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Reply to the reviewers

We are very grateful to the two referees for their constructive comments and suggestions which have helped improve the quality of our manuscript.

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

Ribes et al developed a FACS-based serological assay to detect antibodies against the SARS-CoV-2 spike protein in various hosts. The authors described an assay that is more sensitive and quantitative, allowing the detection of anti-spike antibodies with just a few ul of blood, and highlighted the potential of the assay as an alternative to commercial ELISA-based assays against SARS-CoV-2 spike protein.

Major concerns *

1. * On being quantitative analysis - the authors have used 20/130 reference serum from NIBSC as an example in figure 1. How does the RSS of the described assay compare/correlate with the Ab values in WHO standards? This should be included. * Response: We thank the referee for this helpful suggestion, and have now included the information on the IgG BAU in the legend of figure 1, and alluded to the characterisation of the 20/130 by the Expert Committee on Biological Standardization (Mattiuzzo et al., 2020) on lines 410-414 in the main text of the manuscript

* On sensitivity and specificity - AUC profiles should be performed and included. *

Response: If the Jurkat-flow test was intended for clinical use, the precise determination

of the sensitivity and specificity of the test would indeed be absolutely essential. As was already mentioned at the end of the introduction, the Jurkat-S&R-flow test is only destined to be used by research laboratories, for research purposes. This has now also been clarified at the end of the abstract : “Whilst the Jurkat-flow test is ill-suited and not intended for clinical use ….”

As suggested by the referee, to establish the sensitivity and specificity of a diagnostic test, it is indeed practical to use the Area Under the Receiver Operating Characteristic (ROC) curve (AUC). A ROC curve is a plot of the true positive rate (Sensitivity) in function of the false positive rate (100-Specificity) for different cut-off points of a parameter. Determining properly the sensitivity and specificity of a test thus requires large collections of samples which are known to be either certainly positive or certainly negative, which we did not have access to.

* Are there any cross-reactivity with the other spike proteins from other CoVs? If so, what is the level of cross-reactivity? *

Response: To assess cross-reactivity with other CoVs, we would have needed either Jurkat cells expressing the spike proteins from other CoVs, or sera with known reactivity against CoVs. Since we did not have access to such cells or sera, we were not in a position to address such a question.

* While the authors have showed that the flow-based assay has a more dynamic range, there is insufficient data showing that it is "more sensitive", as stated in the abstract. The authors should reflect this in the text. *

Response: In the abstract, we do not state that the Jurkat-S&R-flow test is more sensitive than the ELISA, but “at least as sensitive”. On the other hand, we state that it is more sensitive than the HAT test, which it clearly is since there are more than a dozen samples on figure 2 that were positive with either or both ELISA and Jurkat-S&R-flow but were negative by HAT.

Of note, we have recently described an improved protocol, called HAT-field, which significantly improves the sensitivity of HAT, albeit at the cost of decreased specificity (https://doi.org/10.1101/2022.01.14.22268980)

* Is trimer or monomer Spike expressed on the surface of the cells? *

Response: Several studies have shown that, when the spike protein is expressed in human cells after transfection or transduction, it is in its native trimeric form at the cells’ surface and can even cause fusion with cells expressing the ACE2 receptor. This has now been clarified in the introduction section.

* While there are significant advantages of the flow-based assay, the authors should discuss the limitations of a flow-based assay as a serological assay, especially for sero-surveillance and cohort studies. For instance, HTS application is usually limited for cell-based assays. In addition, while the assay is relatively cheap, it is worth nothing that the cytometer is an expensive equipment that not all laboratories have. *

Response: We bring the referee’s attention to the fact that those points are discussed at the end of the introduction (line 161-165) : “ Since the Jurkat-flow test calls for the use of both a flow cytometer and cells obtained by tissue culture, it is clearly not destined to be used broadly in a diagnostic context, but its simplicity, modularity, and performances both in terms of sensitivity and quantification capacities should prove very useful for research labs working on characterizing antibody responses directed against SARS-2, both in humans and animal models. “

Minor concerns*: *

1. * Figure 1 - text and numbers in the FACS plots are too small. Please adjust. In addition, for some of the FACS plots shown (eg. neg cont and serum 20/130), the population is right at the axis. Please pan the x-axis to allow better visualisation.
2. Figure 3A - please label axis.
3. Figure S2 - please label axis.
4. In general, please check through all figures for axis labels and also adjust the front size. For most, the text is too small. * Response: Sizes of numbers and text increased, and axis labels added in all figures*

Reviewer #1 (Significance (Required)):

As already discussed by the authors, there have already been quite a number of studies that have demonstrated the advantages of a flow-based assay for serological analysis for SARS-CoV-2. However, Ribes et al showed a new way to separate out alloreactivity from specific staining, which is important in reducing false positivity in serological assay. As more and more people receive their vaccination, there is a significant interest in immune-monitoring following vaccination. Given the more dynamic range of the flow-based assay, this is one good way to monitor antibody response. *

Expertise: My research interest focuses on the study of SARS-CoV-2 antibody responses following infection or vaccination. * *

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

In this paper, Joly and colleagues make use of a flow cytometry-based assay to measure in a reliable and sensitive manner the presence of IgG, IgA and IgM in blood samples from post-COVID human patients and also from laboratory (mouse and hamster) and domestic animals (dogs and cats). They find that the test is appropriate to detect the presence of humoral immunity in all species tested. The manuscript is clearly written and the Figures are clearly presented. The experiments with rodente deliberately infected with inactivated SARS-CoV-2 shows (Fig. 3) that the method is reliable and able to clearly discriminate positive from negative sera. Interestingly, dogs and cats were sampled from households in which the owners had been found to have passed COVID-19 by PCR. Among this cohort of house animals they find more than 90% seroconversion for dogs and slightly less than 30% of clear seroconversion in cats. We find however that the manuscript would benefit by establishing a clear cut-off value of "Specific Stain" for dogs and cats (Fig. 3). This could be implemented by including data from pre-COVID dog and cat sera or in its defect, sera from those species collected at households in which their owners were vaccinated and did not pass the infection. Another point of criticism that could be resolved is that the channels for flow cytometry in Figure 1 do not seem to be adequately compensated and there is evidence of some cross-contamination between FL1 and FL3. *

Responses: We thank the referee for bringing our attention to the fact that we had presented the data on sera from cats and dogs in a confusing manner, which led the referee to believe that the sets of samples presented were representative of the population of animals whose owner had tested positive for Covid-19. In fact, for this experiment, which was only ever intended as a preliminary proof of concept that the test could be adapted very simply to companion animals, we used sets of sera which we knew would contain approximately 50 % of positive and 50 % negative samples because they had previously been screened by sero-neutralisation (incidentally, a manuscript by Bessière et al., describing that work on sera from 131 cats and 156 dogs, has very recently been submitted for publication). To prevent possible confusions, we have now reworded the description of this proof of concept experiment, in the legend of figure 3, the text, and the methods section.

Regarding the question of a clear cut-off value, as when using human samples, we would suggest using a value of 40 for the instruments settings we used, corresponding to an RSS of 20 (i.e. 20 fold the value of the negative control). With such a value, it can be seen that one cat serum would be considered positive whilst showing no neutralising activity, but one dog serum which showed weak neutralising activity would be considered negative. If anything, this example highlights the difficulty in setting a precise cut off value for any biological test.

Regarding the question of inadequate compensation between channels 1 and 3, this is due to the fact that the Cellquest software does not allow for FL1/FL3 compensation, which is explained in the figure legend (see lines 208-210). We decided to simply draw the gates as they appear on figure 1 because attempts at post-acquisition compensation using the Flowjo software did not give satisfactory results. Incidentally, no compensation is required when samples are acquired on a Fortessa flow cytometer, where mCherry can be excited by a different laser (see figure S1) or if one uses the Jurkat-S&G-flow version of the test as in figure 3D for hamster sera (using Jurkat-GFP as negative control, and secondary antibodies conjugated to Alexa 488).

Minor points*: *

*-Figure 1.- Please describe the y- and x-axis. Such as they are is difficult to find out. *

Response: Done

* -It would be advisable to mention in Materials and Methods (page 22) how blood was collected from cats and dogs. *

Response: We thank the referee for highlighting this, and have now provided the information in the relevant method section.

* -Line 856, page 22, "ad libidum" should be "ad libitum" *

Response: We thank the referee for spotting this typo, which has been corrected

* Reviewer #2 (Significance (Required)):

This is another step in the implementation of flow cytometry tests, instead of ELISA or CLIA serological tests based on the use of recombinant proteins, as a more sensitive and reliable method. The description of the high frequency of human-domestic animal transfer of SARS-CoV-2 will also add to the idea that it is humans who transmit the virus to those animals. *

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

In this paper, Joly and colleagues make use of a flow cytometry-based assay to measure in a reliable and sensitive manner the presence of IgG, IgA and IgM in blood samples from post-COVID human patients and also from laboratory (mouse and hamster) and domestic animals (dogs and cats). They find that the test is appropriate to detect the presence of humoral immunity in all species tested.

The manuscript is clearly written and the Figures are clearly presented. The experiments with rodente deliberately infected with inactivated SARS-CoV-2 shows (Fig. 3) that the method is reliable and able to clearly discriminate positive from negative sera. Interestingly, dogs and cats were sampled from households in which the owners had been found to have passed COVID-19 by PCR. Among this cohort of house animals they find more than 90% seroconversion for dogs and slightly less than 30% of clear seroconversion in cats.

We find however that the manuscript would benefit by establishing a clear cut-off value of "Specific Stain" for dogs and cats (Fig. 3). This could be implemented by including data from pre-COVID dog and cat sera or in its defect, sera from those species collected at households in which their owners were vaccinated and did not pass the infection. Another point of criticism that could be resolved is that the channels for flow cytometry in Figure 1 do not seem to be adequately compensated and there is evidence of some cross-contamination between FL1 and FL3.

Minor points:

• Figure 1. Please describe the y- and x-axis. Such as they are is difficult to find out.
• It would be advisable to mention in Materials and Methods (page 22) how blood was collected from cats and dogs.
• Line 856, page 22, "ad libidum" should be "ad libitum"

Significance

This is another step in the implementation of flow cytometry tests, instead of ELISA or CLIA serological tests based on the use of recombinant proteins, as a more sensitive and reliable method. The description of the high frequency of human-domestic animal transfer of SARS-CoV-2 will also add to the idea that it is humans who transmit the virus to those animals.

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

Ribes et al developed a FACS-based serological assay to detect antibodies against the SARS-CoV-2 spike protein in various hosts. The authors described an assay that is more sensitive and quantitative, allowing the detection of anti-spike antibodies with just a few ul of blood, and highlighted the potential of the assay as an alternative to commercial ELISA-based assays against SARS-CoV-2 spike protein.

Major concerns:

1. On being quantitative analysis - the authors have used 20/130 reference serum from NIBSC as an example in figure 1. How does the RSS of the described assay compare/correlate with the Ab values in WHO standards? This should be included.
2. On sensitivity and specificity - AUC profiles should be performed and included.
3. Are there any cross-reactivity with the other spike proteins from other CoVs? If so, what is the level of cross-reactivity?
4. While the authors have showed that the flow-based assay has a more dynamic range, there is insufficient data showing that it is "more sensitive", as stated in the abstract. The authors should reflect this in the text.
5. Is trimer or monomer Spike expressed on the surface of the cells?
6. While there are significant advantages of the flow-based assay, the authors should discuss the limitations of a flow-based assay as a serological assay, especially for sero-surveillance and cohort studies. For instance, HTS application is usually limited for cell-based assays. In addition, while the assay is relatively cheap, it is worth nothing that the cytometer is an expensive equipment that not all laboratories have.

Minor concerns:

1. Figure 1 - text and numbers in the FACS plots are too small. Please adjust. In addition, for some of the FACS plots shown (eg. neg cont and serum 20/130), the population is right at the axis. Please pan the x-axis to allow better visualisation.
2. Figure 3A - please label axis.
3. Figure S2 - please label axis.
4. In general, please check through all figures for axis labels and also adjust the front size. For most, the text is too small.

Significance

As already discussed by the authors, there have already been quite a number of studies that have demonstrated the advantages of a flow-based assay for serological analysis for SARS-CoV-2. However, Ribes et al showed a new way to separate out alloreactivity from specific staining, which is important in reducing false positivity in serological assay. As more and more people receive their vaccination, there is a significant interest in immune-monitoring following vaccination. Given the more dynamic range of the flow-based assay, this is one good way to monitor antibody response.

My research interest focuses on the study of SARS-CoV-2 antibody responses following infection or vaccination.

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

Learn more at Review Commons

Reply to the reviewers

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

Summary: This manuscript documents a very thorough biophysical, structural and functional dissection of interactions between the RNA-binding protein Rrm4 and the endosomal adaptor Upa1 in the filamentous fungus Ustilago maydis. It has been shown previously that the Rrm4-Upa1 interaction is critical for mRNA transport in this system as mRNAs hitchhike on motor-associated endosomes. Here, the authors reveal using modelling that Rrm4 has three MLLE domains, including a cryptic one that had not been identified previously. They then report the crystal structure of MLLE2 and analyze the distribution anf arrangement of the MLLE domains in the protein using SAXS. They then show using pulldowns and isothermal titration calorimetry that MLLE3 is critical for the Upa1 interaction (via the PAM2L domains of Upa1) and that MLLE2 contributes to Rrm4 localization in vivo when the MLLE3-Upa1 interaction is partially impaired. The study suggests that Rrm4 has a platform of MLLE domains for orchestrating Rrm4 function. Overall, this is technically a high quality study. However, a number of points (mostly minor) should be addressed.

Major comments:

__A key part of the study if the in vivo work illustrating a role for MLLE2 in regulating Rrm4 localization when the system is sensitized. Some aspects of this part of the work need clarifying.

a) The authors should show that the abberant staining is indeed microtubule-related with the benomyl experiment that they used in Jankowski et al. 2019. __

We included this important control in Figure EV5F demonstrating that the aberrant staining is no longer visible after the microtubule inhibitor benomyl treatment

b) The authors claim from these experiments that MLLE2 contributes to endosomal targeting (as there is ectopic protein on other structures (presumptive microtubules)). However, to make this claim, the authors would need to measure the intensity of the mutant Rrm4 protein on endosomes and/or the colocalization of these Rrm4 variants with endosomes, as they do in other experiments in this paper. Otherwise, it is possible that the MLLE2 deletion has another effect, e.g. increasing protein stability, and thus increasing the likelihood of binding to structures other than endosomes. If available, data on the relative abundance in the cell of the protein expressed from the wild-type control (rrm4-kat) and MLLE2 deletion constructs (e.g. rrm4-m1,2delta-kat) should be provided.

As indicated by the reviewer, a critical point is identifying a function of MLLE2. Surprisingly, the domain is conserved in evolution, but , we do not see a mutant phenotype under optimal culture conditions. Therefore, we challenged the system and observed the mislocalisation of Rrm4, if the MLLE2 domain is deleted. However, the overall amount of shuttling Rrm4-positive endosomes was not strongly affected according to our kymograph experiments. We observe aberrant staining, which is not seen with the Rrm4 wild-type protein. Thus, under challenging conditions, we do see a function of MLLE2.

To address the valid point of the reviewer, we quantified the signal intensities in kymographs of the most important Rrm4 variants. As indicated in Figure 5E, we observed that the maximum fluorescence intensity in kymograph signals was reduced when Rrm4 variants are mislocalised to microtubules while the minimum intensities were comparable in all strains. This underlines that a subset of Rrm4 molecules are no longer shuttling through the cell and most likely are attached to microtubules (to prove the involvement of microtubules, we did benomyl treatment which is now shown in Figure EV5F). We also included a Western Blot experiment (Figure EV5G) demonstrating that neither MLLE1 nor MLLE2 deletion impacts the total protein amount of Rrm4. These data support the notion that MLLE2 contributes to endosomal targeting.

c) Was the data in Figure 5D scored blind of the identity of the samples? Given that the classification has to be done manually, it is important to confirm the phenotypes are robust to blinding (at least for the key comparisons).

We agree entirely that manual evaluation of microscopic images has to be carried out with utmost care. The phenotype of aberrant microtubule staining is not easily detectable, and it needs an experienced person to quantify this. The data were analyzed by a second experimentalist with experience in evaluating microscopy images to validate the system’s robustness. Notably, the key findings were confirmed in both cases aberrant microtubule staining was only observed when the MLLE domain was mutated. However, the second person reported difficulties in differentiating a bundle of Rrm4 signals or stained microtubules. Therefore, this person quantified higher values with less experience in Rrm4 movement. In essence, we can rely on the key findings. We included the information in the section “Materials and methods” and gave the comparison in Figure EV5H.

