustering analyses.”_

      1. Reviewers #2 and #3 discussed concerns with the outlying nature of libraries S21 and S22. We had also previously held concerns about these samples and had performed some analyses to examine whether the global properties of our dataset are dramatically changed upon removing those samples. We did not observe dramatic changes to the structure of our data in the absence of the S21/S22 samples.

        • a. Samples S21 and S22 appear to be highly separated from the rest of our data using Principal Components Analysis. We had also previously believed that this suggested that these samples might be problematic. However, a colleague indicated to us that researchers in microbiome ecology had observed similar phenomena, often caused by strong single axes of variation (or “linear gradients”) in the datasets. In “Uncovering the Horseshoe Effect in Microbial Analyses” (mSystems, 2017) by Morton et al., the authors describe how a strong linear gradient can create a “horseshoe effect” or “Guttman effect”, where PCA results in the two ends of a linear gradient appearing to come together in ordinal space. The authors also describe a similar “arch effect” which strongly resembles the general shape of our PCA curve. We suggest that the strong apparent “outlier” appearance of S21 and S22 may be exaggerated or induced by the technical “arch effect” phenomenon, and may be caused by a strong single biological gradient – a developmental timecourse – which our data aimed to capture.
        • b. We also performed PCA on our dataset with the S21 and S22 time points removed prior to performing the analysis (see right panel, bottom). When we did so, we observed that the relative positions of the remaining libraries remains largely similar, with time points closer to the middle of development showing a positive loading in PC2, and time points closer to the beginning and end of development showing a negative loading. This suggests that the second major axis of variation in our dataset would remain a contrast between middle vs. terminal timepoints, even without the S21/S22 data, and that the relative positioning of the remaining data within PC-space is not entirely driven by S21/S22.
        • c. To further assess the degree of the S21/S22 samples’ outlying effects, we also performed ImpulseDE2 analysis to generate model fits without S21/S22 data. Doing so allowed us to determine to what degree the S21/S22 stages are necessary for driving the accessibility trajectory of individual peaks, and of the data more broadly. We performed IDE2 with either all data, or the S21/S22 data removed prior to input into IDE2. This generated two sets of model fits to the “cloud” of accessibility vs. time measurements: one that included the S21/S22 data, and one without. We evaluated, for each peak in our dataset, the time point at which the IDE2 model achieved maximum accessibility (the “IDE2 max fit”), and plotted both the “all” and “noS21S22” data as a histogram (see right panel, top graph). The presence of peaks that achieve predicted maximum accessibility in the S21/S22 stages in the “no S21/S22” data is a result of how we calculate “max fit”, which does not require that there is a known accessibility value at a given timepoint; only that the time point during which the model fit is maximum is closest to the timing of that developmental stage. Overall, we still observed early, middle, and late enrichment of IDE2 max fit even when the S21/S22 data are removed. We do see a rightward shift in the middle timepoint histogram in the direction of later stages, although this may be expected given the absence of concrete accessibility values at S21/S22 in the “no S21/S22” data. This indicates that our data globally retain the general trends of early, middle, and late enrichment of accessibility in the absence of the S21/S22 data. Moreover, this suggests that, even without the S21/S22 data, the remaining data from early and late stages result in a model fit that still predicts maximum accessibility at middle developmental stages for many peaks.
        • d. To further measure the influence of the S21/S22 data in IDE2 model fit, we also evaluated the degree of change in the global behavior of a peak when the S21/S22 stages were removed. This analysis aimed to assess whether removing S21/S22 data resulted in an IDE2 model with the same general trajectory as with all data, as opposed to the more stringent requirement of evaluating whether the exact developmental stage of the peak was changed. To perform this analysis, we grouped developmental stages into five quintiles, each representing three stages of development. We asked, for each peak in our dataset, whether that peak’s IDE2 max fit was “stable” when the S21/S22 data were removed; that is, if the quintile of the IDE2 max fit was altered when the S21/S22 data were removed (i.e. if a peak moved more than 3 developmental stages away from its original position), a peak was considered “unstable”. We observed that over 80% of peaks in each quintile remained “stable” after removing the S21/S22 data, suggesting that the vast majority peaks show the same general trajectory of accessibility even without the S21/S22 data. Peaks within the middle time points appeared to be more unstable than peaks at the terminal timepoints, which could be expected given that the S21/S22 timepoints constituted the middle-most timepoints in our dataset.

      We acknowledge that the S21/S22 timepoints still appear to be qualitatively different in other ways. Moreover, we acknowledge that some of the peaks in our dataset are “dependent” on the S21/S22 stages, given that their accessibility trajectory changes when these stages are removed. It is difficult to determine whether a change in accessibility trajectory for a given peak caused by the removal of S21/S22 data is indicative of technical differences in sample preparation, such as batch effects; biological variation, such as a potentially unknown mutant or sick embryo; or due to genuine wildtype biological processes that occur at the S21/S22 stages.

      These caveats acknowledged, a comparative analysis of the data in the absence of the S21/S22 stages suggests that much of the global picture of development remains the same. In the interest of providing the data we generated as a resource, we decided to include the S21/S22 data in the final manuscript we have prepared for submission.

      We have included an additional supplementary figure (Supp. Fig. 2.2) highlighting these further analyses, which we hope future readers will consider when performing their own analyses with these timepoints, as well as a summary of the ways we evaluated this potential concern in the Supplementary Methods. To facilitate future users of this dataset, we will include the model parameters calculated from IDE2 using both the full dataset and the data with S21/S22 removed in the GEO accession data, as well as a Jupyter notebook (ParhyaleATACExplorer.ipynb) that allows users to plot the raw accessibility data and IDE2 model fits for individual peaks of interest (C, example on right panel), so that downstream experiments can consider the potential differences with the S21/S22 samples.

      Reviewer #2, Comment #3:_

      “The majority of ATAC-seq peaks in the distal intergenic regions is a very surprising result. Authors defend this result by suggesting that this organism has big genome. May author perform a short analysis that shows that these peaks are indeed represent nearby genes or may point towards 3D genome organisation. For example, I see that this genome might have regions in the genomes that are densely organised in gene clusters, in those cases does the pattern remains same i.e he majority of the genes are very distant from each other and hence use vital regulatory elements?”_

      Reviewer #3, Cross-comment #3 of 3:_

      Peaks in distal intergenic regions: I agree that this could be elaborated on. It might also be that >10 kb is not actually that distal for Parhyale. I would suggest to split the "distal peaks" further (e.g., in 10 kb or 2-log steps, or whatever makes most sense) and try to understand if >10 kb is mostly <20 kb, or if most of them are hundreds of kb from the nearest gene?_

      1. Reviewers #2 and #3 expressed interest in understanding the absolute distribution of distal intergenic peak distances from nearby genes in our dataset. In generating the analyses to address this question, we stumbled upon an error in our code that reveals that the true number of intergenic peaks is much lower than we had originally reported. We discuss the nature of the error below. Moreover, we address the previous question using the new data, which overall still indicates that distal intergenic peaks remain a large portion of the Parhyale genome.
        • a. To address Reviewer #2’s comments with respect to the presence of potential clusters of intergenic regions, we built a Python tool (included in ParhyaleATACExplorer.ipynb) enabling the visualization of different cis-regulatory element categories along a genomic coordinate. Upon plotting our data with this tool, we observed problems with the categorization of the peaks – namely, that intronic and exonic peaks were erroneously classified as intergenic peaks (see right panel, top). We analyzed our script for classifying annotations more carefully and realized that we had erroneously used “bedtools closest” instead of “bedtools intersect” to try to identify all peaks overlapping with gene annotations in our genome. We corrected this error and observed the expected distribution and categories of peaks in our data (right panel, bottom).
        • b. The revised peak categories have been added to the updated manuscript in Fig. 3H and Fig. 5C. The categories of peaks we observed differ substantially from our previous results, in that we observe a much higher representation of exonic and intronic peaks in our dataset, with intronic peaks now representing 28.2% of all peaks (increased from <1%), and distal intergenic peaks representing 30.2% (decreased from 51.2%). While distal intergenic peaks remain the largest category over time, the proportion is relatively equal to the fraction of intronic peaks. Intergenic peaks (distal and proximal combined) now make up only a slightly larger fraction of peaks (37.2%) than gene body peaks (exon, intron; total 34.4%). This updated result is a significant departure from our previous report, and we have updated the text of the manuscript to correct this mistake.-
        • c. While intergenic and distal intergenic peaks constitute a much smaller portion of our data, we still wanted to address Reviewer #2 and #3’s questions about the distribution of distances between intergenic peaks and nearby genes. We generated a plot to illustrate the number of intergenic peaks at variable distances to the nearest gene (B, right panel). As illustrated in the plot, there are a very large number of distal intergenic peaks, including many peaks >100kb away from the nearest gene. The average distance of intergenic peaks from the nearest gene was 73,351bp. We neglected to mention in the original manuscript that one of the rationales for choosing a 10kb cutoff as “distal intergenic” was that peaks beyond this distance would be considerably more difficult to isolate as single fragments combined with a proximal promoter using PCR, agnostic of their orientation with respect to the promoter element. Such peaks could not have been easily identified using previous transgenic approaches, and are thus distinguished from “proximal” peaks by their necessary identification using techniques such as ATAC-Seq. We have updated the text to reflect this distinction.
        • d. Given that both intergenic and gene body peaks appeared to comprise large fractions of our revised data, we also examined the relative enrichment of intergenic and gene body peaks with respect to time (after normalizing for the fraction of “unknown” peaks, as suggested by Reviewer #3). We observed that the proportion of peaks belonging to intergenic and promoter regions declined slightly as development progressed, while the proportion of gene body peaks increased (E, below). There appeared to be slightly more intergenic peaks than gene body peaks at all developmental time points, and the ratio of intergenic peaks to gene body peaks declined very slightly over time (F, below). These data indicate that intergenic and gene body peaks have different enrichment trajectories over time. As development progresses, gene body peaks are increasingly enriched, and may have a greater impact on gene regulation. We have added these additional observations to the text and to a new Supplementary Figure 2.3.

      We have also addressed the following textual and conceptual concerns with the manuscript:

      Reviewer #3, Comment #1 of 11_

      I felt that the first paragraph of the introduction is not necessary._

      1. We believe the introductory paragraph helps frame the paper in the context of the broader scope of advances in technologies for emerging research organisms – currently, it has become straightforward to both generate a genome sequence and to identify and manipulate coding genes of interest across diverse taxa, but the identification of gene regulatory mechanisms remains more difficult. We have edited the introduction to better reflect this perspective and to link the first paragraph to the rest of the paper.

      Reviewer #2, Comment #1 of 3_

      “In Introductory paragraph 2, sentence one, authors suggest that gene regulation plays more important role in evolutionary process than genes. Although a significant amount of research has been dedicated to gene regulation based evolution still this field is in nascent form. For example evidence of inheritance of the gene regulation pattern across generation is scarce and requires more evidence. I suggest authors to modulate the claim that still gene based evolution is the main paradigm instead otherwise.”_

      Reviewer #3, Cross-comment #1 of 3_

      Evolution via gene regulation vs. coding sequence: While (to my understanding) it is largely accepted in the field that changes to the CDS will often have more deleterious effects than changes to the expression of a gene, I agree that this could be elaborated on a bit.

      1. As requested by Reviewers #2 and #3, we have clarified the language surrounding the debate between gene functional and gene regulatory evolution to indicate that both mechanisms appear to be important for evolutionary processes, with the importance of the latter more recently revealed.

      Reviewer #3, Comment #2 of 11_

      Use of Genrich: I presume this was run on both duplicates simultaneously? This is not clear from the methods section. It might have implications for downstream analyses (e.g., differential accessibility between time points) because running on both sequencing library replicates simultaneously leads to a single "replicate" of peaks per time point, while running it individually leads to two. However, I have never tested if this actually does make a difference. Maybe the authors have and can comment on this?

      1. In response to Reviewer #3’s inquiry about Genrich, we have added additional clarifying information into the Methods section. “Genrich analysis was run on both duplicate libraries simultaneously; Genrich performs peak calling on each peak individually, and then merges the p-values of the replicates using Fisher’s method to generate a q-value, obviating the need to calculate an Irreproducible Discovery Rate (IDR).” We did not test running Genrich on individual libraries, opting for the more conservative approach of using the combined q-value as a filtering score for peak quality. For further information, the reviewer can see the Genrich Github repository section here: < [https://github.com/jsh58/Genrich#multiple-replicates]

      Reviewer #3, Comment #4 of 11_

      The section on the IDE2 models (the paragraph at the end of page 4/beginning of page 5) was unclear to me but appears sound. (The only instance where I didn't quite understand what the program actually does.) Maybe this can be explained a bit easier?_

      1. As requested by Reviewer #3, we have attempted to explain the methods and logic of using ImpulseDE2 a bit more clearly:

      “To identify regions of dynamically accessible chromatin, we used the ImpulseDE2 (IDE2) pipeline (Fischer et al., 2018). IDE2 differs from other software for differential expression analysis in that it allows the investigation of trajectories of dynamic expression over large numbers of timepoints. It does so by modeling a gene expression trajectory as an “impulse” function that is the product of two sigmoid functions (Chechik and Koller, 2009; Yosef and Regev, 2011). This approach enables the modeling of a trajectory of gene expression in three parts: an initial value, a peak value, and a steady state value, thus summarizing an expression trajectory using a fixed number of parameters. With the ability to capture the differences between early, middle, and late expression values for each gene in a dataset, IDE2 also enables the detection of transient changes in gene expression or accessibility during a time course. Identifying differential expression over large numbers of timepoints is difficult for more categorical differential expression software such as edgeR and DESeq2, which generally use pairwise comparisons between timepoints to assess change over time (Love et al., 2014; Robinson et al., 2010).”

      Reviewer #2, Comment #2 of 3_

      2-2) Authors have repeatedly used S21 and S22 throughout the manuscript to support their claims with clustering etc. May authors shed some light on the differences in replicates for these timepoints. Furthermore, I could not find Fig 3J, perhaps author would like to point out Fig 3H.

      Reviewer #3, Comment #5 of 11_

      On page 7, Fig.3J needs changing to 3H. This figure should, in my opinion, also contain the absolute number of peaks for each time point to set the individual proportions into context.

      1. As requested by Reviewer #3, we have added a bar charts representing the number of peaks found at each time point (Fig. 3H) and the number of peaks found in each cluster (Fig. 5C) to the peak type proportion plots. We have also fixed references to Fig. 3J to instead refer to Fig. 3H – we apologize for the confusion.

      Reviewer #3, Comment #6 of 11_

      Last paragraph of the "Improving the Parhyale genome annotation" section: I think this needs to focus on those regions of the genome for which the location is known - after all, the "unknown" regions" could all be "distal transgenic", which would significantly change the relative proportions._

      1. We have revised our analysis of this topic with our updated peak type proportions, as described above in point 2d above under “substantive concerns”.

      Reviewer #3, Comment #7 of 11_

      “On page 9, t-SNE is mentioned but doesn't seem to be cited.”

      1. As requested by Reviewer #3, we have added citations for the t-SNE method, as well as scikit-learn, the software we used for t-SNE visualization.

      Reviewer #3, Comment #8 of 11_

      “The third paragraph on page 9 ("We evaluated the differences...") should mention the fact that clusters 1 and 2 are the only ones with significant proportions of exonic and intronic peaks. In the accompanying figure (5C), the total number of peaks would again be helpful.”_

      1. After identifying the error in our peak category classification pipeline, this observation was no longer true. However, upon examining the new distributions by cluster, we observed that in Clusters 3–7, for which we observed GO enrichment for developmental processes, there appeared to be slightly higher enrichment of intronic regulatory elements than distal intergenic regulatory elements. These results resemble the observation from recent work showing that tissue-specific enhancers are enriched in intronic regions in various human cell types (e.g. Borsari et al. 2021, Genome Research). We have noted this new observation in the text.

      Reviewer #3, Comment #9 of 11_

      In figure 5D, I can't quite make out at which stage the dip in the peak of Cluster 8 occurs. This is quite an unusual pattern of accessibility change, and I can't help but wonder if it has something to do with the quality of one of the libraries? Also, the fact that half of the peaks fall into unmapped regions of the genome is unusual, and I feel this deserves more discussion._

      1. In Figure 5D, Reviewer #3 asks about a dip in accessibility for Cluster 8 peaks. The dip in accessibility was actually observed for Cluster 9 peaks and is marked by the asterisk in that panel. We have updated the figure legend to clarify the significance of the asterisk and have referred readers to examine Supp. Fig. 5.1B, where the IDE2 model fits more clearly show a collective dip in accessibility for Cluster 9 peaks. Upon examining the size distribution of the clusters, we have also noticed that Cluster 8 is the smallest cluster. We have noted the small cluster size and high “unknown” peak enrichment for Cluster 8 in the text.

      Reviewer #3, Comment #10 of 11_

      “On page 10, the abbreviation PFM appears, but it is only explained in the legend of Fig.4. This should appear in the text.”_

      1. Reviewer #3 mentions that on page 10, we use the abbreviation for position frequency matrices (PFMs) without previous reference. We first introduce the abbreviation on page 8, but given the repeated use of “PFM” on page 10, we have added an additional explanation of the abbreviation on page 10, for ease of reading.

      Reviewer #3, Comment #11 of 11_

      “The section on "Concordant and discordant expression and accessibility" is the one I disagree most with. The authors seem to suggest that a repressive cis-regulatory module should become less accessible when the gene is activated. However, they leave trans-acting factors completely out of their conceptualisation here. It is in general likely the availability of transcription factors that leads to repression, while the "silencer" can be well accessible in all cells. Moreover, it has become clear in recent years that CRMs are not just repressors or enhancers per se but can act as either depending on the availability of transcription factors. I think these facts could partially explain the weak correlation and should be discussed.”_

      1. We appreciate the comments from Reviewer #3, which alerted us to the more recent literature around the bifunctional potential of regulatory elements. We have revised our claims to clarify that concordance and discordance analysis cannot be used to directly assign “enhancer” or “silencer” identity to given regulatory elements. Instead, we suggest that evaluating concordance and discordance can be useful for downstream users of our data, such as those aiming to build reporter constructs for a given gene of interest. To facilitate such tool development, we have built additional functions into a Jupyter notebook to enable the visualization of accessibility, gene expression, fold change of accessibility and gene expression, significance of fold change, and concordance/discordance assignment for arbitrary peak-gene pairs. An example of this visualization is shown on the following page. Panel A shows the region around the Engrailed-1 and Engrailed-2 loci in Parhyale (text labels within the plot region were added manually in Illustrator). Panel B shows visualization of the En1 promoter peak alongside En1 expression. Significant log fold changes (DESeq2 padj < 0.05) are marked by asterisks in the bar plots, and concordance/discordance assignment at each time point is indicated by the color of the comparison text (red = concordant, blue = discordant). Panels C and D show accessibility and expression visualization for a single peak (En1 peak5) compared to two nearby genes (En1 and En2). We hope to include sufficient documentation in our GitHub repository such that using these tools is accessible for most researchers, even with limited programming knowledge.

      Description of analyses that authors prefer not to carry out

      We were unable to easily visualize the distribution of regulatory elements across the whole genome as suggested by Reviewer #2. One challenge of working with the Parhyale genome is the lack of complete chromosomes. The genome is distributed across ~290,000 contigs of variable size. We were unable to find any software that could be easily and quickly set up to visualize our data, although we will provide in a Jupyter notebook the tools for local visualization of peak types that we developed.

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      Referee #3

      Evidence, reproducibility and clarity

      In this study, Sun et al. use RNAseq and ATAC-seq in 15 stages of embryonic development of the amphipod crustacean Parhyale hawaiensis to analyse gene regulation genome-wide. They assess the data in multiple ways to provide a more complete genome annotation, understand temporal changes in gene regulation, and identify different classes of cis-regulatory elements including associated GO terms and putative transcription factor binding site enrichment. The authors have made a great effort to account for potential biases in their datasets (one impressive example is the comparison of multiple transcriptome assemblies and the following quality assessment) and I enjoyed reading this manuscript for its great explanations of method usage (i.e., what each bioinformatic package does, why it was used etc.) and the overall style.

      I want to make a few suggestions that would make the study - in my opinion - even better:

      • I felt that the first paragraph of the introduction is not necessary.
      • Use of Genrich: I presume this was run on both duplicates simultaneously? This is not clear from the methods section. It might have implications for downstream analyses (e.g., differential accessibility between time points) because running on both sequencing library replicates simultaneously leads to a single "replicate" of peaks per time point, while running it individually leads to two. However, I have never tested if this actually does make a difference. Maybe the authors have and can comment on this?
      • In general, I thought that the bioinformatic methods (i.e., the code or the options used for each program) would have been helpful for my understanding in some cases. The authors say that these will be published on an accompanying GitHub repository, which should be fine if this is sufficient for journal policy.
      • The section on the IDE2 models (the paragraph at the end of page 4/beginning of page 5) was unclear to me but appears sound. (The only instance where I didn't quite understand what the program actually does.) Maybe this can be explained a bit easier?
      • On page 7, Fig.3J needs changing to 3H. This figure should, in my opinion, also contain the absolute number of peaks for each time point to set the individual proportions into context.
      • Last paragraph of the "Improving the Parhyale genome annotation" section: I think this needs to focus on those regions of the genome for which the location is known - after all, the "unknown" regions" could all be "distal transgenic", which would significantly change the relative proportions.
      • On page 9, t-SNE is mentioned but doesn't seem to be cited.
      • The third paragraph on page 9 ("We evaluated the differences...") should mention the fact that clusters 1 and 2 are the only ones with significant proportions of exonic and intronic peaks. In the accompanying figure (5C), the total number of peaks would again be helpful.
      • In figure 5D, I can't quite make out at which stage the dip in the peak of Cluster 8 occurs. This is quite an unusual pattern of accessibility change, and I can't help but wonder if it has something to do with the quality of one of the libraries? Also, the fact that half of the peaks fall into unmapped regions of the genome is unusual, and I feel this deserves more discussion.
      • On page 10, the abbreviation PFM appears, but it is only explained in the legend of Fig.4. This should appear in the text.
      • The section on "Concordant and discordant expression and accessibility" is the one I disagree most with. The authors seem to suggest that a repressive cis-regulatory module should become less accessible when the gene is activated. However, they leave trans-acting factors completely out of their conceptualisation here. It is in general likely the availability of transcription factors that leads to repression, while the "silencer" can be well accessible in all cells. Moreover, it has become clear in recent years that CRMs are not just repressors or enhancers per se but can act as either depending on the availability of transcription factors. I think these facts could partially explain the weak correlation and should be discussed.

      Significance

      This manuscript will greatly advance research in the emerging model organism Parhyale through a more complete genome annotation and vast amounts of gene expression and chromatin accessibility data (and accompanying analyses) at various stages of development. However, the impact goes far beyond the Parhyale community, and I believe this paper can be seen as a blueprint for similar studies in other organisms. The excellent documentation and comparison of their bioinformatic methods makes their re-use straightforward and much of the authors' pipeline can be used for a "standard" ATAC-seq data analysis - I am likely to use many of their methods myself. Therefore, I think the audience can range from the "classic" evo-devo community to developmental biologists, scientists interested in gene regulation in general, and bioinformaticians.

      My own expertise is in gene regulation through transcriptional control, and I use different seq approaches (ATAC, CUT&RUN, RNAseq) to study this process.

      Referees cross-commenting

      Thank you to my colleagues for their comments. Since Reviewer 1 was happy with the manuscript as it is, I'll only add my views to the points raised by Reviewer 2: - Evolution via gene regulation vs. coding sequence: While (to my understanding) it is largely accepted in the field that changes to the CDS will often have more deleterious effects than changes to the expression of a gene, I agree that this could be elaborated on a bit. - Focus on stages S21/S22: This might indeed be somewhat problematic. The libraries from these two stages (particularly S21) seem to be very different from those from the other stages. In the PCA (Fig. 1C), S21 doesn't cluster well with anything, and the difference between the two replicates is massive compared to other stages. The accessibility pattern (Fig. 1D) also looks odd. The libraries also have the lowest scores for % of mapped reads (Fig. S2B), fragment size distribution (S2E), and Spearman correlation (S2I). All this could be biologically sound and be due to a major developmental transition at this point, but maybe it justifies revisiting the data and testing whether leaving out S21 (and/or S22) makes a big difference for the clustering analyses. - Peaks in distal intergenic regions: I agree that this could be elaborated on. It might also be that >10 kb is not actually that distal for Parhyale. I would suggest to split the "distal peaks" further (e.g., in 10 kb or 2-log steps, or whatever makes most sense) and try to understand if >10 kb is mostly <20 kb, or if most of them are hundreds of kb from the nearest gene?

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      Referee #2

      Evidence, reproducibility and clarity

      Sun et al used omni-ATAC sequencing that is a modified version of classical ATAc-seq to identify and characterise the cis-regulatory elements in the P. hawaiensis genome. They further use long and short reads to improve upon existing gene annotation for this organism. The in-depth analysis ensures the results and conclusions to be sound however few points below might be needed to be addressed before the acceptance of manuscript.

      In Introductory paragraph 2, sentence one, authors suggest that gene regulation plays more important role in evolutionary process than genes. Although a significant amount of research has been dedicated to gene regulation based evolution still this field is in nascent form. For example evidence of inheritance of the gene regulation pattern across generation is scarce and requires more evidence. I suggest authors to modulate the claim that still gene based evolution is the main paradigm instead otherwise.

      Authors have repeatedly used S21 and S22 throughout the manuscript to support their claims with clustering etc. May authors shed some light on the differences in replicates for these timepoints. Furthermore, I could not find Fig 3J, perhaps author would like to point out Fig 3H.

      The majority of ATAC-seq peaks in the distal intergenic regions is a very surprising result. Authors defend this result by suggesting that this organism has big genome. May author perform a short analysis that shows that these peaks are indeed represent nearby genes or may point towards 3D genome organisation. For example, I see that this genome might have regions in the genomes that are densely organised in gene clusters, in those cases does the pattern remains same i.e he majority of the genes are very distant from each other and hence use vital regulatory elements?

      Significance

      The study by Sun et al is timely in nature and significantly improve the gene annotation of P. hawaiensis. It definitely advances the current knowledge for this organism regulatory elements. The comparison to other model organisms can be further improved by extending the discussion of the results especially in context of distal regulatory elements. The resource generated will be helpful for the researchers working in the field of developmental biology.

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      Referee #1

      Evidence, reproducibility and clarity

      The contribution by Sun et al. describes a very deep and thorough analysis of an Omni-ATAC-seq approach to identifying cis-regulatory elements in the crustacean Parhyale. This is a resource paper, so it does not explicitly have a research question or conclusions. The findings are a detailed dataset of putative regulatory elements, tested and validated with a number of different computational approaches, and - to a lesser extent - with a number of experimental approaches.

      The authors' work is very thorough, and while it may be possible to add more analyses and more validations, the work presented in the manuscript is impressive and stands on its own as a useful body of data. No additional work is needed to make this a complete contribution.

      The text is very well written and clear. It is a bit arduous in some places, but that is understandable, given the technical nature of the paper. The figures are clear and many of them are very eye-catching (in a positive sense).

      All in all, I have no criticism of this contribution. It is a very carefully executed and thorough analysis.

      Significance

      I am not aware of any other species outside of the main experimental model organisms for which there is data about putative regulatory elements that is as detailed as that presented in this manuscript. It is thus not only a fantastic resource for people working on Parhyale, but also a model for how such data can and should be generated for other species. The authors say this explicitly in their concluding paragraphs and I agree. The Parhyale community will pounce on this paper as a useful resource, whereas people working on other species might be inspired by it to generate equivalent data for their communities.