If points b and c are addressed, it should be possible to draw an arrow between the gray question mark protein in Figure 6 and the endosome surface, which is what I assume the authors believe to be case based on their discussion.

Having addressed both points, we have also improved the model. To this end, we added a second unknown protein component (grey oval with a question mark) that interacts with MLLE2 and the endosomal surface. Thereby the hierarchical order with the accessory role of MLLE2 during endosomal attachment is stressed.

Minor comments:

1. The first line of the abstract is quite bold. It is hard to quantify the role of transport vs RNA stability for example, so I suggest this sentence is toned down. Correct, the first line now reads, “Spatiotemporal expression can be achieved by transport and translation of mRNAs at defined subcellular sites”.

Line 269: change "amount of motile Rrm4-M12delta-Kat positive signals" to "number of motile Rrm4-M12delta-Kat positive signals".

Changed as mentioned above.

Figure 3 legend: Insert "Variant" before "amino acids of the FxP and FxxP..." to indicate what is labeled in gray. Change "fond" to "font" in the same sentence.

Corrected as mentioned above.

The cartoons of the different protein variants are very helpful but I had problems spotting the Upa1-Pam2L deletions due to the similar gray to the background of the protein. This would perhaps be clearer if the gray used for the background was lighter than it currently is.

We improved the contrast by reducing the background of Upa1 to a lighter grey tone in all the corresponding figures.

The residual motility of wild-type Rrm4 when PAM2L1 and PAM2L2 are both mutated (Figure 5C) is reminiscent of what is seen in a complete Upa1 deletion in the group's previous work. It would be helpful to point this out to the reader, as well as the implication that other proteins are contributing to Rrm4's linkage to endosomes. After all, some of these other adaptors might contact MLLE2 of Rrm4.

We addressed this point by referring to our previous publication with the following sentence: “Comparable to previous reports, we observed residual motility of Rrm4-Kat on shuttling the endosomes if both PAM2L motifs are mutated or if upa1 is deleted. This indicates that additional proteins besides Upa1 are involved in the endosomal attachment of Rrm4 (Pohlmann et al., 2015).”

Some of the y-axes of the charts should be more descriptive so that the reader can understand the plots even before they consult the legends. For example, in Figure EV4A and EV5D and E, which protein is being to referred to in each 'number of signals' plot should be included. In Figure 5D, 'Hyphae [%]' would be clearer as 'Hyphae with MT staining of Rrm4 [%]'

We improved this in Figures EV4, 5D and EV5.

Figure EV5 legend title: this could be misleading as the authors are seeing ectopic MT localization rather than a deficit in microtubule association.

Corrected to “Deletion of MLLE1Rrm4 and -2 cause aberrant staining of microtubules”.

Reviewer #1 (Significance (Required)):

__The Feldbrugge group has previously mapped interactions between Upa1 and Rrm4 (Pohlmann et al., 2015) and some conclusions are corroborated in the paper by Boehm et al. The paper under review is, however, a significant advance due to the identification of the third MLLE domain, detailed biophysical characterization of the interactions, the structural insights, and evidence of a subsidiary role of MLLE2. The work would of course be stronger if the target of MLLE2 had been identified but I think this is beyond the scope of this initial work. To my knowledge, this is one of the most extensive analyses of the interactions mediated by MLLE and PAM domains and will be of interest to others working on these protein features. The work will also appeal to those interested in the links of localizing mRNAs with motor-associated membranes, which is an emerging field.

Reviewer expertise: I have a long-standing interest in molecular analysis of mRNA trafficking mechanisms. I do not have experience in fungal genetics. __

**Referee Cross-commenting**

It seems that we are in agreement that this is solid work and that biochemical and biophysical analysis of the MLLE-PAM interactions will be of significant interest to those working on those domains (or proteins containing those domains). I agree with the comments of the other reviewers and there are clearly some essential minor revisions needed to strengthen the evidence for their conclusions and some clarifications. I think it is a long shot that RNA binding to the RRMs will affect the MLLE-PAM interactions and would require quite a lot of work to show this conclusively. The study would, however, be more impactful if this was shown to be the case, or the target of MLLE2 was found. Nonetheless, I would not say these new avenues of research are necessary to find a home in one of the Review Commons journals.

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

Devan, Schott-Verdugo et al.

Summary

In this study the putative MLLE RNA-binding motifs of the endosomal RNA-binding protein, Rrm4, from Ustilago maydis were examined using structural and genetic analyses. MLLE motifs are conserved in polyA-binding proteins (Pab1/PABPC1) and found also in Rrm4, which was shown to reside on motile endosomes and deliver septin mRNAs for endosome-localized translation during polarized growth. Upa1 on the endosome interacts with Rrm4 via its PAM2L domain that itself interacts with the MLLE domains of proteins like Pab1. Mutations in the known MLLE domain of Rrm4 were earlier shown to affect localization to endosomes. Here, the C-terminal domain of Rrm4 was revealed to have three divergent MLLE motifs using comparative modeling; only two of which were previously predicted. Crystallization and X-ray diffraction analysis of a truncated version of bacterially produced Rrm4, showed MLLE2 is most similar to that of PABPC1 and UBR5, although MLLE1 and 2 are somewhat divergent in the key region of PAM2 binding. Small angle X-ray scattering of recombinant full-length or truncated Rrm4 revealed that the MLLE domains might form a platform that could allow for multiple contacts with different binding partners. In vitro binding studies with different N-terminal GST-tagged versions of the Rrm4 were used to examine for interactions with PAM2 sequences of Upa1 using N-terminal hexa-histidine-SUMO fusions. It was found that Pab1-MLLE interacts with the PAM2, but not PAM2L, domain of Upa1. In contrast, the complete Rrm4 MLLE region (G-Rrm4-NT4) interacted with the PAM2L domain, but not the PAM2 of Upa1. Notably, the interaction with PAM2L required the third MLLE and neither MLLE1 nor MLLE2, nor both. No significant differences in affinity were observed and were similar to that of the Pab1 MLLE. The results also show that the MLLE3 has a higher affinity for the PAM2L2 than PAM2L1 of Upa1.

To examine the biological role of the Rrm4 MLLEs, U. maydis strains bearing deletions in the domains of Rrm4 were examined for hyphal growth and endosomal transport (latter using Upa1-GFP and Rrm4-mKate2). Only the loss of the MLLE3 domain inhibited polarized growth (as seen with the full deletion of RRM4) and not the deletion of either MLLE1 or 2. Similar results were obtained regarding endosome shuttling. Thus, in line with the biochemical experiments performed the MLLE3 domain alone (of the three identified) is necessary for the biological actions of Rrm4. This suggested the MLLE1 and 2 are not necessary for function under these conditions.

To examine this further, Upa1 carrying mutations in the PAM2L 1or PAM2L2 domains were examined. It was found that the deletion of both PAM2L domains affected unipolar growth resulting in bipolar growth similar to the deletion of UPA1 alone. This phenotype was observed even upon the deletion of Rrm4 MLLE1 and 2 in the same background as the PAM2L mutants. The mutation of both PAM2L domains led to a reduction in Rrm4-labeled shuttling endosomes, which suggests that these domains help anchor Rrm4 to endosomes. When only the PAM2L1 domain is present in Upa1 there was a larger increase in hyphae with aberrant microtubule staining than upon the loss of PAM2L1. The authors suggest that this indicates PAM2L2 is more important and prescribes an accessory role for MLLE2 in endosome association.

Comments: Overall, the study seems well conducted. We cannot comment on the structural aspect of the work since this is not our field of expertise. That said, the biochemical and genetic/functional studies appear solid, well thought-out, and clearly presented. No new experiments are necessary to support the general claims of the paper, however, experiments suggested below might make it more revealing with regards to the connection between RNA binding and MLLE-PAM2L interactions (i.e. endosome localization and RNA binding functions).

1. Line 286 - It reads the they "Next, we investigated the association of Rrm4 -M12D-Kat in strains expressing PAM2L1. Thus, the endosomal attachment was solely dependent on the interaction of MLLE3 with the PAM2L2 sequence of Upa1." Unclear - wouldn't lacking PAM2L1 (and not expressing) fit the logic of the sentence? We corrected this with the sentence, “Next, we investigated the association of Rrm4-M1,2D-Kat in strains expressing Upa1 with mutated PAM2L1”.

Several questions regarding the specificity of PAM2 vs. PAM2L domains. What happens when you switch/replace the PAM2L1 or 2 of Upa1 with Upa1 PAM2 domains? Are they exclusive? What happens when the MLLE3 of Rrm4 is switched with that of Pab1? And if one does both - does that restore functionality to Rrm4?

These are very interesting suggestions. Previously, we have shown that a single PAM2L1 or PAM2L2 sequence of Upa1 is sufficient for unipolar growth and recruitment of Rrm4 to endosomes. Please note that Upa1 with mutated PAM2L1 and L2 still contains a PAM2 motif. Furthermore, mutating the PAM2 motif of Upa1 did not affect Rrm4 shuttling or unipolar growth. Thus, switching the domains would mostly address whether the precise location within Upa1 would be important. This is interesting but, unfortunately very labour-intensive and beyond the manuscript’s current scope.

Switching MLLE3 with MLLE of PAB1 is an interesting approach. One might expect that Rrm4 can be recruited to endosomes again. However, Rrm4 would also interact with numerous other proteins containing PAM2 motifs like deadenylase Not4. Here it would compete with the MLLE of Pab1. Thus, it would be expected that Rrm4 is on the surface, but the protein will be mistargeted to other proteins causing pleiotropic alterations. It will be difficult to judge whether Rrm4 functionality is restored or whether other processes are disturbed. In essence, these are stimulating ideas, but we believe that these experiments are beyond the scope of the current study. In the future, we might address this point by using a heterologous peptide-binding pocket or tethering approach.

Likewise, what happens if Upa1 only has PAM2L2 instead of only PAM2L1 domains? Does that alter function - perhaps now one can observe a contribution of MLLE1? If it it's there it's likely to have function. Anything known about the post-translational modification of these MLLE or PAM domains? Does it change during unipolar vs. bipolar growth? Perhaps the different MLLE domains are regulated in such a fashion?

Again also very valid points. Upa1 with two PAM2L2 motifs might interact stronger. The problem is that one PAM2L motif is sufficient for interaction, and we do not see a strong phenotype.

Currently, we do not know if post-translational modifications regulate the MLLE domains. This could alter the binding affinity or specificity, and by expressing fungal proteins in E. coli, we might have missed this type of regulation. However, we addressed the function of MLLE1 and MLLE2 in U. maydis using a genetic approach. We deleted the corresponding domains and interfered with potential regulation by posttranslational modification. Thus, we cannot exclude post-translational modification, but it appears to be not essential for function. We will address the posttranslational regulation of Rrm4 in more detail in the future.

Can the authors show whether the binding of mRNA cargo (e.g. Cdc3 mRNA) to the RRM motifs of Rrm4 affects the interaction between any of the MLLE-PAM2L pairs, or vice versa (i.e. does the MLLE-PAM2L interaction affect mRNA binding)?

In previous studies, we have investigated a version of Rrm4 carrying a mutation in the first RRM motif of Rrm4. According to RNA live imaging, the respective strains exhibit a loss of function phenotype and mRNA transport is strongly affected. However, the endosomal association of Rrm4-mR1-Gfp is not affected, indicating no direct cross-talk between RNA-binding via RRM1 and endosomal attachment via MLLE3. Also, a version of Rrm4 carrying a deletion of all three RRM domains is still shuttling on endosomes. The two functions, i.e. RNA binding and endosomal binding, appears to be carried out by two independent platforms, i.e. three RRMs and three MLLEs, respectively. The overall structure of the protein also reflects this. The RRM domains are structurally clearly separated from the flexible MLLE domains.

Discussion line 311 It is written that the three MLLE domains "collaborate for optimal functionality..." Perhaps there's a misunderstanding here, but the authors show that MLLE3 domain alone is necessary & sufficient for function, so where is the collaboration? MLLE2 may have an accessory role according to the authors, but we do not know if it is in collaboration with MLLE3 or independent thereof. Since the KD of MLLE3 is not affected by the presence or absence of MLLE1,2 in vitro at least, it may be that they have independent, and not collaborative, roles.

Correct, we rephrased this more carefully. We omitted the collaboration aspect. It now reads, ”but a sophisticated binding platform consisting of three MLLE domains with MLLE2 and MLLE3 functioning in linking the key RNA transporter to endosomes.”

Reviewer #2 (Significance (Required)):

This paper concerns functional domains found in an endosome-localized RNA binding protein, U. maydis Rrm4, which is necessary for localized translation on endosomes and subsequent unipolar growth. Here the authors show using structural, biochemical, and genetic studies that instead of one or two MLLE protein-protein interacting domain in Rrm4 there are three, although one (MLLE3) is necessary and sufficient for full function. This work is for an audience interested in those studying RNA trafficking and its role in cell physiology, which is our expertise. The work is interesting, but it could be made more so especially if a connection was established between the RNA-binding function of the RRM domains and the MLLE-PAM2L interaction(s). At this point it is solid technical work and could be published after minor revisions.

**Referee Cross-commenting**

I concur with the comments of the other reviewers in that the work is solid and necessitates minor revisions in order to be published. Clearly, establishing a connection between the RNA-binding function and the MLLE-PAM interactions of Rrm4 would be an interesting and worthy pursuit that might enhance the novelty of the work, but I agree that it could belong to future studies.

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

__ Summary: Long-distance subcellular transport of mRNAs is achieved through selective and dynamic interaction with the transport machinery. Using the highly polarized hyphae of Ustilago maydis, the authors previously showed i- that mRNAs can hitchhike on actively transported endosomes for proper distribution, and ii- that the connection between mRNAs and endosomes is mediated by the interaction between a C-terminal MademoiseLLE (MLE) domain of the RNA binding protein Rrm4 and the Upa1 adapter protein. In this study, the authors aimed at more precisely characterizing the structural and molecular bases underlying the Rrm4-Upa1 interaction. Combining structural modeling and X-ray analyses, they discovered a non-canonical, and previously missed, MLE domain (MLE1) in Rrm4, and characterized the structure of the second MLE domains (MLE2) of Rrm4. Through binding assays, they showed that the three MLE domains exhibit different binding properties, and that MLE3 is the only domain capable of binding to the PAM2 domain of Upa1. Consistent with this finding, functional assays performed in U. maydis revealed that MLE3 is the main domain involved in interaction with endosomes and trafficking, MLE1 and 2 having either no or minor functions in this process.

The manuscript is very-well written, the data are of high quality and clearly presented. A wide range of complementary approaches has been used to molecularly and functionally characterize the different MLE domains of Rrm4. From an "RNA transport" perspective, this manuscript falls short of a main novel findings as the domains characterized in this study (MLE1 and 2) don't have a clear function in connecting mRNAs to the transport machinery. From an "MLE domain" perspective, this work however provides interesting information about non-canonical domains and structures, and about binding and function specificity. As described below, my major concern relates to the role played by the ML2 domain of Rrm4, a role referred to as "accessory" by the authors. __

__

Major comments: __

The authors conclude from their results that ML2 has an accessory role in promoting association with endosomes.

1- This conclusion is made based on in vivo experiments showing that a form of Rrm4 lacking the M2 domain, in contrast to wild-type Rrm4, aberrantly attached to MTs in a context where the Rrm4-Upa1 interaction mediated by MLE3Rrm4 has been weakened (Upa1-pl2m). Although the results are convincing, their interpretation is less. The authors, indeed, claim that the observed phenotype results from "the static accumulation of Rrm4" due to reduced interaction with endosomes. Why then don't they see a decrease in the motility/transport properties of Rrm4-M2Δ in this context then? Also, do the authors see a decrease in the co-localization of Rrm4-M2Δ with endosomes (which would be expected if the interaction is decreased)? Can the authors perform IP or co-sedimentation experiments to strengthen their hypothesis?

This is a fair criticism that was also raised by reviewer 1. In the improved version of the manuscript, we now include important control experiments demonstrating that (i) the aberrant localisation is microtubule-dependent (Fig. EV5F) (ii) the mutations do not cause differences in protein amounts of Rrm4 (Fig. EV5G) (iii) the key findings of the aberrant microtubule staining, which were scored manually in microscopic images were verified independently by two persons (Fig. EV5H) and (iv) most importantly, Rrm4 signal intensity is decreased in processive signals of our kymograph analysis (Fig. 5E). We firmly believe that this set of experiments strengthens our conclusion that MLLE2 plays an accessory role in the endosomal attachment (Fig. 6).

2- Whether MLE2Rrm4 mediates interaction with endosomes through association with Upa1 is unclear, as the binding assays performed in Figure 3 test for association of Rrm4 variants with single isolated domains of Upa1, not with the full-length protein. Assessing the binding of Rrm4-M2Δ variants with Upa1-PL2m would help interpreting the phenotypes described in Figure 5.

Unfortunately, it is difficult to express full-length Upa1 protein in E. coli due to the presence of extended unstructured regions. To overcome this limitation, we performed yeast two-hybrid experiments with full-length proteins of Rrm4 and Upa1. We were able to recapitulate qualitatively the results observed in vitro using the individual domains.

Notably, the Rrm4 version carrying a deletion in MLLE1 and MLLE2 interacted with Upa1 versions carrying mutations in PAM2L1 or PAM2L2 (Fig. EV3C), suggesting that both MLLE domains of Rrm4 are dispensable for interaction with Upa1. MLLE3 is sufficient to interact with a single PAM2L sequence of Upa1. This suggests the presence of additional interaction partners for MLLE1 and MLLE2 and is entirely consistent with our genetic and cell biological analysis described in Fig. 5.

__

Minor comments: __

1- The authors have previously characterized the effect of a C-terminal deletion of Rrm4 on Rrm4 motility and binding to Upa1 (Becht et al., 2006; Pohlmann et al., 2015). How their previously-described construct compares to the Rrm4-M3Δ used in this study is unclear (is it the same?).

It is the identical mutation to allele rrm4GPD from Becht et al. 2006. We indicate the information in the text “(Fig. 4B-C; mutation identical to allele rrm4GPD in Becht et al., 2006).”

2- page 6, line 141: refer to Fig. 1B rather than Fig. EV1A ?

We included the reference to Fig. 1B.

3- page 10, line 274: "Rrm4-Kat was found"

We corrected this.

4- page 11, line 286: "in strains expressing Upa1-PAM2L1", replace by "in strains expressing Upa1 with mutated PAM2L1"?

We corrected this.

5- The Figures and accompanying legends are overall very clear and detailed. In Figures EV4A and EV5D-E, it would however help if the authors would indicate on the Figure itself, left to each panel which markers/signals is being analyzed (e.g Rrm4-Kat (top) and Upa1-GFP (down) for Figure EV4).

We clarified this.

Reviewer #3 (Significance (Required)):

Active transport of mRNAs along microtubule tracks has been shown to play a key role in the spatio-temporal control of gene expression in various cell types and species. How specific mRNAs mechanistically connect to molecular motors for their transport to their subcellular destination has however for long remained largely unclear. Recent work, including work from the authors, has uncovered that RNAs can hitchhike on membranous organelles through adapter proteins linking mRNAs and RNA binding proteins with trafficking membrane-bound organelles.

This study aimed at investigating the structural and molecular bases underlying the interaction between RNA binding proteins and endosomes. While their identification and characterization of the MLE1 and MLE2 domains of Rrm4 did not provide significant new insight into the mechanisms involved in the endosome-mediated transport of mRNAs, it uncovered interesting new properties of MLE domains, including structural variations, selective binding and functional specificity. This work should thus be of interest for structural biologists and researchers interested in protein-protein interaction platforms.

**Referee Cross-commenting**

Our comments all converge to the idea that this study is solid as it is and requires only minor revision work to support the authors conclusions. Although characterizing further MLE/PAM2 binding specificity and MLE2 interactors would be of great interest and indeed provide a more complete understanding of interaction networks at play, I feel that this is beyond expected revision work.

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Referee #3

Evidence, reproducibility and clarity

Summary:

Long-distance subcellular transport of mRNAs is achieved through selective and dynamic interaction with the transport machinery. Using the highly polarized hyphae of Ustilago maydis, the authors previously showed i- that mRNAs can hitchhike on actively transported endosomes for proper distribution, and ii- that the connection between mRNAs and endosomes is mediated by the interaction between a C-terminal MademoiseLLE (MLE) domain of the RNA binding protein Rrm4 and the Upa1 adapter protein.

In this study, the authors aimed at more precisely characterizing the structural and molecular bases underlying the Rrm4-Upa1 interaction. Combining structural modeling and X-ray analyses, they discovered a non-canonical, and previously missed, MLE domain (MLE1) in Rrm4, and characterized the structure of the second MLE domains (MLE2) of Rrm4. Through binding assays, they showed that the three MLE domains exhibit different binding properties, and that MLE3 is the only domain capable of binding to the PAM2 domain of Upa1. Consistent with this finding, functional assays performed in U. maydis revealed that MLE3 is the main domain involved in interaction with endosomes and trafficking, MLE1 and 2 having either no or minor functions in this process.

The manuscript is very-well written, the data are of high quality and clearly presented. A wide range of complementary approaches has been used to molecularly and functionally characterize the different MLE domains of Rrm4. From an "RNA transport" perspective, this manuscript falls short of a main novel findings as the domains characterized in this study (MLE1 and 2) don't have a clear function in connecting mRNAs to the transport machinery. From an "MLE domain" perspective, this work however provides interesting information about non-canonical domains and structures, and about binding and function specificity.

As described below, my major concern relates to the role played by the ML2 domain of Rrm4, a role referred to as "accessory" by the authors.

Major comments:

The authors conclude from their results that ML2 has an accessory role in promoting association with endosomes.

1- This conclusion is made based on in vivo experiments showing that a form of Rrm4 lacking the M2 domain, in contrast to wild-type Rrm4, aberrantly attached to MTs in a context where the Rrm4-Upa1 interaction mediated by MLE3Rrm4 has been weakened (Upa1-pl2m). Although the results are convincing, their interpretation is less. The authors, indeed, claim that the observed phenotype results from "the static accumulation of Rrm4" due to reduced interaction with endosomes. Why then don't they see a decrease in the motility/transport properties of Rrm4-M2Δ in this context then? Also, do the authors see a decrease in the co-localization of Rrm4-M2Δ with endosomes (which would be expected if the interaction is decreased)? Can the authors perform IP or co-sedimentation experiments to strengthen their hypothesis?

2- Whether MLE2Rrm4 mediates interaction with endosomes through association with Upa1 is unclear, as the binding assays performed in Figure 3 test for association of Rrm4 variants with single isolated domains of Upa1, not with the full-length protein. Assessing the binding of Rrm4-M2Δ variants with Upa1-PL2m would help interpreting the phenotypes described in Figure 5.

Minor comments:

1- The authors have previously characterized the effect of a C-terminal deletion of Rrm4 on Rrm4 motility and binding to Upa1 (Becht et al., 2006; Pohlmann et al., 2015). How their previously-described construct compares to the Rrm4-M3Δ used in this study is unclear (is it the same?).

2- page 6, line 141: refer to Fig. 1B rather than Fig. EV1A ?

3- page 10, line 274: "Rrm4-Kat was found"

4- page 11, line 286: "in strains expressing Upa1-PAM2L1", replace by "in strains expressing Upa1 with mutated PAM2L1"?

5- The Figures and accompanying legends are overall very clear and detailed. In Figures EV4A and EV5D-E, it would however help if the authors would indicate on the Figure itself, left to each panel which markers/signals is being analyzed (e.g Rrm4-Kat (top) and Upa1-GFP (down) for Figure EV4).

Significance

Active transport of mRNAs along microtubule tracks has been shown to play a key role in the spatio-temporal control of gene expression in various cell types and species. How specific mRNAs mechanistically connect to molecular motors for their transport to their subcellular destination has however for long remained largely unclear. Recent work, including work from the authors, has uncovered that RNAs can hitchhike on membranous organelles through adapter proteins linking mRNAs and RNA binding proteins with trafficking membrane-bound organelles.

This study aimed at investigating the structural and molecular bases underlying the interaction between RNA binding proteins and endosomes. While their identification and characterization of the MLE1 and MLE2 domains of Rrm4 did not provide significant new insight into the mechanisms involved in the endosome-mediated transport of mRNAs, it uncovered interesting new properties of MLE domains, including structural variations, selective binding and functional specificity. This work should thus be of interest for structural biologists and researchers interested in protein-protein interaction platforms.

Referee Cross-commenting

Our comments all converge to the idea that this study is solid as it is and requires only minor revision work to support the authors conclusions. Although characterizing further MLE/PAM2 binding specificity and MLE2 interactors would be of great interest and indeed provide a more complete understanding of interaction networks at play, I feel that this is beyond expected revision work.

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Referee #2

Evidence, reproducibility and clarity

Devan, Schott-Verdugo et al.

Summary

In this study the putative MLLE RNA-binding motifs of the endosomal RNA-binding protein, Rrm4, from Ustilago maydis were examined using structural and genetic analyses. MLLE motifs are conserved in polyA-binding proteins (Pab1/PABPC1) and found also in Rrm4, which was shown to reside on motile endosomes and deliver septin mRNAs for endosome-localized translation during polarized growth. Upa1 on the endosome interacts with Rrm4 via its PAM2L domain that itself interacts with the MLLE domains of proteins like Pab1. Mutations in the known MLLE domain of Rrm4 were earlier shown to affect localization to endosomes.

Here, the C-terminal domain of Rrm4 was revealed to have three divergent MLLE motifs using comparative modeling; only two of which were previously predicted. Crystallization and X-ray diffraction analysis of a truncated version of bacterially produced Rrm4, showed MLLE2 is most similar to that of PABPC1 and UBR5, although MLLE1 and 2 are somewhat divergent in the key region of PAM2 binding. Small angle X-ray scattering of recombinant full-length or truncated Rrm4 revealed that the MLLE domains might form a platform that could allow for multiple contacts with different binding partners. In vitro binding studies with different N-terminal GST-tagged versions of the Rrm4 were used to examine for interactions with PAM2 sequences of Upa1 using N-terminal hexa-histidine-SUMO fusions. It was found that Pab1-MLLE interacts with the PAM2, but not PAM2L, domain of Upa1. In contrast, the complete Rrm4 MLLE region (G-Rrm4-NT4) interacted with the PAM2L domain, but not the PAM2 of Upa1. Notably, the interaction with PAM2L required the third MLLE and neither MLLE1 nor MLLE2, nor both. No significant differences in affinity were observed and were similar to that of the Pab1 MLLE. The results also show that the MLLE3 has a higher affinity for the PAM2L2 than PAM2L1 of Upa1. To examine the biological role of the Rrm4 MLLEs, U. maydis strains bearing deletions in the domains of Rrm4 were examined for hyphal growth and endosomal transport (latter using Upa1-GFP and Rrm4-mKate2). Only the loss of the MLLE3 domain inhibited polarized growth (as seen with the full deletion of RRM4) and not the deletion of either MLLE1 or 2. Similar results were obtained regarding endosome shuttling. Thus, in line with the biochemical experiments performed the MLLE3 domain alone (of the three identified) is necessary for the biological actions of Rrm4. This suggested the MLLE1 and 2 are not necessary for function under these conditions.

To examine this further, Upa1 carrying mutations in the PAM2L 1or PAM2L2 domains were examined. It was found that the deletion of both PAM2L domains affected unipolar growth resulting in bipolar growth similar to the deletion of UPA1 alone. This phenotype was observed even upon the deletion of Rrm4 MLLE1 and 2 in the same background as the PAM2L mutants. The mutation of both PAM2L domains led to a reduction in Rrm4-labeled shuttling endosomes, which suggests that these domains help anchor Rrm4 to endosomes. When only the PAM2L1 domain is present in Upa1 there was a larger increase in hyphae with aberrant microtubule staining than upon the loss of PAM2L1. The authors suggest that this indicates PAM2L2 is more important and prescribes an accessory role for MLLE2 in endosome association.

Comments:

Overall, the study seems well conducted. We cannot comment on the structural aspect of the work since this is not our field of expertise. That said, the biochemical and genetic/functional studies appear solid, well thought-out, and clearly presented. No new experiments are necessary to support the general claims of the paper, however, experiments suggested below might make it more revealing with regards to the connection between RNA binding and MLLE-PAM2L interactions (i.e. endosome localization and RNA binding functions).

1. Line 286 - It reads the they "Next, we investigated the association of Rrm4 -M12D-Kat in strains expressing PAM2L1. Thus, the endosomal attachment was solely dependent on the interaction of MLLE3 with the PAM2L2 sequence of Upa1." Unclear - wouldn't lacking PAM2L1 (and not expressing) fit the logic of the sentence?
2. Several questions regarding the specificity of PAM2 vs. PAM2L domains. What happens when you switch/replace the PAM2L1 or 2 of Upa1 with Upa1 PAM2 domains? Are they exclusive? What happens when the MLLE3 of Rrm4 is switched with that of Pab1? And if one does both - does that restore functionality to Rrm4?
3. Likewise, what happens if Upa1 only has PAM2L2 instead of only PAM2L1 domains? Does that alter function - perhaps now one can observe a contribution of MLLE1? If it it's there it's likely to have function. Anything known about the post-translational modification of these MLLE or PAM domains? Does it change during unipolar vs. bipolar growth? Perhaps the different MLLE domains are regulated in such a fashion?
4. Can the authors show whether the binding of mRNA cargo (e.g. Cdc3 mRNA) to the RRM motifs of Rrm4 affects the interaction between any of the MLLE-PAM2L pairs, or vice versa (i.e. does the MLLE-PAM2L interaction affect mRNA binding)?
5. Discussion line 311 It is written that the three MLLE domains "collaborate for optimal functionality..." Perhaps there's a misunderstanding here, but the authors show that MLLE3 domain alone is necessary & sufficient for function, so where is the collaboration? MLLE2 may have an accessory role according to the authors, but we do not know if it is in collaboration with MLLE3 or independent thereof. Since the KD of MLLE3 is not affected by the presence or absence of MLLE1,2 in vitro at least, it may be that they have independent, and not collaborative, roles.

Significance

This paper concerns functional domains found in an endosome-localized RNA binding protein, U. maydis Rrm4, which is necessary for localized translation on endosomes and subsequent unipolar growth. Here the authors show using structural, biochemical, and genetic studies that instead of one or two MLLE protein-protein interacting domain in Rrm4 there are three, although one (MLLE3) is necessary and sufficient for full function. This work is for an audience interested in those studying RNA trafficking and its role in cell physiology, which is our expertise. The work is interesting, but it could be made more so especially if a connection was established between the RNA-binding function of the RRM domains and the MLLE-PAM2L interaction(s). At this point it is solid technical work and could be published after minor revisions.

Referee Cross-commenting

I concur with the comments of the other reviewers in that the work is solid and necessitates minor revisions in order to be published. Clearly, establishing a connection between the RNA-binding function and the MLLE-PAM interactions of Rrm4 would be an interesting and worthy pursuit that might enhance the novelty of the work, but I agree that it could belong to future studies.

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Referee #1

Evidence, reproducibility and clarity

Summary:

This manuscript documents a very thorough biophysical, structural and functional dissection of interactions between the RNA-binding protein Rrm4 and the endosomal adaptor Upa1 in the filamentous fungus Ustilago maydis. It has been shown previously that the Rrm4-Upa1 interaction is critical for mRNA transport in this system as mRNAs hitchhike on motor-associated endosomes. Here, the authors reveal using modelling that Rrm4 has three MLLE domains, including a cryptic one that had not been identified previously. They then report the crystal structure of MLLE2 and analyze the distribution anf arrangement of the MLLE domains in the protein using SAXS. They then show using pulldowns and isothermal titration calorimetry that MLLE3 is critical for the Upa1 interaction (via the PAM2L domains of Upa1) and that MLLE2 contributes to Rrm4 localization in vivo when the MLLE3-Upa1 interaction is partially impaired. The study suggests that Rrm4 has a platform of MLLE domains for orchestrating Rrm4 function. Overall, this is technically a high quality study. However, a number of points (mostly minor) should be addressed.