      I am an evolutionary developmental biologist who has worked on a number of species that are not traditional model species (I avoid the term "non-model", since every species is a model for something). I for one, fall into the category of people who will be inspired to generate equivalent data, although I must confess that I do not have the bioinformatic expertise of the authors, and therefore I am not able to critically assess the specifics of the tools they have used to generate and validate their data.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity (Required)): Excellent quality of cell biology and biochemistry. the additional supports are needed for the claim of actin elongation using different formin variants.

      Reviewer #1 (Significance (Required)): Ingrid Billault-Chaumartin and co-authors described interesting research that provides insights on formin-isoform specific function in fission yeast and a new role of Fus1 FH2 domain in cell-cell fusion event. While three formin isoforms have different localization, research proposed an additional dissection in their functional differences by having different functions in C-terminus, including FH1 FH2 and formin C-terminus. The work also described additional factors that regulate cell fusions from autotrophy effect and formin expression level, in addition to the well-accepted formin biochemical activities. Here are my comments regarding the strengths of the work and improvements that could further strengthen the story.

      Major comments 1. Fig.1 shows Cdc12C could recapitulate Fus1 function by ~80% if fused with Fus1C, whereas deletion of the C-terminal tail of Cdc12 following FH2 introduces drastic dysfunction. Together with Fig. 3, these results indicate Cdc12 Cter plays more important roles than Fus1 Cter for there respective functions. Such results suggested a Cter-mediated mechanism that differentiates the functions of three fission yeast formin isoforms. The authors examined contributions from the difference in FH1 (Figs 4,5) and FH2 residues (Fig. 6). Whereas the obvious phenotype of Cter was not further investigated and not much discussed. The Cter of budding yeast formins interacts with nucleation-promoting factors, Bud6 and Aip5. Although S. Pombe does not have orthologs of budding yeast Bud6 and Aip5, I wonder would the author discuss the potential contribution of Cter in differentiating S. Pombe formins.

      The reviewer is correct that the C-terminal tail region of Cdc12 beyond the FH1-FH2 domains has a strong influence on the ability of Cdc12C to replace Fus1C. This is one reason why we specifically investigated the possible role of Fus1 C-terminal tail, which is much shorter than that of Cdc12. We found that Fus1 C-terminal tail plays only very minor role in regulating Fus1 function, as described in Figure 3. We note that contrary to what the reviewer states, Bud6 exists in S. pombe and binds the C-terminal tail of the formin For3 (see Martin et al, MBoC 2007), but whether it binds Fus1 is unknown. We have expanded our discussion to include a paragraph on the role of formin C-termini.

      Because the manuscript is focused on the function of Fus1 formin, we did not explore further the role of the Cdc12 C-terminal tail. It was previously shown that this region of Cdc12 contains an oligomerization domain that promotes actin bundling (Bohnert et al, Genes and Dev 2013). It is thus likely that this helps Cdc12 FH1-FH2 perform well in replacement of Fus1. In fact, it is likely that oligomerization boosts formin function, as we have discovered that Fus1 N-terminus contains a disordered region that fulfils exactly this function. This is described in a distinct manuscript under review elsewhere and just deposited on BioRxiv (Billault-Chaumartin et al, BioRxiv 2022; DOI: 10.1101/2022.05.05.490810). We have now cited this point in the discussion.

      1. Here, the study focuses on the FH1 between Fus1 and Cdc12 to understand their different functions in actin polymerization. FH1 mediated actin elongation through its interaction with profilin via polyP. The transfer rate of G-actin from profilin and profilin sliding depends on the polyP patterns regarding the length of each polyp motif and their distance to FH2 (Naomi Courtemanche and Thomas D. Pollard, JBC, 2012). To better understand the mechanisms by which these engineered FH1 variants on both Fus1 and Cdc12 in Fig. 4, the author may want to list the sequence of these engineered FH1 domains, including the information of the number and length of polyp motifs, and discuss these patterns.

      This list and discussion were available in the initial paper that characterized each of the constructs in vitro (Scott et al, MBoC 2011). We have now re-drawn it in a supplemental figure for convenience (as also answered in response to minor point 2), which is already provided in the revised manuscript as Figure S1. (Previous supplementary figures are re-numbered S1>S2, S2>S3 and S3>S4).

      1. Figs.4,5 cell biology results do not directly support the point of specific elongation rate unless the LifeAct-labeled actin cable elongation speed could be followed and quantified. The fluorescent tagging of tropomyosin does not show the actin cable pattern, which makes it very difficult to be used to study actin cable dynamics, such as elongation. Therefore, I feel the data in current Fig. 4 and Fig. 5 could not claim the differences in actin elongation without a quantitative comparison of elongation rate. I suggest a CK666 treatment to increase the visibility of the actin cable pattern of LifeAct, used before in both fission and budding yeasts, which would allow the author to quantify the actin cable elongation rate. Another way is to use the TIRF assay used in this study, which would give a better quantitation of formin nucleation and profilin-aided elongation.

      We respectfully disagree with the reviewer on this point. All the constructs we use in vivo have been characterized in vitro and their elongation rate carefully measured (Scott et al, MBoC 2011). These values are thus known and can be directly compared to our results in vivo.

      Of course, it would be fantastic to be able to directly measure formin elongation rates in vivo, but we are not aware that this has been done in any system. The proxy experiments that the reviewer suggests would be good ones, but each faces technical challenges that make them impossible in our system. First, because the fusion focus is a structure that forms in response to cell-cell pheromonal communication, we cannot add CK-666 or any other drug during this phase, as this perturbs the pheromone signal. Indeed, we had shown that simple buffer wash leads to loss of the fusion focus (see Dudin et al, Genes and Dev 2016). Second, the fusion focus is at the contact site between partner cells, i-e somewhat distant (1-2µm) from the coverslip during imaging. It is thus impossible to use TIRF. Finally, the fusion focus is a tightly packed actin structure. This is the reason why (rather than use of the tropomyosin marker) we cannot image single actin filaments (or even bundles) of which we could follow the dynamics as has been done to measure the retrograde flow of actin cables in yeast.

      What we have done is to use a better tropomyosin tag, mNeonGreen-Cdc8, which was just described (Hatano et al, BioRxiv 2022; DOI: 10.1101/2022.05.19.492673) to quantify amounts of linear actin. Although this is not a measure of elongation rate, it would give some sense about amounts of polymer assembled. We have obtained images with mNeonGreen-Cdc8 of all experiments previously conducted with GFP-Cdc8 and have replaced them in Figure 4C, Figure 5E, Figure 6E and Figure S2B. We have also quantified the relevant strains. The relative intensities of mNeonGreen-Cdc8 at the fusion focus at fusion time reflect remarkably well the measured elongation rates of the various formin constructs characterized in vitro. These data are now provided as new panels Figure 4F and Figure 5F.

      1. I appreciated the detailed biochemical dissections of multiple aspects of WTFus1 and Fus1R1054E, although the biochemical assays could not identify the mechanism by which R1054E causes the cell fusion. In many cases, the formin functions are diverse in diverse biological processes and sophisticated that cannot be explained well only from its biochemical activities in actin polymerization, such as the bundling, nucleation, and elongation studied in this story regarding fusion. This exciting information allows us to think of more possibilities that might regulate formin function rather than a direct change of formin activities in actin polymerization. I think a discussion of different aspects of functional regulation of formin might inspire society to investigate new possibilities to solve the mysteries. For example, the changes in formin behaviors and functions could be regulated by stress-induced formin turnover by degradation, cell signaling-regulated formin clustering and complex assembly, and their potential relevance to recruit protein constituents for fusion progression.

      We have added a paragraph on the role of Fus1 C-terminus. If you feel we should expand more on the diverse modes of regulation of formins, we could, but we have so far kept the discussion centred around the points of investigation in this paper, whose aim was to probe how changes in nucleation and elongation rates, rather than other regulations, affect the in vivo function of Fus1.

      Minor comments. 1. There are two types of "C", one includes FH1/FH2 and one following FH2, used in the manuscript, and it is a bit confusing. Better to differentiate them that allows an easy following. Fig. 1 uses Cdc12C-deltaC, Fig. 3 uses Fus1-delta Cter.

      We have updated the nomenclature to make this clearer: the C-terminal region beyond the FH1-FH2 domains is now called Cter throughout the manuscript.

      1. It's better to specify the amino acid position on the schematic of formins, such as panel A in many figures. It's always more informative to compare formin activities by considering the domain lengths, especially for the C-terminal tail that is variable in lengths and sequences. With similar thoughts, I suggest a supplementary figure that lists the sequence of all FH1 domains variants and Cter domains, such as the FH2 domain in Fig. S1.

      We have made a supplementary figure (new Figure S1) listing all constructs with specific aa positions as well as the FH1 domain variants and their sequences (see also answer to point 2 above). We have not added the sequence of the Cter domains in this figure, as these are extremely divergent and not particularly informative at this point.

      1. "n" for the statistic needs to be provided for Fig. S3.

      We have added the information to the legend of the figure (now Fig S4).

      1. The SDS-PAGE staining gel of the purified recombinant proteins for biochemical assays should be provided, particularly for these newly reported mutant variants.

      This is now provided as new panel S4C. We show the purified recombinant Cdc122FH1-Fus1FH2 proteins, which are the newly reported ones.

      Reviewer #2 (Evidence, reproducibility and clarity (Required)): In this study, Billaut-Chaumartin and colleagues investigate the molecular specialization of the S. pombe formin, Fus1. The authors systematically modulate the actin filament elongation and nucleation activities of Fus1 by expressing chimeric constructs that contain Formin Homology 1 and 2 domains from two other formins with known polymerization activities. By characterizing the architecture of the fusion focus and the efficiency of cell fusion, they find that both the elongation and nucleation properties of Fus1 are specifically tailored for its cellular role. Comparison of formin constructs with similar elongation and nucleation activities also reveals that the Fus1 FH2 domain possesses a specific property that promotes efficient cell fusion. Using sequence alignment and homology modeling, the authors identify R1054 as the residue that confers this novel, fusion-specific activity to Fus1, despite producing no effect on its bundling or polymerization properties in vitro.

      Overall, this study is well motivated, and the results support the conclusions that are drawn. I have only minor suggestions, as described below.

      Minor comments: (1) The schematic diagrams of the chimeric formin constructs are very helpful. However, it is difficult to distinguish the colors from one another, especially in the case of the Cdc12FH1-Fus1FH2 variant, which requires discernment of the relatively small purple region within the dark blue molecule. Would it be possible to modify the colors to increase their contrast? Similarly, the blue and gray data sets in Figure 3B are very difficult to discern.

      We have changed the colours to improve contrasts.

      (2) The affinities (Kd) with which the formins bind the barbed ends as described in the second-to-last paragraph on page 8, in Figure Legend 7G, and in the "Analysis of pyrene data" section of the Materials and Methods should be defined as dissociation "constants", rather than dissociation "rates". Also, these affinities are lacking units in the following sentence on page 8.

      We have corrected this. The unit is nM.

      (3) When comparing the TIRF micrographs in Figure S3A, it looks as though both formins (but especially the R1054E variant) nucleate more filaments in the presence of profilin than in its absence. Is this a reproducible effect? If so, can the authors provide an explanation for this?

      There is strong variability in the filament numbers observed by TIRF in replicate experiments, which makes it difficult to use this technique to determine the nucleation efficiency. This may be due for instance to the stickiness of the glass, which may influence the number of observed filaments. We have measured the number of filaments after 130s of polymerization for each condition to test whether there are any significant differences between conditions despite overall variability. The measurements suggest that the addition of profilin increases the number of actin filaments. However, these results should be taken very carefully due to the experimental variations (very large error bars). Additionally, because Fus1-associated filaments are very short in absence of profilin, it is quite likely that this influences their crowding at the glass surface compared to longer filaments (in presence of profilin). Since in TIRF we can only observe the filaments at the glass surface, we may miss a portion of short Fus1-bound actin filaments in absence of profilin.

      For these reasons, and because the possible role of profilin in modulating nucleation efficiency by formins is not the object of the work here, would thus prefer not to include this graph in the manuscript.

      Reviewer #2 (Significance (Required)): This study contributes a key advancement towards understanding how the polymerization activities of formins are tailored to support diverse and specific cellular functions. The results in this study nicely complement and expand upon similar recent work that dissected the polymerization requirements of the formin Cdc12, which mediates cytokinetic ring assembly in S. pombe, and For2, which drives the assembly of apical networks that are necessary for polarized growth in Physcomitrella patens. As such, this work will likely be of significant interest to scientists who study mechanisms of actin dynamics regulation. The identification of R1054 as a residue that confers a novel regulatory activity to the FH2 domain of Fus1 will also likely be of great interest to biochemists and other scientists who study formins at the molecular level.

      My expertise is in the field of formins and actin polymerization.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this study, Billaut-Chaumartin and colleagues investigate the molecular specialization of the S. pombe formin, Fus1. The authors systematically modulate the actin filament elongation and nucleation activities of Fus1 by expressing chimeric constructs that contain Formin Homology 1 and 2 domains from two other formins with known polymerization activities. By characterizing the architecture of the fusion focus and the efficiency of cell fusion, they find that both the elongation and nucleation properties of Fus1 are specifically tailored for its cellular role. Comparison of formin constructs with similar elongation and nucleation activities also reveals that the Fus1 FH2 domain possesses a specific property that promotes efficient cell fusion. Using sequence alignment and homology modeling, the authors identify R1054 as the residue that confers this novel, fusion-specific activity to Fus1, despite producing no effect on its bundling or polymerization properties in vitro.

      Overall, this study is well motivated, and the results support the conclusions that are drawn. I have only minor suggestions, as described below.

      Minor comments:

      1. The schematic diagrams of the chimeric formin constructs are very helpful. However, it is difficult to distinguish the colors from one another, especially in the case of the Cdc12FH1-Fus1FH2 variant, which requires discernment of the relatively small purple region within the dark blue molecule. Would it be possible to modify the colors to increase their contrast? Similarly, the blue and gray data sets in Figure 3B are very difficult to discern.
      2. The affinities (Kd) with which the formins bind the barbed ends as described in the second-to-last paragraph on page 8, in Figure Legend 7G, and in the "Analysis of pyrene data" section of the Materials and Methods should be defined as dissociation "constants", rather than dissociation "rates". Also, these affinities are lacking units in the following sentence on page 8.
      3. When comparing the TIRF micrographs in Figure S3A, it looks as though both formins (but especially the R1054E variant) nucleate more filaments in the presence of profilin than in its absence. Is this a reproducible effect? If so, can the authors provide an explanation for this?

      Significance

      This study contributes a key advancement towards understanding how the polymerization activities of formins are tailored to support diverse and specific cellular functions. The results in this study nicely complement and expand upon similar recent work that dissected the polymerization requirements of the formin Cdc12, which mediates cytokinetic ring assembly in S. pombe, and For2, which drives the assembly of apical networks that are necessary for polarized growth in Physcomitrella patens. As such, this work will likely be of significant interest to scientists who study mechanisms of actin dynamics regulation. The identification of R1054 as a residue that confers a novel regulatory activity to the FH2 domain of Fus1 will also likely be of great interest to biochemists and other scientists who study formins at the molecular level.

      My expertise is in the field of formins and actin polymerization.

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      Referee #1

      Evidence, reproducibility and clarity

      Excellent quality of cell biology and biochemistry. the additional supports are needed for the claim of actin elongation using different formin variants.

      Significance

      Ingrid Billault-Chaumartin and co-authors described interesting research that provides insights on formin-isoform specific function in fission yeast and a new role of Fus1 FH2 domain in cell-cell fusion event. While three formin isoforms have different localization, research proposed an additional dissection in their functional differences by having different functions in C-terminus, including FH1 FH2 and formin C-terminus. The work also described additional factors that regulate cell fusions from autotrophy effect and formin expression level, in addition to the well-accepted formin biochemical activities. Here are my comments regarding the strengths of the work and improvements that could further strengthen the story.

      Major comments

      1. Fig.1 shows Cdc12C could recapitulate Fus1 function by ~80% if fused with Fus1C, whereas deletion of the C-terminal tail of Cdc12 following FH2 introduces drastic dysfunction. Together with Fig. 3, these results indicate Cdc12 Cter plays more important roles than Fus1 Cter for there respective functions. Such results suggested a Cter-mediated mechanism that differentiates the functions of three fission yeast formin isoforms. The authors examined contributions from the difference in FH1 (Figs 4,5) and FH2 residues (Fig. 6). Whereas the obvious phenotype of Cter was not further investigated and not much discussed. The Cter of budding yeast formins interacts with nucleation-promoting factors, Bud6 and Aip5. Although S. Pombe does not have orthologs of budding yeast Bud6 and Aip5, I wonder would the author discuss the potential contribution of Cter in differentiating S. Pombe formins.
      2. Here, the study focuses on the FH1 between Fus1 and Cdc12 to understand their different functions in actin polymerization. FH1 mediated actin elongation through its interaction with profilin via polyP. The transfer rate of G-actin from profilin and profilin sliding depends on the polyP patterns regarding the length of each polyp motif and their distance to FH2 (Naomi Courtemanche and Thomas D. Pollard, JBC, 2012). To better understand the mechanisms by which these engineered FH1 variants on both Fus1 and Cdc12 in Fig. 4, the author may want to list the sequence of these engineered FH1 domains, including the information of the number and length of polyp motifs, and discuss these patterns.
      3. Figs.4,5 cell biology results do not directly support the point of specific elongation rate unless the LifeAct-labeled actin cable elongation speed could be followed and quantified. The fluorescent tagging of tropomyosin does not show the actin cable pattern, which makes it very difficult to be used to study actin cable dynamics, such as elongation. Therefore, I feel the data in current Fig. 4 and Fig. 5 could not claim the differences in actin elongation without a quantitative comparison of elongation rate. I suggest a CK666 treatment to increase the visibility of the actin cable pattern of LifeAct, used before in both fission and budding yeasts, which would allow the author to quantify the actin cable elongation rate. Another way is to use the TIRF assay used in this study, which would give a better quantitation of formin nucleation and profilin-aided elongation.
      4. I appreciated the detailed biochemical dissections of multiple aspects of WTFus1 and Fus1R1054E, although the biochemical assays could not identify the mechanism by which R1054E causes the cell fusion. In many cases, the formin functions are diverse in diverse biological processes and sophisticated that cannot be explained well only from its biochemical activities in actin polymerization, such as the bundling, nucleation, and elongation studied in this story regarding fusion. This exciting information allows us to think of more possibilities that might regulate formin function rather than a direct change of formin activities in actin polymerization. I think a discussion of different aspects of functional regulation of formin might inspire society to investigate new possibilities to solve the mysteries. For example, the changes in formin behaviors and functions could be regulated by stress-induced formin turnover by degradation, cell signaling-regulated formin clustering and complex assembly, and their potential relevance to recruit protein constituents for fusion progression.

      Minor comments.

      1. There are two types of "C", one includes FH1/FH2 and one following FH2, used in the manuscript, and it is a bit confusing. Better to differentiate them that allows an easy following. Fig. 1 uses Cdc12C-deltaC, Fig. 3 uses Fus1-delta Cter.
      2. It's better to specify the amino acid position on the schematic of formins, such as panel A in many figures. It's always more informative to compare formin activities by considering the domain lengths, especially for the C-terminal tail that is variable in lengths and sequences. With similar thoughts, I suggest a supplementary figure that lists the sequence of all FH1 domains variants and Cter domains, such as the FH2 domain in Fig. S1.
      3. "n" for the statistic needs to be provided for Fig. S3.
      4. The SDS-PAGE staining gel of the purified recombinant proteins for biochemical assays should be provided, particularly for these newly reported mutant variants.
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      Reply to the reviewers

      From the start, the authors would like to thank all the reviewers for their careful and constructive consideration of our manuscript. We have now made several changes to the paper and believe it to be better for the feedback.

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

      In this study, Rees et al. perform an RNA-seq circadian time course experiment in the recently formed allopolyploid wheat. Through comparisons with other circadian transcriptomic datasets in other species it appears that the period of rhythmic genes is much more variable in wheat with a shift to longer periods compared to the other species examined. Interestingly, by analyzing circadian parameters among expressed genes, they find evidence that this newly formed allopolyploid already shows signs of divergence in circadian traits among homoeologs. A thorough comparison with circadian regulated genes in Arabidopsis reveals overlap in phasing of genes involved in certain biological processes such as photosynthesis and light signaling whereas genes involved in starch metabolism were found to have different levels of rhythmicity and phasing. This dataset will be a great resource for the community and enable new predictions about the influence of polyploidy on the circadian control of important crop improvement traits and the circadian regulation of gene expression.

      Major Comments

      1. The results section starts with very little explanation of the experiment. It would help to provide a little more detail at the start of the results to explain the context for the experiment and what was done, when samples were collected and for how long. For the methods section, it isn't until line 650 that it is clearly stated that the sampling started at ZT0. It would be better to put this in the plant materials and growth condition section.

      Thank you for highlighting the need for this context, we agree that the manuscript is improved by an introduction to the experiments. We have now included an “Experimental context” section in the results and have taken the opportunity to explain how the full 0-68h and 24-68h datasets are used within our analysis. Ln 74-82. We have also edited the Methods as suggested Ln 610-615.

      The low proportion of circadian regulated genes is likely due to the very low cutoff for calling a gene expressed, especially when there are three days of repeated timepoints. If a gene is expressed across the time course it should have values above TPM 0 for at least 3 time points in order for it to be expressed each day. I'd also be suspicious of a gene with a TPM value less than 0.5. Comparing these types of numbers is always challenging due to the various cutoffs used. Along those lines, why was a different filtering scheme used for Arabidopsis (line 657)?

      We completely agree that the proportion of genes described as rhythmic changes a great deal with the threshold at which you exclude low expression transcripts as well as the window over which measurements are taken and the q-value cut-off for rhythmicity. We performed an analysis to test the effects of applying a pre-filtering step to exclude low-expression genes and discuss our findings in Supplementary Note 1. Briefly, we removed genes with expression less than 0.1 TPM in six or more timepoints and again ran Metacycle to define numbers of rhythmic genes. Our results are discussed in Supplementary Note 1 and are presented in Supplementary Table 1. Regardless of the cut-offs applied, Arabidopsis and wheat data was treated identically, and our findings reported in the main results were consistent with those reported in the Supplementary analysis. Thank you for raising this point, as we have now improved our description of this analysis in the main text (Ln 92-95).

      Regarding the different filtering schemes, the filtering mentioned by Reviewer 1 was applied to both Arabidopsis and wheat data for a stricter retention of rhythmic genes, as part of the pre-WGCNA clustering analysis. Filtering to retain genes with >0.5TPM across 3 timepoints was applied to reduce lowly expressed genes, that act as background 'noise' when defining clusters. We applied this across 3 timepoints rather than the WGCNA suggestion of 90% of samples - because the patterns of expression in our rhythmically filtered datasets were cyclical in nature.

      In reference to the shortening of the period every day, this should be interpreted with caution. Period estimate of a single cycle are not very reliable and the SD for each day is around 3h so it is difficult to draw any conclusions about changes in period each day. One option would be to only include genes with an SD less than 1h or alternatively to remove the discussion surrounding the comparison of period across the three days and focus on the period results for the full 24h-68h window shown in 1b. While 2 days is better it is still not ideal for calling period; however, your first day will still have a strong diurnal driven pattern that will likely skew your circadian period.

      Thank you for your comments. Our question here was to determine whether the mean period lengths of rhythmic transcripts in wheat were always immediately longer upon transfer to constant light, or whether they got progressively longer over time. Upon reading the reviewer’s comment, we realize that the explanation provided of how we conducted this analysis was misleading. Our approach was to take a 44h sliding window (almost 2 days) and measure period at 0-44h, 12-56h and 24-68h. We have now added the previously missing statistics that support our findings in the main text, and which hopefully show the significance of the period changes over time (supplementary note 2). One of the most surprising findings from this analysis was that the periods in the first window were the longest 28.61h (SD=3.421), suggesting that the diel (driven) oscillation had little impact upon immediate transfer to free run. Our interpretation is that the mean period initially lengthens trying to follow the missing dusk signal, before the free-running endogenous period asserts itself in later cycles (Ln 129-128).

      Line 87-93: If the dusk cue is important for clock expression you would think this would be biased towards genes that peak later in the day or near dusk. This argument should be connected better to the period results discussed on lines 98-101.

      Following on from our statement above, we have now combined our hypothesis for why wheat transcripts expressed at dusk have longer periods with the discussion about longer periods upon transfer to constant light. We agree that the two processes are likely to be connected and have now placed them together in Ln 129-128.

      1. Lines 650-652 of the Methods mentions that one of the main interests was the response to transfer to L:L, but this isn't mentioned in the introduction and doesn't come up much in the Results section. Most of the expression comparisons are focused on the 24-68h window. It also isn't clearly explained why the first day in LL is still a diurnal cycle. This would be helpful for non-circadian readers who may wonder why the first day is not included in all the analyses.

      We believe this point is now also addressed by the addition of an Experimental Context section in the results (Ln 74-82), in response to the reviewer’s previous comment.

      1. The phase comparisons shown in Figure suppl 4 are confusing. Suppl. Note 3 states that the period from the 24-68h data window was used to establish the bins but then the phase is shown for 3 different windows for each column? When calculating the phase for each of those 3 windows which period was used as the denominator in the phase calculation? Was it the period that matches the window used to calculate phase? What does the plot look like if phase is called on the same window used to calculate period (24-68)? What method was used to call phase in Suppl. Fig 4? As shown in Suppl Fig. 3 the method can influence the phase distributions. The methods suggest that the phase was determined with Metacycle but then FFT and MESA were used to verify. What does this mean verify, were they adjusted if FFT/MESA didn't agree?

      We agree that this Figure was unnecessarily complicated. We have now simplified Supplementary Figure 4 so that only the phases from 24-68h are presented. We have also clarified the legend to explain why we used FFT-NLLS to improve accuracy of Metacycle predictions.

      It is difficult to interpret the value of the period and phase comparisons shown in Fig. 1b, c, e and f after the preceding section about how variable the period and phase is across days. It is also surprising that the full 3 days were used to calculate the circadian statistics considering the first day is still under diurnal control. Do the ratios remain the same if the statistics are performed only on the 24h-68h window? For consistency with the rest of the paper and avoid confusion it would be best to have all circadian parameters measured using the same time window (24h-68h).

      Thank you for your comments, we can see how our logic in using the different data windows was not clear enough. As mentioned above, we have now explained the use of the full and shortened data windows in Experimental context section (Ln 74-82). Fig 1c is a comparison between different circadian datasets and as such we have only compared periods across 24-68h window. Similarly, Fig 1b is a global analysis of periods in rhythmic genes in comparison with Arabidopsis and so is again measured from 24-68h. We have now clarified this in the Figure legend for 1b.

      For comparisons of homoeologs within wheat triads, our question was in identifying homoeologs which behaved differently when placed under free-running conditions. We therefore still feel justified in using the full 0-68h dataset to identify homoeolog periods and phases which indicate differential circadian regulation, but we have now clarified that we are using the full dataset for the triad analysis in the results (Ln 140).

      Fig 1h-m. How were those genes chosen? It would help to see the SD of the replicates shown, since this is just showing one triad. It would be helpful to see a plot that represents the full set of triads rather than just one that looks best. If normalized to a standard phase they could be put on the same plot. For example, panel j is meant to show the 8h lag of subgenome D. If the data is normalized so that A and B are set to the same phase all the triads could be displayed with shaded SD bars to show the variation. Something like this would be a better representation of the data rather than showing just one example.