Major comments:

A key part of the study if the in vivo work illustrating a role for MLLE2 in regulating Rrm4 localization when the system is sensitized. Some aspects of this part of the work need clarifying.

a) The authors should show that the abberant staining is indeed microtubule-related with the benomyl experiment that they used in Jankowski et al. 2019.

b) The authors claim from these experiments that MLLE2 contributes to endosomal targeting (as there is ectopic protein on other structures (presumptive microtubules)). However, to make this claim, the authors would need to measure the intensity of the mutant Rrm4 protein on endosomes and/or the colocalization of these Rrm4 variants with endosomes, as they do in other experiments in this paper. Otherwise, it is possible that the MLLE2 deletion has another effect, e.g. increasing protein stability, and thus increasing the likelihood of binding to structures other than endosomes. If available, data on the relative abundance in the cell of the protein expressed from the wild-type control (rrm4-kat) and MLLE2 deletion constructs (e.g. rrm4-m1,2delta-kat) should be provided.

c) Was the data in Figure 5D scored blind of the identity of the samples? Given that the classification has to be done manually, it is important to confirm the phenotypes are robust to blinding (at least for the key comparisons).

If points b and c are addressed, it should be possible to draw an arrow between the gray question mark protein in Figure 6 and the endosome surface, which is what I assume the authors believe to be case based on their discussion.

Minor comments:

1. The first line of the abstract is quite bold. It is hard to quantify the role of transport vs RNA stability for example, so I suggest this sentence is toned down.
2. Line 269: change "amount of motile Rrm4-M12delta-Kat positive signals" to "number of motile Rrm4-M12delta-Kat positive signals".
3. Figure 3 legend: Insert "Variant" before "amino acids of the FxP and FxxP..." to indicate what is labeled in gray. Change "fond" to "font" in the same sentence.
4. The cartoons of the different protein variants are very helpful but I had problems spotting the Upa1-Pam2L deletions due to the similar gray to the background of the protein. This would perhaps be clearer if the gray used for the background was lighter than it currently is.
5. The residual motility of wild-type Rrm4 when PAM2L1 and PAM2L2 are both mutated (Figure 5C) is reminiscent of what is seen in a complete Upa1 deletion in the group's previous work. It would be helpful to point this out to the reader, as well as the implication that other proteins are contributing to Rrm4's linkage to endosomes. After all, some of these other adaptors might contact MLLE2 of Rrm4.
6. Some of the y-axes of the charts should be more descriptive so that the reader can understand the plots even before they consult the legends. For example, in Figure EV4A and EV5D and E, which protein is being to referred to in each 'number of signals' plot should be included. In Figure 5D, 'Hyphae [%]' would be clearer as 'Hyphae with MT staining of Rrm4 [%]'
7. Figure EV5 legend title: this could be misleading as the authors are seeing ectopic MT localization rather than a deficit in microtubule association.

Significance

The Feldbrugge group has previously mapped interactions between Upa1 and Rrm4 (Pohlmann et al., 2015) and some conclusions are corroborated in the paper by Boehm et al. The paper under review is, however, a significant advance due to the identification of the third MLLE domain, detailed biophysical characterization of the interactions, the structural insights, and evidence of a subsidiary role of MLLE2. The work would of course be stronger if the target of MLLE2 had been identified but I think this is beyond the scope of this initial work. To my knowledge, this is one of the most extensive analyses of the interactions mediated by MLLE and PAM domains and will be of interest to others working on these protein features. The work will also appeal to those interested in the links of localizing mRNAs with motor-associated membranes, which is an emerging field.

Reviewer expertise: I have a long-standing interest in molecular analysis of mRNA trafficking mechanisms. I do not have experience in fungal genetics.

Referee Cross-commenting

It seems that we are in agreement that this is solid work and that biochemical and biophysical analysis of the MLLE-PAM interactions will be of significant interest to those working on those domains (or proteins containing those domains). I agree with the comments of the other reviewers and there are clearly some essential minor revisions needed to strengthen the evidence for their conclusions and some clarifications. I think it is a long shot that RNA binding to the RRMs will affect the MLLE-PAM interactions and would require quite a lot of work to show this conclusively. The study would, however, be more impactful if this was shown to be the case, or the target of MLLE2 was found. Nonetheless, I would not say these new avenues of research are necessary to find a home in one of the Review Commons journals.

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Reply to the reviewers

Reviewer #1: General comments:

Fujimoto and collaborators use Nanopore-based cDNA sequencing for genome-wide transcriptome analysis of a collection of hepatocellular carcinomas (HCCs) and matched normal liver tissues. To improve detection of alternatively spliced isoforms and hybrid transcripts potentially deriving from genomic rearrangements, they develop a dedicated pipeline SPLICE, which they benchmark against available software used for the same analysis. Besides having dual functionality (calls both alternative transcripts and fused transcripts), SPLICE seems to outperform previous software in calling alternative/fused transcripts and accuracy. They use the SPLICE pipeline to call isoforms and gene fusions in normal liver cells and HCCs and perform basic functional validations on novel fusions identified. The manuscript is well written, and the analyses are well performed. Perhaps the benchmarking of the SPLICE pipeline could have been more extensive (i.e., performed on additional independent datasets).

Major points: 1. Line 149-150: "We compared the results of mapping to the reference genome and the reference transcriptome sequences, and removed candidates if both were inconsistent (removal of mapping errors). " Please specify what "both were inconsistent" means.

Our reply; Thank you for this comment. The accuracy of fusion gene detection is influenced by mapping errors. To remove possible mapping errors, SPLICE aligned reads to the reference genome and the reference transcriptome sequences and compared the results. If the results are inconsistent (for example, GeneA-GeneB in the reference genome and GeneA-GeneB in the transcriptome genome, or GeneA-GeneB in the reference genome and GeneA in the transcriptome genome), SPLICE considers the candidates as false positive and removes them from the analysis.

          We changed the sentence “We compared the results of mapping to the reference genome and the reference transcriptome sequences, and removed candidates if both were inconsistent (removal of mapping errors).” to “we compared the results of mapping to the reference genome and the reference transcriptome sequences, and removed candidates if both results did not detect same fusion genes (removal of mapping errors).”  (line 150-152).

• Concerning TE-derived novel exons, in principle, this may lead to altered expression of the TE-transcript (as the Authors report for L1-MET) or to altered splicing of the transcript (i.e., other exon/introns could be retained or excluded). Can the Authors assess whether the inclusion of the TE in a transcript enhances its expression or affects the splicing of the "parental" transcript? If so, can they verify if the position of the insertion of the TE has any effect on expression and splicing?*

Our reply; Thank you very much for this important comment. As the reviewer mentioned, exonization of TE may affect the splicing patterns and gene expression levels of transcripts. To determine the effect of TE on expression levels, we compared the expression levels of transcripts with TE-derived novel exons with those of known transcripts of the gene. We found that the expression levels of transcripts with TE-derived novel exon were lower than those of known transcripts (Figure 1 in the reply). Since the same results were observed in all novel transcripts (Fig. 1E,F), most TE exonization would not affect the expression level of transcripts.

          We then analyzed the effects of TE in the splicing change, we compared the numbers of novel splicing junctions between transcripts with TE-derived novel exons and other transcripts in each gene. The proportions of genes with novel splicing junctions were not significantly different between the transcripts with TE-derived novel exons and others (transcripts with TE-derived novel exons; 9.1% and others; 11.9%)  (Figure 2 in the reply). As observed in L1-*MET* and L2-*RHR1*, transposons can affect expression levels and structures of transcripts, however, their effect would be limited to a part of genes.

Figure 1

Comparison of expression levels of transcripts with TE-derived novel exon and known transcripts. Only transcripts derived from genes with TE-derived novel exons were compared. The total number of transcripts is shown below the plot. Transcript abundance was measured in reads per million reads (RPM), and log10 converted values for RPM were shown in the violinplot. P-values were calculated by Wilcoxon rank-sum test.

Figure 2

Comparison of the percentage of novel splicing junction in transcripts with novel TE-derived exon and other transcripts. The total number of genes are shown below the plot. Transcripts with TE-derived novel exons and other transcripts were compared. P-value was calculated by Fisher’s exact test.

• Can the Authors explain why the NBEAL1-RPL12 was not detected by SPLICE?*

Our reply; Thank you for this comment. Although NBEAL1-RPL12 fusion was detected by SPLICE, mapping results to the reference genome and the reference transcriptome were inconsistent and removed from the final result. AsNBEAL1-RPL12 was not validated by PCR (Supplemental Fig. S4B) (line 183-184), we consider that this fusion-gene is a false positive, and filtering of SPLICE successfully removed false-positive fusions.

• Line 332: Can the Authors explain how the total amount of HVB mRNA was determined in each sample? Is it a relative amount calculated from the sequencing data? If so, it should be made clear in the text that this is a fractional measure.*

Our reply; Thank you very much for this comment. Expression levels were calculated by log10 converted reads per million reads (log10(RPM)) for each sample. We added the following sentences to the "Expression from HBV" subsection in the Results (line 337-338); “Expression levels were estimated by log10 converted support reads per million reads (log10(RPM)) for each sample.”.

• Fig4a: please specify if the y-axis "number of support reads" reports library normalized values.*

Our reply; Thank you for this comment. The values of the y-axis are row read counts. We added the following sentences to the Figure legend (line 348); “Y-axis shows the total number of support reads (raw counts).”.

• HCCs have more HBV-human genome fusion transcripts than normal liver. Could the authors clarify if these HCC transcripts are selectively found in tumors? or whether they are also expressed in normal liver samples? The paragraph starting from line 356 is confusing, and it is difficult to retrieve the above information for both HBs and HBx fusions.*

Our reply; We apologize for the confusing description. All HBV-human genome fusion transcripts were selectively expressed in tumor or normal liver. We added the following sentence to the "Expression from HBV" subsection in the Results (line 365-366); “All of these HBV-human genome fusion transcripts were selectively expressed in the HCCs and the livers.”.

• Figure 4C: what was the control used to calculate the relative viability in these analyses?*

Our reply; Thank you for this comment. Fig. 4C shows the number of HBV-human fusion transcripts in the six categories. If this comment refers to Fig. 4H, cell lines transfected with the empty vector (pIRES2-AcGFP1-Nuc) was used as controls. This has been described in the "Gene overexpression" subsection of Methods (line 716-717).

• MYT1L: the Authors report the identification of a novel MYT1L transcript downregulated in HCC, and argue it may have a potential tumor-suppressive function. For the sake of clarity, it will be advisable to show also the differential expression (HCC vs. Liver) of the other transcripts expressed from the same locus.*

Our reply; Thank you for this important comment. In HCCs and normal livers, only the novel MYT1L transcript was expressed from this locus, and no known transcript of MYT1L was expressed. We changed the sentence “In the MYT1Lgene, a highly-conserved novel exon was detected (Fig. 2E), and this transcript was significantly down-regulated in the HCCs” to “In the MYT1L gene, a highly-conserved novel exon was detected (Fig. 2E), and only a transcript with the novel exon was expressed.” (line 471-472).

• *

Minor points: 1. Table S4: there is a typo, correct “secific” in “specific”

Our reply; Thank you very much for this comment. We corrected the typo of Table S4.

• *

• *

*Reviewer #2: General comments:

Summary: This is both a presentation of a pipeline for analysis of Nanopore RNA-seq data, as well as an analysis of a cohort of 44 hepatocellular carcinomas against matched-normal liver tissue. It presents a number of quite intriguing results from the long-read RNA analysis, and suggests potential new targets for study in HCC. It is also worth noting that the current version of guppy (6) has functionality to detect primer sequences in the middle of reads and split those reads, which may obviate one of the steps in SPLICE.*

*Major comments:

1) The work done in this study used data that was basecalled using guppy 3.0.3. Since that version, I am aware of at least two major upgrades to the base caller accuracy, which would likely also improve the accuracy of isoform resolution. Given that the data is relatively low-coverage and that you have an automated workflow for the analysis, I would recommend re-basecalling using an updated basecaller and re-running your analysis using that. This is especially important given your comments in the paper about splice site misalignment.*

Our reply; Thank you very much for this important comment. We performed basecalling of a sequence data of MCF7 using the latest guppy v6.0.6 and compared the result with that by guppy v3.0.3. We randomly extracted 1M reads from MCF-7 reads that passed qscore filtering in guppy basecaller. The same reads were extracted and basecalled by guppy v3.0.3. These two data were analyzed by SPLICE.

The average error rate was 4.6 % for v6.0.6 and 6.8 % for v3.0.3. The number of transcripts was 9,674 for v6.0.6 and 9,329 for v3.0.3. Of these, the number of novel transcripts was 446 and 410, respectively. The number of fusion genes was 2 (BCAS3-BCAS4, and BCAS3-ATXN7) by v6.0.6 and one (BCAS3-BCAS4) by v3.0.3. As the reviewer mentioned, we found that using the latest version of guppy improved the accuracy and detected a larger number of transcripts.

We added the results to Supplemental Table S12. We also changed the sentences from “Second, our analysis removed the change of splicing sites within 5 bp to remove alignment errors (Fig. 1B). We consider that this cutoff value is necessary due to currently available high-error reads (S____upplemental Data S____2). However, sequencing technologies and basecallers are improving, and in the near future, we should be able to use a smaller cutoff value and identify larger numbers of splicing changes.” to “Second, the accuracy of the analysis depends on the sequencing error rate. Although several filters are used for currently available high-error reads (Fig. 1B and ____Supplemental____ Fig. S1), sequencing errors would affect the accuracy of the result. Sequencing technologies and basecallers are improving, and in the near future, we should be able to identify larger numbers of splicing changes with high accuracy (Supplemental Table S10).” (line 538-542).

2) You have compared your software to another tool for isoform analysis on Nanopore sequencing data, TALON. But a number of other tools exist for this purpose, including stringtie2, flair and bambu. My own testing has shown that stringtie2 outperforms TALON in terms of concordance with Illumina RNA-seq. It is quite important that you perform a complete comparison of your software to the state of the art for this purpose.

Our reply; Thank you very much for this important comment. We compared our tool with four tools (TALON, FLAIR, StringTie, and bambu). For this comparison, we used sequence data of MCF-7 and HCC (RK107C). We randomly extracted 1 M reads from MCF-7 and HCC (RK107C) sequence data using Seqtk (v1.3) (params: sample -s1 1000000). Reads were mapped to the reference genome sequence (hg38) with minimap2 (v2.17) (params: -ax splice --MD), and the output SAM files were converted to BAM files and sorted with samtools (v1.7) (Li et al. 2009).

For benchmarking of TALON (v5.0), we corrected aligned reads with TranscriptClean (v2.0.3) (Wyman and Mortazavi 2018). Next, we ran the talon_label_reads module to flagging reads for internal priming (params: --ar 20). TALON database was initialized by running the talon_initialize_database module (params: --l o --5p 500 --3p 300). Then, we ran the talon module to annotate the reads (params: --cov 0.8 --identity 0.8). To output transcript abundance, we first obtained a whitelist using the talon_filter_transcripts module (params: --maxFracA 0.5 --minCount 5), and then quantified transcripts using the talon_abundance module based on the whitelist. For FLAIR (v1.5), the sorted BAM file was converted to BED12 using bin/bam2Bed12.py. We then corrected misaligned splice sites with the flair-correct module. High-confidence isoforms were defined from the corrected reads using the flair-collapse module (params: -s 3 --generate_map). For benchmarking of StringTie (v2.2.1), Stringtie was performed with input files consisting of long-read alignment and reference annotation (params: -L -c 3). For benchmarking of bambu (v2.0.0), Bambu was performed with input files consisting of long-read alignment, reference annotation and reference genome (hg38) (params: min.readCount = 3). Candidates with low expression levels (support reads As a result, SPLICE identified the third-highest number of transcripts followed by FLAIR and StringTie (Supplemental Fig. S3A). In MCF-7 the concordance rate with IsoSeq MCF-7 transcriptome data was the highest in SPLICE for known transcripts and the second highest in SPLICE for novel transcripts (Supplemental Fig. S3B). These results indicate that SPLICE has sufficient accuracy for analyzing transcript aberrations.

We added the text to the "Comparison of SPLICE method with other tools" subsection of the Results (line 165-177) and the "Benchmarking" subsection of the Methods (line 640-679). We added the results to Supplemental Fig. S3.

3) Likewise, for fusion detection, you compare to LongGF. You should also compare to (and cite) JAFFAL.

Our reply; Thank you very much for this important comment. We compared our tool with the two tools (LongGF and JAFFAL). We used 1 M reads randomly extracted from MCF-7 and HCC (RK107C) sequence data as described above.