      Fig. 1h-m are case-studies illustrating the different forms of circadian imbalance between homoeologs. We agree that it is helpful to see the standard deviation as error bars on these triad plots and have added it as suggested. In line with another Reviewer 2’s suggestion we have removed Fig 1k and have replaced this with a comparison of mean normalised data for Triad 408 and Triad 2454, highlighting the difference between imbalanced rhythmicity and imbalanced amplitudes between homoeologs. Fig 1 I and m do not have error bars as adding standard deviations to mean normalised data wasn’t appropriate.

      Thank you for your suggestion on how to display the different phases between homoeologs. We feel that if we were to plot all of the triads displaying imbalanced phases, the differences in period length and accompanying noise differences would make the plot so busy as to be unreadable. We hope that the pie charts Fig 1 d-g give a global overview of the proportions of triads with circadian imbalance, but agree with the point that it is useful to allow readers to view triads of their own preference. Therefore, we have now provided the replicate level TPM data with the triad IDs annotated (Supplementary File 12) and Supplementary file 11 provides the classification of each triad alongside Metacycle statistics, ortholog identification and cluster information discussed elsewhere in the paper. Readers can now look up a triad or gene of interest and see how it was classified and what the expression looks like over the full dataset.

      It is surprising that there aren't more comparisons with the B. rapa dataset, especially when discussing the clock genes that show balanced or imbalanced expression. Are they similar in B. rapa and does it support your hypothesis that unbalance for certain genes are selected against?

      While we agree that a thorough, multiple species, comparative transcriptomic analysis is undoubtably of interest for the future, we feel it is beyond the scope of the questions being addressed in this paper. We do compare paralogs defined as “similar” in the Greenham dataset with homoeologs described as “balanced” in our dataset and find that genes involved with “photosynthesis” and “generation of precursor metabolites and energy” tend to be common between the two groups, potentially suggesting conservation of balance for certain types of genes (Ln 206-217).

      Figure 2 networks. Why were these specific modules selected? Is it actually appropriate to directly compare these modules? I do see that some of the comparisons have high correlations from panel a, but not all. For example, in panel b the W9 and A9 modules have a correlation value of 0.92, which seems appropriate. However, panel c (modules W3 and A2) have a correlation of 0.42, which seems far too low to make any sort of comparison meaningful.

      The modules were selected to simplify the comparison of genes expressed in the dawn, midday, dusk, and night. We were interested in identifying common GO-enrichment in genes peaking throughout the day, although as you have identified, the differences in period length between Arabidopsis and wheat made this difficult. Our reasons for comparing module W3 with module A2, were that, even though their eigengenes are not highly correlated per se, when period length is taken into account, both modules peak during the subjective day (CT 6.34h and 6.19h) and they share commonly enriched GO terms which make sense for day peaking genes.

      Further, as described in methods comments, using a cutHeight as low as 0.15 will likely lead to some number of genes in any given module that do not necessarily "share" a similar expression pattern. These genes could have a pattern that has very low correlation to their module eigengene and were only placed in that module because the pattern was "less similar" to other module eigengenes. The current expression plots in this figure follow a clear pattern, but I suspect this would be even more apparent if the genes within these modules had a higher correlation to the module eigengene. Perhaps the current genes in these modules could just be filtered to have a higher correlation score?

      Thank you for your comments, we have now made changes to the Results and Methods to clarify our approach (Ln 237-239 and Ln738-765). Merging modules with highly correlated module eigengenes (ME) is the final step in constructing our co-expression networks. To do this, as the reviewer describes - we used the WGCNA default parameter of a mergeCutHeight() of 0.15. This results in the merging of modules with highly correlated ME as the 0.15 mergeCutHeight() refers to the dissimilarity metric of 1 minus the eigengene correlation. So for WGCNA, a mergeCutHeight() of 0.15 corresponded to a correlation of 0.85. For the wheat modules, we took the additional step of merging closely related modules (mergeCloseModules()) using a cutHeight of 0.25, again a dissimilarity metric of 1 minus the eigengene correlation (corresponding to a correlation of 0.75). Reducing the stringency of the cutHeight to merge highly correlated wheat modules enabled us to more easily compare significantly correlated wheat and Arabidopsis co-expression modules to identify groups of genes in wheat and Arabidopsis expressed at similar times in the day, and enable the comparison of whether similar phased transcripts in wheat and Arabidopsis had similar biological roles.

      Lines 327-334: I am not following the connection between 'response to abiotic stimulus' and the photoreceptor and light signaling proteins. At the start of this section (line 308) the authors say that the GO analysis was only done on rhythmically expressed genes but the reference to only one PHYA being rhythmic and yet multiple genes are shown in the plot in fig. S16. Does this mean that all the genes were shown and not just the rhythmic ones? This would explain why many of the PHY and CRY genes don't seem to have rhythms. This should be clarified better in the text or indicated in the plot which ones were called rhythmic. Since the first day following transfer is still the diel pattern from the entrainment condition, what does the PHY and CRY expression look like? Does it appear rhythmic under diel but lose rhythmicity in LL? It should be noted in the text that arrhythmicity in circadian conditions doesn't mean there isn't rhythmicity under diel conditions. This could be an additional explanation apart from the current one in the text that the regulation is at the level of protein stability/localization. Overall, this entire section is very long and entirely based on data shown in the supplemental material. I do appreciate having the individual gene plots that supplement Figure 4 and would suggest either providing a main figure to highlight a small subset of genes or pathways in this section or shorten it and focus on the results shown in the main figures.

      Upon reading the reviewer’s comment, we realize that we should have made our motivations and processes clearer within this section. We used the data filtered for rhythmicity to conduct the GO-enrichment analysis and then used that to identify processes which should be of interest for further investigation. We have now added an additional sentence (Ln 352-354) to explain this more clearly. We then considered the orthologs of well-known Arabidopsis gene networks and extracted their expression from our circadian dataset, whether rhythmic or not. Supplementary Table 10 contains all of the genes we investigated, their expression and their MetaCycle statistics. We have also indicated here which genes are plotted in which Supplementary Figure 18-20. The reasons for plotting non-rhythmic genes in some cases was that it illustrates the differences between circadian control in Arabidopsis versus wheat (as is the case for the PHY and CRY genes). We understand that it is useful to see at a glance which genes are classified as rhythmic or arrhythmic, so have now highlighted each row in Supplementary Table 10 to make this more intuitive, and added a read me tab.

      Regarding your point about oscillation under diel cycles, we agree that some transcripts will show rhythmic behaviour under entraining environments but not under constant conditions, and may perform time-of-day specific functions. However, these transcripts are likely to not be regulated by the circadian clock (at the transcriptional level) and so are not discussed in the context of a circadian transcriptome.

      For your interest, here is the full expression of PHY and CRY transcripts starting at ZT0:

      [Image]

      It is difficult to say for definite, but it seems likely that some of these photoreceptors will have rhythmic patterns of expression under diel cycles, but these rhythms do not endogenously persist under constant conditions.

      We appreciate your feedback that this section would benefit from cutting down of text and addition of a Figure to illustrate the text. We have now cut some of this section down and created a new main figure based on some of the oscillation plots from Supplementary Figure 18 and 19. We chose examples that reflect a conservation of relationships between transcripts of different peak phases, as we find it interesting that both species have similar patterns. (Main Figure 4, Ln 361--363, 382).

      1. Primary metabolism section: in terms of the supplemental figure, similar to the previous one I think it would declutter the plots if the genes that are not rhythmic were left out and simply indicate below the plot that they didn't meet the rhythmicity cutoff. This is another area where there is more discussion surrounding the supplemental figures than the main figure 4.

      One of the overall findings of this section was that many of the genes involved in Starch and T6P metabolism which are rhythmically expressed in Arabidopsis are not rhythmically expressed in wheat. We feel removing these genes from the results would detract from the importance of this finding. We have now edited Supplementary Table 10 to highlight which genes are classified as rhythmic. We have also added in a sentence to the start of this section which lays out our motivations for this analysis, summarises our findings and better connects the text with an explanation of Fig. 5 (Ln 408-430).

      For all gene expression figures there should be SD or SE shown either as bars or ribbons to represent the variation in replicates.

      Although we agree that error bars are informative for showing variation between replicates (and have added them to Fig. 1 to show differences within wheat triads) we feel that adding error bars to the gene expression plots in Fig. 3, Fig 4 and Supplementary Fig 19-20 would make these plots difficult to read, particularly where the wheat homeologs are very similar. The purpose of these gene expression plots is to compare circadian profiles in Arabidopsis and wheat orthologs rather than to claim significant differences in expression at any particular timepoint. This is fairly common in other circadian biology studies:

      https://www.pnas.org/doi/10.1073/pnas.1408886111 ,

      https://www.jbc.org/article/S0021-9258(17)49454-3/fulltext#seccestitle20 , https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0169923 , https://www.science.org/doi/10.1126/science.290.5499.2110?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed,

      https://www.frontiersin.org/articles/10.3389/fgene.2021.664334/full,

      https://www.science.org/doi/full/10.1126/science.1161403

      The replication level information for each gene has now been made available in Supplementary file 12.

      1. It would be very helpful to include the code used to generate the networks and perform the cross-correlation of eigengenes across networks should be included in the Methods. This will also save you from responding to email requests!

      Thank you for your comment, Code for the cross-correlation analysis, Loom plots and WGCNA network construction is now available from our groups GitHub repository: https://github.com/AHallLab/circadian_transcriptome_regulation_paper_2022/tree/main

      Minor Comments

      1. Figure 1, panel d: - The "unbalanced" triads that are depicted by the lighter shading; do these in fact have a different cutoff than the original rhythmic homoeologs? In the figure it says qThank you for bringing this to our attention, this has now been corrected.

      Hard to directly compare the GO term overlap in Figure 2f. Might be better to only show the results for the 4 pairs shown in b-e and put them side by side in the bubble plot.

      Thank you for this feedback, We have tried to make this plot easier to understand without losing any of the available information. Hopefully it is now more intuitive to understand which columns are being compared. We have changed the coloured lines to make them slightly wider, put the modules in corresponding coloured boxes and highlighted GO-slim terms shared by modules being compared.

      1. Line 314 -316 don't see supp tables 10, 11

      Our apologies, these files were missed previously from the upload are now available.

      1. For the selection of B. rapa circadian paralogs with similar and differential expression patterns (starting line 714), the authors choose a hard cut off of 0.001 (differentially patterned) OR 0.1 (similarly patterned). What happens to the genes that are between these two cut offs or is this a typo. Since all the other cutoffs for rhythmicity was set at 0.01 it seems likely that this is a typo.

      We have now clarified this in the methods, (Ln 807-822). This is not a typo, but it is a different method to the Metacycle approach we have used for our wheat data. We defined similar/different paralogs as characterized in Greenham et al, (2020) using DiPALM p-values. We chose these DiPALM p-value cut-offs as they gave us approximately equal numbers of paralogs in each category, which represent tails of similarly expressed or differently expressed circadian genes. We checked these cut-offs by calculating average Pearson’s correlation statistics between paralogs and found that differential Brassica paralogs had a mean Pearson correlation coefficient of 0.31 (SD = 0.43) and similar Brassica paralogs had a mean Pearson correlation of 0.75 (SD= 0.23) which confirms that the DiPALM method of defining expression patterns makes sense in the context of this analysis.

      Line 681. Should be supplemental Figure 6 not 9.

      1. References to most supplemental figures are not the correct number.

      2. Labels above the plots in Supp Fig5 do not match the legend.

      We apologise for these mistakes. We realize that we had mistakenly submitted an earlier draft of the Supplementary materials file, which was missing Supplementary Figure 5, 6 and 9 which therefore shifted the order of the remaining figures. This is now updated.

      1. Suppl table 7 should be as a separate .csv file or similar to be able to see the full table.

      This is a good suggestion, and we have added this.

      1. Line 723 should be B. rapa not B. napus.

      Thank you for catching this! Corrected.

      1. Figure 4. There is no explanation for what the black boxes represent in the figure legend.

      Thank you for your comment. Figure 4 (new Figure 5) has now been updated.

      Reviewer #1 (Significance (Required)):

      This study provides new insight into the circadian regulation of the transcriptome in a new allopolyploid. It adds a valuable resource to a growing collection of circadian studies in important crops and will greatly improve our efforts to learn more about the circadian control of important crop improvement traits. The dataset will be of interest to other plant circadian biologists as well as the general plant biology community who focus on monocot crops. My expertise is more on the transcriptomic side and I do not have the expertise to evaluate the phylogenetic work presented in this study.

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

      Summary Rees et al. present an RNAseq time course of bread wheat. Its recent polyploidisation is one motivation for this study as gene expression dosage is known to be important for clock function in other plants. The time course covers 3 days at sampling intervals of 4h of 2-week old wheat plants (all aerial tissues), in triplicates. The subsequent analysis of the RNAseq data includes analysis of the generated data by itself (e.g. GO analysis, rhythmicity, period and phase analysis, rhythmicity of transcription factor families as well as TF binding sites) as well as thorough comparison with published datasets of other species (Arabidopsis, Brassica rapa, Brachypodium dystachion). One of the key findings is that the mean period length and the period spread are larger in wheat than in these other species). Circadian clock genes largely have similar dynamics in wheat compared to Arabidopsis. In addition, one focus is the analysis of the dynamics of three genes of one triad and imbalance / balance of such triads. To the surprise of the authors, circadian regulated and clock genes were not necessarily balanced. Silencing is one of their explanation for imbalance of circadian genes as arrhythmic genes of one triad are typically those with the lowest expression level. Finally, the authors point out more examples of rhythmic processes and genes (photoreceptors and signalling, auxin, carbon metabolism) and their commonalities and differences with Arabidopsis.

      Major comments - The key conclusions and the data are convincing

      We thank the reviewer for their supportive comments.

      • line 120 and figure 1: In my opinion, q > 0.05 is not a good definition of arrhythmicity as non-significant q-values can result from either noise in spite of rhythmicity or from arrhythmicity. A more statistically sound way to detect arrhythmicity could for example be two-one-side tests (for example in the R package 'equivalence', e.g. see usage for time courses by Noordally et al. 2018, https://www.biorxiv.org/content/10.1101/287862v1).

      Thank you for pointing us in the direction of this package, we agree that choosing methods for circadian quantification and q-value cut-offs is always tricky and different approaches will perform better for noisier or non-sinusoidal waveforms. For future work, we will investigate the application of the suggested method in circadian rhythmicity analysis. However, we believe that the criteria used in this paper for rhythmicity quantification is suitable for addressing our questions, and overall, we are satisfied that rhythms with a q-value of >0.05 would also be classified by eye as being arrhythmic, and rhythms with a q-value Many other studies have used meta2d B.H q-values as a metric of rhythmicity: e.g. (https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-022-03565-1 , https://link.springer.com/content/pdf/10.1186%2Fs12915-022-01258-7 , https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8782462/pdf/pcbi.1009762.pdf )

      • lines 480-484 and intro: In the introduction, the authors write that expression levels of clock components are important for the function of the clock, and that this is one motivation for the current study where polyploidisation is expected to affect the expression levels of clock genes and their outputs. I wonder what answers or speculations this study provides in the end, or whether such answers / speculations should be made clearer. For example, do the authors think that the higher variability of periods in wheat could be a consequence of lower robustness (in addition to possible spatial differences that are mentioned) due to polyploidisation? Is anything known about the period of rhythms of close wheat relatives that did not undergo polyploidisation? Did you look at dampening over the time course in wheat vs. Arabidopsis?

      The point above is an interesting one, and we thank the reviewer for raising it. We agree that the high variability of periods in wheat may be a product of polyploidisation, as functional redundancy between homoeologs may allow a tolerance for less tightly regulated, non-dominantly expressed circadian transcripts. We have now added this hypothesis to our discussion: Ln536-550.

      In our comparative analysis of period distributions, we looked at periods of transcripts from a diploid relative of hexaploid wheat, Brachypodium distachyon. In Brachypodium, period lengths have around the same SD as in Arabidopsis but the mean period length is slightly longer (Supplementary table 2). We have now edited our results to make the relationship between wheat and Brachypodium clearer (ln 109-110).

      Minor comments:

      Introduction - lines 49: it is unclear what is meant by ppd-1 at this position of the sentence

      We agree this was unclear and have revised it to “notably the ppd-1 locus within TaPRR3/7” Ln 52

      • line 54/55: clarify that this refers to Arabidopsis thaliana

      Corrected.

      Results - line 69 and 76: cite references for these tools here (not only in the methods section)

      Corrected.

      • line 90-93: Why wouldn't the same thing happen on subsequent subjective evenings?

      Thank you for your comments. We have now combined our hypothesis for why wheat transcripts expressed at dusk have longer periods with the discussion about longer periods upon transfer to constant light. We think that the two processes are likely to be connected and have now placed them together in Ln 126-131.

      The behaviour of mean period lengths of wheat transcripts upon transfer to constant light was unexpected and we believe is quite interesting. One explanation is that the influence of the ongoing light zeitgeber when dusk was expected causes a delay in the expression of evening peaking genes which are delayed by the continuous light signal. Then, on subsequent evenings the influence of the diel dusk signal is ‘forgotten’ as the governance of the endogenous clock takes over. The very long period observed at 0-24h (28.61h) may be due to a phase shift rather than an intrinsic lengthening of period per se. Whether this trait is unique to wheat or can also be seen in other plant species is, to our knowledge, unknown.

      • line 118: what is your defined cutoff for significance of the Chi square test (p=0.03 not regarded significant?)

      The reviewer is completely right, we have now clarified this. Ln 145-149

      • figure 1h,i: In order for the reader to see whether A and D (Figure 1h) or A (figure 1i) are indeed arrhythmic, one would need to see plots with a normalisation as done in figure 1m for 1l.

      We have now removed the triad showing one rhythmic gene and two arhythmic genes (as Fig. 1h already illustrates this type of circadian imbalance) and replaced this with a side by side comparison of how imbalance in rhythmicity differs from imbalance in relative amplitude as suggested.

      • figure 1h-m (and others with circadian time course traces): could a measure of variation (e.g. SD, SEM, confidence interval) be plotted as a shaded region around the curves (unless they're so small that they are there but not visible)?

      We have now added error bars to these plots to show standard deviation between replicates, in Fig. 1 h, j, k and l. We could not think of an accurate way to display this information for the mean normalised data (Fig 1. i and m) so have not put error bars on these plots.

      • line 139 (also in 737 and 450): give reference to Ramirez-Gonzalez et al in the same style as the rest of the manuscript (number)

      Thank you for raising this, we believe we have corrected all in-text citations (both narrative and fully parenthetical form) for consistency with the APA format used by the majority of Review Commons affiliate journals.

      • Clustering (modules): What is the reason for choosing 9 clusters? Was this number optimised or chosen for other reasons?

      WGCNA uses an unsupervised clustering algorithm that works within the supplied parameters to determine the optimum number of clusters to explain the dataset, without prior specification of the number of clusters. We have amended the manuscript text to clarify this Ln237-239.

      • lines 280 - 284: The TaELF3-1D phenotype could be explained a bit better to the non-wheat specialist, for example by mentioning in the beginning of this set of sentences.

      Done (Ln 314-318).

      • The authors present an analysis of TF binding sites. Can they say something about binding sites in a less sophisticated manner, such as on some very well-known motifs in promoters like the evening element?

      We agree that this is a very interesting question, and one that we may investigate in more detail with our data in the future. In this paper, we performed a global analysis of wheat TFBS predicted from orthologous Arabidopsis TF targets. These targets have been experimentally validated in Arabidopsis using DAP-seq, but we have not validated that these binding sites exist in wheat promoters. We therefore took a tentative approach, and presented only enrichments at the superfamily level rather than talking about specific regulatory motifs.

      The evening element would fit most likely fit within the MYB or MYB-related TFBS superfamily, however the diversity of transcription factors in this family means that there is significant enrichment of these TFBS in multiple modules throughout the day (Supplementary Figure 11). In summary, a more in depth TFBS analysis of known circadian motifs is of great interest, but we feel would be a substantial work in its own right.

      • Figure 1h-l: If known or meaningful, it would be interesting to know the gene identities behind the triads shown, as in supplementary figure 5.

      These triads were selected as case studies to exemplify the ways in which we were defining imbalanced circadian triads. They have no particular relevance to the figure, but out of curiosity, these are the closest Arabidopsis orthologs for the triads displayed in Fig. 1:

      Triad 408 has highest identity to a hypothetical protein (AT4G26415).

      Triad 2454 is similar to AT3G07600, a heavy metal transport/detoxification superfamily protein

      Triad 13405 is similar to AT3G22360, encoding an ALTERNATIVE OXIDASE 1B, AOX1B

      Triad 10854 is similar to NSE4A, a δ-kleisin component of the SMC5/6 complex, possibly involved in synaptonemal complex formation (AT1G51130).

      Information about wheat gene names in each triad and their Arabidopsis orthologs can be viewed in Supplementary Table 11, so that readers can search for genes of particular interest to them.

      • Figure 4 and text: The illustration of starch metabolism is very helpful. However, I think the paper would benefit from giving a better reason for the selection of this specific set of processes, for example by relating these findings to functional differences in starch metabolism in the two species (in contrast to Arabidopsis, wheat stores little starch in leaves but uses fructans as main reserve carbohydrate)? Are there known differences in the dynamics of starch degradation during the night?

      The reviewer raises an interesting point, and we have now clarified in our results that the stated differences between starch regulation in Arabidopsis and wheat was part of the motivation behind studying this pathway. Starch is at the centre of plant primary metabolism as a carbon storage source and is arguably one of the most important features that breeders look for in regard to grain filling and yields. Additionally, it is of interest to circadian biologists as starch (as well as sucrose) have been shown to transiently cycle and to be regulated by the circadian clock. However, in wheat, carbon storage primarily uses sucrose rather than starch, and we have now added sucrose to Figure 5 to place it in this context. We think your suggestion has now improved our explanation for why we focused on starch in the manuscript, and we are grateful for your input (Ln 408-421).

      We also agree that the differences in the ways that Arbaidopsis and wheat utilise starch versus sucrose, and perhaps the role that fructans have in as a reserve carbohydrate and in protection against freezing in wheat may be one of the reasons we are seeing differences in circadian regulation of starch. We have now added this to our discussion (Ln 584-592).

      • Figure 4: triose-phosphates can be transported in and out of the chloroplast, as is illustrated in the figure. However, the illustration looks as though they are converted to hexose phosphates during the transport process. In order to be consistent with other transport processes of the figure (maltose and glucose), triose-phosphate should be repeated on the cytosolic side.

      We have now amended this (new Fig. 5). Thank you for your feedback.

      Methods - line 543: if I understand correctly that triplicates were collected and analysed for each time point, '18 samples' is mis-leading (18 time points would be more accurate).

      We agree this was badly worded. Changed Ln 615.

      Supplementary - Supplementary figure 3: x axis label very small and contains typo

      Now corrected. Also enlarged axis for Supplementary Figure 2.

      • Supplementary table 1: Romanowski et al 2020 (add year), or use ref. number citation style as in the rest of the manuscript

      Thank you for raising this, we have now hopefully corrected all in text citations (both narrative and fully parenthetical form) to be consistent with APA format used by the majority of Review commons affiliate journals.

      • Supplementary table 9, primary metabolism: does bold highlighting of Arabidopsis accession numbers have a meaning or is it accidental?

      We apologise that this was unclear. We have corrected this. Supplementary Table 10 now also has a “Read me” tab which explains that table.

      Reviewer #2 (Significance (Required)):

      I believe this is a precious, carefully generated and analysed dataset which many biologists will benefit from, beyond wheat or circadian specialists. The dataset expands the knowledge of circadian transcriptome regulation to an important crop and contributes a resource of which only a handful of others exist in other species. Many high impact papers on RNAseq include some follow-up on candidates, for example in Romanowski et al 2020, which is admittedly easier to do in Arabidopsis than wheat due to the availability of genetic resources.

      My expertise: Plant circadian clock (Arabidopsis), dataset analysis (but not specifically for RNAseq)

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

      This manuscript is based on the analysis of a single experiment consisting in transcriptomic profiling of one (hexaploid) wheat genotype along 3 days (samples taken every 4 hours). The experiment is performed in constant light conditions, allowing detection of transcripts controlled by the circadian clock. The bioinformatic analysis studies the dynamics of the different homoeologous transcript in the polyploid genome and compares cycling transcripts in wheat with what is known from Arabidopsis.

      The manuscript is well written, the methods are correct, the analysis performed is sufficiently extensive and the figures are clear. The manuscript finds interesting expression patterns among homeologous genes, and goes into detail on important differences in circadian regulation of relevant gene families between Arabidopsis and wheat. The work is purely descriptive and does not aim at associations with physiological phenotypes, but the bioinformatic analysis is very thorough and uncovers interesting examples.

      Only one caveat: For what I gather, there is no replication in the RNA-seq experiment, although the exact method does not appear in the text. From the Methods section: "tissue was sampled every 4h for 3 days (18 samples in total)" and "At each timepoint, we sampled the entire aerial tissue from 3 replicate plants". Whether these samples were pooled or not is not described. The "Data Availability" section links to 18 RNA-seq paired end libraries, which suggest that the replicates were pooled, although some type of barcoding might have been used. The text should mention if the replicates were pooled or not, and, if so, what was the method used for poling (tissue, RNA or libraries). Even in the case of no biological replication the manuscript brings interesting insights into wheat transcriptomics and circadian biology. The editor (or the rules of the journal) should decide if they accept articles with no "real" biological replication (I am sure we all understand by now the benefits and limitations of pooling biological replicates into a single RNA-seq library).

      There was replication within the RNA sequencing experiment, and we apologise that this was unclear from our manuscript. Each timepoint consisted of three independent biological replicates. We have now created a new “Experimental context” section in the results to explain this (Ln 74-82) and have clarified in the methods how our data was processed (Ln 609-615 and 636-638).

      We have now included an additional matrix with TPMs at the replicate level to assist readers in looking at specific genes of interest (Supplementary Table 12).

      Minor comments:

      The description of the experimental setup in the first sentence of the Results section is too brief. Could you please talk about for how long the experiment was running? At what intervals the samples were taken? What conditions were used?

      We apologise that this was unclear. We hope that the new Experimental Context section, added in response to comments from several reviewers, makes this much clearer, alongside the clarification in the methods (Ln 609-615 and 636-638).

      Line 280: "...due *to* an introgression..."

      Corrected. Ln 315

      The legend of Figure 3l says elf4 instead of elf3

      We thank the reviewer for noticing this mistake that we have now corrected.

      Line 306 "says Supplementary Note 7 instead of Supplementary Note 7

      We are not sure what is to be corrected here!

      Reviewer #3 (Significance (Required)):

      This works advances our knowledge on how genome wide expression levels are controlled by the circadian clock in polyploids. Although previous works had performed similar analyses in other polyploid plants, this is the first time this is done in an hexaploid. This work is a starting step to understand gene regulation in this important crop, and have interest for researchers working in fundamental and applied plant biology.

      Thank you for your positive comments and your feedback in improving this manuscript. We would like to clarify that to our knowledge, this work presents the first analysis of a circadian transcriptome in a polyploid crop. The work by Greenham et al, although undoubtably providing insight into circadian regulation of ancient paralogs, was performed in the diploid Brassica rapa.

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      Referee #3

      Evidence, reproducibility and clarity

      This manuscript is based on the analysis of a single experiment consisting in transcriptomic profiling of one (hexaploid) wheat genotype along 3 days (samples taken every 4 hours). The experiment is performed in constant light conditions, allowing detection of transcripts controlled by the circadian clock. The bioinformatic analysis studies the dynamics of the different homoeologous transcript in the polyploid genome and compares cycling transcripts in wheat with what is known from Arabidopsis.