          For benchmarking of LongGF (v0.1.2), reads were mapped to the reference genome sequence (hg38) with minimap2 (v2.17) (params: -ax splice --MD), and the output SAM files were converted to BAM files and sorted with samtools (v1.7). We then ran the *longgf* module and obtained the list of fusion genes (params: min-overlap-len 100 bin_size 50 min-map-len 200 pseudogene 0 secondary_alignment 0 min_sup_read 3). For benchmarking of JAFFAL (v2.2), we ran the *JAFFAL.groovy* module with zipped fastq files.

          In this comparison, close gene pairs (We added the text to the "Comparison of SPLICE method with other tools" subsection in the Results (line 178-186) and the "Benchmarking" subsection in the Methods (line 667-679). We showed the results in Supplemental Fig. 4.

4) In terms of the source code, I have questions. Why did you use BASH to run the Python code, instead of making this into a Python package? Why did you not use the functionality already available in BioPython for a number of basic sequence data handling tasks? Why is there not even a single function defined anywhere, let alone classes?

At some level, if it works, it works. But I have serious concerns about the long-term maintainability of the code in its current state.

Our reply; Thank you very much for this critical comment. As the reviewer mentioned, we think it is better to make a python package and use BioPython for maintenance and long-term maintainability of the code. We have been building our analysis pipeline by trial and error, and at this stage, the current scripts are convenient for us (our group may need to learn software development). We provided a Docker package (see the reply to comment 5)), and this would promote usability.

5) Also related to the code, it is generally the standard now to create a BioConda package or Docker container for a bioinformatics package. BioConda has the advantage that the BioContainers project automatically generate Docker and Singularity containers from it. Please provide one of these.

Our reply; Thank you very much for this critical comment. We made a Docker file and provided it from our github page. It is available from the "Installation and usage via Docker" section.

6) There is some quite nice functional validation work done on some of the DE transcripts that would have been hidden in a gene-level analysis. There is also some nice work on detecting HBV fusion genes. These both contain important results which are not mentioned at all in the abstract. I feel like the abstract as it stands is selling the paper short.

Our reply; Thank you very much for this important comment. We added the following sentences to the abstract; “Comparison of expression levels identified 9,933 differentially expressed transcripts (DETs) in 4,744 genes. Interestingly, 746 genes with DETs, including LINE1-MET transcript, were not found by the gene-level analysis. We also found that fusion transcripts of transposable elements and hepatitis B virus (HBV) were overexpressed in HCCs. In vitro experiments on DETs showed that LINE1-MET and HBV-human transposable elements promoted cell growth.”.

7) Fig 5C shows a Venn diagram of fusions detected by short-read vs long-read sequencing, in which there is quite low overlap between these. You make the statement in the paper that "a combination of short- and long-reads can detect more fusion genes". I find it more likely that the short-read ICGC data had much greater depth of coverage than the MinION data you produced, which allowed for the detection of fusions that were expressed at much lower levels. This could be easily tested by downsampling the ICGC data to the same amount of sequence data as was generated on the MinION, and re-creating the Venn diagram with the fusions detected that way.

Our reply; Thank you very much for this very important comment. We compared the amount of data between our long-reads and the previous short-reads. However, the amounts of data were not quite different (Supplemental Fig. S14A). Therefore, differences in depth are not likely to be the cause of the low overlap. We considered that two possibilities could explain the low overlap. First, most of the fusion genes missed by short-read were very low expression levels, less than 1 reads per million reads (RPM) (Supplemental Fig. S14B), therefore, there are many fusion-genes with low expression levels, and they are difficult to be detected. Second, 28.9 % of transcripts in long-reads lacked 5' region (Supplemental Fig. S5 and Supplemental Fig. S14C,D). Therefore fusion-genes whose breakpoints are located in the 5' region were difficult to detect by long-read.

We added the following sentences to the "Fusion genes" subsection in the Results (line 400-405); “We considered that two possibilities could explain the low overlap. Since the most of the fusion genes missed by short-reads had very low expression levels (Supplemental Fig. S14B), many fusion-genes with low expression levels would be missed by a single approach. In addition, 28.9 % of transcripts in long-reads lacked 5' region (Supplemental Fig. S5 and Supplemental Fig. S14C, D). Therefore fusion-genes whose breakpoints are located in the 5' region would be difficult to detect by long-read.”. We also added a figure on the amount of data to Supplemental Information (Supplemental Fig. S14A).

8) Figure 5D is very interesting. What do you conclude from that result? Please comment in the manuscript.

Our reply; Thank you very much for this important comment. We used samples that used for whole-genome sequencing in our previous study. Therefore, a list of SVs is available. We classified fusion-gene to these supported by SVs (SV detected fusion-genes) and others (no SV detected fusion-genes), and compared the expression levels of them (Figure 5D).

Whole-genome sequencing can accurately identify clonal (high frequency) SVs, however, would miss sub-clonal (low frequency) SVs. Therefore, we considered that no SV detected fusion-genes were generated by sub-clonal SVs. This result suggests that there are a lot of sub-clonal fusion genes, and their expression levels are lower than clonal fusion genes. Although the functional importance of sub-clonal fusion genes is currently unknown, deeper RNA sequencing would detect a larger number of fusion genes.

          We added the following sentences to the “Fusion genes” subsection in the Results (line 410-412); “This result suggests that there are a lot of sub-clonal fusion genes, and their expression levels are lower than clonal fusion genes. Although the functional importance of sub-clonal fusion genes is currently unknown, deeper RNA sequencing would detect a larger number of fusion genes.”.

*Minor comments:

1) The manuscript has many small errors in English grammar, spelling and style. I would strongly recommend sending it for copy editing before submitting it to a journal.*

Our reply; Thank you very much for this comment. Due to the limitation of time, the current version has not been proofread by a native-English speaker. We are planning to review English grammar by a native-English speaker.

2) Neither the results section nor the methods section describing the sequencing that was performed specify whether it was done on a MinION or PromethION (or flongle). While this is implied elsewhere in the paper, it should definitely be specified in the methods at a minimum.

Our reply; Thank you for this comment. We used a MinION for sequencing. We added the following sentences to the Method section (line 579-580); “Libraries were sequenced on a SpotON FlowCell MKⅠ(R9.4) (Oxford Nanopore), using the MinION sequencer (Oxford Nanopore)”.

3) You also write in the introduction that your method, SPLICE, was developed for the MinION specifically. Please comment on its applicability to data generated on the PromethION and flongle Nanopore sequencers.

Our reply; Thank you very much for this comment. We consider that our method is applicable to data from MinION, PromethION, and flongle. We added the following sentence to the Methods section (line 592-593); “In the present study, we analyzed sequence data from MinION. We consider that our method is applicable to data from MinION, PromethION, and flongle.”.

4) The volcano plot in Fig 3A is missing its dots.

Our reply; Thank you very much for this comment. We modified the Fig. 3A.

*Reviewer #3: General comments:

Summary: In this manuscript, Kiyose et al have developed and tested a novel methodology for identifying splicing alterations, and fusions, from full-length transcript or long read sequencing data. They apply this approach to liver cancer and paired, non-cancerous liver tissue from a prior publication, and use wet-lab/experimental methods to validate their in silico findings. They conclude that their new methodology, SPLICE, outperforms one existing method, and is uniquely suitable to identifying fusion genes.*

Major Comments: 1) Figure 1B shows a schematic of common error patterns from MinION cDNA sequencing, and the text of the manuscript describes how the authors' new approach (SPLICE), overcomes several of these, e.g. sequencing errors, artificial chimeras, and mapping errors of highly homologous genes. However, there is a fundamental disconnect between the text and the graphic in Figure 1B. This should either be revised for clarity, or an additional graphic or flowchart placed in the supplementary materials to clearly show *how* SPLICE overcomes each of these limitations.

Our reply; We apologize for the insufficient explanation in Figure 1. We showed a detailed explanation of the data analysis procedure in Supplemental Fig. S1.

2) Why was TALON the only alternative approach chosen for validation of SPLICE performance? There are a number of other, more advanced pipelines such as SUPPA2, and IsoformSwitchAnalyzeR. It would strengthen the manuscript, and its conclusions, to incorporate at least one of these methods as a second comparator. This is particularly true for IsoformSwitchAnalyzeR, since Kiyose et al identify a number of differentially expressed transcripts (DETs) for genes that are not differentially expressed.

Our reply; Thank you very much for this important comment. Another reviewer also requested additional benchmarking, therefore we performed an additional performance comparison for the revised manuscript. As SUPPA2 and IsoformSwichAnalyzeR are used to analyze the annotated output GTF files, and direct comparison with SPLICE is difficult. Since IsoformSwichAnalyzeR recommends StringTie as an annotation soft, we compared using StringTie instead.

We compared the performance of SPLICE with that of four other methods (TALON, FLAIR, StringTie and Bambu) for splicing variant detection. SPLICE identified the third-highest number of transcripts followed by FLAIR and StringTie (Supplemental Fig. S3A). In MCF-7 the concordance rate with IsoSeq MCF-7 transcriptome data was the highest in SPLICE for known transcripts and the second highest in SPLICE for novel transcripts (Supplemental Fig. S3B).

We added the text to the "Comparison of SPLICE method with other tools" subsection of the Results (line 165-177) and the "Benchmarking" subsection of the Methods (line 640-665). We added the results to Supplemental Fig. 3.

3) The Venn diagram in Figure 5C appears to show that conventional short read sequencing identifies 46 fusion genes that are not also detected by long read sequencing. However, this result, and its implications are never addressed in the text.

Our reply; Thank you very much for this important comment. We apologize for the insufficient explanation. We considered that two possibilities could explain the low overlap. First, most of the fusion genes missed by short-read were very low expression levels, less than 1 reads per million reads (RPM) (Supplemental Fig. S14B), therefore these are many fusion-gene with low expression level and they are difficult to be detected. Second, 28.9 % of transcripts in long-reads lacked 5' region (Supplemental Fig. S5 and Supplemental Fig. S14C,D). Therefore fusion-genes whose breakpoints are located in the 5' region were difficult to detect by long-read.

          We added the following sentences to the "Fusion genes" subsection in the Results (line 400-405); “We considered that two possibilities could explain the low overlap. The most of the fusion genes missed by short-reads had very low expression levels (Supplemental Fig. S14B). This result suggests that there are many missed fusion-genes with low expression levels. In addition, 28.9 % of transcripts in long-reads lacked 5' region (Supplemental Fig. S5 and Supplemental Fig. S14C, D). Therefore fusion-genes whose breakpoints are located in the 5' region would be difficult to detect by long-read.”. We also added a figure on the amount of data to Supplemental Information (Supplemental Fig. S14A).

Minor Comments: 1) On pages 20-21, the language used to describe the HBV and/or HCV postive vs negative materials is very confusing. Please clarify that by "HBV- and HCV-related tissues" you in fact mean "HBV-and HCV-infected samples."

Our reply; We apologize for the confusing wording. We converted "HBV and HCV-related tissues" to " HBV and HCV-infected samples" in the manuscript.

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Referee #3

Evidence, reproducibility and clarity

Summary:

In this manuscript, Kiyose et al have developed and tested a novel methodology for identifying splicing alterations, and fusions, from full-length transcript or long read sequencing data. They apply this approach to liver cancer and paired, non-cancerous liver tissue from a prior publication, and use wet-lab/experimental methods to validate their in silico findings. They conclude that their new methodology, SPLICE, outperforms one existing method, and is uniquely suitable to identifying fusion genes.

Major Comments:

1. Figure 1B shows a schematic of common error patterns from MinION cDNA sequencing, and the text of the manuscript describes how the authors' new approach (SPLICE), overcomes several of these, e.g. sequencing errors, artificial chimeras, and mapping errors of highly homologous genes. However, there is a fundamental disconnect between the text and the graphic in Figure 1B. This should either be revised for clarity, or an additional graphic or flowchart placed in the supplementary materials to clearly show how SPLICE overcomes each of these limitations.
2. Why was TALON the only alternative approach chosen for validation of SPLICE performance? There are a number of other, more advanced pipelines such as SUPPA2, and IsoformSwitchAnalyzeR. It would strengthen the manuscript, and its conclusions, to incorporate at least one of these methods as a second comparator. This is particularly true for IsoformSwitchAnalyzeR, since Kiyose et al identify a number of differentially expressed transcripts (DETs) for genes that are not differentially expressed.
3. The Venn diagram in Figure 5C appears to show that conventional short read sequencing identifies 46 fusion genes that are not also detected by long read sequencing. However, this result, and its implications are never addressed in the text.

Minor Comments:

1. On pages 20-21, the language used to describe the HBV and/or HCV postive vs negative materials is very confusing. Please clarify that by "HBV- and HCV-related tissues" you in fact mean "HBV-and HCV-infected samples."

Significance

There is somewhat strong significance to this advance. As promising as long read, full-transcript sequencing is for the field, current limitations such as its high error rate have limited applicability, and most of the current analytic pipelines require complementary short read RNA sequencing to be performed in parallel for error correction. The authors assert that SPLICE overcomes these limitations, and to some extent demonstrates this. As a predominantly wet-lab experimentalist in the area of RNA processing, I have the relevant expertise to most rigorously assess the downstream impacts of findings from pipelines such as SPLICE, e.g. the validation experiments shown in the latter portion of the manuscript. These are uniformly strong. Where I was challenged some is in the authors' explanations of how and why SPLICE's specific design, as an algorithm, overcomes the known limitations in current analytic pipelines for long-read sequencing.

Referees cross-commenting

I concur with Reviewer 2. I think the 3 of us were broadly enthusiastic, yet raised some of the same concerns. In my view, these concerns should be able to be readily addressed by the authors.

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Referee #2

Evidence, reproducibility and clarity

Summary:

This is both a presentation of a pipeline for analysis of Nanopore RNA-seq data, as well as an analysis of a cohort of 44 hepatocellular carcinomas against matched-normal liver tissue. It presents a number of quite intriguing results from the long-read RNA analysis, and suggests potential new targets for study in HCC. It is also worth noting that the current version of guppy (6) has functionality to detect primer sequences in the middle of reads and split those reads, which may obviate one of the steps in SPLICE.

Major comments:

1. The work done in this study used data that was basecalled using guppy 3.0.3. Since that version, I am aware of at least two major upgrades to the base caller accuracy, which would likely also improve the accuracy of isoform resolution. Given that the data is relatively low-coverage and that you have an automated workflow for the analysis, I would recommend re-basecalling using an updated basecaller and re-running your analysis using that. This is especially important given your comments in the paper about splice site misalignment.
2. You have compared your software to another tool for isoform analysis on Nanopore sequencing data, TALON. But a number of other tools exist for this purpose, including stringtie2, flair and bambu. My own testing has shown that stringtie2 outperforms TALON in terms of concordance with Illumina RNA-seq. It is quite important that you perform a complete comparison of your software to the state of the art for this purpose.
3. Likewise, for fusion detection, you compare to LongGF. You should also compare to (and cite) JAFFAL.
4. In terms of the source code, I have questions. Why did you use BASH to run the Python code, instead of making this into a Python package? Why did you not use the functionality already available in BioPython for a number of basic sequence data handling tasks? Why is there not even a single function defined anywhere, let alone classes?

At some level, if it works, it works. But I have serious concerns about the long-term maintainability of the code in its current state. 5. Also related to the code, it is generally the standard now to create a BioConda package or Docker container for a bioinformatics package. BioConda has the advantage that the BioContainers project automatically generate Docker and Singularity containers from it. Please provide one of these. 6. There is some quite nice functional validation work done on some of the DE transcripts that would have been hidden in a gene-level analysis. There is also some nice work on detecting HBV fusion genes. These both contain important results which are not mentioned at all in the abstract. I feel like the abstract as it stands is selling the paper short. 7. Fig 5C shows a Venn diagram of fusions detected by short-read vs long-read sequencing, in which there is quite low overlap between these. You make the statement in the paper that "a combination of short- and long-reads can detect more fusion genes". I find it more likely that the short-read ICGC data had much greater depth of coverage than the MinION data you produced, which allowed for the detection of fusions that were expressed at much lower levels. This could be easily tested by downsampling the ICGC data to the same amount of sequence data as was generated on the MinION, and re-creating the Venn diagram with the fusions detected that way. 8. Figure 5D is very interesting. What do you conclude from that result? Please comment in the manuscript.