      The manuscript is well written, the methods are correct, the analysis performed is sufficiently extensive and the figures are clear. The manuscript finds interesting expression patterns among homeologous genes, and goes into detail on important differences in circadian regulation of relevant gene families between Arabidopsis and wheat. The work is purely descriptive and does not aim at associations with physiological phenotypes, but the bioinformatic analysis is very thorough and uncovers interesting examples.

      Only one caveat: For what I gather, there is no replication in the RNA-seq experiment, although the exact method does not appear in the text. From the Methods section: "tissue was sampled every 4h for 3 days (18 samples in total)" and "At each timepoint, we sampled the entire aerial tissue from 3 replicate plants". Whether these samples were pooled or not is not described. The "Data Availability" section links to 18 RNA-seq paired end libraries, which suggest that the replicates were pooled, although some type of barcoding might have been used. The text should mention if the replicates were pooled or not, and, if so, what was the method used for poling (tissue, RNA or libraries). Even in the case of no biological replication the manuscript brings interesting insights into wheat transcriptomics and circadian biology. The editor (or the rules of the journal) should decide if they accept articles with no "real" biological replication (I am sure we all understand by now the benefits and limitations of pooling biological replicates into a single RNA-seq library).

      Minor comments:

      The description of the experimental setup in the first sentence of the Results section is too brief. Could you please talk about for how long the experiment was running? At what intervals the samples were taken? What conditions were used?

      Line 280: "...due to an introgression..."

      The legend of Figure 3l says elf4 instead of elf3

      Line 306 "says Supplementary Note 7 instead of Supplementary Note 7

      Significance

      This works advances our knowledge on how genome wide expression levels are controlled by the circadian clock in polyploids. Although previous works had performed similar analyses in other polyploid plants, this is the first time this is done in an hexaploid. This work is a starting step to understand gene regulation in this important crop, and have interest for researchers working in fundamental and applied plant biology.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      Rees et al. present an RNAseq time course of bread wheat. Its recent polyploidisation is one motivation for this study as gene expression dosage is known to be important for clock function in other plants. The time course covers 3 days at sampling intervals of 4h of 2-week old wheat plants (all aerial tissues), in triplicates. The subsequent analysis of the RNAseq data includes analysis of the generated data by itself (e.g. GO analysis, rhythmicity, period and phase analysis, rhythmicity of transcription factor families as well as TF binding sites) as well as thorough comparison with published datasets of other species (Arabidopsis, Brassica rapa, Brachypodium dystachion). One of the key findings is that the mean period length and the period spread are larger in wheat than in these other species). Circadian clock genes largely have similar dynamics in wheat compared to Arabidopsis. In addition, one focus is the analysis of the dynamics of three genes of one triad and imbalance / balance of such triads. To the surprise of the authors, circadian regulated and clock genes were not necessarily balanced. Silencing is one of their explanation for imbalance of circadian genes as arrhythmic genes of one triad are typically those with the lowest expression level. Finally, the authors point out more examples of rhythmic processes and genes (photoreceptors and signalling, auxin, carbon metabolism) and their commonalities and differences with Arabidopsis.

      Major comments

      • The key conclusions and the data are convincing
      • line 120 and figure 1: In my opinion, q > 0.05 is not a good definition of arrhythmicity as non-significant q-values can result from either noise in spite of rhythmicity or from arrhythmicity. A more statistically sound way to detect arrhythmicity could for example be two-one-side tests (for example in the R package 'equivalence', e.g. see usage for time courses by Noordally et al. 2018, https://www.biorxiv.org/content/10.1101/287862v1).
      • lines 480-484 and intro: In the introduction, the authors write that expression levels of clock components are important for the function of the clock, and that this is one motivation for the current study where polyploidisation is expected to affect the expression levels of clock genes and their outputs. I wonder what answers or speculations this study provides in the end, or whether such answers / speculations should be made clearer. For example, do the authors think that the higher variability of periods in wheat could be a consequence of lower robustness (in addition to possible spatial differences that are mentioned) due to polyploidisation? Is anything known about the period of rhythms of close wheat relatives that did not undergo polyploidisation? Did you look at dampening over the time course in wheat vs. Arabidopsis?

      Minor comments:

      Introduction

      • lines 49: it is unclear what is meant by ppd-1 at this position of the sentence
      • line 54/55: clarify that this refers to Arabidopsis thaliana

      Results

      • line 69 and 76: cite references for these tools here (not only in the methods section)
      • line 90-93: Why wouldn't the same thing happen on subsequent subjective evenings?
      • line 118: what is your defined cutoff for significance of the Chi square test (p=0.03 not regarded significant?)
      • figure 1h,i: In order for the reader to see whether A and D (Figure 1h) or A (figure 1i) are indeed arrhythmic, one would need to see plots with a normalisation as done in figure 1m for 1l.
      • figure 1h-m (and others with circadian time course traces): could a measure of variation (e.g. SD, SEM, confidence interval) be plotted as a shaded region around the curves (unless they're so small that they are there but not visible)?
      • line 139 (also in 737 and 450): give reference to Ramirez-Gonzalez et al in the same style as the rest of the manuscript (number)
      • Clustering (modules): What is the reason for choosing 9 clusters? Was this number optimised or chosen for other reasons?
      • lines 280 - 284: The TaELF3-1D phenotype could be explained a bit better to the non-wheat specialist, for example by mentioning in the beginning of this set of sentences.
      • The authors present an analysis of TF binding sites. Can they say something about binding sites in a less sophisticated manner, such as on some very well-known motifs in promoters like the evening element?
      • Figure 1h-l: If known or meaningful, it would be interesting to know the gene identities behind the triads shown, as in supplementary figure 5.
      • Figure 4 and text: The illustration of starch metabolism is very helpful. However, I think the paper would benefit from giving a better reason for the selection of this specific set of processes, for example by relating these findings to functional differences in starch metabolism in the two species (in contrast to Arabidopsis, wheat stores little starch in leaves but uses fructans as main reserve carbohydrate)? Are there known differences in the dynamics of starch degradation during the night?
      • Figure 4: triose-phosphates can be transported in and out of the chloroplast, as is illustrated in the figure. However, the illustration looks as though they are converted to hexose phosphates during the transport process. In order to be consistent with other transport processes of the figure (maltose and glucose), triose-phosphate should be repeated on the cytosolic side.

      Methods

      • line 543: if I understand correctly that triplicates were collected and analysed for each time point, '18 samples' is mis-leading (18 time points would be more accurate)

      Supplementary

      • Supplementary figure 3: x axis label very small and contains typo
      • Supplementary table 1: Romanowski et al 2020 (add year), or use ref. number citation style as in the rest of the manuscript
      • Supplementary table 9, primary metabolism: does bold highlighting of Arabidopsis accession numbers have a meaning or is it accidental?

      Significance

      I believe this is a precious, carefully generated and analysed dataset which many biologists will benefit from, beyond wheat or circadian specialists. The dataset expands the knowledge of circadian transcriptome regulation to an important crop and contributes a resource of which only a handful of others exist in other species. Many high impact papers on RNAseq include some follow-up on candidates, for example in Romanowski et al 2020, which is admittedly easier to do in Arabidopsis than wheat due to the availability of genetic resources.

      My expertise: Plant circadian clock (Arabidopsis), dataset analysis (but not specifically for RNAseq)

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      Referee #1

      Evidence, reproducibility and clarity

      In this study, Rees et al. perform an RNA-seq circadian time course experiment in the recently formed allopolyploid wheat. Through comparisons with other circadian transcriptomic datasets in other species it appears that the period of rhythmic genes is much more variable in wheat with a shift to longer periods compared to the other species examined. Interestingly, by analyzing circadian parameters among expressed genes, they find evidence that this newly formed allopolyploid already shows signs of divergence in circadian traits among homoeologs. A thorough comparison with circadian regulated genes in Arabidopsis reveals overlap in phasing of genes involved in certain biological processes such as photosynthesis and light signaling whereas genes involved in starch metabolism were found to have different levels of rhythmicity and phasing. This dataset will be a great resource for the community and enable new predictions about the influence of polyploidy on the circadian control of important crop improvement traits and the circadian regulation of gene expression.

      Major Comments

      1. The results section starts with very little explanation of the experiment. It would help to provide a little more detail at the start of the results to explain the context for the experiment and what was done, when samples were collected and for how long. For the methods section, it isn't until line 650 that it is clearly stated that the sampling started at ZT0. It would be better to put this in the plant materials and growth condition section.
      2. The low proportion of circadian regulated genes is likely due to the very low cutoff for calling a gene expressed, especially when there are three days of repeated timepoints. If a gene is expressed across the time course it should have values above TPM 0 for at least 3 time points in order for it to be expressed each day. I'd also be suspicious of a gene with a TPM value less than 0.5. Comparing these types of numbers is always challenging due to the various cutoffs used. Along those lines, why was a different filtering scheme used for Arabidopsis (line 657)?
      3. In reference to the shortening of the period every day, this should be interpreted with caution. Period estimate of a single cycle are not very reliable and the SD for each day is around 3h so it is difficult to draw any conclusions about changes in period each day. One option would be to only include genes with an SD less than 1h or alternatively to remove the discussion surrounding the comparison of period across the three days and focus on the period results for the full 24h-68h window shown in 1b. While 2 days is better it is still not ideal for calling period; however, your first day will still have a strong diurnal driven pattern that will likely skew your circadian period.
      4. Line 87-93: If the dusk cue is important for clock expression you would think this would be biased towards genes that peak later in the day or near dusk. This argument should be connected better to the period results discussed on lines 98-101.
      5. Lines 650-652 of the Methods mentions that one of the main interests was the response to transfer to L:L, but this isn't mentioned in the introduction and doesn't come up much in the Results section. Most of the expression comparisons are focused on the 24-68h window. It also isn't clearly explained why the first day in LL is still a diurnal cycle. This would be helpful for non-circadian readers who may wonder why the first day is not included in all the analyses.
      6. The phase comparisons shown in Figure suppl 4 are confusing. Suppl. Note 3 states that the period from the 24-68h data window was used to establish the bins but then the phase is shown for 3 different windows for each column? When calculating the phase for each of those 3 windows which period was used as the denominator in the phase calculation? Was it the period that matches the window used to calculate phase? What does the plot look like if phase is called on the same window used to calculate period (24-68)? What method was used to call phase in Suppl. Fig 4? As shown in Suppl Fig. 3 the method can influence the phase distributions. The methods suggest that the phase was determined with Metacycle but then FFT and MESA were used to verify. What does this mean verify, were they adjusted if FFT/MESA didn't agree?
      7. It is difficult to interpret the value of the period and phase comparisons shown in Fig. 1b, c, e and f after the preceding section about how variable the period and phase is across days. It is also surprising that the full 3 days were used to calculate the circadian statistics considering the first day is still under diurnal control. Do the ratios remain the same if the statistics are performed only on the 24h-68h window? For consistency with the rest of the paper and avoid confusion it would be best to have all circadian parameters measured using the same time window (24h-68h).
      8. Fig 1h-m. How were those genes chosen? It would help to see the SD of the replicates shown, since this is just showing one triad. It would be helpful to see a plot that represents the full set of triads rather than just one that looks best. If normalized to a standard phase they could be put on the same plot. For example, panel j is meant to show the 8h lag of subgenome D. If the data is normalized so that A and B are set to the same phase all the triads could be displayed with shaded SD bars to show the variation. Something like this would be a better representation of the data rather than showing just one example.
      9. It is surprising that there aren't more comparisons with the B. rapa dataset, especially when discussing the clock genes that show balanced or imbalanced expression. Are they similar in B. rapa and does it support your hypothesis that unbalance for certain genes are selected against?
      10. Figure 2 networks. Why were these specific modules selected? Is it actually appropriate to directly compare these modules? I do see that some of the comparisons have high correlations from panel a, but not all. For example, in panel b the W9 and A9 modules have a correlation value of 0.92, which seems appropriate. However, panel c (modules W3 and A2) have a correlation of 0.42, which seems far too low to make any sort of comparison meaningful. Further, as described in methods comments, using a cutHeight as low as 0.15 will likely lead to some number of genes in any given module that do not necessarily "share" a similar expression pattern. These genes could have a pattern that has very low correlation to their module eigengene and were only placed in that module because the pattern was "less similar" to other module eigengenes. The current expression plots in this figure follow a clear pattern, but I suspect this would be even more apparent if the genes within these modules had a higher correlation to the module eigengene. Perhaps the current genes in these modules could just be filtered to have a higher correlation score?
      11. Lines 327-334: I am not following the connection between 'response to abiotic stimulus' and the photoreceptor and light signaling proteins. At the start of this section (line 308) the authors say that the GO analysis was only done on rhythmically expressed genes but the reference to only one PHYA being rhythmic and yet multiple genes are shown in the plot in fig. S16. Does this mean that all the genes were shown and not just the rhythmic ones? This would explain why many of the PHY and CRY genes don't seem to have rhythms. This should be clarified better in the text or indicated in the plot which ones were called rhythmic. Since the first day following transfer is still the diel pattern from the entrainment condition, what does the PHY and CRY expression look like? Does it appear rhythmic under diel but lose rhythmicity in LL? It should be noted in the text that arrhythmicity in circadian conditions doesn't mean there isn't rhythmicity under diel conditions. This could be an additional explanation apart from the current one in the text that the regulation is at the level of protein stability/localization. Overall, this entire section is very long and entirely based on data shown in the supplemental material. I do appreciate having the individual gene plots that supplement figure 4 and would suggest either providing a main figure to highlight a small subset of genes or pathways in this section or shorten it and focus on the results shown in the main figures.
      12. Primary metabolism section: in terms of the supplemental figure, similar to the previous one I think it would declutter the plots if the genes that are not rhythmic were left out and simply indicate below the plot that they didn't meet the rhythmicity cutoff. This is another area where there is more discussion surrounding the supplemental figures than the main figure 4.
      13. For all gene expression figures there should be SD or SE shown either as bars or ribbons to represent the variation in replicates.
      14. It would be very helpful to include the code used to generate the networks and perform the cross-correlation of eigengenes across networks should be included in the Methods. This will also save you from responding to email requests!

      Minor Comments

      1. Figure 1, panel d: - The "unbalanced" triads that are depicted by the lighter shading; do these in fact have a different cutoff than the original rhythmic homoeologs? In the figure it says q<0.1 but I thought it was q<0.01.
      2. Hard to directly compare the GO term overlap in Figure 2f. Might be better to only show the results for the 4 pairs shown in b-e and put them side by side in the bubble plot.
      3. Line 314 -316 don't see supp tables 10, 11
      4. For the selection of B. rapa circadian paralogs with similar and differential expression patterns (starting line 714), the authors choose a hard cut off of 0.001 (differentially patterned) OR 0.1 (similarly patterned). What happens to the genes that are between these two cut offs or is this a typo. Since all the other cutoffs for rhythmicity was set at 0.01 it seems likely that this is a typo.
      5. Line 681. Should be supplemental Figure 6 not 9.
      6. References to most supplemental figures are not the correct number.
      7. Labels above the plots in Supp Fig5 do not match the legend.
      8. Suppl table 7 should be as a separate .csv file or similar to be able to see the full table.
      9. Line 723 should be B. rapa not B. napus.
      10. Figure 4. There is no explanation for what the black boxes represent in the figure legend.

      Significance

      This study provides new insight into the circadian regulation of the transcriptome in a new allopolyploid. It adds a valuable resource to a growing collection of circadian studies in important crops and will greatly improve our efforts to learn more about the circadian control of important crop improvement traits. The dataset will be of interest to other plant circadian biologists as well as the general plant biology community who focus on monocot crops. My expertise is more on the transcriptomic side and I do not have the expertise to evaluate the phylogenetic work presented in this study.

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      Reply to the reviewers

      Manuscript number: RC-2021-01219

      Corresponding author(s): Rajan, Akhila

      1) General Statements [optional]

      This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

      The goal of this study is to:

      • Define how prolonged exposure to a high-sugar diet (HSD) regime alters both the lipid landscape and feeding behavior.
      • Determine how changes in lipid classes within the adipose tissue regulates feeding behavior. Key findings:

      In this study, by taking an unbiased systems level and genetic approach, we reveal that phospholipid status of the fat tissue controls global satiety sensing.

      Impact of Key findings:

      By uncovering a critical role for adipose tissue phospholipid balance as a key regulator of organismal feeding, our work raises the possibility that the rate-limiting enzymes in phospholipid synthesis, including Pect, are potential targets for therapeutic interventions for obesity and feeding disorders.

      Peer review comments:

      This study has immensely benefited from the thoughtful peer-review of three reviewers. As per their recommendations, we have performed a major revision by performing additional experiments (see summary table below in next section) and strived to address the major concerns raised. Based on our reading, there were two major concerns that overlapped between all three reviewers raised. They are as follows:

      • Does the genetic disruption of Pect in fly fat body alter phospholipid levels? Two reviewers (#2 and #3) recommended that we perform lipidomic analyses on adult flies with adipose tissue specific knockdown of For the revised version, we have completed this lipidomic experiment, and present results as a new main Figure 6, Supplemental S7 and S9.
      • Is the dampened HSD induced hunger-driven feeding (HDF) behavior because of increased baseline feeding (#1 and #3)? In addition, reviewer #1, asked us whether HSD flies experience an energy-deficit? In other words, we were asked to uncouple whether what we observed was HSD-driven allostasis or indeed, as we had interpreted, that HSD dampened hunger-driven feeding response.

      Hence, they recommended that we:

      1. Re-analyze our hunger-driven feeding datasets and present non-normalized data (also requested by Reviewer #3) and show baseline feeding behavior on HSD. To address this, we have completed this analysis and present our results in Figure 1B-D and S1.
      2. Determine whether the HSD fed flies display an energy deficit on starvation. To this end, we performed an assayed starvation-induced fat mobilization on HSD, results for this are now presented on Figure 1E-G and S2. Conclusions after the revision:

      First, it is important to note here that the additional experiments have not caused a significant revision of the major conclusions of the original version of our study. In fact, we hope that the revised version provides clarity and further substantiation to our original arguments.

      • The lipidomics experiments on Pect fat-specific knock-down flies show that reducing Pect in fat-body causes a significant reduction in certain PE lipid species (PE 36.2 specifically- Figure 6B). This is consistent with a prior report on lipidomics of the Pect null allele by Tom Clandinin’s group (PMID: 30737130). Furthermore, we note that when Pect is knocked down in the fat body, there is a significant increase in two other classes of phospholipids LPC and LPE (Figure 6A). Together, this suggests that an imbalance in phospholipid composition in the absence of Pect activity in fat.
      • The starvation-induced fat mobilization experiments show that despite being fed a prolonged HSD, adult flies sense starvation and effectively mobilize fat stores, at a level comparable to Normal food (NF) fed adult flies, suggesting that even despite HSD exposure, adult flies experience an energy deficit on starvation.
      • In our non-normalized data, we find that the baseline feeding events are not significantly altered between HSD and NF-fed flies (Figure 1D). This suggests that the effects we observe are not due to an increase in the “denominator”, but a dampening of hunger-driven feeding on HSD. With regard to our original version, all three peer-reviewers found that the study was interesting, significant, important, and novel – Reviewer #1: “The work is potentially novel and interesting”; #2 : “I find the study to be potentially very important - the authors combine a longitudinal study that would be difficult in any other model with the powerful genetic tools available in the fly. The conclusions are mostly convincing”; #3: “This manuscript demonstrates how fat body Pect levels affect HSD induced changes in hunger-driven feeding response. I agree with all the reviewers points; potentially very interesting”. But had requested that we provide further substantiation and clarification.

      We sincerely hope that the peer-reviewers find that our revised version with additional new experimental datasets, improved data visualization, and the presentation of non-normalized raw data points, makes this study clear, compelling, and well-substantiated.

      • 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. *

      Below we summarize in Part A, the key experiments that were performed to address the major concerns. In Part B, we provide a point-point response to each reviewer with embedded datasets.

      Part a:

      We performed several new experiments, including:

      • To address the primary concern of Reviewer #1 regarding whether the HSD flies have a similar energy deficit to Normal food (NF) fed flies, we performed analysis of stored neutral fat Triacylglycerol (TAG) reserves and how HSD fed flies mobilized fat stores on starvation. We present these results in Figure 1E-G, S2. These results show that HSD-flies despite accumulating more TAG (S2), breakdown a similar amount of fat reserves as NF-fed flies on starvation at any time-point (Figure 1E-G). This suggests that HSD-fed flies do sense and respond to energy deficit.
      • To address concerns of reviewer #2 and #3 on whether Pect genetic manipulation affects specific phospholipid classes, we performed lipidomic analyses. The table below summarizes the new 3 new figures and 4 supplemental figures (blue text are all new figure numbers and figure panels) and three new Supplementary files as per reviewer’s request.

      Figure #

      Main point

      New datasets in revision

      Companion Supplement

      1

      HSD alters feeding behavior, but flies still breakdown TAG on starvation.

      TAG storage and breakdown over longitudinal HSD shows that HSD and NF fed flies show similar levels of TAG breakdown on starvation, despite consistently elevated TAG on HSD. This supports the idea that flies do sense starvation even on HSD, but there is a uncoupling of the feeding behavior after Day 14. Revised the data representation of Figure 1 to show non-normalized data over time. S1 and S2 companions are new in the revision. Panels 1D to 1E are new for the revision.

      S1- Raw data of feeding events plotted.

      S2 Elevated TAG at all time points.

      2

      HSD causes insulin resistance

      S3A added to show that insulin transcript levels remain the same in response to reviewer #3’s concerns.

      S3

      3

      Phospholipid concentration raw data from lipidomic on Day 7 and Day 14 HSD suggest that PC, PE levels are increased on Day 14 HSD.

      Figure 3 revamped to show new data visualization and non-normalized raw data to address Reviewer #2’s major concerns. S4A and S4B added. In addition Supplementary File 1 and 2 provided with raw lipidomics data as per reviewer #2’s request.

      S4.

      S4A- non normalized raw data of all other lipid classes on HSD.

      S4B- fatty acid species data on Day 14 added as per request of rev.#2.

      4

      HSD regulate Apo-I levels in the IPCs and phenocopies Pect KD.

      Added Figure 4A to show that HSD phenocopies Pect-KD in terms of delivery to brain

      S5 showing the validation of the Apo-I antibody.

      S6 validation of Pect KD and over-expression and Pect mRNA levels dysregulation on HSD.

      5

      Pect RNAi is insulin resistant

      N/A

      N/A

      6

      Pect knockdown shows significant increase in LPC and LPE, and a non-significant reduction in PC, PE levels. Specifically, the PE lipid class PE36.2 is downregulated.

      Fig 6, S7, S9 are completely new based on reviewer #2 and #3 requests. In addition Supplementary File 3 provided with raw lipidomics data as per reviewer #2’s request

      S7, S8, S9#.

      S7- new Pect KD other classes

      S8- new PE classes for day 14 and Pect associated classes.

      S9- Pect OE lipidomics

      7

      Pisd and Pect activity in adipocytes are required for hunger-driven feeding behavior in normal diets

      Pisd RNAi data was moved from supplement to main figure.

      N/A

      Note on revised text: We have revised text not only in the results section, but also as per reviewer #2’s recommendation, we have revamped our introduction and discussion as well. Since the manuscript has been significantly revised to include a main figure 6, fully altered Figure 1 and 3, multiple new supplemental figures, the changes in text are extensive. Hence, they are unmarked in the main text. Nonetheless, we hope that the reviewers will be able to evaluate these changes, as we have provided the specific locations in text and embed key figures in the point-point response below.

      __Part B: __Point-Point responses to reviewer comments.

      Reviewer #1 comments in Blue, author response in black.

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

      In this manuscript, Kelly et al. show that the difference between the feeding behavior of fed and starved flies (hunger-driven feeding; HDF) is absent in animals fed a high-sugar diet (HSD) for two weeks or more. The disappearance of HDF with HSD coincides with changes in phospholipid profiles caused by HSD. Furthermore, RNAi-mediated downregulation of Pect in the fat body-a key enzyme in the PE biosynthesis pathway-phenocopies physiological effects of HSD. Moreover, downregulation or overexpression in the fat body abolishes or induces HDF, respectively, abolishes or induces HDF, respectively, independent of HSD treatment.

      Overall, the manuscript is well-written and the phenotypes are clear. However, I have major concerns regarding the authors' interpretation of the data and their conclusion. Most importantly, while it is clear that the authors' high-sugar dietary treatment affects feeding behavior and physiology, I am not convinced that the changes can be considered "hunger-driven"-which is central to the main point of the manuscript. Therefore, it is my recommendation that the authors substantially revise the manuscript by either showing additional/re-analyzed data that rule out alternative hypotheses, or rewriting the manuscript keeping alternative interpretations in mind.

      We are thankful to this reviewer for their thoughtful critique, and constructive and specific suggestions on how we can redress these concerns. We have taken on board the concerns of this reviewer regarding our interpretation of whether the changes in feeding behavior can be considered hunger-driven or not. Based on their advice, we have made significant changes by addressing: i) does HSD increased baseline feeding- we now show non-normalized raw data and data supports conclusion that baseline feeding is not higher; ii) whether HSD- fed flies can sense an energy deficit at levels similar to NF fed flies- we show that HSD flies sense energy deficit. We have provided detailed response below, and we hope the reviewer finds the additional datasets and re-analyzed data are consistent with the interpretation that prolonged HSD dampens starvation induced feeding. In addition to this key concern this reviewer has made a many other salient points that we have addressed with additional data or by clarifying the text.

      Major comments: 1) The data do not sufficiently show that the long-term HSD regime disrupts "hunger-sensing." The manuscript should address alternative hypotheses by showing raw instead of normalized data, rewriting the manuscript with a new central conclusion, or running additional experiments that actually show a defect in hunger-driven response. a. The main results that the authors rely on for the argument is that the ratio of feeding events that the starved and non-starved flies eat is different between the groups fed normal or HSD. However, because the authors only show normalized data (normalized to non-starved flies; Fig. 1), it is difficult to tell whether the change is due to a chronically increased feeding in non-starved HSD flies-maybe in perpetual hunger-like allostasis-or dampened starvation response. Indeed, the data shown in Fig S1 show that flies fed HSD for as short as 5 days show more frequent feeding events compared to age-matched controls fed normal food. It is possible that because the HSD-fed flies eat more than NF-fed flies, even without being starved, the ratio of starved/non-starved feeding is lower in the HSD-fed group-due to changes in the denominator, rather than the numerator.

      We have taken onboard this concern regarding presenting only normalized data, and that clouded the interpretation and left open other possibilities. In the completely revised figure 1 and S1. We now show non-normalized data, as a function of time. First we note that HSD-fed flies, do not show higher baseline feeding that NF fed flies, except on Day 10 of HSD, when there is a modest but significant elevation (Figure 1D).

      Nonetheless, on Day 10 HSD, flies still display increased hunger-driven feeding HDF (Figure 1C), it is only after Day 14 HSD that HSD dampens the starvation induced feeding.

      1. It is also possible that the HSD-fed flies are simply not in as big an energy deficit physiologically, due to the increased fat deposits they've accumulated (as the authors show later in the manuscript). It may take longer for the fat HSD flies to reach substantial energy deficiency than the NF flies, but they still may eventually be able to appropriately respond to hunger, just like NF flies. In such case, it would be a misnomer to call this behavioral change a 'defect in hunger-driven feeding behavior.' Maybe an experiment with a dose-response curve of "hunger driven feeding response" as a function of duration of starvation would help? Prompted by this reviewers question, we asked whether HSD fed flies, that have a higher baseline neutral fat store (Triacylglycerol-TAG) level, and if HSD-fed flies can sense energy deficit. For this, we revisited the longitudinal assays for neutral fat triacylglycerol (TAG) storage that our lab had generated, along with the HSD-HDF studies. We now present this evidence as Figure 1E-1G and Figure S2. Overall, our experiments point to the idea that adult flies fed HSD, are able to sense and mobilize TAG stores effectively throughout the 28-day time point that we analysed.