Minor comments:

1. The manuscript has many small errors in English grammar, spelling and style. I would strongly recommend sending it for copy editing before submitting it to a journal.
2. Neither the results section nor the methods section describing the sequencing that was performed specify whether it was done on a MinION or PromethION (or flongle). While this is implied elsewhere in the paper, it should definitely be specified in the methods at a minimum.
3. You also write in the introduction that your method, SPLICE, was developed for the MinION specifically. Please comment on its applicability to data generated on the PromethION and flongle Nanopore sequencers.
4. The volcano plot in Fig 3A is missing its dots.

Significance

Nature and significance of the advance: The paper presents several exciting advances in terms of tumour biology. The authors demonstrate how alternative splicing can drive liver cancer, while being undetectable by short-read sequencing. They also show a large number of fusion transcripts that were validated by RT-PCR but were undetectable with short-read sequencing. The analysis method they present, SPLICE, contains a number of smaller advances, but raises major concerns about its capacity to act as a maintainable piece of bioinformatics software.

Comparison to existing published knowledge: The authors compare the software they present to a single tool in the same class for the two functions it performs (isoform analysis and fusion detection). A more thorough comparison to a broader range of available tools would be better.

In terms of biology, the authors extensively cite related literature to place their discoveries in context.

Audience: Cancer researchers, anyone interested in doing isoform-level differential expression analysis or gene fusion detection using Nanopore RNA-seq data.

My expertise: I am a staff scientist working on developing and testing tools for Nanopore sequencing analysis at a cancer research centre.

Referees cross-commenting

I fully agree with all of the comments by the other two reviewers.

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Referee #1

Evidence, reproducibility and clarity

Fujimoto and collaborators use Nanopore-based cDNA sequencing for genome-wide transcriptome analysis of a collection of hepatocellular carcinomas (HCCs) and matched normal liver tissues. To improve detection of alternatively spliced isoforms and hybrid transcripts potentially deriving from genomic rearrangements, they develop a dedicated pipeline SPLICE, which they benchmark against available software used for the same analysis. Besides having dual functionality (calls both alternative transcripts and fused transcripts), SPLICE seems to outperform previous software in calling alternative/fused transcripts and accuracy. They use the SPLICE pipeline to call isoforms and gene fusions in normal liver cells and HCCs and perform basic functional validations on novel fusions identified. The manuscript is well written, and the analyses are well performed. Perhaps the benchmarking of the SPLICE pipeline could have been more extensive (i.e., performed on additional independent datasets).

Major points:

1. Line 149-150: "We compared the results of mapping to the reference genome and the reference transcriptome sequences, and removed candidates if both were inconsistent (removal of mapping errors). " Please specify what "both were inconsistent" means.
2. Concerning TE-derived novel exons, in principle, this may lead to altered expression of the TE-transcript (as the Authors report for L1-MET) or to altered splicing of the transcript (i.e., other exon/introns could be retained or excluded). Can the Authors assess whether the inclusion of the TE in a transcript enhances its expression or affects the splicing of the "parental" transcript? If so, can they verify if the position of the insertion of the TE has any effect on expression and splicing?
3. Can the Authors explain why the NBEAL1-RPL12 was not detected by SPLICE?
4. Line 332: Can the Authors explain how the total amount of HVB mRNA was determined in each sample? Is it a relative amount calculated from the sequencing data? If so, it should be made clear in the text that this is a fractional measure.
5. Fig4a: please specify if the y-axis "number of support reads" reports library normalized values.
6. HCCs have more HBV-human genome fusion transcripts than normal liver. Could the authors clarify if these HCC transcripts are selectively found in tumors? or whether they are also expressed in normal liver samples? The paragraph starting from line 356 is confusing, and it is difficult to retrieve the above information for both HBs and HBx fusions.
7. Figure 4C: what was the control used to calculate the relative viability in these analyses?
8. MYT1L: the Authors report the identification of a novel MYT1L transcript downregulated in HCC, and argue it may have a potential tumor-suppressive function. For the sake of clarity, it will be advisable to show also the differential expression (HCC vs. Liver) of the other transcripts expressed from the same locus.

Minor points:

1. Table S4: there is a typo, correct "secific" in "specific"

Significance

The Authors show that applying long-reads sequencing to the study of the transcriptome, combined with their improved in-house analyses pipeline, leads to the identification of novel transcripts, which are alternative splicing isoforms and transcripts originating from novel gene fusions with potential oncogenic function. This provides a proof of principle study which show the advantages of long-reads sequencing and offers a solid data for further mechanistic studies on liver cancer.

Referees cross-commenting

I also agree with the other reviewers. All the concerns expressed by the reviewers seem addressable in a reasonable timeframe.

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Reply to the reviewers

1. General Statements [optional]

Overall we were elated to have received such positive comments on the manuscript, with requests for only minor changes. We have made all suggested changes to clarify or tone down the language as suggested.

We would like to thank each of the three reviewers for their assessment of our work. We note that all three reviewers agreed the phylogenetic analysis was interesting and convincing. Two of the three reviewers felt the study sufficiently demonstrated roles for Baramicin in the nervous system. We have responded to comments from Reviewer 2 to draw attention to some aspects of the data that they may have been overlooked, which we hope reassures them that our proposal of BaraB and BaraC involvement in the nervous system is robust, coming from different approaches that show consistent results.

Reviewer 1 and Reviewer 3 compliment the study as being very worthwhile, and for suggesting concrete routes for how an AMP evolved non-immune functions. Both compliment its comprehensiveness, and describe the study as having striking findings that should have broad appeal to audiences interested in the crosstalk between the nervous system and the innate immune system.

2. Point-by-point description of the revisions

In the revised manuscript file, we have highlighted all text where changes were made.

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

The authors provide convincing evidence for an evolutionary scenario in which duplications of an AMP gene with ancestral immune function led to paralogs specialist for neural functions. They focus on the Baramicin genes, coding for major Toll signalling targets in the context of antifungal defence. Their study uses infection experiments in several Drosophila species, a careful annotation of the Baramicin genes of D. melanogaster, the demonstration of neural expression of BaraB and BaraC, the KD analysis of Bara B revealing lethality and neurological phenotypes, a reconstruction of the evolutionary history of Baramicn genes in Drosophilids and an analysis of the sequence evolution of the IM24 domain providing the neural functions. In general the paper is well written. There are a few places in the manuscript where the language can be improved and one point, which needs clarification: - ine 297: ...,which did not present with... - line 314/315: ...to just 14% that of...to 63% that of - line 459: ..., we this motif... - line 518: What does "... genomic relatedness (by speciation and locus)..." mean? - line 527/528: ...drive behaviour or disease through interactions... - line 532: ... ancestrally encodes distinct peptides involved with either the nervous system or the immune response... line 535: ...with either the nervous system (IM24) or.... Do the data provide enough evidence suggesting that IM24 had a neural function in the ancestor? Ideally the authors should look at neural expression of the Baramicin gene in the ourgroup, S. lebanonensis. The authors later (line571) admit, that they cannot rule out that IM24 is also antimicrobial.

We thank reviewer #1 for drawing attention to these points. We have made changes to each line to be more concise, clarify our meaning, or fix typos.

Reviewer #1 (Significance (Required)):

This is a very comprehensive study, which, to my knowledge for the first time, suggests concrete routes of how an AMP evolved non-immune functions. One of the striking findings of this paper is that duplications and subsequent truncations of the ancestral Baramicin locus linked to specialisation for neural functions occurred independently in different Drosophila lineages.

We thank reviewer #1 for their very positive comments. We also agree with all suggested changes, including more careful phrasing to emphasize that we have not described a mechanism, just an involvement in the nervous system. For instance, see lines 556-568 are reworked to soften language and explicitly state the ancestral function of IM24 is unknown, and our suggestion that IM24 could underlie Dmel\BaraA interactions with the nervous system is speculation that should be tested.

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

Hanson and Lemaitre present a genomic and phylogenetic characterization of the Baramicin family of antimicrobial peptide genes in different species. They discover new Baramicin paralogs, united by the presence of an IM24 domain at the N-terminus. They show that among Baramicins, those that are not inducible by infection (which they improperly call non-immune since a protein can be non-inducible by infection and have very important immune functions), are truncated. They propose that an ancestor peptide with immune functions evolved into a neuronal regulator/effector via truncation.

Although the hypothesis is interesting, the data do not really support it. This manuscript is rather descriptive at this point. The demonstration that IM24 is necessary for neural function is very tenuous. For example, in the paragraphs titled Dmel\BaraB is required in the nervous system during development and Baramicin B plays an important role in the nervous system, I did not find convincing data demonstrating that BaraB is required in the nervous system. The only data that links BaraB to the nervous system is a weak locomotion defect observed in the BaraB mutant. But how many genes, when inactivated, give a locomotion defect? This remains totally unexplained at the molecular level. The authors also mentioned that BaraB is expressed in a subset of mechanosensory neuron cells in the wing. What is the link between this expression and the nubbin phenotype? The authors also mention that data in the literature indicate that BaraC is expressed in glial cells but also in other tissues. Finally, we have no idea what role, if any, these peptides have in the nervous system.

While the characterization of the Baramicin gene family and its evolution across species is convincing, the link between these AMPs and the nervous system is really too preliminary to be convincing. The manuscript would greatly benefit from being more concise.

Reviewer #2 (Significance (Required)):

see above

We thank reviewer #2 for their fair assessment. We have made edits to soften our phrasing, and to emphasize that we have not described a mechanism, just an involvement, in the nervous system.

Examples:

line 270: “integral development role” -> “important for development”

line 277: “Baramicin B plays an important role in the nervous system“ -> “Baramicin B suppression in the nervous system mimics mutant phenotypes”

line 532: “Here we demonstrate that the Baramicin antimicrobial peptide gene of Drosophila ancestrally encodes distinct peptides involved with either the nervous system or the immune response.“ -> “Here we demonstrate that the Baramicin antimicrobial peptide gene of Drosophila ancestrally encodes distinct peptides that may interact with either the nervous system (IM24) or invading pathogens (IM10-like, IM22).”

line 562 new text: “Thus while our results suggest that IM24 of different Baramicin genes might underlie Baramicin interactions with the nervous system, we cannot exclude the possibility that IM24 is also antimicrobial, or even that antimicrobial activity is IM24’s ancestral purpose. Future studies could use tagged IM24 transgenes or synthetic peptides to determine the host binding partner(s) of secreted IM24 from the immune-induced Dmel\BaraA, and/or to see if IM24 binds to microbial membranes.”

We have also changed all instances of “non-immune Baramicins” to “Baramicins lacking immune induction” or something to that effect (e.g. new Lines 25,464, 469,478-82).

We also made some small changes to be more concise (e.g. line 387, 447, cut lines 492-495 from previous version, cut lines 506-507 from previous version).

We have responded below in the reviewer-to-reviewer comments for a few of the specific points raised there, which we hope further assuage some of Reviewer 2’s concerns.

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

Antimicrobial peptides are main effectors in (insect) immune defenses. It is becoming more and more clear, that AMPs can have pleiotropic effects or even acquire new functions. In the present paper, the authors investigate Baramicin, an antifungal AMP that they described first in publication last year. Here they show that in Drosophila melanogaster Baramicin A, which they described before, has paralogs, that are not immune-inducible. They then show that these paralogs, named BarB and BarC, which are truncated versions of BarA, are expressed in the head and neural tissues. That they have neural functions is supported by targeted gene-silencing experiments. They go on to show, using a comparative approach across Drosophila, that Baramicin A with its antimicrobial function constitutes the ancestral state. Moreover, Baramicin is also enriched in head samples of some of the other Drosophila species they study. This manuscript, which according to the acknowledgements has already been seen by reviewers, is in a very good shape.

I have only a number of minor points, that might help to clarify the presentation.

Lines 34-36: I would delete this sentence and replace it with a statement based on the main findings of the manuscript

We now conclude the abstract with “As many AMP genes encode polypeptides, a full understanding of how immune effectors interact with the nervous system will require consideration of all their peptide products.”

Lines 56-60. May be tone down a bit. Anti-inflammatory activities of AMPs have been known for a long time. I think the next paragraph makes a very good case what is already known and is hence a nice motivation for the current study.

Toned down. This part now reads: “However AMPs and AMP-like genes in many species have recently been implicated in non-immune roles in flies, nematodes, and humans, suggesting non-immune functions might help explain AMP evolutionary patterns.”

Line 125: classical instead of classically

done

Line 200: what is a 'novel' time course? I would just describe what has been done.

Now reads: “We next measured Baramicin expression over development from egg to adult.”

Line 268: hypomorph, I guess in the literature usually hypomorphic is used.

done

Line 279: I would suggest to tone this headline down. This is not a criticism of the paper, but the actual mechanisms of the roles in the nervous system are not studied here.

Done. Now reads: “Baramicin B suppression in the nervous system mimics mutant phenotypes”

Line 505: what does not really become clear is whether IM24 plays an important role in the nervous system of fly species that only have BarA.

Edits from lines 556-568 now help highlight this question.

Line 540-549. This comparison I find a bit far-fetched, or maybe it needs clarification how doublesex expression is related to Baramicins.

Being completely honest: the doublesex discussion was requested during previous review at another journal. We agree that it is a bit of a tangent, and so we have removed these sentences.

Line 584-585. I think that this has been known for much longer from studies in frogs and beetles.

Our use of “in vivo” might have been a bit squishy here. We have edited this to reflect endogenous loss-of-function study, rather than simply “in vivo,” to clarify our intended sentiment.

Reviewer #3 (Significance (Required)):

Overall, I think that this is a very worthwhile and convincing story about the evolution AMPs and how they can acquire new functions. All the main statements are supported by careful experiments and data analysis. The paper does not go into any detail, of how the neurological role of BarB and BarC is achieved, but I think this is beyond the scope of the current manuscript. In short, this is a very worthwhile contribution to the growing literature of the role of AMPs in the nervous system. The authors provide the context of the main published papers in the area in the introduction. As opposed to most papers on this so far, the current manuscript also provides very interesting data on the evolutionary history of the Baramicin genes, both within the main study species, and within other Drosophila species. This paper should appeal to a rather broad audience of researchers interested in innate defenses, AMPs and the crosstalk between the nervous system and the innate immune system.

My background is insect immunology with a focus on AMPs and evolutionary approach.

We thank reviewer #3 for their very positive comments. We agree with all suggested changes.

**Referees cross-commenting**

This session contains the comments of all reviewers

Reviewer 3

Reviewer 2 and I share the view, that the evidence for the effects of BarB and C on the nervous system is rather limited. But I still think, that the paper provides enough new and interesting data that make it a very useful contribution. Though not a neurobiologist, I would assume that providing functional evidence for the role of BarA and B in the nervous system would justify a paper on its own. I agree though, that the relevant sections should be toned down.

Reviewer 2

As I mentioned in my review, I found the genomic and phylogenetic analysis interesting and convincing. I therefore totally agréé with reviewers 2 and 3 on that. Whether BarA and B are playing a role in the nervous system and how it does remain speculative. BaraB mutants show locomotion defects. But mutants in mitochondrial genes have locomotion defects. Can we conclude that mitochondria play a role in the nervous system? If I understand correctly, downregulating Bara in neurons only (With Elav-Gal4 driver) does not show the locomotion phenotype. it induces early lethality. How many genes when inactivated in neurons will give rise to such a phenotype? A lot. I really think that the implication of Bara in the nervous system should be seriously toned done and more presented as an hypothesis than a validated fact.

We would like to note for Reviewer 2 here that it is specifically elav> BaraB-IR that results in lethality, and in weaker gene silencing experiments, adult elav>BaraB-IR flies emerge, and they do suffer locomotor defects. Often, they got stuck in the food shortly after emerging, or would move haphazardly (which was common in flies with nubbin-like wings). We have added explicit mention that elav>BaraB-IR also results in locomotor defects (Line 288-289).

Our private speculation is that the reason flies fail to emerge from their pupae is because they are so uncoordinated that they sometimes cannot wriggle out of the pupal case before their cuticle hardens. In some instances, both using mutants and RNAi, we observed fully developed adults with mature abdominal pigmentation that died trapped inside their pupal cases.

We’d also like to emphasize here that despite testing many other Gal4 drivers, including mef2-Gal4 (muscle/myocytes), nubbin-like wings and lethality were only found using elav-Gal4. A role interacting with mitochondria would likely have been revealed using mef2-Gal4, given the importance of mitochondrial function in muscle.