      First as shown in Figure S2, flies fed HSD display an increase in TAG levels. But it is to be noted that while TAG stores increase, the increase is not linear with time. This suggests that adult flies exposed to HSD store excess energy as TAG, but the increased TAG stores stay within a certain range despite the length of HSD exposure. This suggests that adult flies on HSD still display TAG homeostasis.

      Next, to directly address the reviewers point about HSD fed flies not sensing an energy deficit, we subject HSD-fed flies to an overnight starvation, same regime as used in the overnight feeding experiments, and asked whether they mobilize TAG. We noted that flies exposed to HSD breakdown TAG throughout the 28-day exposure at statistically significant levels for Day 3- Day 28, except on 14 and 21 days (Figure 1F). While there is TAG mobilization on Day 14 and 21, the difference is not statistically significant. Nonetheless, we note the same levels TAG breakdown for normal lab food (NF) fed flies on Day 14 and 21 (Figure 1E). Overall, HSD fed flies sense and display energy deficit, as measured by TAG store mobilization, throughout the 28 days of HSD exposure, at levels comparable to NF-fed flies (Figure 1G).

      Taken together, these results suggest that while HSD-fed flies experience an energy deficit on starvation, at levels comparable to NF-fed flies, throughout the 28-day time point assayed. But, their starvation driven feeding-response is dampened by Day 14 and by Day 28, the HSD-fed flies display more feeding events than HSD starved flies. These results are consistent with the interpretation that in HSD-fed flies the starvation-induced feeding behavior becomes desynchronized from the starvation induced TAG-mobilization, suggesting that there is an absence of hunger-driven feeding.

      2) How can you be sure that lower Dilp5 immunofluorescence is indicative of increased Dilp5 secretion? Wouldn't decreased production of dilp5 also have the same results?

      It has been shown previously in HSD fed larvae are hyperinsulinemic, i.e., they have 55% increase in circulating Dilp2 ( PMID: 22567167). Additionally, we have shown that ectopic activation of the insulin-producing neurons by expressing TRPA1, an ion channel that activates neurons, reduces Dilp5 accumulation without a change in Dilp5 mRNA levels (PMID: 32976758), suggesting that reduced Dilp5 accumulation, without alterations to mRNA levels is a proxy for increased secretion. Now, in response to this concern, in the revised manuscript, we have added qPCR data of Dilp2 and 5 (Figure S3A), which show no difference in expression levels after 14 days on HSD. Therefore, there is no dip in Dilp5 mRNA production. Given that Dilp2 and Dilp5 mRNA levels remain the same, but we see reduced Dilp5 accumulation, we interpret this to mean that Dilp5 secretion is increased.

      1. Also, the authors should state in the main text that it is Dilp5, not just any Dilp. Thanks for this suggestion and we have fixed this and referred to Dilp5 specifically throughout the text in the results section.

      3) Data presentation: a. Sometimes the data are normalized to NF (Fig 4B-C), sometimes not (ex. Fig 4A, S4C). Unless there is a specific rationale for the data transformation, it would be more appropriate to show untransformed data (ex. Fig 4A, S4C), especially as the authors use two-way ANOVA to determine significance. Only showing the differences implies comparison against a hypothetical mean (i.e. μ0=0), not between two group means.

      We thank the reviewers for bringing this issue to our attention. We updated all the figures to show untransformed data in the revised manuscript.

      1. Some figures show both individual data points and summary statistics (mean, SD, ... ex. Fig 2A)-which I believe is ideal-but some show only one or the other (ex. Fig 2B, no summary statistics; Fig. 3, no data points. The manuscript would read more convincing if data visualization is consistent across figures. We thank the reviewers for their feedback. We have made changes to all the figures in the revised manuscript to improve visual consistency.

      Minor comments: 1) High sugar diet: what is the actual sugar concentration in the NF v. HSD diets? The authors write that the HSD diet contains "30% more sugar" than the NF, but providing the final sugar concentrations-sucrose or others-would be informative for other scientists studying the effect of high sugar diets.

      We thank the reviewer for their suggestion and now we have updated the methods to include this sentence. After 7 days, flies were either maintained on normal diet or moved to a high sugar diet (HSD), composed of the same composition as normal diet but with an additional 300g of sucrose per liter”.

      1. Additionally, the definition of HSD is inconsistent. Main text (Page 5, line 17) states that their HSD is "60% more sugar than normal media," whereas the figure legend (Fig 1) and the Methods state that the HSD contains "30% more sugar." We apologize for this egregious typo in the figure legend! We have now fixed this to say 30% HSD. Only 30% HSD was used throughout this study.

      2) Starvation medium: please provide justification for why the authors used 1% sucrose/agar for starvation medium, instead of plain agar/water that most labs use. At least clarify and provide a reference for the claim that the 1% sucrose/agar "is a minimal food media to elicit a starvation response."

      We are very grateful for this reviewer identifying this this methods description error and bring it to our attention. We used 0% sucrose agar for overnight starvation in this study as most labs do. The error occurred because we were using another manuscript from the lab to help draft the methods section (PMID: 29017032). In that study, where we assayed the effect of chronic starvation our lab used: “1% sucrose agar for 5 days at 25C”. However, in this current study, because we are testing acute effects of overnight starvation, we are using 0% sucrose agar.

      3) Pect mRNA level is higher with HSD. This is surprising because not only, as authors mention, is increased PC32.2 with HSD suggests lower Pect activity, but also because Pect RNAi phenocopies long-term HSD in HDF behavior, lipid morphology, FOXO accumulation in fat body. The authors speculate that the data "likely shown an upregulation in an attempt to mediate the Pect dysregulation occurring at the protein level." If that were true, a western blot may be informative. Zhao and Wang (2020, PLoS Genetics) generated a Pect antibody that seems compatible with western blot applications. That being said, I don't think such data is critical for the manuscript. I mention this simply as a suggestion for the authors. a. page 8, line 22-23, did you mean to write "Given how PC32.2 is elevated after 14 days of exposure to HSD, we assumed that Pect levels would be low for flies under HSD," not "high?" Otherwise the subsequent 2 sentences don't make sense.

      We agree that the most confusing aspect of the study was that Pect mRNA levels being very high on Day 14 HSD, but nonetheless the effects of Pect-KD phenocopied HSD. To resolve this, we have now performed lipidomic analyses on whole adult flies, when Pect is knocked-down (KD) by RNAi in the fat tissue. We now present a new dataset in Figure 6. Two striking changes occur. They are:

      1. Pect-KD shows increase in the phospholipid classes LPC and LPE (Figure 6A). In contrast, LPE is significantly downregulated on HSD Day 14 (Figure 3).
      2. Pect-KD shows a significant reduction in specific class of PE 36.2 (Figure 6B). Our data regarding increase in PE 36.2 agree with a previous lipidomic analyses of Pect mutant retina (PMID: 30737130). In contrast, PE 36.2 trends upwards on 14 day HSD (Figure S7C) though not significantly. On 14-day HSD consistent with extreme upregulation of Pect mRNA fed flies (Figure S6A; Pect mRNA 200-250 fold), PE trends upwards on 14-day HSD (Figure 3) and PE 36.2 trends higher (Figure S7C). We note that on the surface of it PE and LPE per se are contrasting between 14-day HSD lipidome and fat-specifc Pect-KD. But there is a significant commonality that under both states there is an imbalance of phospholipids classes PE and LPE. Hence, we propose that maintaining the compositional balance of phospholipid classes PE and LPE is critical to hunger-driven feeding and insulin sensitivity. Hence, either increase or decrease, of these key phospholipid species, may lead to abnormal hunger-driven feeding.

      We agree that a western blot would be informative as well, but we were unable to obtain the reagent from Dr. Wang’s group, precluding us from performing this request. See email snapshot.

      To ensure that we appropriately discuss and clarify this issue, we have now included a section in the discussion - Page 14 Lines 26-34- under the subtitle “The implications of relationship between Pect levels and HSD”. We have pasted an excerpt from that subsection below for this reviewers assessment.

      Also, we note that over-expression of Pect cDNA in the fat-body does not alter phospholipid balance (Figure S9) and indeed improves HDF on HSD (Figure 7B). While this may appear inconsistent, it is critical to note that over-expression of Pect cDNA using UAS/Gal4 only increases Pect mRNA expression by 7-fold (Figure S6A), whereas HSD causes its upregulation by 250-fold (Figure S6B). Hence, we speculate that an increased ‘basal’ level of Pect such as by that provided by a cDNA over-expression in fat, may be protective to the negative effects of HSD (Figure 7B) without affecting overall phospholipid levels (Figure S9) , but extreme upregulation Pect on HSD affects the PE and LPE balance (Figure 3).”

      Reviewer #1 (Significance (Required)):

      The work is potentially novel and interesting, but at this stage it's difficult to interpret what the phenotype signifies. Although the manuscript could be revised simply by modifying the text, experimentally addressing the concerns would significantly improve the work.

      In sum, we hope we have addressed the key concern for Reviewer #1 as to whether the behavior we report here is indeed a dampening of starvation-induced feeding, or an effect of increase in baseline feeding. We hope that by reviewing our non-normalized data, they can appreciate that it is the former. Also, we hope that Reviewer #1 appreciates that we have strived to address the concerns by additional experiments, to clarify our findings and improve the impact of the work.

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

      This intriguing manuscript by Kelly and colleagues uses the fruit fly Drosophila melanogaster as a model to understand how diet-induced obesity alters the feeding response over time. In particular, the authors findings indicate that chronic exposure to a high-sugar diet significantly alters the starvation-induced feeding response. These behavioral studies are complemented by a lipidomics approach that reveals how a chronic high sugar affects many lipid species, including phospholipids. The authors then pursue mechanistic studies that indicate phospholipid metabolism within the fat body appears to remotely affect insulin secretion from the insulin producing cells. Moreover, the changes in phospholipid abundance are associated with changes in insulin-signaling, including increased insulin secretion from the IPCs and elevated levels of FOXO within the nucleus.

      I find the study to be potentially very important - the authors combine a longitudinal study that would be difficult in any other model with the powerful genetic tools available in the fly. The conclusions are mostly convincing, but a few follow-up experiments are required:

      We are grateful for the reviewers constructive, detail-oriented, and balanced feedback, and their recognition of the value of this study. Now, we have performed additional experiments to address the key concerns raised by all reviewers. We hope that on reading the revised version of our study, that the reviewer continues to feel positive about the message of this study and its potential impact.

      1. The key conclusions from the manuscript assume that manipulation of Pect expression levels alters phosphatidylethanolamine (PE) levels. However, the authors make no attempt to verify that the genetic experiments described herein actually affect PE levels. At a minimum, changes in PE levels should be verified for the Pect knockdown and overexpression lines. Similarly, there is no evidence that manipulation of either EAS or Pcyt2 induces the expected metabolic effects. I'm not asking that the longitudinal feeding experiments be repeated, simply that the authors measure the relevant lipid species, preferably with a targeted LC-MS approach.

      Prompted by this reviewer, we performed targeted LC-MS on whole adult flies, on normal diet, to assess lipid levels for fat-specific Pect-KD and overexpression. We decided to focus on Pect, as its knock-down even on normal diet causes a dampened hunger-driven feeding behavior (Figure 7A) and phenocopied a 14-day HSD feeding phenotype.

      We now present a new dataset in Figure 6. Two striking changes occur:

      They are:

      Pect-KD shows a significant reduction in specific class of PE 36.2 (Figure 6B). Our data regarding decrease in PE 36.2 agree with a previous lipidomic analyses of Pect mutant retina (PMID: 30737130). It is to be noted that though overall levels of all PE species trend downwards, like the Clandinin lab study on Pect (PMID: 30737130), we did not find a significant change in the overall PC and PE levels.

      • Pect-KD shows increase in the phospholipid classes LPC and LPE (Figure 6A). In contrast, LPE is significantly downregulated on HSD Day 14 (Figure 3). On 14-day HSD consistent with extreme upregulation of Pect mRNA fed flies (Figure S6A; Pect mRNA 200-250 fold), PE trends upwards on 14-day HSD (Figure 3) and PE 36.2 trends higher (Figure S7C). We note that on the surface of it PE and LPE per se are contrasting between 14-day HSD lipidome and fat-specifc Pect-KD. But there is a significant commonality that under both states there is an imbalance of phospholipids classes PE and LPE. Hence, we propose that maintaining the compositional balance of phospholipid classes PE and LPE is critical to hunger-driven feeding and insulin sensitivity. Hence, either increase or decrease, of these key phospholipid species, may lead to abnormal hunger-driven feeding.

      Finally, fat-specific Pect-OE did not cause significant changes to lipid species (Figure S9). This could either be due to the fact that in fat-specific Pect-OE flies under normal food and that we were assaying whole body lipid levels and not fat-specific lipid changes. But to counter that, even a 60% reduction in Pect mRNA levels (Figure S6A), was sufficient to produce an effect on whole body phospholipid balance (Figure 6). Hence, we speculate that by maintaining a basally higher (7-fold higher Pect mRNA level Figure S6A), might allow 14-day HSD-fed flies to buffer the negative effects of HSD and we predict that it might take longer to disrupt the phospholipid balance and HDF response.

      We have now included a section in the discussion - Page 14 Lines 26-34- under the subtitle “The implications of relationship between Pect levels and HSD”. We have pasted an excerpt from that subsection below for this reviewers assessment.

      Also, we note that over-expression of Pect cDNA in the fat-body does not alter phospholipid balance (Figure S9) and indeed improves HDF on HSD (Figure 7B). While this may appear inconsistent, it is critical to note that over-expression of Pect cDNA using UAS/Gal4 only increases Pect mRNA expression by 7-fold (Figure S6A), whereas HSD causes its upregulation by 250-fold (Figure S6B). Hence, we speculate that an increased ‘basal’ level of Pect such as by that provided by a cDNA over-expression in fat, may be protective to the negative effects of HSD (Figure 7B) without affecting overall phospholipid levels (Figure S9), but extreme upregulation Pect on HSD affects the PE and LPE balance (Figure 3).”

      A central hypothesis in the study is that the HSD over a period of 14 days results in insulin resistant and that these changes are leading to changes in hunger dependent feeding. I would encourage the authors to determine if Foxo mutants are resistant to these HSD-induced effects on HFD.

      We thank the reviewers for this suggestion. However, given that dFOXO nuclear localization rather than expression levels regulate insulin sensitivity, we feel that disrupting dFOXO levels via mutation or knockdown will produce a plethora of indirect effects including developmental abnormalities (PMID: 24778227, PMID: 16179433, PMID: 29180716, PMID: 12893776). Our data suggest that chronic HSD treatment and Pect affect insulin sensitivity in fat tissue. However, we feel that investigating whether insulin sensitivity/FOXO signaling in fat tissue regulates feeding behavior is outside the scope of our work.

      1. In lines 25-30, the authors draw the conclusion that an increase in unsaturated fatty acid species is associated with the HSD and that these changes results in a more fluid lipid environment. While I agree with the model, the manuscript contains no evidence to support such a model. Either test the hypothesis or move the last line of the section to the discussion.

      We thank the reviewer for this important and insightful comment. We agree that the data we presented and discussed in the original version is at the moment speculative. Addressing the hypothesis that increase in unsaturated fatty acid species result in a more fluid lipid environment will require us to build tools and expertise. Hence, this hypothesis is better suited for exploration in a future study. Given this, we have moved this out of the results section into the Discussion section titled “HSD and fat-specific PECT-KD causes changes to phospholipid profile” (See excerpt below from page 13, lines 24-35).

      In addition to changes in phospholipid classes, we found that HSD caused an increase in the concentration of PE and PC species with double bonds (Figure S4C and S4D). Double bonds create kinks in the lipid bilayer, leading to increased lipid membrane fluidity which impacts vesicle budding, endocytosis, and molecular transport14,92. Hence it is possible that a mechanism by which HSD induces changes to signaling is by altering the membrane biophysical properties, such as by increased fluidity, which would have a significant impact on numerous biological processes including synaptic firing and inter-organ vesicle transport.”

      Also, as per the reviewer’s guidance, given that we are speculating here, we have also shifted this dataset from Main figure 4 to supplement S4C and S4D.

      In addition, lines 25-30 state that FFAs are increased after 14 days of a HSD. Figure 3A shows the exact opposite - FFAs are significantly decreased in 14 day fed animals despite being elevated in the 7 day fed animals. This is an interesting result that warrants discussion. Moreover, I would encourage to examine the lipidomic data more carefully to ensure that the text accurately portrays the lipid profiles.

      We apologize for misstating that FFAs are decreased on 14-day HSD in the lines 25-30. It was an error and we have corrected this. We agree with the reviewer that the reduction of FFA on Day 14-HSD is an intriguing and unexpected observation that needs to be emphasized and further discussed. To this end, we have added figure S4B, wherein we have provided the difference in FFA concentration (by species) after days 7 and 14.

      Furthermore, we have discussed what the potential meaning of reduced FFA at Day 14 implies in page 12, lines 19-27 of the Discussion section titled “HSD and fat-specific PECT-KD causes changes to phospholipid profile”. We have stated the following-

      We speculate that this reduction in FFA maybe due to their involvement in TAG biogenesis (PMID: 13843753). We were interested to see if the decrease in FFA correlated to a particular lipid species, as PE and PC are made from DAGs with specific fatty acid chains. However, further analysis of FFAs at the species level did not reveal any distinct patterns. The majority of FFA chains decreased in HSD, including 12.0, 16.0, 16.1, 18.0, 18.1, and 18.2 (Figure S4B). This data was more suggestive of a global decrease in FFA, likely being converted to TAG and DAG, rather than a specific fatty acid chain being depleted.”

      The processed lipidomics data should also be included as supplementary data table so that they can be independently analyzed by the reader.

      We thank the reviewer for this suggestion. As per the reviewers request, we have included the raw data as an attachment in our supplementary material (Supplementary Files 1-3.), so that interested readers can use the datasets generated in this study for future work and further analysis.

      Beyond these experimental suggestions, the manuscript needs significant editing for clarity. While I won't provide a comprehensive list, the authors need to provide accurate descriptions and annotation of genotypes (including w[1118], which is written as W1118), typos, and formatting. I've listed a few examples below:

      1. Page 3, Line 1 and 2: "...have been shown to impact feeding behavior and metabolism that leads to..." This is an awkward and grammatically incorrect sentence.
      2. Page 3, Lines 7-32 is one very large paragraph but contains concepts that should be broken down over at least three paragraphs.
      3. Page 3, Line 25: A description of the reaction catalyzed by Pect would be helpful for a manuscript focused on Pecte activity.
      4. Page 4, Line 10: "previously characterized method of eliciting diet induced feeding behavior." As stated in the text, the method is previously described yet the manuscript characterizing the method isn't cited.
      5. Figure legend 3 contains a random assortment of capitalized lipid species. Also, the names of lipid species are inappropriately broken into multiple names. Please use correct nomenclature throughout the manuscript.

      The list above is nowhere near comprehensive. The manuscript requires significant editing.

      We are grateful to the reviewer for drawing our attention to these errors. We have made significant edits to the revised manuscript to address the above-mentioned concerns, as well as made additional textual changes throughout and copyedited it. We hope that the reviewer will find the manuscript reads better and the clarity and preciseness is significantly improved.

      Reviewer #2 (Significance (Required)):

      I find the study to be potentially very important - the authors combine a longitudinal study that would be difficult in any other model with the powerful genetic tools available in the fly. The findings will significantly advance our understanding of how lipid metabolism links dietary nutrition with feeding behavior.

      Once again, we are grateful for this reviewer’s thoughtful critique and encouraging words regarding our work and its potential impact.

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

      Summary: This manuscript uses Drosophila to investigate how diet-induced obesity and the changes in the lipid metabolism of the fat boy modulate hunger-driven feeding (HDF) response. The authors first demonstrate that chronic exposure (14 days) of high sugar diet (HSD) suppresses HDF response. Through lipidome analysis, the authors identify a specific class of lipids to be elevated upon chronic HSD feeding. This coincided with the changes in expression of Pect, an enzyme that regulates the biosynthesis of these lipids. Modulating the expression of Pect specifically in the fat body affected HDF response.

      We thank this reviewer for their rigorous and thoughtful critique and for identifying a key issue with our original study pertaining to a gap in how Pect mRNA levels on 14-day HSD are elevated but the Pect-KD phenocopies the HDF. Now by performing whole-body adult fly lipidomic on fat-specific Pect-KD we have resolved this issue and provided clarity on role of Pect in maintaining phospholipid homeostasis and thus subsequently impacts hunger-driven feeding. We hope the reviewer finds that the revised manuscript provides further clarity to the functional link between Pect’s role in fat-body and hunger-driven feeding.

      Major comments: The author claim that the HDF response in HSD is distinct between early (5d, 7d) and chronic (day 14) HSD feeding. However, the data seem to indicate that HDF response is significantly decreased at all time points in HSD. For example, at day 5 HDF response was increased only 3-fold in HSD (Figure 1C) compared to around 50-fold increase in NF (Figure 1B). The scale of the Y-axis in Figure 1B and 1C is an order of magnitude different. Including the starved data (NFstv and HSDstv) in Figure S1, normalized to NF fed group, would better visualize the overall trends. Related to this, having the source data for the actual number of feeding events would be useful (e.g., to see the baseline changes in feeding in different time points in Figure 1 and the effect of genetic manipulations in Figure 7).

      As per the reviewers request, we now have modified our graphs to show source data (Figure S1) and show the raw feeding events.

      Then in the non-normalized graphs we plot, over a longitudinal time course, baseline and hunger-driven feeding events (Figure 1B-D). We also show that HSD fed flies do not display increased baseline feeding (Figure 1D) suggesting that the effect we see on HDF are no clouded by increased baseline feeding.

      Yes, the reviewer makes an important point that HDF response on HSD fed flies is of a lower magnitude than NF fed flies. We think that is a biologically meaningful observation, as it suggests that flies have a remarkably fine-tuned ability to coordinate food-intake with nutrient store levels.

      ­­Now we have included a paragraph in the Discussion, Page 11 Lines 23-27, that say the following to ensure the readers appreciate this salient point raised by this reviewer.

      *It is to be noted that the HDF response of HSD-fed flies (Figure 1C, Days 3-10) is of lower order of magnitude than the NF-fed flies. This suggests that that in addition to sensing an energy deficit and mobilizing fat stores (Figure 1F, 1G, S1), HSD fed flies calibrate their starvation-induced feeding to compensate only for the lost amount of fat. Overall, this suggests that flies have a remarkably fine-tuned ability to coordinate food-intake with nutrient store levels. *

      The association between fat body Pect level and phospholipid levels is not clear. Day 14 of HSD feeding shows high expression of Pect in the fat body and elevated levels of PC32.0 and PC32.2. The authors assume the high expression of Pect in the fat body is due to the compensatory response, but there are no data indicating downregulation of Pect levels at the earlier time points of HSD feeding. A previous study demonstrated that Pect mutant flies have lower levels of PC32.0 but higher PC32.2 (PMID: 30737130).

      We agree that one puzzling aspect of the original version of this study was that Pect mRNA levels being very high on Day 14 HSD, but nonetheless the effects of Pect-KD phenocopied HSD. To resolve this, prompted by Reviewer #2 and #3 concerns, for this revised version we have now performed lipidomic analyses on whole adult flies, when Pect is knocked down (KD) by RNAi in the fat tissue. We now present a new dataset in Figure 6. Two striking changes occu. They are:

      1. Pect-KD shows increase in the phospholipid classes LPC and LPE (Figure 6A). In contrast, LPE is significantly downregulated on HSD Day 14 (Figure 3).
      2. Pect-KD shows a significant reduction in specific class of PE 36.2 (Figure 6B). Our data regarding increase in PE 36.2 agree with a previous lipidomic analyses of Pect mutant retina (PMID: 30737130). In contrast, PE 36.2 trends upwards on 14 day HSD (Figure S7C) though not significantly. On 14-day HSD consistent with extreme upregulation of Pect mRNA fed flies (Figure S6A; Pect mRNA 200-250 fold), PE trends upwards on 14-day HSD (Figure 3) and PE 36.2 trends higher (Figure S7C). We note that on the surface of it PE and LPE per se are contrasting between 14-day HSD lipidome and fat-specifc Pect-KD. But there is a significant commonality that under both states there is an imbalance of phospholipids classes PE and LPE. Hence, we propose that maintaining the compositional balance of phospholipid classes PE and LPE is critical to hunger-driven feeding and insulin sensitivity. Hence, either increase or decrease, of these key phospholipid species, may lead to abnormal hunger-driven feeding.

      On day 14, HDF response was increased 70-fold in w1118 flies in NF (Figure 1B; w1118), but only 2.5-fold in lpp>LucRNAi control flies in NF (Figure 7A). This suggests that lpp-gal4 driver lines have a significant effect on HDF response. Using a different fat-body specific Gal4 line would be necessary to validate conclusions.

      Regards reduced HDF magnitude, in our experience using UAS-Gal4 reduces HDF response magnitude consistently and cannot be compared to w1118 which is more robust. To account for background differences, we use Uas-Gal4 with control RNAi. It clearly shows differences in HDF response on starvation, but Pect and Pisd RNAi does not (Figure 7A). Hence, given that this experiment internally controls for any changes in HDF response for UAS-Gal4>RNAi, we conclude that HDF response in disrupted in Pect and PISD KD (Figure 7).

      We only presented the Lpp-driver in our study, as this driver is the only fat-specific driver that has no leaky expression in other tissues, and is specific to fat as apolpp promoter used to generate this Gal4 line is only expressed in fat tissue (Eaton and colleagues, PMID: 22844248). Other widely used fat-specific drivers, including the pumpless-Gal4 (ppl-Gal4) driver has leaky expression in gut or other tissues (See Table 2 of this detailed study by Dr. Drummond- Barbosa https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7642949/). If the reviewer is aware of a fat-specific Gal4 line, other than Lpp-Gal4, which has a highly specific expression in the fat tissue without leaky expression in other tissues, then we are happy to take onboard the reviewer’s suggestion and try that fat-specific Gal4 that they suggest.

      HSD feeding promotes Pect expression (Figure S3C) and global changes in phospholipid levels (Figure 3, 4). Therefore, shouldn't Pect overexpression (not Pect RNAi) in a normal diet mimic HSD feeding state and promote loss of HDF response? Conversely shouldn't knockdown of Pect in HSD rescue loss of HDF response?

      We agree that a puzzling aspect is that Pect mRNA levels are significantly elevated in HSD Day-14, but Pect-KD showed displays the inappropriate HDF response. As we have described in our response to this reviewer on Page 19, we believe that Pect-KD and HSD disrupt PE and LPE balance overall but in different ways. Whereas Pect-OE using cDNA expression in fat body does not cause a significant change to any lipid class (Figure S9), and our results suggest that basally higher level of PECT is likely to be protective on HSD with respect to HDF(Figure 7B).

      To ensure that we appropriately discuss and clarify this issue, we have now included a section in the discussion - Page 14 Lines 26-33- under the subtitle “The implications of relationship between Pect levels and HSD”. We have pasted an excerpt from that subsection below for this reviewers assessment.

      Also, we note that over-expression of Pect cDNA in the fat-body does not alter phospholipid balance (Figure S9) and indeed improves HDF on HSD (Figure 7B). While this may appear inconsistent, it is critical to note that over-expression of Pect cDNA using UAS/Gal4 only increases Pect mRNA expression by 7-fold (Figure S6A), whereas HSD causes its upregulation by 250-fold (Figure S6B). Hence, we speculate that an increased ‘basal’ level of Pect such as by that provided by a cDNA over-expression in fat, may be protective to the negative effects of HSD (Figure 7B) without affecting overall phospholipid levels (Figure S9) , but extreme upregulation Pect on HSD affects the PE and LPE balance (Figure 3).”