For BaraC: expression in other tissues (like the rectal pad) could nevertheless be from e.g. nerves innervating the muscles controlling the sphincter. Or it could indeed be entirely unrelated to the nervous system. However we feel the nearly perfect overlap with Repo-expressing cells is a strong argument for a neural role. We also made an effort using RNAi to validate this pattern suggested by scRNAseq, which confirmed a strong knockdown of BaraC-IR with Repo-Gal4 (Fig. 3, Fig. S4).

We hope these comments clarify for Reviewer 2 why we feel confident in proposing a role for Baramicins in the nervous system, even if we do not investigate a mechanism in this study.

Reviewer 1

I agree with reviewer 3 that the main message of the paper providing a concrete scenario of how non-immune functions of AMPs may evolve is an important contribution. A deep investigation of the neural function is definitely going beyond the scope of the paper. Indeed this might be quite tricky. But it would help if the authors could clarify their idea about the ancestral condition. Is there the possibility that IM24 had ancestrally already non-immune function? They are not really clear about this point.

Reviewer 2

I agree with the other reviewers that determining the exact role of Bara peptides could be complicated. I just ask that the authors limit themselves to proposing that the peptides have lost their immune function. I stress that this argument is not very strong. It relies solely on the lack of inducibility of these peptides following infection. I still think that the demonstration of the role of Bara in the nervous system is not provided.

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 #3

Evidence, reproducibility and clarity

Antimicrobial peptides are main effectors in (insect) immune defenses. It is becoming more and more clear, that AMPs can have pleiotropic effects or even acquire new functions. In the present paper, the authors investigate Baramicin, an antifungal AMP that they described first in publication last year. Here they show that in Drosophila melanogaster Baramicin A, which they described before, has paralogs, that are not immune-inducible. They then show that these paralogs, named BarB and BarC, which are truncated versions of BarA, are expressed in the head and neural tissues. That they have neural functions is supported by targeted gene-silencing experiments. They go on to show, using a comparative approach across Drosophila, that Baramicin A with its antimicrobial function constitutes the ancestral state. Moreover, Baramicin is also enriched in head samples of some of the other Drosophila species they study. This manuscript, which according to the acknowledgements has already been seen by reviewers, is in a very good shape.

I have only a number of minor points, that might help to clarify the presentation.

Lines 34-36: I would delete this sentence and replace it with a statement based on the main findings of the manuscript

Lines 56-60. May be tone down a bit. Anti-inflammatory activities of AMPs have been known for a long time. I think the next paragraph makes a very good case what is already known and is hence a nice motivation for the current study.

Line 125: classical instead of classically

Line 200: what is a 'novel' time course? I would just describe what has been done.

Line 268: hypomorph, I guess in the literature usually hypomorphic is used.

Line 279: I would suggest to tone this headline down. This is not a criticism of the paper, but the actual mechanisms of the roles in the nervous system are not studied here.

Line 505: what does not really become clear is whether IM24 plays an important role in the nervous system of fly species that only have BarA.

Line 540-549. This comparison I find a bit far-fetched, or maybe it needs clarification how doublesex expression is related to Baramicins.

Line 584-585. I think that this has been known for much longer from studies in frogs and beetles.

Significance

Overall, I think that this is a very worthwhile and convincing story about the evolution AMPs and how they can acquire new functions. All the main statements are supported by careful experiments and data analysis. The paper does not go into any detail, of how the neurological role of BarB and BarC is achieved, but I think this is beyond the scope of the current manuscript.

In short, this is a very worthwhile contribution to the growing literature of the role of AMPs in the nervous system. The authors provide the context of the main published papers in the area in the introduction. As opposed to most papers on this so far, the current manuscript also provides very interesting data on the evolutionary history of the Baramicin genes, both within the main study species, and within other Drosophila species.

This paper should appeal to a rather broad audience of researchers interested in innate defenses, AMPs and the crosstalk between the nervous system and the innate immune system.

My background is insect immunology with a focus on AMPs and evolutionary approach.

Referees cross-commenting

This session contains the comments of all reviewers

Reviewer 3

Reviewer 2 and I share the view, that the evidence for the effects of BarB and C on the nervous system is rather limited. But I still think, that the paper provides enough new and interesting data that make it a very useful contribution. Though not a neurobiologist, I would assume that providing functional evidence for the role of BarA and B in the nervous system would justify a paper on its own. I agree though, that the relevant sections should be toned down.

Reviewer 2

As I mentioned in my review, I found the genomic and phylogenetic analysis interesting and convincing. I therefore totally agréé with reviewers 2 and 3 on that. Whether BarA and B are playing a role in the nervous system and how it does remain speculative. BaraB mutants show locomotion defects. But mutants in mitochondrial genes have locomotion defects. Can we conclude that mitochondria play a role in the nervous system? If I understand correctly, downregulating Bara in neurons only (With Elav-Gal4 driver) does not show the locomotion phenotype. it induces early lethality. How many genes when inactivated in neurons will give rise to such a phenotype? A lot. I really think that the implication of Bara in the nervous system should be seriously toned done and more presented as an hypothesis than a validated fact.

Reviewer 1

I agree with reviewer 3 that the main message of the paper providing a concrete scenario of how non-immune functions of AMPs may evolve is an important contribution. A deep investigation of the neural function is definitely going beyond the scope of the paper. Indeed this might be quite tricky. But it would help if the authors could clarify their idea about the ancestral condition. Is there the possibility that IM24 had ancestrally already non-immune function? They are not really clear about this point.

Reviewer 2

I agree with the other reviewers that determining the exact role of Bara peptides could be complicated. I just ask that the authors limit themselves to proposing that the peptides have lost their immune function. I stress that this argument is not very strong. It relies solely on the lack of inducibility of these peptides following infection. I still think that the demonstration of the role of Bara in the nervous system is not provided.

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

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

Hanson and Lemaitre present a genomic and phylogenetic characterization of the Baramicin family of antimicrobial peptide genes in different species. They discover new Baramicin paralogs, united by the presence of an IM24 domain at the N-terminus. They show that among Baramicins, those that are not inducible by infection (which they improperly call non-immune since a protein can be non-inducible by infection and have very important immune functions), are truncated. They propose that an ancestor peptide with immune functions evolved into a neuronal regulator/effector via truncation.

Although the hypothesis is interesting, the data do not really support it. This manuscript is rather descriptive at this point. The demonstration that IM24 is necessary for neural function is very tenuous. For example, in the paragraphs titled Dmel\BaraB is required in the nervous system during development and Baramicin B plays an important role in the nervous system, I did not find convincing data demonstrating that BaraB is required in the nervous system. The only data that links BaraB to the nervous system is a weak locomotion defect observed in the BaraB mutant. But how many genes, when inactivated, give a locomotion defect? This remains totally unexplained at the molecular level. The authors also mentioned that BaraB is expressed in a subset of mechanosensory neuron cells in the wing. What is the link between this expression and the nubbin phenotype?

The authors also mention that data in the literature indicate that BaraC is expressed in glial cells but also in other tissues.

Finally, we have no idea what role, if any, these peptides have in the nervous system.

While the characterization of the Baramicin gene family and its evolution across species is convincing, the link between these AMPs and the nervous system is really too preliminary to be convincing. The manuscript would greatly benefit from being more concise.

Significance

see above

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 authors provide convincing evidence for an evolutionary scenario in which duplications of an AMP gene with ancestral immune function led to paralogs specialist for neural functions. They focus on the Baramicin genes, coding for major Toll signalling targets in the context of antifungal defence. Their study uses infection experiments in several Drosophila species, a careful annotation of the Baramicin genes of D. melanogaster, the demonstration of neural expression of BaraB and BaraC, the KD analysis of Bara B revealing lethality and neurological phenotypes, a reconstruction of the evolutionary history of Baramicn genes in Drosophilids and an analysis of the sequence evolution of the IM24 domain providing the neural functions. In general the paper is well written. There are a few places in the manuscript where the language can be improved and one point, which needs clarification:

• line 297: ...,which did not present with...
• line 314/315: ...to just 14% that of...to 63% that of
• line 459: ..., we this motif...
• line 518: What does "... genomic relatedness (by speciation and locus)..." mean?
• line 527/528: ...drive behaviour or disease through interactions...
• line 532: ... ancestrally encodes distinct peptides involved with either the nervous system or the immune response... line 535: ...with either the nervous system (IM24) or.... Do the data provide enough evidence suggesting that IM24 had a neural function in the ancestor? Ideally the authors should look at neural expression of the Baramicin gene in the ourgroup, S. lebanonensis. The authors later (line571) admit, that they cannot rule out that IM24 is also antimicrobial.

Significance

This is a very comprehensive study, which, to my knowledge for the first time, suggests concrete routes of how an AMP evolved non-immune functions.<br /> One of the striking findings of this paper is that duplications and subsequent truncations of the ancestral Baramicin locus linked to specialisation for neural functions occurred independently in different Drosophila lineages.

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Reply to the reviewers

We thank the reviewers for carefully reading our manuscript. We found their comments to be incredibly thoughtful and constructive and greatly appreciate their feedback. We are confident that addressing the reviewers’ concerns has strengthened our manuscript.

Reviewer #1 (Evidence, reproducibility and clarity (Required)): Camuglia, Chanet and Martin investigate the mechanisms that control cell division orientation in vivo, using the mitotic domains (MDs) in the head of the Drosophila embryo as their main model system. They find that cells in the head mitotic domains rotate and align their spindles within 30 degress of the anterior-posterior axis of the embryo. The Pins protein, implicated in spindle orientation in other systems, is planar polarized in mitotic cells. Pins polarization precedes spindle rotation and is correlated with the division angle (but cell shape is not, violating Hertwig's rule). Overexpression of myristoylated Pins results in uniform Pins distribution on the membrane and affects spindle orientation. alpha-catenin RNAi (but not canoe RNAi) disrupts Pins polarity and spindle orientation in MDs 1, 3 and 5. Low dose CytoD injections (which should disrupt force transmission) also result in defective Pins polarity and spindle orientations. Finally, mechanical isolation by laser ablation also disrupts spindle orienttion. The authors find that preventing mesoderm invagination by snail dsRNA disrupts Pins polarity and spindle orientation in the head. MAJOR 1. Is there a certain chirality in the rotation of the spindles? From Movie 1, it seems like in MDs 1 and 3 at least, a majority of spindles on the right side of the embryo rotate clockwise, while spindles on the left side rotate counter-clockwise? Is that so, and in that case, are there geometric/molecular considerations that could explain that chirality?

We thank the reviewer for pointing this out. They are correct in that there is a tilt to the spindle orientation relative to the AP axis. To illustrate this tilt, we performed our spindle analysis separately on the right and left sides of MD1 and found that spindles on the left side align with an average division angle of about 30 from the AP axis whereas spindles on the right side align with an average division angle of -30 from the AP axis. To determine whether spindles on either side rotated with a certain chirality, we found there was no preference in rotating clockwise or counterclockwise on the left and right sides (on the left side of MD1 53% of measured spindles rotated counterclockwise and 47% rotated clockwise, on the right side 46% rotated counterclockwise and 54% clockwise). We have added this data as Fig. 1I-J and discussed in the Results lines 134-145.

1. The authors are experts in mesoderm invagination, and understandably concentrate on the role that forces from that process may have in the orientation of head MD divisions. However, the cephalic furrow forms much closer to the head MDs, and in an orientation that might also explain the alignment of spindles in the head. Is cephalic furrow formation important for Pins polarity and spindle orientation in the head MDs?

This was certainly a possibility, but our experimental results strongly argues that mesoderm invagination is most relevant.

1) Perturbing the ventral furrow (e.g. by Snail depletion) does not block the cephalic furrow (Vincent et al., 1997; Leptin and Grunewald, 1990), but does block mesoderm invagination. Snail depletion strikingly disrupted spindle orientation and Pins localization, which suggests mesoderm is most important.

2) In addition, depletion of -catenin blocks ventral furrow invagination but not cephalic furrow formation. We see a disruption in spindle orientation and Pins localization in -catenin RNAi, which suggests cephalic furrow itself cannot orient spindles.

3) Furthermore, light sheet imaging of the Drosophila embryo has shown that the head region of the embryo undergoes tissue movement in the direction of the cell division and that this is associated with mesoderm invagination (Streichan et al., 2018; Stern et al., 2022).

See movies here: https://www.youtube.com/watch?v=kC11Upr30JY

To further test the importance of mesoderm invagination, we will perform additional ablation experiments trying to disrupt forces transmitted to the mitotic domains from distinct directions. Once we get this experimental result we will include language in the Discussion that will summarize the experimental results and the weight of the evidence for the roles of either ventral or cephalic furrow.

1. Does expression of myristoylated Pins affect mesoderm invagination (or cephalic furrow formation)? From Table S1 it seems that a maternal Gal4 driver was used to express myristoylated Pins, which could affect other tissues in the embryo. So it is in principle possible that effects of myristoylated Pins on mesoderm internalization/cephalic furrow formation could affect cell division orientation much like sna loss of function does, but in a mechanism that does not depend on Pins polarity. There is definitely an effect on mesoderm invagination in alpha-catenin RNAi (but not in canoe RNAi) embryos, so I wonder if the effect could be consistently through defects in mesoderm invagination (or cephalic furrow formation), and Pins polarity is really dispensable for spindle orientation. Are there head-specific Gal4 drivers that could be used to drive myristoylated Pins exclusively in the head?

We apologize that we did not clarify this in the text. Maternal overexpression of myr-Pins does not obviously disrupt mesoderm internalization/cephalic furrow formation. But, we do see that targeted disruption of mesoderm internalization via a Snail depletion affects the orientation of division. Note that our paper demonstrates the effect of force transmission on Pins polarity and division orientation, which is new and the main conclusion. The role of these divisions in morphogenesis is more complicated and is beyond the scope of this study.

In response to this comment we: 1) added language in the Results that states that gastrulation proceeds in myr-Pins expressing embryos (lines 206-208), 2) Added to the Discussion of the role of these oriented divisions to morphogenesis (lines 443-449), and 3) will add a figure showing ventral furrow and cephalic furrow formation in embryos ectopically expressing the myr-Pins.

1. Related to the previous point, does mechanical isolation by laser ablation (Figure 6I-N) affect Pins polarity? This experiment could alleviate some of my concerns above, as it certainly does not (should not?) disrupt neither mesoderm invagination nor cephalic furrow formation.

We agree that it would be useful to look at Pins polarity in laser ablated embryos. Currently, we have been unable to analyze Pins polarity after laser ablation, because the ablation to fully isolate the mitotic domain has bleached our Pins::GFP signal. Also, we have shown that Pins polarity is disrupted by 1) alpha-catenin-RNAi, 2) low dose CytoD injection, and 3) Snail depletion, all of which are expected to disrupt force generation and transmission through tissues.

In response to the reviewer comment, we will determine if Pins::GFP can be analyzed in less aggressive (directional) laser ablations. Again, remember that myr-Pins does not affect mesoderm internalization and that Snail depletion affects Pins polarity.

MINOR 1. Figure S5: I am a bit confused about the role of Toll 2, 6, 8 in orienting spindle orientation. In Figure S5D it seems that dsRNA treatment against these genes does not disrupt spindle orientation, but Figure S5F shows quite a significant (p=0.0057) effect in triple mutants. The authors favor the idea that Toll receptors do not affect spindle orientation, but the difference with the mutant should be addressed. Furthermore, what happens in MDs 3, 5 and 14 (if the germband extension defect does not affect those divisions)? Is there a difference between dsRNA and triple mutant embryos in these other MDs?

We think this is a great point. We stated in the text that TLRs are not solely responsible (line 247) for spindle orientation as they do not recapitulate the random pattern of division seen in the myr-Pins expression condition. We acknowledge the differences between the dsRNA injection and TLR triple mutant in the manuscript (lines 242-247), but our data show a greater importance for the role of force transmission. We favor the idea that other mechanisms contribute to spindle orientation because of the small effect of mutating all three Tolls and the dramatic effects of depleting AJs, inhibiting actin (with CytoD), laser ablation, and blocking mesoderm invagination. The planned laser ablation experiments (described above) will also contribute to addressing this point.

1. No statistical analysis is provided for any of the differences in polarity between Pins and Gap43, and this should be done to demonstrate the significance of the polarization of Pins. Also, particularly for MD14, they should compare anterior vs. posterior polarity, as based on the images in Figure 2H it is not clear that there is a difference between the anterior and posterior side of cells.