      We would have liked to test Pect protein expression on HSD, but since we were unable to access antibodies for Pect published in a prior study (PMID: 33064773) from Dr. Wang’s lab (see Page 10-11, of response to Reviewer #1). Hence, we were unable to test how the proteins levels of Pect correlate with the 250-fold increase mRNA expression.

      In conclusion, we hope the reviewer appreciates that our results regarding Pect function are consistent with the main conclusion that achieving the right phospholipid balance between PE and LPE, is critical for an organism to display an appropriate HDF response.

      Minor comments: All graphs should plot individual data points and showed as box and whisker plot as much as possible.

      Thanks for this suggestion, we have added individual data points to the vast majority of figures in the paper. We have made exceptions to graphs such as seen in figure 1 and FigureS4B-D where we find individual data points add an unnecessary layer of complexity. We hope these changes provide additional clarity and strength to the claims made in this manuscript.

      Data for day 14 missing in Figure S4A and S4B.

      We have provided Day 14 for the PC composition and PE composition, due to changes in Figures, they are now S7A and S7B.

      Reviewer #3 (Significance (Required)):

      The interactions between diet-induced obesity, peripheral tissue homeostasis and feeding behavior is an interesting topic that can be addressed using Drosophila. This manuscript demonstrates how fat body Pect levels affect HSD induced changes in hunger-driven feeding response. However, at this point, the functional association between fat body Pect level, global phospholipid level, and loss of hunger-driven feeding response in chronic HSD feeding is not clear.

      We hope the revised data, and discussion of the paper, provides well-substantiated functional association on the importance of maintaining phospholipid balance, driven by Pect enzyme, as a critical regulator of hunger-driven feeding behavior. As stated in the revised discussion, the key take home message of our manuscript is that on prolonged HSD exposure PC, PE and LPE levels are dysregulated, the loss of phospholipid homeostasis coincided with a loss of hunger-driven feeding. Following this lead on phospholipid imbalance, we then uncovered a critical requirement for the activity of the rate-limiting PE enzyme PECT within the fat tissue in controlling hunger-driven feeding.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      This manuscript uses Drosophila to investigate how diet-induced obesity and the changes in the lipid metabolism of the fat boy modulate hunger-driven feeding (HDF) response. The authors first demonstrate that chronic exposure (14 days) of high sugar diet (HSD) suppresses HDF response. Through lipidome analysis, the authors identify a specific class of lipids to be elevated upon chronic HSD feeding. This coincided with the changes in expression of PECT, an enzyme that regulates the biosynthesis of these lipids. Modulating the expression of PECT specifically in the fat body affected HDF response.

      Major comments:

      The author claim that the HDF response in HSD is distinct between early (5d, 7d) and chronic (day 14) HSD feeding. However, the data seem to indicate that HDF response is significantly decreased at all time points in HSD. For example, at day 5 HDF response was increased only 3-fold in HSD (Figure 1C) compared to around 50-fold increase in NF (Figure 1B). The scale of the Y-axis in Figure 1B and 1C is an order of magnitude different. Including the starved data (NFstv and HSDstv) in Figure S1, normalized to NF fed group, would better visualize the overall trends. Related to this, having the source data for the actual number of feeding events would be useful (e.g., to see the baseline changes in feeding in different time points in Figure 1 and the effect of genetic manipulations in Figure 7).

      The association between fat body PECT level and phospholipid levels is not clear. Day 14 of HSD feeding shows high expression of pect in the fat body and elevated levels of PC32.0 and PC32.2. The authors assume the high expression of pect in the fat body is due to the compensatory response, but there are no data indicating downregulation of pect levels at the earlier time points of HSD feeding. A previous study demonstrated that pect mutant flies have lower levels of PC32.0 but higher PC32.2 (PMID: 30737130). To better understand the link the authors should knockdown/OE PECT specifically in the fat body and assess changes in phospholipids.

      On day 14, HDF response was increased 70-fold in w1118 flies in NF (Figure 1B; w1118), but only 2.5-fold in lpp>LucRNAi control flies in NF (Figure 7A). This suggests that lpp-gal4 driver lines have a significant effect on HDF response. Using a different fat-body specific Gal4 line would be necessary to validate conclusions.

      HSD feeding promotes PECT expression (Figure S3C) and global changes in phospholipid levels (Figure 3, 4). Therefore, shouldn't PECT overexpression (not PECT RNAi) in a normal diet mimic HSD feeding state and promote loss of HDF response? Conversely shouldn't knockdown of PECT in HSD rescue loss of HDF response?

      Minor comments:

      All graphs should plot individual data points and showed as box and whisker plot as much as possible. Data for day 14 missing in Figure S4A and S4B.

      Significance

      The interactions between diet-induced obesity, peripheral tissue homeostasis and feeding behavior is an interesting topic that can be addressed using Drosophila. This manuscript demonstrates how fat body PECT levels affect HSD induced changes in hunger-driven feeding response. However, at this point, the functional association between fat body PETC level, global phospholipid level, and loss of hunger-driven feeding response in chronic HSD feeding is not clear.

      Referees cross-commenting

      I agree with all the reviwers points; potentially very interesting, but requires a significant amount of work.

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      Referee #2

      Evidence, reproducibility and clarity

      This intriguing manuscript by Kelly and colleagues uses the fruit fly Drosophila melanogaster as a model to understand how diet-induced obesity alters the feeding response over time. In particular, the authors findings indicate that chronic exposure to a high-sugar diet significantly alters the starvation-induced feeding response. These behavioral studies are complemented by a lipidomics approach that reveals how a chronic high sugar affects many lipid species, including phospholipids. The authors then pursue mechanistic studies that indicate phospholipid metabolism within the fat body appears to remotely affect insulin secretion from the insulin producing cells. Moreover, the changes in phospholipid abundance are associated with changes in insulin-signaling, including increased insulin secretion from the IPCs and elevated levels of FOXO within the nucleus.

      I find the study to be potentially very important - the authors combine a longitudinal study that would be difficult in any other model with the powerful genetic tools available in the fly. The conclusions are mostly convincing, but a few follow-up experiments are required:

      1. The key conclusions from the manuscript assume that manipulation of PECT expression levels alters phosphatidylethanolamine (PE) levels. However, the authors make no attempt to verify that the genetic experiments described herein actually affect PE levels. At a minimum, changes in PE levels should be verified for the PECT knockdown and overexpression lines. Similarly, there is no evidence that manipulation of either EAS or Pcyt2 induces the expected metabolic effects. I'm not asking that the longitudinal feeding experiments be repeated, simply that the authors measure the relevant lipid species, preferably with a targeted LC-MS approach.
      2. A central hypothesis in the study is that the HSD over a period of 14 days results in insulin resistant and that these changes are leading to changes in hunger dependent feeding. I would encourage the authors to determine if Foxo mutants are resistant to these HSD-induced effects on HFD.
      3. In lines 25-30, the authors draw the conclusion that an increase in unsaturated fatty acid species is associated with the HSD and that these changes results in a more fluid lipid environment. While I agree with the model, the manuscript contains no evidence to support such a model. Either test the hypothesis or move the last line of the section to the discussion.

      In addition, lines 25-30 state that FFAs are increased after 14 days of a HSD. Figure 3A shows the exact opposite - FFAs are significantly decreased in 14 day fed animals despite being elevated in the 7 day fed animals. This is an interesting result that warrants discussion. Moreover, I would encourage to examine the lipidomic data more carefully to ensure that the text accurately portrays the lipid profiles.

      The processed lipidomics data should also be included as supplementary data table so that they can be independently analyzed by the reader.

      Beyond these experimental suggestions, the manuscript needs significant editing for clarity. While I won't provide a comprehensive list, the authors need to provide accurate descriptions and annotation of genotypes (including w[1118], which is written as W1118), typos, and formatting. I've listed a few examples below:

      1. Page 3, Line 1 and 2: "...have been shown to impact feeding behavior and metabolism that leads to..." This is an awkward and grammatically incorrect sentence.
      2. Page 3, Lines 7-32 is one very large paragraph but contains concepts that should be broken down over at least three paragraphs.
      3. Page 3, Line 25: A description of the reaction catalyzed by PECT would be helpful for a manuscript focused on PECT activity.
      4. Page 4, Line 10: "previously characterized method of eliciting diet induced feeding behavior." As stated in the text, the method is previously described yet the manuscript characterizing the method isn't cited.
      5. Figure legend 3 contains a random assortment of capitalized lipid species. Also, the names of lipid species are inappropriately broken into multiple names. Please use correct nomenclature throughout the manuscript.

      The list above is nowhere near comprehensive. The manuscript requires significant editing.

      Significance

      I find the study to be potentially very important - the authors combine a longitudinal study that would be difficult in any other model with the powerful genetic tools available in the fly. The findings will significantly advance our understanding of how lipid metabolism links dietary nutrition with feeding behavior.

      Referees cross-commenting

      I agree. We all think the manuscript is potentially interesting and important, but requires further experimentation. I agree with all concerns raised by the other reviewers. A revision would likely represent a significant amount of work.

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      Referee #1

      Evidence, reproducibility and clarity

      In this manuscript, Kelly et al. show that the difference between the feeding behavior of fed and starved flies (hunger-driven feeding; HDF) is absent in animals fed a high-sugar diet (HSD) for two weeks or more. The disappearance of HDF with HSD coincides with changes in phospholipid profiles caused by HSD. Furthermore, RNAi-mediated downregulation of PECT in the fat body-a key enzyme in the PE biosynthesis pathway-phenocopies physiological effects of HSD. Moreover, downregulation or overexpression in the fat body abolishes or induces HDF, respectively, abolishes or induces HDF, respectively, independent of HSD treatment.

      Overall, the manuscript is well-written and the phenotypes are clear. However, I have major concerns regarding the authors' interpretation of the data and their conclusion. Most importantly, while it is clear that the authors' high-sugar dietary treatment affects feeding behavior and physiology, I am not convinced that the changes can be considered "hunger-driven"-which is central to the main point of the manuscript. Therefore, it is my recommendation that the authors substantially revise the manuscript by either showing additional/re-analyzed data that rule out alternative hypotheses, or rewriting the manuscript keeping alternative interpretations in mind.

      Major comments:

      1. The data do not sufficiently show that the long-term HSD regime disrupts "hunger-sensing." The manuscript should address alternative hypotheses by showing raw instead of normalized data, rewriting the manuscript with a new central conclusion, or running additional experiments that actually show a defect in hunger-driven response.
        • a. The main results that the authors rely on for the argument is that the ratio of feeding events that the starved and non-starved flies eat is different between the groups fed normal or HSD. However, because the authors only show normalized data (normalized to non-starved flies; Fig. 1), it is difficult to tell whether the change is due to a chronically increased feeding in non-starved HSD flies-maybe in perpetual hunger-like allostasis-or dampened starvation response. Indeed, the data shown in Fig S1 show that flies fed HSD for as short as 5 days show more frequent feeding events compared to age-matched controls fed normal food. It is possible that because the HSD-fed flies eat more than NF-fed flies, even without being starved, the ratio of starved/non-starved feeding is lower in the HSD-fed group-due to changes in the denominator, rather than the numerator.
        • b. It is also possible that the HSD-fed flies are simply not in as big an energy deficit physiologically, due to the increased fat deposits they've accumulated (as the authors show later in the manuscript). It may take longer for the fat HSD flies to reach substantial energy deficiency than the NF flies, but they still may eventually be able to appropriately respond to hunger, just like NF flies. In such case, it would be a misnomer to call this behavioral change a 'defect in hunger-driven feeding behavior.' Maybe an experiment with a dose-response curve of "hunger driven feeding response" as a function of duration of starvation would help?
      2. How can you be sure that lower Dilp5 immunofluorescence is indicative of increased Dilp5 secretion? Wouldn't decreased production of dilp5 also have the same results?
        • a. Also, the authors should state in the main text that it is Dilp5, not just any Dilp.
      3. Data presentation:
        • a. Sometimes the data are normalized to NF (Fig 4B-C), sometimes not (ex. Fig 4A, S4C). Unless there is a specific rationale for the data transformation, it would be more appropriate to show untransformed data (ex. Fig 4A, S4C), especially as the authors use two-way ANOVA to determine significance. Only showing the differences implies comparison against a hypothetical mean (i.e. μ0=0), not between two group means.
        • b. Some figures show both individual data points and summary statistics (mean, SD, ... ex. Fig 2A)-which I believe is ideal-but some show only one or the other (ex. Fig 2B, no summary statistics; Fig. 3, no data points. The manuscript would read more convincing if data visualization is consistent across figures.

      Minor comments:

      1. High sugar diet: what is the actual sugar concentration in the NF v. HSD diets? The authors write that the HSD diet contains "30% more sugar" than the NF, but providing the final sugar concentrations-sucrose or others-would be informative for other scientists studying the effect of high sugar diets.
        • a. Additionally, the definition of HSD is inconsistent. Main text (Page 5, line 17) states that their HSD is "60% more sugar than normal media," whereas the figure legend (Fig 1) and the Methods state that the HSD contains "30% more sugar."
      2. Starvation medium: please provide justification for why the authors used 1% sucrose/agar for starvation medium, instead of plain agar/water that most labs use. At least clarify and provide a reference for the claim that the 1% sucrose/agar "is a minimal food media to elicit a starvation response."
      3. PECT mRNA level is higher with HSD. This is surprising because not only, as authors mention, is increased PC32.2 with HSD suggests lower PECT activity, but also because PECT RNAi phenocopies long-term HSD in HDF behavior, lipid morphology, FOXO accumulation in fat body. The authors speculate that the data "likely shown an upregulation in an attempt to mediate the PECT dysregulation occurring at the protein level." If that were true, a western blot may be informative. Zhao and Wang (2020, PLoS Genetics) generated a PECT antibody that seems compatible with western blot applications. That being said, I don't think such data is critical for the manuscript. I mention this simply as a suggestion for the authors.
        • a. page 8, line 22-23, did you mean to write "Given how PC32.2 is elevated after 14 days of exposure to HSD, we assumed that PECT levels would be low for flies under HSD," not "high?" Otherwise the subsequent 2 sentences don't make sense.

      Significance

      The work is potentially novel and interesting, but at this stage it's difficult to interpret what the phenotype signifies. Although the manuscript could be revised simply by modifying the text, experimentally addressing the concerns would significantly improve the work.

      The co-reviewer and I have expertise in Drosophila neurobiology and behavior.

      Referees cross-commenting

      Hi all, although the reviews hit upon some overlapping, but mostly different points, I agree with all of the concerns raised. There's some really interesting stuff here but some of the results, as presented, don't make sense. It's possible this will be clarified by revising the text, although I suspect it's more likely that the authors will have to add a number of the experimental suggestions made by the reviewers.

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      Reply to the reviewers

      Reviewer 1

      Summary: The authors used conventional confocal and super-resolution STED microscopy to characterize the actin filament network in response to SARS-CoV-2 infection in pulmonary cells. They demonstrate that, although total levels of actin are unchanged, F-actin polymerization increases upon infection, with the most significant changes occurring at 48 hours post infection. Notably, F-actin remodels from primarily stress-fiber architectures to circularized, F-actin nanostructures that tend to colocalize with viral M cluster rings at 48 hours post infection. Additionally, there is a significant increase in F-actin-associated filopodia-like structures, with an example of a possible cell-to-cell filopodia that could possibly be a mode of inter-cellular viral transmission. The authors complement their imaging-based experiments with RNAseq to profile the cellular gene expression of SARS-CoV-2 infected pulmonary cells, revealing an upregulation of RHO GTPases activate PKNs and alpha-actinins. They show that treatment of pulmonary cells with Rho/SFR and PKN inhibitors during infection decreases the size of viral M clusters and release to comparable levels as the known viral therapeutic, Remdesivir.

      Major comments:

      1. The majority of the author's conclusions are based off of qualitative and quantitative analysis of their fluorescence images. While they do mention briefly an ImageJ plug-in and the statistical tests performed, the description of their quantitative image-based analyses for each experiment is lacking. For example, how was viral M cluster and actin intensity measured? How was the signal intensity normalized to account for variations in antibody labeling or other cell-to-cell variations? For figures 3C&D, how did the authors calculate viral and actin ring diameter? It is necessary to expand on the details of the quantitative analysis for each parameter mentioned in the methods section and/or include a figure panel demonstrating the details of the analysis (similar to what is nicely displayed for M cluster size in Figure S1B). Response:

      We would like to thank reviewer suggestions to improve the material and methods section. We have incorporated all the suggested details for image analysis and also schematic where ever it is necessary in the figures and SI figures in the revised manuscript to clarify:

      • viral M cluster measurement (Figure S1A) ; no variation in M antibody labelling or in cells was observed per se. the pic of infection regarding M clusters was always 48h pi (maximum of M clusters intensity and area.
      • F-actin intensity was considered for each cells, labelling cells with Phalloidin (for at least 30 cells in each condition), imaging z-stack and then considering the whole F-actin content for each cell.
      • Intracellular viral and F-actin ring diameter was calculating using the scale bar on 3D STED images using ImagJ.

      In particular, the details regarding the F-actin orientation measurements is lacking. Is there a consistent reference point for the orientation of the actin filaments? When comparing across two different cells, it is unclear how the orientations are normalized. Perhaps it would be more informative to plot the difference or the range in angles? Or the distribution of the differences in angles? Another point that is a bit misleading is describing this analysis as "F-actin orientation" since the term "orientation" can has a specific meaning for polar filaments such as actin. For example, given resolution limitations of the imaging approaches used in this manuscript, the authors are reporting on the orientations of bundles/populations of actins and not orientations of individual filaments relative to one another within the bundle (e.g. anti-parallel vs parallel vs branched). The authors should clarify this in the text and also further expand on the utility of their F-actin orientation analysis and how it informs us on the mechanisms of actin-mediated viral infection.

      Response:

      To quantify F-actin rearrangements, we have analyzed the orientation angle of actin nano-fibers from STED images (as in Nature Communications. 8 (2017), doi:10.1038/ncomms14347).

      For this analysis all the images were imaged with STED 2D microscopy for better resolution (axial 60 nm resolution). From STED 2D microscopy images of F-actin, the orientation angle of nano-fibers were evaluated based on the structure tensor of each nano-fibers compares to its local neighborhood using the Java plugin for ImageJ “OrientationJ”. From the given images, the OrientationJ plugin computes the structure tensor for each pixel in the image by sliding the Gaussian analysis window over the entire image. The local angle of orientation properties encoded in color and it is also generating a distribution of angles for each nano-fibers for a given image. Here, in the STED images, it is considered the vertically elongated nano-fibers as the major orientation angle (as around +90 Deg and – 90 Deg from the cell edge) and others orientation angles were calculated accordingly. Area are normalized to the distribution curve of angles to compare the changes in distribution for infected and non-infected cell (as in Fig. 3B).

      We have incorporated above explanations in the material and method section (Image analysis section, Page 11) in the revised manuscript.

      For the majority of figures and findings, they report that between "22

      Response:

      We have incorporated the exact number of cells analyzed for each condition and details about data sets used for analysis in each figure legend in the revised manuscript.

      The actin filament network can assemble into different architectures that are dependent on subcellular location. For example, actin at the basal region of the cell closest to the coverslip often assembles into stress fibers, whereas the cortical actin network often forms astral, highly branched networks. It would be important to take this into account when comparing across different cellular conditions. It is unclear if the authors were consistent with the z-slice examined for the different cellular treatment/infection conditions. Were the analyses performed on individual z-stacks or max projection images?

      Response:

      We agree with the reviewer views on actin network in different planes. Thus, to ensure reasonable quantification and comparison among conditions, all images were taken with the same objective (63x oil N 1.4) and microscope settings (same gain, same laser power). For post-processing, we mainly choose individual cells, which are not in contact with others and individual z-stacks were taken. Z-stacks images with fixed 0.3 micrometer slices for each cells were taken to ensure the whole cell was in focus. The Z-projection images of individual cells were then performed and used to calculate the F-actin or viral M cluster or ER mean intensity in the whole cell. We have analyzed the mean intensity per individual cell using a Fiji/Image J.

      We have incorporated above details in material method (image analysis) section in the revised manuscript.

      Since a major impact of this paper is the first imaging-based characterization of actin filament assembly in response to infection, the authors should provide a more comprehensive display of the raw data images. For example, figure S2 provides a nice gallery of images of actin and viral M particles, however it should show separate image channels in gray scales and consistent scaling across all images. Furthermore, all figure panels showing distinct imaging experiments and quantitative results should be complemented with a supplemental figure showing a gallery of images. This would apply to actin nanostructure rings (Figure 3C/E), filopodia and cell-to-cell contacts (Figure 4A/D), treatment with remdesivir/PKN inihibitor (Figure 6B), and ER localization of M particles (Figure S5).

      Response:

      As the reviewer suggested, we have now created an image gallery for each figure panel (Figures 3, 4, 6 and S5, S3, S8, S9) including STED images that were added as supplemental figures.

      The results in Figure 3D are difficult to interpret. The images should be larger and labeled. Also, based on the 3D STED image in Figure 3D, it appears that the brightest actin signal is actually at the center portion of the viral M cluster. Does this contradict the TEM image and what is described in the text? For Figure 3E: a more relevant analysis might be line scans across multiple images showing how relative actin-M cluster intensity varies within the dimensions of the nanostructure to demonstrate more clearly a pattern of ring assembly of both M clusters and actin.

      Response:

      Since the virus “rings” were mostly found in intracellular places, far from membrane surface, some times during imaging we observed F-actin signal from the upper plane, which is possibly the reason for brightest F-actin signal appears at the center portion of the viral M cluster. Thus, for better clarity of the image and to support our statements we have now incorporated other new images in the Figure 3E (STED 3D images) showing that an heterogeneity of the F-actin labelling but strongly associated with intracellular viral M clusters.

      The authors should address the implications and significance of the described cellular morphological changes in the context of the more physiologically relevant tissue/organ system. How do the changes they observe upon infection in isolated cultured cells compare to when these cells are assembled into tissue/organs?

      Response:

      The significance of the cellular morphological changes upon SARS-CoV2 infection showing a contraction-like effect on the cells as well as higher cells and less contact area could account in a pulmonary tissue by the destructuration of the lung tissue, consistent with the lung damaged seen in the case of COVID19. A sentence in that sense was added in the Discussion section.

      For Figure 6 and S5, the authors infected and treated cells with an inhibitor at the same time point and demonstrate that M cluster size and release is reduced to somewhat comparable levels as treatment with Remdesivir. The authors should expand their analyses for this experiment to include the other quantitative parameters outlined in the paper: F actin/M cluster nanostructures, cellular morphology, filopodia formation, orientation of actin, etc. Additionally, it would be more informative to treat cells post-infection to more closely mimic cellular conditions of infection/treatment.

      Response:

      We have now included quantitative analysis for cellular morphological changes of cells with or without drug treatment (both in the presence of PKN inhibitor and Remdesivir upon SARS-CoV-2 infection) in the revised manuscript (Supplemental Figure S7). We observed a restoration of F-actin nanostructures as well as did not observe any filopodia-like structure formation upon treatment with PKN inhibitor in infected cells.

      Minor comments:

      1. The individual data points should be overlaid on the violin plots for better interpretability of the variability in the data. Response:

      We have incorporated new violin plots with overlaid data points in the revised version of the manuscript for each figure with quantitative data (Figures 1,2,5,6).

      For Figure 3E: the images look "stretched" with an altered relative aspect ratio.

      Response:

      For sake of clarity, we have incorporated new (better) 3D STED images for a better visualization of intracellular F-actin/M clusters “rings” in revised manuscripts (Figure 3E).

      1. The authors should include a cartoon model figure highlighting both (1) how their results contribute to our knowledge of actin-mediated viral assembly/replication and (2) unknown portions of the pathway that need to be further probed to better understand the mechanistic underpinnings of this process.

      Response:

      We have now included a model scheme figure summarizing our results in the revised manuscript, as a new figure 7.

      There have been several high resolution cellular imaging studies using other complementary 3D volumetric imaging approaches (e.g. cryo-electron tomography and FIB/SEM) to characterize the subcellular ultrastructure’s of SARS-CoV-2 infection. The authors should include a brief discussion on how their study complement or compare to these reports, in particular noting whether or not actin filament assemblies were observed in these data.

      Response:

      Thanks to the reviewers for this very pertinent remark, we have added in the Discussion (Page 7,8), a section commenting previous high-resolution cellular imaging studies (REFERENCES: Mendonçà et al Nature Comm 12, 4629, 2021; Klein et al 2020) comparing our 2D/3D STED imaging with complementary 3D EM or 3D cryo-ET or FIB/SEM of SARS-CoV-2 infected cells recently published.

      From Mendonca et al 2021, one can see some intracellular dense structure underneath the CoV-2 budding membrane area, but not able to see if F-actin filaments were present or not. It would be difficult to observe because the vRNP underneath the Spike decorating membrane are very dense. The study was focus on viral assembly and egress using cryo-ET/FIB but not on F-actin filament per se. We don’t know if their imaging conditions would preserve F-actin fibers on membranes. On the other side, when studying virus egress, then we can clearly see CoV-2 individual particles surfing on giant filopodia-like structures very much resembling our STED imaging of viruses on filopodia 48h pi. We can clearly see and recognize parallel F-actin filament bundles inside the enlarged filopodia (Figure 5 D/E) with viruses on it.

      Same results were observed using Cryo-EM tomography in another study (Klein et al 2020) where one can see viruses on filopodia for many cell types A549-hACE2, VeroE6, Calu3 infected cells.

      Reviewer 2

      The authors investigate the role of F-actin in infected human pulmonary alveolar A549-hACE2 cells. They investigate infection progression at different time points by the detection of the M protein by confocal microscopy and western blot. They compare the detection of M with S and N in western blot and with viral RNA detection by Q-PCR. The authors correlate M cluster formation to peak at 48h p.i. with particle assembly and particle release at 72h p.i. An increase in F-actin at 24h and 48h p.i. was monitored by confocal microscopy and z-stacks, whereas the overall amount of actin determined by western blot was not changed. Using 2D STED microscopy the authors identified F-actin rearrangement from stress fibers to filamentous protrusions at 24h-48h p.i. and conclude importance for particle assembly and release. By 3D STED microscopy M labeled intracellular organelles called "viral rings" surrounded by actin called "actin rings" are shown. By transmission electron microscopy (TEM) vesicular structures with budding particles were shown at intracellular membranes. The authors conclude from these findings that F-actin stabilizes assembly platforms at membranes or support the transport of virus loaded vesicles to the plasma membrane. The authors found more and longer filopodia in infected cells which were loaded with virus particles bridging cells suggesting role in cell-cell spread. At the plasma membrane they found bigger particles and at the filopodia smaller, suggesting release from the plasma membrane in packages.

      Transcriptom analysis of non infected and SARS-CoV-2 infected A549-hACE2 revealed upregulation of Rho-GTPases activated proteins like PKN and α-actinins upon infection. The levels of α-actinins in WB were 2-fold higher in infected cells. The authors show that inhibitors of Rho/SRF and PKN restored cell morphology, reduced M cluster formation and virus release. The PKN inhibitor blocked M in the ER. The authors conclude from this data a role of the alpha-actinins superfamily in SARS-CoV-2 assembly and egress.

      Major comments:

      The presented data are convincing but some figures may need improvements, see in minor comments. For some conclusions, more evidences like marker staining may be needed.

      Response:

      In accordance with the reviewer, we have significantly improve the figures in the revised manuscript. We identified that the intracellular compartment containing budding viruses were derived from the ER (gpr78 marker) – shown in revised Figure 6 - and not in lysosomes (Lamp1 marker) or extracellular vesicles (CD81 marker) – See new supplemental revised Figure S10. We have included all the new results and discussion in revised manuscripts.

      Minor comments:

      The authors conclude that F-actin stabilizes assembly platforms at membranes like ERGIC, but an ERGIC marker staining is not provided. The authors suggest that F-actin might also be involved in transport of virus loaded vesicles to the plasma membrane. Here a plasma membrane marker or native staining of particles may help to descriminate between intracellular Exosomes and extracellular particles. Co-staining with exosomal markers would also be more convincing.

      Response:

      Also as per reviewer suggestion we have identified that the intracellular compartment containing budding viruses were derived from the ER (gpr78 marker) – shown in revised Figure 6, Fig. S8. New quantitative analysis (including with PKN inhibitor) to support the data also included in the figures. Also we have used lysosomes marker LAMP1 and extracellular vesicles (EV) marker CD81 and we found that there was no colocalization with Viral M clusters ( Supporting Figure 10). we have tried the ERGIC marker grp53 without any success so far,

      Further, It is well documented on CryoFIB/SEM study of SARS-CoV-2-infected cells suggested the presence of “exit tunnels”, linking virion-rich intracellular vacuoles to the extracellular space (Mendonça, L. et al. Nature Communications 12, (2021)). The size of this vacuoles observed in the cell periphery was approximately 1 µm, which is well corelated with actin and viral ring we have observed from STED 2D images Also the author suggested that SARS-CoV-2 could possibly egress through these tunnels by a mechanism of exocytosis from these large intracellular vacuoles.

      We have now included all above results and discussions in revised manuscripts to support our claims.

      Figure S1 A. Align individual pictures in one line and do not overlap, scale bars not readable, Is in each picture the same magnification shown? Show representative pictures with the same area magnification!

      Response:

      Thanks to the reviewer to point out these imperfections, so we have improved the figures accordingly in the revised manuscript. Individual images are aligned properly, scale bars are readable, images are with the same magnification.

      Figure 3C and 3E for better orientation magnified areas should be indicated as squares, not in circles.

      Response:

      As suggested, we have modified the figure 3 accordingly in the revised manuscript.

      Figure S4 quality of pictures not appropriate to see differences.

      Response:

      We improved the figure quality in the revised manuscript (see new Figures 6 and S8)

      Fig S5 All pictures overlap in one? ER marker in blue very difficult to read.

      Response:

      We have modified the new figure S8 as such as the ER marker is visible (in magenta color) in the revised manuscript.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      The authors investigate the role of F-actin in infected human pulmonary alveolar A549-hACE2 cells. They investigate infection progression at different time points by the detection of the M protein by confocal microscopy and western blot. They compare the detection of M with S and N in western blot and with viral RNA detection by Q-PCR. The authors correlate M cluster formation to peak at 48h p.i. with particle assembly and particle release at 72h p.i. An increase in F-actin at 24h and 48h p.i. was monitored by confocal mircroskopy and z-stacks, whereas the overall amount of actin determined by western blot was not changed. Using 2D STED microscopy the authors identified F-actin rearrangement from stress fibers to filamentous protrusions at 24h-48h p.i. and conclude importance for particle assembly and release. By 3D STED microscopy M labeled intracellular organelles called "viral rings" surrounded by actin called "actin rings" are shown. By transmission electron microscopy (TEM) vesicular structures with budding particles were shown at intracellular membranes. The authors conclude from these findings that F-actin stabilizes assembly platforms at membranes or support the transport of virus loaded vesicles to the plasma membrane. The authors found more and longer filopodia in infected cells which were loaded with virus particles bridging cells suggesting role in cell-cell spread. At the plasma membrane they found bigger particles and at the filopodia smaller, suggesting release from the plasma membrane in packages. Transcriptom analysis of non infected and SARS-CoV-2 infected A549-hACE2 revealed upregulation of Rho-GTPaes activated proteins like PKN and α-actinins upon infection. The levels of α-actinins in WB were 2-fold higher in infected cells. The authors show that inhibitors of Rho/SRF and PKN restored cell morphology, reduced M cluster formation and virus release. The PKN inhibitor blocked M in the ER. The authors conclud from this data a role of the alpha-actinins superfamily in SARS-CoV-2 assembly and egress.

      Major comments:

      The presented data are convincing but some figures may need improvements, see in minor comments. For some conclusions more evidences like marker staining may be needed.

      Minor comments:

      The authors conclude that F-actin stabilizes assembly platforms at membranes like ERGIC, but an ERGIC marker staining is not provided. The autors suggest that F-actin migth also be involved in transport of virus loaded vesicles to the plasma membrane. Here a plasma membrane marker or native staining of particles may help to descriminate between intracellular Exosomes and extracellular particles. Co-staining with exosomal markers would also be more convincing. - Figure S1 A. Align individual pictures in one line and do not overlap, scale bars not readable, Is in each picture the same magnification shown? Show representative pictures with the same area magnification! - Figure 3C and 3E for better orientation magnified areas should be indicated as squares, not in circles. - Figure S4 quality of pictures not appropriate to see differences. - Fig S5 All pictures overlap in one? ER marker in blue very difficult to read.

      Significance

      The presented data provide a nice peace of work to the knoweledge on SARS-COV-2 replication in human pulmonary cells. The authors use advanced imaging and molecular biology methods for their experiments. The indentified cellular target may help to develop specific inhibitors for antiviral therapy.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      The authors used conventional confocal and super-resolution STED microscopy to characterize the actin filament network in response to SARS-CoV-2 infection in pulmonary cells. They demonstrate that, although total levels of actin are unchanged, F-actin polymerization increases upon infection, with the most significant changes occurring at 48 hours post infection. Notably, F-actin remodels from primarily stress-fiber architectures to circularized, F-actin nanostructures that tend to colocalize with viral M cluster rings at 48 hours post infection. Additionally, there is a significant increase in F-actin-associated filopodia-like structures, with an example of a possible cell-to-cell filopodia that could possibly be a mode of inter-cellular viral transmission. The authors complement their imaging-based experiments with RNAseq to profile the cellular gene expression of SARS-CoV-2 infected pulmonary cells, revealing an upregulation of RHO GTPases activate PKNs and alpha-actinins. They show that treatment of pulmonary cells with Rho/SFR and PKN inhibitors during infection decreases the size of viral M clusters and release to comparable levels as the known viral therapeutic, Remdesivir.

      Major comments:

      1. The majority of the author's conclusions are based off of qualitative and quantitative analysis of their fluorescence images. While they do mention briefly an ImageJ plug-in and the statistical tests performed, the description of their quantitative image-based analyses for each experiment is lacking. For example, how was viral M cluster and actin intensity measured? How was the signal intensity normalized to account for variations in antibody labeling or other cell-to-cell variations? For figures 3C&D, how did the authors calculate viral and actin ring diameter? It is necessary to expand on the details of the quantitative analysis for each parameter mentioned in the methods section and/or include a figure panel demonstrating the details of the analysis (similar to what is nicely displayed for M cluster size in Figure S1B).
      2. In particular, the details regarding the F-actin orientation measurements is lacking. Is there a consistent reference point for the orientation of the actin filaments? When comparing across two different cells, it is unclear how the orientations are normalized. Perhaps it would be more informative to plot the difference or the range in angles? Or the distribution of the differences in angles? Another point that is a bit misleading is describing this analysis as "F-actin orientation" since the term "orientation" can has a specific meaning for polar filaments such as actin. For example, given resolution limitations of the imaging approaches used in this manuscript, the authors are reporting on the orientations of bundles/populations of actins and not orientations of individual filaments relative to one another within the bundle (e.g. anti-parallel vs parallel vs branched). The authors should clarify this in the text and also further expand on the utility of their F-actin orientation analysis and how it informs us on the mechanisms of actin-mediated viral infection.
      3. For the majority of figures and findings, they report that between "22<n<50 cells" were analyzed. The authors should be more specific of the exact sample size for each experiment/figure panel displayed. In particular, it is unclear in a few figure panels showing exemplar images whether or not this is the full sample size (n=1) or just an exemplar image. I recommend reporting specifically in the figure legend and/or a supplemental table outlining the sample size and analysis used for each imaging experiment to add clarify to their quantitative analysis and strengthen their claims.
      4. The actin filament network can assemble into different architectures that are dependent on subcellular location. For example, actin at the basal region of the cell closest to the coverslip often assembles into stress fibers, whereas the cortical actin network often forms astral, highly branched networks. It would be important to take this into account when comparing across different cellular conditions. It is unclear if the authors were consistent with the z-slice examined for the different cellular treatment/infection conditions. Were the analyses performed on individual z-stacks or max projection images?
      5. Since a major impact of this paper is the first imaging-based characterization of actin filament assembly in response to infection, the authors should provide a more comprehensive display of the raw data images. For example, figure S2 provides a nice gallery of images of actin and viral M particles, however it should show separate image channels in gray scales and consistent scaling across all images. Furthermore, all figure panels showing distinct imaging experiments and quantitative results should be complemented with a supplemental figure showing a gallery of images. This would apply to actin nanostructure rings (Figure 3C/E), filopodia and cell-to-cell contacts (Figure 4A/D), treatment with remdesivir/PKN inihibitor (Figure 6B), and ER localization of M particles (Figure S5).
      6. The results in Figure 3D are difficult to interpret. The images should be larger and labeled. Also, based on the 3D STED image in Figure 3D, it appears that the brightest actin signal is actually at the center portion of the viral M cluster. Does this contradict the TEM image and what is described in the text? For Figure 3E: a more relevant analysis might be line scans across multiple images showing how relative actin-M cluster intensity varies within the dimensions of the nanostructure to demonstrate more clearly a pattern of ring assembly of both M clusters and actin.
      7. The authors should address the implications and significance of the described cellular morphological changes in the context of the more physiologically relevant tissue/organ system. How do the changes they observe upon infection in isolated cultured cells compare to when these cells are assembled into tissue/organs?
      8. For Figure 6 and S5, the authors infected and treated cells with an inhibitor at the same time point and demonstrate that M cluster size and release is reduced to somewhat comparable levels as treatment with Remdesivir. The authors should expand their analyses for this experiment to include the other quantitative parameters outlined in the paper: F actin/M cluster nanostructures, cellular morphology, filopodia formation, orientation of actin, etc. Additionally, it would be more informative to treat cells post-infection to more closely mimic cellular conditions of infection/treatment.

      Minor comments:

      1. The individual data points should be overlaid on the violin plots for better interpretability of the variability in the data.
      2. For Figure 3E: the images look "stretched" with an altered relative aspect ratio.
      3. The authors should include a cartoon model figure highlighting both (1) how their results contribute to our knowledge of actin-mediated viral assembly/replication and (2) unknown portions of the pathway that need to be further probed to better understand the mechanistic underpinnings of this process.
      4. There have been several high resolution cellular imaging studies using other complementary 3D volumetric imaging approaches (e.g. cryo-electron tomography and FIB/SEM) to characterize the subcellular ultrastructures of SARS-CoV-2 infection. The authors should include a brief discussion on how their study complement or compare to these reports, in particular noting whether or not actin filament assemblies were observed in these data.

      Significance

      Impact:

      This manuscript provides the first characterization of the architecture of the actin filament network upon SARS-CoV-2 infection. Since actin filament remodeling is a mechanism used by several other viruses, there is considerable interest in targeting these assemblies for the development of therapeutics to prevent and treat infection. This manuscript lays the groundwork for more detailed analysis probing the mechanisms mediating actin-mediated viral entry, replication, and release. Furthermore, it establishes some quantitative tools to standardize how this process is studied and analyzed in future studies.

      Audience:

      I anticipate that this work will motivate future studies aimed at further ultrastructural characterization of actin and other cytoskeletal filaments by complementary, high-resolution imaging techniques, as well as studies aimed at screening for small molecule drugs to inhibit actin-mediated viral infection.

      Field of expertise:

      cellular cryo-electron tomography, quantitative imaging, cytoskeletal-based motility, functional cytoskeleton-organelle interactions. Insufficient expertise to evaluate RNAseq experiments.

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      Reply to the reviewers

      *Reviewers comments in italics *

      We thank all reviewers for their positive and encouraging comments and criticisms to improve our work. Here we present a reviewed version of the manuscript according to the comments risen.

      • Reviewer #1 (Evidence, reproducibility and clarity (Required)): This is an interesting paper that identifies Tns3 as a potential effector of oligodendrocytes differentiation based on an ingenious strategy comparing regulatory binding sites of known master regulators of differentiation, and then shows using in vivo genetics that this role is indeed correct. Next, a potential mechanism is identified by showing co-localization with beta 1 integrin, known to regulate apoptosis of newly-formed oligodendrocytes. The results are well illustrated and the experiments performed with appropriate power using a broad range of techniques that combine in silico, in vitro and in vivo work to great effect.

      I think this represents an important contribution that will be of significant interest to neuroscientists - the mechanisms regulating oligodendrocytes generation remain poorly understood and the evidence that this contributes to adult learning (adaptive myelination) and CNS regeneration makes this a key question. I would suggest that the following are considered before publication: We thank the reviewer for this positive comments and critics to improve the manuscript. The work describing the KO mice that were not used as they proved unsuitable need not be described - it breaks the logical flow.*

      In agreement with the reviewer comment, we have reduced this part to a sort paragraph indicating that our analyses of several Tns3 constitutive KO lines showed developmental lethality and possible genetic compensation in Tns3 expression, leading us to conclude them inappropriate tools to study Tns3 function in oligodendrogenesis. We have summarized the data in Fig. S7 and the description in the method section.

      It would be useful to compare the extent of cell death in the Tns3 cKO mice with that described in the alpha6 integrin KO and the integrin beta1 cKO (the Colognato and Benninger papers). Do they match? If not (and I suspect the Tns3 cKO death is greater) could other mechanisms be downstream of the Tns3?

      In agreement with the reviewer comment, we have added the following paragraph to the discussion:

      ‘Knockout mice for integrin-a6 present a 50% reduction in brainstem MBP+ OLs at E18.5, just before they die at birth, accompanied by an increase in TUNEL+ dying OLs (Colognato et al, 2002). Similarly, conditional deletion of integrin-b1 in immature OLs by Cnp-Cre also leads to a 50% reduction in cerebellar OLs at P5, with a parallel increase in TUNEL+ dying OLs (Benninger et al., 2006). Therefore, given that Tns3-induced deletion in postnatal OPCs also leads to 40-50% reduction in OLs in both grey and white matter regions of the postnatal telencephalon (this study), paralleled by similar increase in TUNEL+ apoptotic oligodendroglia, we suggest that Tns3 is required for integrin-b1 mediated survival signal in immature oligodendrocytes.’

      I'm not sure why the authors argue that the activation of beta 1 would not be informative experiment? This will regulate actin dynamics just as it regulates other integrin signaling pathways. Indeed, I would argue that an integrin activation experiments would be a neat way to prove mechanism (as it would be predicted to rescue the Tns3 cKO phenotype).

      In agreement with the reviewer comment, we have removed this sentence: ‘If so, exogenous activation of integrin a6b1 in cultured OPCs by Mn2+ (Colognato et al., 2004) would not be expected to increase oligodendrogenesis in Tns3-iKO oligodendroglia.’

      In an effort, to understand Tns3 function by acute Tns3-deletion in postnatal OPCs, we have compared the transcriptome of Tns3-iKO oligodendroglia compared to control cells, and we present these results in figure 7 pinpointing deregulated genes leading to reduced oligodendroglial differentiation, integrin dysregulation, increase apoptosis, and conflicting cell cycle signaling, and leaving for further studies the full characterization how the loss of Tns3 leads to the deregulation of these processes.

      Can the authors provide any data on GM oligos and their OPCs? Is the requirement for Tns3 the same, and if so what might the implications be in the adult where new oligodendrocytes are being generated throughout life?

      Indeed, in our analyses of Tns3-iKO mice, we provide quantifications of the cortex as a grey matter territory, showing a similar 40-50% reduction in OLs as in white matter areas (corpus callosum and fimbria, and mixed regions such as the striatum.

      I note in S13 that integrin beta1 is not highly expressed in human oligos at the time in question. Does this call into question the relevance for human disease?

      We realize that scRNAseq plots are never easy to interpret but it is important to note that the levels of expression are coded by the intensity of the color scale, while the surface of the dot plots indicate the experimental sensitivity to detect transcript expression in a larger or smaller proportion of the cells in a given cluster/cell type (due to the drop out limitation of current single cell RNA-seq technologies). Considering this, please note that beyond a stronger expression in neural progenitor cells (NPCs, blue color), integrin-b1 (Itgb1) transcripts are expressed at medium to high levels (green to blue) in human immature OLs (Fig. S13B), similar to their pattern of expression in mouse oligodendroglia (Fig. S13A).

      Reviewer #1 (Significance (Required)): See above

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

      *In this article, the authors identify and characterise Tensin3 (Tns3) as a target of key oligodendroglial transcription factors driving differentiation in the mouse. They use multiple transgenic models to describe loss of function, and suggest Tns3's action through integrin B1 signalling, with the key function being oligodendroglial survival.

      There is extensive and impressive work here, including identification of Tns3 by ChIPseq, expression of Tns3 in brain development, analysis of human (ES-derived) and mouse scRNAseq to infer timing of expression in the differentiation pathway, generation of V5-tagged Tns3-KI mice to overcome antibody limitations, identification of its expression in mouse remyelination, generation of a new Tns3KO mouse, in vivo Crispr Tns3KO in development, generation of a conditional KO, for deletion in adulthood, and finally some culture work to investigate potential mechanisms of actions. The bottom line is that Tns3 is required for survival of OPCs and immature oligodendrocytes in development/remyelination in mouse at least, and loss leads to apoptosis (through p53 increase and loss of integrin-B1 signalling), leading to a failure of proper differentiation.

      The experiments are carefully done, convincing and the tools generated impressive. There is clearly more to be done on clarifying the mechanism of action of Tns3, but I do not think further experiments on this topic are needed for this paper - they can wait for the next.*

      We thank the reviewer for the positive and encouraging reviewing comments. In an effort, to understand Tns3 function by acute Tns3-deletion in postnatal OPCs, we have compared the transcriptome of Tns3-iKO oligodendroglia compared to control cells, and we present these results in figure 7 pinpointing deregulated genes leading to reduced oligodendroglial differentiation, integrin dysregulation, increase apoptosis, and conflicting cell cycle signaling, and leaving for further studies the full characterization how the loss of Tns3 leads to the deregulation of these processes.

      My only query is whether the expression of Tns3 is also in immature OLs in human brain (rather than human ES-derived OLs). This should be easily checked with interrogation of online Shiny apps from already published snRNAseq from various groups on human post mortem adult brain, but if not present then in also baby/fetal brain. This would be interesting and may well be different from the ES_derived cells which tend to be very immature and would add interest to the possible translational impact.

      According to the suggestion of the reviewer, we analyzed 69,174 snRNAseq GW9-GW22 from fetal cerebellum,; Aldinger & Miller, 2021; https://doi-org.proxy.insermbiblio.inist.fr/10.1038/s41593-021-00872-y), which we present now in Figure S3, finding a cluster of cells expressing iOL markers, including NKX2-2, TNS3, ITPR2, and BCAS1, similar to the hiPSCs-derived iOL1/iOL2 clusters and mouse iOL1/iOL2 clusters shown in Fig. S2.

      We also analyzed other datasets without finding iOLs given their age or numbers, including:

      • Immunopanned PDGFRA+ cells from human cortex GW20-GW24 (2690 cells, Huang and Kriegstein, Cell 2020) finding OPCs but not iOLs.

      -The recently published dataset from GW8-GW10 human forebrain oligodendroglia (van Brugen & Castelo-Branco, Dev Cell 2022; https://doi.org/10.1016/j.devcel.2022.04.016) containing OPCs but not iOLs.

      -The GW17 to GW18 human cortex (40,000 cells, Polioudakis & Geschwind, 2019, https://doi.org/10.1016/j.neuron.2019.06.011) containing OPCs but not iOLs.

      Reviewer #2 (Significance (Required)): This work extends our knowledge of oligodendroglial differentiation, links it to the ECM and provides interest in manipulating this in diseases including glioma. My expertise: myelin, oligodendroglia, remyelination, human neuropathology

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

      see below Reviewer #3 (Significance (Required)): Using purified oligodendrocytes target genes of key regulators of oligodendrocyte differentiation were analyzed, which led to the identification of Tensin-3. The authors performed a detail characterization of Tensin-3 expression. They found that Tensin-3 is highly expressed in immature mouse and human oligodendrocytes. Interestingly, Tensin-3 is selectively enriched in immature oligodendrocytes, and not present at detectable levels in OPCs and mature oligodendrocytes. Subsequently, the authors characterized Tensin-3 function by a series of knockdown approaches in vitro and in vivo. These series of experiments revealed an essential function of Tensin-3 in supporting oligodendrocytes survival. In the absence of Tensin-3 a large fraction of oligodendrocytes undergo apoptosis while differentiating to mature oligodendrocytes. This is a remarkable study applying an impressive array of methods that led to an important discovery in the field of oligodendrocyte biology. The main advances for the field are: 1) identification of a novel marker for premyelinating oligodendrocytes, 2) elucidation of Tensin-3 as a pro-survival factor in oligodendrocytes differentiation, 3) evidence of link of Tensin-3-integrin signal in survival of oligodendrocytes. The data is well presented and organized, and the paper well written. I recommend publication with only minor suggestions for a revision:

      • *

      We thank the reviewer for this positive comments and critics to improve the manuscript.

      In Figure 2, only images are shown, and the data is referred to as highly expressed or strong co-localization. Even if the data looks clear, the authors should provide some quantification of the data in the figure.

      We thank the reviewer for his comment and we have now provided a quantification of the fraction of Tns3+ cells expressing different markers of oligodendrocyte lineage progression/stages, and the percentage of each stage expressing Tns3.

      Figure 3 is given too much weight in the manuscript text. I would recommend to shorten the text in the result section, and to move this figure to the supplement as it does not advance the story. It mainly shows that the KO mice still express transcripts in the brain. Were the transcripts lost in peripheral tissue?

      • *

      As mentioned above, in agreement with the reviewers #1 and #3 comments, we have reduced this part to a sort paragraph indicating that our analyses of several Tns3 constitutive KO lines showed developmental lethality and possible genetic compensation in Tns3 expression, leading us to conclude them inappropriate tools to study Tns3 function in oligodendrogenesis. We have summarized the data in Fig. S7 and the description in the method section.

      Page 11: the authors describe in the text how the floxed allele was generated. This should be shifted to the supplement.

      According to reviewers suggestion, we have moved the description of Tns3 floxed allele generation to the Methods section. Page 16: the authors refer to Bcas1 as a problematic marker for immature oligodendrocytes, because the transcript is also expressed in mature oligodendrocytes. The authors are correct that the transcript is expressed in mature oligodendrocytes. However, the proteins changes its localization when oligodendrocytes mature. On protein level, it is valuable and a selective marker, as antibodies only label pre-myelinating and actively myelinating cells. In mature oligodendrocytes, antibodies against Bcas1 do not label the cell, only myelin. The text is misleading and needs to be corrected.

      In agreement with reviewers comment we have modified the text as follows: ‘An optimized protocol for immunodetection using Bcas1-recognizing antibodies has been shown to label iOLs (Fard et al., 2017).’

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      Referee #3

      Evidence, reproducibility and clarity

      see below

      Significance

      Using purified oligodendrocytes target genes of key regulators of oligodendrocyte differentiation were analyzed, which led to the identification of Tensin-3. The authors performed a detail characterization of Tensin-3 expression. They found that Tensin-3 is highly expressed in immature mouse and human oligodendrocytes. Interestingly, Tensin-3 is selectively enriched in immature oligodendrocytes, and not present at detectable levels in OPCs and mature oligodendrocytes. Subsequently, the authors characterized Tensin-3 function by a series of knockdown approaches in vitro and in vivo. These series of experiments revealed an essential function of Tensin-3 in supporting oligodendrocytes survival. In the absence of Tensin-3 a large fraction of oligodendrocytes undergo apoptosis while differentiating to mature oligodendrocytes.

      This is a remarkable study applying an impressive array of methods that led to an important discovery in the field of oligodendrocyte biology. The main advances for the field are: 1) identification of a novel marker for premyelinating oligodendrocytes, 2) elucidation of Tensin-3 as a pro-survival factor in oligodendrocytes differentiation, 3) evidence of link of Tensin-3-integrin signal in survival of oligodendrocytes. The data is well presented and organized, and the paper well written.

      I recommend publication with only minor suggestions for a revision:

      In Figure 2, only images are shown, and the data is referred to as highly expressed or strong co-localization. Even if the data looks clear, the authors should provide some quantification of the data in the figure.

      Figure 3 is given too much weight in the manuscript text. I would recommend to shorten the text in the result section, and to move this figure to the supplement as it does not advance the story. It mainly shows that the KO mice still express transcripts in the brain. Were the transcripts lost in peripheral tissue?

      Page 11: the authors describe in the text how the floxed allel was generated. This should be shifted to the supplement.

      Page 16: the authors refer to Bcas1 as a problematic marker for immature oligodendrocytes, because the transcript is also expressed in mature oligodendrocytes. The authors are correct that the transcript is expressed in mature oligodendrocytes. However, the proteins changes its localization when oligodendrocytes mature. On protein level, it is valuable and a selective marker, as antibodies only label pre-myelinating and actively myelinating cells. In mature oligodendrocytes, antibodies against Bcas1 do not label the cell, only myelin. The text is misleading and needs to be corrected.

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      Referee #2

      Evidence, reproducibility and clarity

      In this article, the authors identify and characterise Tensin3 (Tns3) as a target of key oligodendroglial transcription factors driving differentiation in the mouse. They use multiple transgenic models to describe loss of function, and suggest Tns3's action through integrin B1 signalling, with the key function being oligodendroglial survival.

      There is extensive and impressive work here, including identification of Tns3 by CHIPseq, expression of Tns3 in brain development, analysis of human(ES-derived) and mouse scRNAseq to infer timing of expression in the differentiation pathway, generation of V5-tagged Tns-KI mice to overcome antibody limitations, identification of its expression in mouse remyelination, generation of a new Tns3KO mouse, in vivo crispr Tns3KO in development, generation of a conditional KO, for deletion in adulthood, and finally some culture work to investigate potential mechanisms of actions. The bottom line is that Tns3 is required for survival of OPCs and immature oligodendrocytes in development/remyelination in mouse at least, and loss leads to apoptosis (through p53 increase and loss of integrinB1 signalling), leading to a failure of proper differentiation.

      The experiments are carefully done, convincing and the tools generated impressive. There is clearly more to be done on clarifying the mechanism of action of Tns3, but I do not think further experiments on this topic are needed for this paper - they can wait for the next.

      My only query is whether the expression of Tns3 is also in immature OLs in human brain (rather than human ES-derived OLs). This should be easily checked with interrogation of online Shiny apps from already published snRNAseq from various groups on human post mortem adult brain, but if not present then in also baby/fetal brain. This would be interesting and may well be different from the ES_derived cells which tend to be very immature and would add interest to the possible translational impact.

      Significance

      This work extends our knowledge of oligodendroglial differentiation, links it to the ECM and provides interest in manipulating this in diseases including glioma.

      My expertise: myelin, oligodendroglia, remyelination, human neuropathology

    4. 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

      This is an interesting paper that identifies Tns3 as a potential effector of oligodendrocytes differentiation based on an ingenious strategy comparing regulatory binding sites of known master regulators of differentiation, and then shows using in vivo genetics that this role is indeed correct. Next, a potential mechanism is identified by showing co-localization with beta 1 integrin, known to regulate apoptosis of newly-formed oligodendrocytes. The results are well illustrated and the experiments performed with appropriate power using a broad range of techniques that combine in silico, in vitro and in vivo work to great effect.

      I think this represents an important contribution that will be of significant interest to neuroscientists - the mechanisms regulating oligodendrocytes generation remain poorly understood and the evidence that this contributes to adult learning (adaptive myelination) and CNS regeneration makes this a key question. I would suggest that the following are considered before publication:

      The work describing the KO mice that were not used as they proved unsuitable need not be described - it breaks the logical flow.

      It would be useful to compare the extent of cell death in the Tns3 cKO mice with that described in the alpha6 integrin KO and the integrin beta1 cKO (the Colognato and Benninger papers). Do they match? If not (and I suspect the Tns3 cKO death is greater) could other mechanisms be downstream of the Tns3?

      I'm not sure why the authors argue that the activation of beta 1 would not be informative experiment? This will regulate actin dynamics just as it regulates other integrin signaling pathways. Indeed, I would argue that an integrin activation experiments would be a neat way to prove mechanism (as it would be predicted to rescue the Tns3 cKO phenotype).

      Can the authors provide any data on GM oligos and their OPCs? Is the requirement for Tns3 the same, and if so what might the implications be in the adult where new olligodendrocytes are being generated throughout life?

      I note in S13 that integrin beta1 is not highly expressed in human oligos at the time in question. Does this call into question the relevance for human disease?

      Significance

      See above

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity):

      The manuscript by Tran et al. describes the mechanism by which IFNa treatment prevents the development of liver CRC metastasis in several mouse models. They show how continuous administration of IFNa strength liver vascular barrier by a direct effect on endothelial cells and avoids the trans-sinusoidal migration of tumour cells.

      Major points:

      1. Authors use an elegant orthotopic model of liver metastasis to confirm the effect of continuous IFNa on hepatic colonization (Fig.3). Although they extensively characterize the metastatic lesions, they do not show data on the potential impact of IFNa treatment in the primary caecum tumour. Authors should clarify if the described effects are taken place in the liver or/and in the caecum. It would be interesting to show if IFNa affects the primary tumour size, the extravasation of cancer cells and the immune infiltration since all these factors could have an impact in the number of liver lesions.

      We thank the reviewer for acknowledging the importance of our results particularly in the context of the orthotopic mouse model we developed. We agree that displaying the results of continuous IFNα therapy on primary intracecal tumors, as well as the results pertaining to the few mice that develop microscopic or macroscopic liver metastasis, is important for the interpretation of our work. Thus, we evaluated the dimension of primary intracecal CRC lesions (Fig 3D,E) and we performed additional IHC characterization of the primary tumors (Fig S4A,B). The analysis showed that the dimension of the primary lesions and the markers we analyzed were non significantly modified by continuous IFNα therapy (Fig 3D,E and Fig S4A,B). These results favor the hypothesis that IFNα therapy does not modify the number of cells that spread from the primary tumors and seed into the liver, but it rather impinges on the intravascular containment of CRC cells circulating within the liver (Fig 3F). As said earlier, the data also highlight the possibility that CRC tumors may become refractory to IFNα or that the dose and schedule we adopted does not significantly affect the growth of established liver CRCs at late time points. The data are also consistent with results obtained with MC38Ifnar1_KO CRC cells indicating that continuous IFNα therapy does not require Ifnar1 expression by tumor cells to exert its antimetastatic function (Fig 4A,C-D). This is also in line with the high IFNα concentrations required to activate the "tunable" direct antiproliferative functions of this cytokine that exceed those achieved in our system (Catarinella et al, 2016; Schreiber, 2017). Text has been added in the revised manuscript at lines 175-197 and in the discussion lines 425-431.

      1. Figure 3f right shows liver images without any obvious metastatic lesion. Since authors are analysing the effect of IFNa treatment in proliferation, vascularization and immune composition in liver tumours, they may show and quantify images with metastatic lesions and restrict the analysis to the tumour area.

      Since the main finding of our manuscript regards the prevention of hepatic colonization by continuous IFNα therapy, we think that the original data presented in Fig 3G,H are representative of the overall efficacy of our strategy that confers protection in up to 60% of the mice carrying intramesenteric tumors of increasing dimensions (Fig 3H). We have thus maintained our original results, adding the quantification of all IHC data on groups of Sham control livers (n=6), as suggested. In any case, we also included the same IHC characterization of the few and small intrahepatic lesions that have bypassed the intravascular antimetastatic barrier (Fig S4C,D). Indeed, in agreement with the results observed in primary intracecal lesions, these metastatic lesions that developed in IFNαtreated mice showed similar markers of cell proliferation, neoangiogenesis, F4/80 macrophages and CD3+ T cells, as control lesions detected in NaCl-treated mice. Once again, the results highlight the possibility that CRC tumors, once established as micro/macroscopic metastases, may become refractory and resistant to IFNα therapy by downregulating the Ifnar1 in various components of the tumor microenvironment (Boukhaled et al., 2021; Katlinski et al., 2017). Text has been added in the revised manuscript at lines 175-197 and in the discussion lines 496-515.

      1. Authors analyse the recombination efficiency of different mouse CRE lines by non-quantitative methods (PCR of hepatic genomic DNA and GFP expression by immunofluorescence in healthy liver). Since PDGFRβ-Cre/ERT2 and CD11c-Cre lines are used to exclude a role of IFNa on the targeted cells, authors should provide stronger evidences to support this. They may consider studding the ablation of Ifnar1 in FACS sorted fibroblasts and myeloid cells. Moreover, it would be important showing the proportion of GFP+ cells in the sorted populations to understand how broadly these stromal populations are targeted.

      We thank the referee for raising this important issue, which is related to the relative efficiency of Ifnar1 recombination in each of the Cre-expressing mouse models we have used in the study. To this regard, we newly performed an extensive colocalization analysis quantifying the percentage of GFP+ cells that colocalize with cell specific markers (i.e., PDGFRβ, CD11c, F4/80 and CD31) of the various mouse models (PDGFRβCreERT2, CD11cCre and VeCadCreERT2, respectively) crossed with RosaZsGreen reporter mice. Colocalization analysis of GFP in the different systems was performed using the ImageJ “colocalization” algorithm developed by Pierre Bourdoncle (Institut Jacques Monod, Service Imagerie, Paris; 2003–2004). The method allows the generation of unsupervised profiles of co-localized pixels between two channels. This methodology has been included in the section Methods and Protocols, line 806-809. Of note, we observed an almost complete recombination in liver fibroblast (GFP+/PDGFRβ+), with about 98.2 ± 0.72% hepatic stellate cells that co-expressed GFP+ and PDGFRβ+ signals (see the new Fig S5E). Similarly, hepatic DCs (GFP+/CD11c+) had 94.17 ± 2.16% colocalization, while F4/80+ KCs or LCMs (GFP+/F4/80+) colocalized in 78.14 ± 5.03% (see the new Fig S5E). Finally, HECs, including LSECs, (GFP+/CD31+) showed 85.3 ± 5.03% colocalization (see the new Fig S5E,F), with no expression of GFP signals in cells other than CD31+. Note that these values indicate an almost complete colocalization of the Cre recombinase in the target cell types analyzed (see representative IF shown in Fig S5E). Text has been added in the revised manuscript at lines 225-233. Moreover, DEGs analysis between NaCl-treated VeCadIfnar1_KO and Ifnar1fl/fl HECs showed a significant downregulation of Ifnar1 expression in CD31+ VeCadIfnar1_KO cells, with a log2 fold-change of -0.387 and an adjusted p-value of 0.033, further confirming Cre recombination in HECs isolated from VeCadIfnar1_KO mice (as depicted in the heatmap of Fig 6B; the 12th gene of the Type I IFN response is Ifnar1). We have prepared all source images at higher dimension to better appreciate the colocalization within liver microvasculature. In addition, we performed several flow cytometry analyses to identify liver cell populations of Cre-recombinant mice that express Ifnar1. Unfortunately, the predicted low cellular surface expression of this molecule coupled with the experimental conditions needed to extract viable non-parenchymal cells from the liver have prevented us from obtaining informative results.

      1. Ifnar1 ablation in VeCad+ cells prevents the effect of IFNa on tumour growth (Fig. 4d), suggesting the existence of anti-tumour mechanisms beyond the effects on hepatic colonization. Authors may consider checking proliferation, vascularization and immune infiltration in these tumours to enhance their conclusion.

      We fully agree with the referee’s concern and as above mentioned, we have followed his/her suggestion and examined the existence of antitumor mechanisms beyond the effects on hepatic colonization in VeCadIfnar1_KO mice treated with NaCl or IFNα. To this end, 4 NaCl-Ifnar1fl/fl, 7 IFNα-Ifnar1fl/fl, 4 NaCl-VeCadIfnar1_KO and 4 IFNα-VeCadIfnar1_KO mice were intrasplenically injected with MC38 CRC cells (Fig S7A,B). Twenty-one days after injection, mice were euthanized and their livers analyzed for tumor size, proliferation, signs of angiogenesis (as denoted by CD34 staining) and immune infiltration (F4/80+ macrophages and CD3+ T cells). Consistent with data presented in Fig 4D, histological analysis showed that Ifnar1fl/fl mice did not develop liver metastases in IFNα-treated mice. Furthermore, metastatic lesions detected in VeCadIfnar1_KO mice treated or not with IFNα did not show significant differences in Ki67 positivity, CD34 staining or the amount of F4/80+ resident macrophages and CD3+ T cells. This further supports that the antimetastatic potential of IFNα therapy may be primarily depend on the inhibition of hepatic trans-sinusoidal migration, a limiting step in the metastatic cascade that could secondarily influence colonization and outgrowth (Chambers et al, 2002). Corresponding text has been added at lines 248-252.

      1. Immune properties of LSECs are analysed in vivo by using a mouse CRE line that targets all endothelial cells, including those ones located in lymphoid organs, and evaluating T cell composition in the spleen. I found difficult to conclude that these properties are exerted directly by LSECs and not by other endothelial cells in vivo. To clarify the local effect of LSECs in modulating anti-tumour immunity, T cell composition and activation should be checked in tumours shortly after tamoxifen administration.

      We thank the reviewer for pointing out this issue, which cannot not be tested directly because - as also mentioned by reviewer 2 - LSEC-specific Cre-recombinant driver mice do not exist . As also indicated in the cited literature, central memory T cells accumulate after peripheral priming in secondary lymphoid organs such as the spleen (Sallusto et al, 2004; Stone et al, 2009; Yu et al, 2019). To this end, the generation and regulation of antitumor immunity is a highly orchestrated multistep process involving the uptake of tumor-associated antigens by professional APCs, their time-consuming migration to draining lymph nodes and the generation of protective T cells. Unlike other APCs, HECs/LSECs do not need to migrate to draining lymph nodes to activate effector T cells, leading to a rapid intrahepatic CD8+ T cell activation. In this context, LSECs must not only efficiently uptake, process and present CRC-derived antigens coming from intravascularly contained tumor cells, but they also require the attraction and retention within the liver micro-vasculature of T cell populations necessary for the generation of effective antitumor immune responses, where chemokines play an important role (Lalor et al, 2002). As shown in Fig 6A-C, two prominent chemokines (Cxcl10 and Cxcl9) required for T cell recruitment to the liver are specifically upregulated only in HECs/LSECs from IFNα-treated Ifnar1fl/fl mice, whereas HECs from VeCadIfnar1_KO mice maintained low expression of these chemoattractants in both NaCl- and IFNα-treated mice. These data are also consistent with the in vitro cross-priming results (see Fig 7A,B) showing that in the absence of IFNα, HECs have a low capacity to prime naïve T cells (Katz et al, 2004), indicating that LSEC-primed by tumor-derived antigens coming from apoptotic intravascular CRC metastatic cells play an important role in inducing tolerance (Berg et al, 2006; Katz et al., 2004), especially when CRC cells quickly extravasate and position within the space of Disse, likely becoming less accessible to intravascular patrolling by naïve and effector T cells (Benechet et al, 2019; Guidotti et al, 2015). On the contrary, in IFNα-treated Ifnar1fl/fl mice, CRC cells are rapidly contained in the liver microvasculature (Fig 5A,B) with CRC-derived antigens that could be immediately taken up by LSECs due to their anatomical proximity and efficient endocytosis capacity, which is among the highest of all cell types in the body (Sorensen, 2020). Here, the continuous sensing of IFNα by LSECs upregulates several genes related to antigen processing and presentation pathways (Fig. 6B,D), leading to efficient cross-priming of tumor-specific CD8+ T cells to the same extent as professional APCs, such as splenic DCs (Fig 7B). Text has been added in the revised manuscript at lines 496-515. Finally, regarding the suggestion to analyze the role of HECs/LSECs in inducing antitumor T cell immunity shortly after tamoxifen administration, while we agree that it would be interesting to analyze HEC/LSEC-mediated T cell activation by treating NaCl- and IFNαtreated Ifnar1fl/fl and VeCadIfnar1_KO mice with tamoxifen after CRC cell injection, we would like to point out that tamoxifen treatment will not only induce Cre recombination and Ifnar1 loss on endothelial cells but it may also induce several “off-target” effects complicating the interpretation of the results. Indeed, tamoxifen is known to i) inhibit the in vitro proliferation of several CRC cell lines (Ziv et al, 1994), ii) impair the growth of CRC liver metastases in vivo (Kuruppu et al, 1998) and iii) modify matrix stiffness to reduce tumor cell survival (Cortes et al, 2019). Further, as IFNα modifies the hepatic vascular barrier and the accessibility of antigens by LSECs, the specific timing of tamoxifen treatment could also affect the immunological consequences of Ifnar1 deletion making these experiment impractical. For these reasons, we’d like not to perform the suggested experiment with tamoxifen.

      Reviewer #1 (Significance):

      The conclusions of this study are consistent with previously published literature and the biological insights are potentially useful to the cancer biology community.

      Reviewer #2 (Evidence, reproducibility and clarity):

      In this study Dr. Sitia's group investigated the effect of IFNα1 as perioperative agent preventing liver metastasis formation of colorectal carcinoma (CRC). To this end, various mouse models were used such as liver colonization models, i.e. intrasplenic and mesenterial injections of MC38 and CT26 CRC cell lines. Besides, spontaneous metastasis of CRC was analyzed by orthotopic injection of MC38 into the cecum. To study the influence of IFNα1 in these settings mini-osmotic pumps releasing IFNα1 were used. Moreover, conditional mouse models with a cell-type specific deficiency of Ifnar1 were compared. Altogether, the application of IFNα1 led to a reduction in liver colonization of CRC in all models studied. This was ascribed to decreased trans-sinusoidal migration of CRC and increased cross-priming by LSEC entailing in T cell activation.

      Major comments:

      Overall the study is well performed and the major conclusions seem to be drawn well. However, there are certain points I like to address:

      • First, the authors started their experiments with MC38 and CT26 CRC cell lines. At the end they just applied MC38. The rational behind this should be clearly stated. Second, as in their previous publication (Catarinella et al, 2016) F1 hybrids of C57BL/6 x BALB/c mice were used for the experiments. However, I believe that the genetic heterogeneity might be strongly increased by this approach which might lead to difficult reproducibility of the results.

      We thank the referee for raising this important issue; additional text describing the reason of our choice has been introduced at lines: 203-205. We respectfully disagree with the comment that CB6F1 hybrids may increase genetic heterogeneity and impair reproducibility of our results. Each CB6F1 hybrid individual is genetically identical to its littermates, sharing 50% of genes of each parental mouse line and being tolerant to reciprocal MHC-I genes (thus permitting the correct engraftment of both cell lines). We agree that the use of mismatched backcrosses after the F1 generation would increase genetic heterogeneity and thus may affect outcome. This is also the reason why we could not perform experiments with CT26 in the Ifnar1fl/fl conditional lines that are in C57BL/6 background and would have needed at least 10 generations of backcrossing in the BALB/c background before being suitable to such experiments. Finally, all experiments described in Fig 4, 5, 6 and 7 were performed in C57BL/6 mice using MC38 CRC cells with results that reproduced those obtained in CB6F1 hybrids, and very similarly to what we have previously reported with MC38 in C57BL/6 mice (see Fig 5 (Catarinella et al., 2016)).

      • At page 16 the authors conclude that "patients suffering from chronic liver fibrotic disease... display lower incidence of hepatic metastases". In the community there is contradictory data (see Kondo et al, BJC, 2016, https://www.nature.com/articles/bjc2016155). This should be precisely discussed, otherwise this claim should be removed.

      We thank the referee for raising this issue and modified the discussion accordingly. Text has been added in the revised manuscript at lines 455-457.

      We agree with the reviewer's suggestion and added new text to recognized the interplay between different cell types such as dendritic cells within the hepatic niche (see new text at lines 505-515).

      • Last, multiple times the authors write about data that is "not shown". Please either include these data in the manuscript or delete corresponding phrases because it is not possible for the reader to scrutinize it.

      We fully agree with the referee’s concern and displayed all “not shown results” in Fig S1E and Fig S9C-I.

      • Besides, I suggest additional experiments further substantiating the study:
      • To see if this effect of IFNα1 is cell type-specific liver metastasis of other solid tumors such as breast cancer or melanoma should be investigated.

      We agree with the reviewer's suggestion, as also indicated in our original discussion. We believe that additional experiments with other solid tumor cell lines would be important to generalize the potential of perioperative IFNα therapy. In particular, we believe that pancreatic ductal adenocarcinoma (PDAC), a highly lethal disease that most commonly metastasizes to the liver (Lambert et al, 2017), may benefit from our approach. It should be noted, however, that the pleotropic nature of IFNα allows this cytokine to inhibit tumor growth by several mechanisms. Above all, the ability of IFNα therapy to directly reduce tumor growth depends on the relative surface expression of Ifnar1 on each tumor cell and the ability to maintain such expression in the harsh tumor microenvironment during IFNα therapy. As the degradation of Ifnar1 by CRC tumors has been well described (Katlinski et al., 2017), it is possible that CRC tumors thus escaping the antitumor properties of endogenous type I interferons may respond less efficiently to therapeutic IFNα regimens such as those herein described. This notion is consistent with our data on primary orthotopic tumors (Fig. 3D,E), which are no longer responsive to continuous IFNα therapy as early as 7 days after implantation of CT26LM3 cells. In addition, the definition of the HEC/LSEC antimetastatic barrier has been possible only because CRC cells are not directly susceptible to the IFNα antiproliferative activity, which we observed in vitro at extremely high IFNα dosages (Catarinella et al., 2016) but not in vivo (as formally demonstrated by using MC38Ifnar_ko cells, Fig 4A). At any rate, we followed the reviewer’s suggestion and performed an additional experiment in which we intramesenterically injected the PDAC cell line Panc02 (H-2b, C57BL/6-derived) (Soares et al, 2014) into C57BL/6 mice 7 days after of NaCl or IFNα therapy initiation. As shown below, MRI analysis at day 21 showed that none of the IFNα-treated Panc02 challenged mice developed metastatic lesions, while NaCl controls displayed a high metastatic burden that required euthanization for ethical reasons of about 67% of these mice shortly after MRI analysis. These data indicate that perioperative IFNα therapy completely curbs metastatic development in IFNα-treated PDAC animals. The notion that these cells may be more IFNα-susceptible than CRCs may well depend on the relative capacity of the former cells to maintain Ifnar1 expression, as suggested by others (Zhu et al, 2014). Properly addressing the reviewer’s comment would thus require extensive investigations involving the establishment of new mouse models of metastases from other solid tumors, starting from the in vitro and in vivo regulation of surface Ifnar1 expression in each tumor cell. We strongly believe that this work has merit but we think that it should be reported separately.

      • The authors applied a broad range of cell type-specific mice. However, a thorough characterization of the deletion of Ifnar1 in the corresponding cell types is missing. This is crucial for the manuscript.

      We fully agree with the referee’s concern and as previously mentioned, we have improved the characterization of Ifnar1 deletion (see response to the same critique received from reviewer 1, comment 3).

      • The capillarization of the hepatic vascular niche is a crucial point in this story. I believe that the hepatic endothelium should be further characterized by additional vascular markers.

      In response to the reviewer’s suggestion, we have included in our analysis the characterization of Lyve-1, a marker of hepatic capillarization (Pandey et al, 2020; Wohlfeil et al, 2019). Indeed, IFNα treatment of Ifnar1fl/fl mice significantly increased the expression of Lyve-1, whereas IFNα treatment of VeCadIfnar1_KO mice showed no effect (Fig S9A,B), further corroborating our findings. Text has been added in the revised manuscript at lines 291-294. To better aid readers, we have prepared high-resolution images for each IF channel and have provided these data as source date for Fig S9A.

      • Last, the data and methods appear adequately presented and experiments seem to be reproducible. Just in Figure 4 the exact number of mice and replicates are not clearly presented. Otherwise, everything is fine.

      We thank the reviewer for raising this issue, which apparently was not properly described in our original submission. We have now included the exact number of mice in each experimental group in the figure legend to Fig 4.

      Minor comments:

      Overall the text and figures are accurately presented. However, I would like to add further minor comments:

      • In Fig. 1 you present the IFNα dosing regimen. How do you explain the decrease in serum IFNα after day 2? Besides, the data points at day 0 should be excluded since measuring startet from day 2! Why did you decide to treat for seven days until the start of the experiment? One could think 2 days might already be enough.

      We thank the reviewer for raising these important points. Regarding the pharmacokineticpharmacodynamic (PK-PD) behavior of our approach, we do not believe that MOP reduced its pumping efficacy after day 2 (Theeuwes & Yum, 1976), nor that counterregulatory mechanisms, such as the induction of anti-IFNα blocking antibodies, occurred in such a short time frame (Wang et al, 2001). It is neither feasible that IFNα treatment significantly downregulated Ifnar1 in the liver (as demonstrated by pSTAT1 activation after MOP treatment in Fig S1E). Rather, our results reflect the PK-PD behavior of other long-lasting formulations of IFNα, which depend on intrinsic pharmacological properties of IFNα already described in (Jeon et al, 2013). Text has been added in the revised manuscript at lines 110-112. We also corrected the figures in which we quantified serum IFNα. Indeed, blood was drawn one day before MOP implantation rather than on the same day of surgery to avoid additional blood loss, which could be a source of unnecessary stress for the animals. Therefore, we corrected the results section and Fig S1A-C and Fig 1A,B. The decision to start treatment 7 days rather than 2 days before seeding was made for several reasons: i) this study follows our previous gene/cell therapy approach, in which the time interval between reconstitution of the transduced bone marrow with Tie2-IFNα and tumor challenge was at least 7-8 weeks. We therefore thought that 7 days might be a sufficient/necessary time period to induce similar phenotypes in the liver after continuous IFNα administration; ii) 7 days is a time frame compatible with the perioperative period in humans (Horowitz et al, 2015). Furthermore, the side effects that patients may experience after IFNα therapy are generally limited to the first few days after administration, allowing patients to benefit from IFNα-induced vascular antimetastatic barriers at the time of surgery without potential side effects of IFNα. Because oncologic guidelines recommend starting adjuvant chemotherapy at least 4 weeks after surgery in stage 2-3 CRC patients at risk of later developing liver metastases (Engstrand et al, 2019; van Gestel et al, 2014), our proposed perioperative time frame does not even conflict with these indications (Van Cutsem et al, 2016). We have included additional text in the lines 131-132 to motivate the timing of our regimens.

      • Fig. 2: Did you check for metastases in other organs than the liver at the timepoint of euthanization, e.g. lungs. In the discussion section you talk about a potential influence of IFNα1 on other organs. Therefore, I think that the mice should be thoroughly analyzed and the data presented. The manuscript will benefit from it.

      We thank the reviewer for this valuable comment. Indeed, we always check for dissemination of CRC metastases on MRI analysis and necroscopy. As stated at lines 146-147 and 158 CRC tumors seeded in the liver vasculature after colonizing the liver do not spread to other organs such as the lungs. Indeed, CRC cells intravascularly seeded in the portal circulation, are trapped at the beginning of hepatic sinusoids because their diameter is bigger than that of liver sinusoids (Fig S8A,B). These micro-anatomic peculiarities are also thought to impede the spreading of tumor cells from periportal to centrilobular areas and to the general circulation (Catarinella et al., 2016; Vidal-Vanaclocha, 2008), and this is consistent with studies showing that in CRC patients undergoing surgery the majority of CRC-derived circulating tumor cells are found in the portal vein (Deneve et al, 2013).

      • Overall, MRI pictures and pictures of IHC or IF are sometimes too small to see. Please provide pictures with larger magnification or enlarge the images.

      We thank you for this suggestion and we have indeed increased the size of all MRI, IHC, and IF images to the maximum that will fit within the figure. In addition, we presented the images at the highest magnification available, without making digital enlargements that would significantly reduce resolution.

      • Fig. 3 F, G: immune cell infiltration in the liver was analyzed. Please compare it to untreated, tumor-free wildtype liver tissue.

      We appreciated the reviewer's suggestion and included the results of six Sham mice per each marker in our analysis. The text was added on the figure legends to Fig 3H and Fig S4B,D.

      • Fig. 6: the graphs are too small to be read, especially the volcano plot and the gene names of the heatmap.

      We increased the font size of genes in the volcano plots and heatmap in Fig 6A,B, as suggested.

      • Fig. S6: Pictures of co-immunofluorescences are presented. For the reader it is really hard to distinguish the stainings and to identify colocalized areas. Please provide pictures with one channel to better compare the marker expression.

      We thank the reviewer for pointing this out and we have tried to make each panel as large as possible to fit into a two-column figure. We have also prepared high magnification images of each channel for all immunofluorescence images, which we provide as source data. We hope that this is sufficient to help readers to interpret our results without increasing the number of main or supplementary figures.

      • From page 8 onwards (section about transgenic mice) LSEC was used as kind of synonym for hepatic endothelial cells. Since there is still no LSEC-specific driver mouse, it should be stated "hepatic endothelial cells" instead.

      We agree with this suggestion and thus have indicated that the results refer to HECs but include a large majority of LSECs. Indeed, LSECs make up the majority (~89%) of the total HEC population (Su et al, 2021). In addition, some SEM and TEM analyses were performed only on LSECs, as well as the IF analyses. Therefore, we believe that LSECs play an important role in this process. Although not specifically suggested, we have also changed the title of our manuscript to reflect the reviewer's suggestion. Thus, we propose "Continuous sensing of IFNα by hepatic endothelial cells shapes a vascular antimetastatic barrier" as new title.

      • P. 11: there is a typo: Fig. Fig. S6G,H

      We corrected this typo.

      • P. 13: the authors describe Gata4 as inhibitor of subendothelial matrix deposition. This should be precisely written, since Gata4 originally is described as master-regulator of liver sinusoidal differentiation which leads to liver fibrosis development upon loss of Gata4.<br /> Besides, I came across a study of the same group that investigated the role of Notch signaling in hepatic CRC and melanoma metastasis (Wohlfeil et al, Cancer Res, 2019, https://aacrjournals.org/cancerres/article/79/3/598/638600/Hepatic-Endothelial-Notch-Activation-Protects). Similar to your study they tie the reduction in hepatic metastasis to capillarization of the hepatic microvasculature.

      We agree with this suggestion and modified text accordingly. We are also glad that our results agree with previous reported literature that has now been correctly cited at lines 351-356 and in the discussion lines 474-476.

      • The discussion reads like paraphrasing the results section. The manuscript would clearly benefit if the discussion section had been rewritten short and concisely.

      We agree with this suggestion, and we have modified discussion accordingly. We are also willing to shorten the discussion by removing the schematic model that could possibly be used as a graphical abstract.

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      Reviewer #2 (Significance):