We thank the reviewer for this point. We have added the statistical comparison.

1. Figure 2A-D: the authors propose that Pins localizes preferentially to the posterior end of cells (instead of both anterior and posterior ends) in MDs 1, 3 and 14 (and anterior in MD 5). How is the asymmetry in the distribution of Pins along the AP axis accomplished, and is there any significance to it? This should be discussed in a bit more detail (currently no potential mechanisms provided in the discussion, just an acknowledgment of the question).

__We agree the localization of Pins to the posterior end of cells in MDs 1, 3, and 14 and anterior end in MD 5 is of great interest. The details and further mechanism of this preferential localization are beyond the scope of this paper, but we have added an acknowledgment of the question and discuss possible models that could explain the result (lines 458-460). __TYPOS 1. Line 49: "one daughter cells" should be "one daughter cell". 2. Line 193: "rotation. (Figure 3E-F)." should be "rotation (Figure 3E-F)." 3. Lines 232-237: please review. 4. Line 238: "epithelia cells" should be "epithelial cells".

We thank the reviewers for carefully reading our manuscript. We have fixed the typos mentioned.

Reviewer #1 (Significance (Required)): This is the first study to my knowledge that demonstrates the role of mechanical forces in polarizing Pins, and provides a nice model to further investigate how mechanical forces generated in one tissue may affect cell division orientation in distant ones. The paper is clear, well written, and quantitative analysis is present for most results. I have some issues with the statistics (or lack thereof) for a couple of results, and potential alternative interpretations for some experiments that in my opinion should be addressed prior to publication. Specifically, it is not clear to me if Pins polarity is at all necessary for spindle orientation in any of the examined MDs.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): Overview: In this manuscript, Camuglia et al. show Pins/LGN, which is understood to drive spindle orientation, can localize asymmetrically (with respect to the tissue plane) in the Drosophila embryo. Experimental work (including drug treatments, laser ablation, and knockdowns) lead the authors to propose that this asymmetry is driven by tissue-level tension. The findings are quite interesting and the manuscript is well-written overall. Major Comments: • The authors propose that localization is driven by tissue-level tension, but the direction of the tension isn't clear from the experimental work. For example, the laser ablation experiments cut around the entire perimeter of the mitotic domain, rather than along just one tension axis. Similarly, the finding that disruption of the ventral furrow (by Snail RNAi) interferes with spindle orientation in the head is very puzzling; the furrow is A) outside the embryonic head and B) runs in the parallel direction to the divisions considered. The authors need to address the directionality of tension experimentally.

We thank the reviewer for this comment and agree that better defining the direction of tension would strengthen our manuscript. We showed that blocking mesoderm invagination with Snail depletion disrupts spindle orientation, despite Snail not being required for cephalic furrow formation (refs). Recent light sheet data has shown that mesoderm invagination is associated with global movements throughout the embryo. Furthermore, the ventral furrow extends into the head region just past the anterior of MD5. To address the reviewer’s comments, we plan to: 1) Perform directional laser ablations to determine the directionality of the tension that orients the spindle, 2) Analyze strain rates in the mitotic domains prior to and during division, and 3) Add to our Discussion more about what is said in the literature about the movements that occur in the head during mesoderm invagination.

• As acknowledged in the text, the asymmetric enrichment of Pins in MD14 is fairly weak. Since the cells being examined here border a divot in the tissue, and might therefore be curving relative to the focal plane, it would be good to rule out the possibility that some of the asymmetry in Pins intensity is just a consequence of cell/tissue geometry. One way this could be achieved is by showing multiple focal planes.

Good point. We do not think that the asymmetric Pins enrichment in MD14 is due to tissue geometry or junction tilt. 1) MD14 divides ~10-15 minutes after mesoderm invagination is completed, so the cells do not border a divot (as seen with Gap43::mCh, Fig. 2I). The cells do round up, which can be seen as gaps between cells (Fig. 3E). 2) We compare Pins to GapCh and only see an enrichment with Pins (Fig. 2H-K). If the enrichment was due to tissue curvature or junction orientation relative to imaging axis, we would see the same enrichment in GapCh. 3) Expression of myr-Pins randomizes spindle orientation in MD14 (Fig. 3M, N).

• In Figure 3I (and 3M?), it appears that there are fewer cell divisions in the presence of myr-Pins. Is this the case? Since cell shapes change during division, and cell shapes influence tissue tension, an increase in cell divisions could lead to a change in tissue tension. This would be important to address, since tissue tension plays an important role in the proposed model.

These images are not taken at the same point of MD1 division ‘wave’, there are the same number of divisions in each condition. These mitotic domains exhibit a ‘wave’ of cell division (Di Talia and Wieschaus, 2012), and so the number of divisions in each image reflect the timing at which we captured the image. Quantifications involved divisions throughout this wave, but we have chosen images for figures which are most representative of what we see. We will add this to the text in the final version of the manuscript.

• The alpha-catenin and Canoe results are a bit confusing: - The rose plot in Figure 4D doesn't show a random distribution of spindle angles, but rather a modest change; most spindles still orient in the normal range. The p value in the figure legend (0.0012) is very different from the one in the figure (5.8284e-04). - Alpha-catenin is the strongest way to disrupt AJs, but A) the epithelium appears to be intact in the knockdown condition and B) spindle orientation is impacted but not randomized. Does this mean that the knockdown is incomplete? Or is Cadherin-mediated adhesion (in which alpha-catenin participates) only partially responsible for force transduction?

We acknowledge that perturbation using ____alpha-cat RNAi does not recapitulate the complete disruption of division orientation seen in embryos expressing myr-Pins. This is likely due to the variability in the strength of RNAi knockdown, which is observed for most RNAi lines that we use. To address the reviewer’s comment, we have added rose plots for individual embryos showing extremes in the severity of division orientation disruption (Fig. 4E and F). For the main plot (Fig. 4D), we have included all the data that we took because we obviously did not want to pick and choose which embryos were used for analysis. So Fig. 4D includes all the variability.

• Given that previous studies implicate Canoe in Pins localization, it seems important to lock down the question of whether Canoe is participating in the mechanism described in this paper. How do the authors know the extent of Canoe knockdown? As suggested by the alpha-catenin results (described above), is it possible that Canoe knockdown is simply not strong enough to impact spindle orientation? Aren't there genetic nulls available? We thank the reviewer for bringing these points to our attention. There are certainly genetic nulls available (Sawyer et al., 2009), but the experiment suggested by the reviewer would not establish the necessity of Canoe in mitotic domain cells. This is because Canoe nulls severely disrupt mesoderm invagination (Sawyer et al., 2009; Jodoin et al., 2015), as well as affecting junctions in the ectoderm during germband extension (Sawyer et al., 2011). Therefore, we would not be able to distinguish what effect of Canoe would be responsible for the spindle orientation using a null mutation. We did better experiments, we used 1) a mutant which specifically compromised mesoderm invagination (snail), 2) laser isolation to show the importance of external force transmission in orienting mitotic domain divisions, and 3) RNAi to deplete Canoe so that mesoderm invagination initiates and pulls on the ectoderm, but where there is clearly compromised Canoe function. This treatment did not cause any effect on spindle orientation arguing against a role of Canoe in this case. In response to the reviewers comment, we added language to the Results to indicate that it is possible that the Canoe knockdown is not strong enough and our rationale for why we did not perform the experiment in a Canoe null (lines 279-282).

Minor Comments:

• It can be difficult to interpret some of the spindle orientation data since the AP axis is vertical in the diagrams but horizontal in the rose plots. Can one of these be flipped so they go together?

We thank the reviewer for this suggestion and have flipped the rose plots so they match the images. Note that because of the large size of the figures, we have had to consistently orient anterior towards the top, which we establish at the beginning of the Results.

• Figure S3 is important information for the reader and should be ideally moved into the main paper. - Protein localizations referred to in text should be annotated on images, as they can be hard to see.

We disagree that S3 should be included in the main paper. The myr-Pins reagent has been used previously so the information in S3 is not new (Chanet et al., 2017).

• There are some discrepancies between figures, legends and text. - p-values differ between figures, legends, and/or text. - Fluorescent markers are labelled differently in figures and legend (CLIP170 in Figure 1) - Graphs appear to show that MD3 polarizes on posterior side, but figure legend says anterior in Figure S1. Vice versa for MD5.

We thank the reviewer for catching these typos. We have fixed these issues.

• Ideally, multichannel image overlays should be shown along with individual channels (b/w). However, it is appreciated that the fluorescent signals are exceptionally weak in this study, presenting a challenge to presentation and to quantification.

We agree the overlays would be nice. However, the Pins::GFP signal is weak compared to the tubulin and Gap43 signals, the merge does not provide more clarity, and the figures are already quite large. Therefore, we have only included the separated the images.

• Graph axes depicting spindle orientation would be more clear if shown in degrees, instead of normalized or in radians.

We thank the reviewer for this suggestion. We have changed the graph axes to be in degrees.

Reviewer #2 (Significance (Required)): Several recent studies have demonstrated that division orientation (in the tissue plane) is governed by tissue level tension. Remarkably, it appears that diverse mechanisms link tension with spindle orientation. Here the authors provide the first in vivo evidence connecting tension to the asymmetric localization of Pins, an important and evolutionarily conserved spindle orientation factor.

Reviewer #3 (Evidence, reproducibility and clarity (Required)): This beautiful manuscript uncovers a role for planar polarized PINS/LGN in orienting the mitotic spindle in Drosophila epithelia. In response to morphogenetic forces acting on adherens junctions, PINS/LGN localises to junctions in a planar polarized fashion to orient the spindle, and de-polarization of PINS/LGN prevents planar spindle orientation. The experiments are very well performed and the findings are robust. The conclusions are well supported by the data. Reviewer #3 (Significance (Required)): These important findings mirror previous work in human cell culture, but crucially reveal that the same phenomenon occurs in vivo in the Drosophila embryo. Thus, the findings underscore the highly conserved nature and in vivo relevance of this phenomenon.

We thank this reviewer for reading the manuscript and their encouraging words.

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Referee #3

Evidence, reproducibility and clarity

This beautiful manuscript uncovers a role for planar polarized PINS/LGN in orienting the mitotic spindle in Drosophila epithelia. In response to morphogenetic forces acting on adherens junctions, PINS/LGN localises to junctions in a planar polarized fashion to orient the spindle, and de-polarization of PINS/LGN prevents planar spindle orientation. The experiments are very well performed and the findings are robust. The conclusions are well supported by the data.

Significance

These important findings mirror previous work in human cell culture, but crucially reveal that the same phenomenon occurs in vivo in the Drosophila embryo. Thus, the findings underscore the highly conserved nature and in vivo relevance of this phenomenon.

Referees cross-commenting

this session contains comments of all reviewers

Reviewer 2

My biggest concern was that the direction of tension isn't obvious. I was particularly puzzled over the ventral furrow experiments, since I'm not clear on how that manipulation impacts the head. I agree with Reviewer #1 that it makes more sense to disrupt the cephalic furrow, but I'm not sure how to do that.

Reviewer 1

Agreed. I guess the question is whether there are cephalic furrow mutants in which mesoderm invagination is not affected. If so, those would be ideal.

Reviewer 3

Hi both. I understand your comments, but I felt that the direction of tension was apparent from the spindle orientation and the cell division axis itself. So, I wasn't concerned about using the snail mutant to prevent gastrulation and thus abolish forces generally.

Reviewer 2

I see. Well I certainly suspect that you and the authors are correct - and I'm enthusiastic about that! - but I'm concerned that using the direction of division to define the direction of tension is getting a little bit circular with the argument. I noticed that their ablation experiments aren't directional; instead they isolate the entire MD. Reviewer 1, as an expert in ablations, do you think it would make sense to make cuts that are only AP or DV?

Reviewer 1

I agree with Reviewer 2 about the circularity of the argument. I was going to propose AP vs DV cuts in sna mutants,with the idea that the wound healing response to those would pull in specific directions. My concern is that It won't be an effect of the same magnitude as the entire mesodermal placode going in, but maybe worth trying?

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Referee #2

Evidence, reproducibility and clarity

Overview:

In this manuscript, Camuglia et al. show Pins/LGN, which is understood to drive spindle orientation, can localize asymmetrically (with respect to the tissue plane) in the Drosophila embryo. Experimental work (including drug treatments, laser ablation, and knockdowns) lead the authors to propose that this asymmetry is driven by tissue-level tension. The findings are quite interesting and the manuscript is well-written overall.

Major Comments:

• The authors propose that localization is driven by tissue-level tension, but the direction of the tension isn't clear from the experimental work. For example, the laser ablation experiments cut around the entire perimeter of the mitotic domain, rather than along just one tension axis. Similarly, the finding that disruption of the ventral furrow (by Snail RNAi) interferes with spindle orientation in the head is very puzzling; the furrow is A) outside the embryonic head and B) runs in the parallel direction to the divisions considered. The authors need to address the directionality of tension experimentally.
• As acknowledged in the text, the asymmetric enrichment of Pins in MD14 is fairly weak. Since the cells being examined here border a divot in the tissue, and might therefore be curving relative to the focal plane, it would be good to rule out the possibility that some of the asymmetry in Pins intensity is just a consequence of cell/tissue geometry. One way this could be achieved is by showing multiple focal planes.
• In Figure 3I (and 3M?), it appears that there are fewer cell divisions in the presence of myr-Pins. Is this the case? Since cell shapes change during division, and cell shapes influence tissue tension, an increase in cell divisions could lead to a change in tissue tension. This would be important to address, since tissue tension plays an important role in the proposed model.
• The alpha-catenin and Canoe results are a bit confusing:
  • The rose plot in Figure 4D doesn't show a random distribution of spindle angles, but rather a modest change; most spindles still orient in the normal range. The p value in the figure legend (0.0012) is very different from the one in the figure (5.8284e-04).
  • Alpha-catenin is the strongest way to disrupt AJs, but A) the epithelium appears to be intact in the knockdown condition and B) spindle orientation is impacted but not randomized. Does this mean that the knockdown is incomplete? Or is Cadherin-mediated adhesion (in which alpha-catenin participates) only partially responsible for force transduction?
  • Given that previous studies implicate Canoe in Pins localization, it seems important to lock down the question of whether Canoe is participating in the mechanism described in this paper. How do the authors know the extent of Canoe knockdown? As suggested by the alpha-catenin results (described above), is it possible that Canoe knockdown is simply not strong enough to impact spindle orientation? Aren't there genetic nulls available?

Minor Comments:

• It can be difficult to interpret some of the spindle orientation data since the AP axis is vertical in the diagrams but horizontal in the rose plots. Can one of these be flipped so they go together?
• Figure S3 is important information for the reader and should be ideally moved into the main paper.
  • Protein localizations referred to in text should be annotated on images, as they can be hard to see.
• There are some discrepancies between figures, legends and text.
  • p-values differ between figures, legends, and/or text.
  • Fluorescent markers are labelled differently in figures and legend (CLIP170 in Figure 1)
  • Graphs appear to show that MD3 polarizes on posterior side, but figure legend says anterior in Figure S1. Vice versa for MD5.
• Ideally, multichannel image overlays should be shown along with individual channels (b/w). However, it is appreciated that the fluorescent signals are exceptionally weak in this study, presenting a challenge to presentation and to quantification.
• Graph axes depicting spindle orientation would be more clear if shown in degrees, instead of normalized or in radians.

Significance

Several recent studies have demonstrated that division orientation (in the tissue plane) is governed by tissue level tension. Remarkably, it appears that diverse mechanisms link tension with spindle orientation. Here the authors provide the first in vivo evidence connecting tension to the asymmetric localization of Pins, an important and evolutionarily conserved spindle orientation factor.

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Referee #1

Evidence, reproducibility and clarity

Camuglia, Chanet and Martin investigate the mechanisms that control cell division orientation in vivo, using the mitotic domains (MDs) in the head of the Drosophila embryo as their main model system. They find that cells in the head mitotic domains rotate and align their spindles within 30 degress of the anterior-posterior axis of the embryo. The Pins protein, implicated in spindle orientation in other systems, is planar polarized in mitotic cells. Pins polarization precedes spindle rotation and is correlated with the division angle (but cell shape is not, violating Hertwig's rule). Overexpression of myristoylated Pins results in uniform Pins distribution on the membrane and affects spindle orientation. alpha-catenin RNAi (but not canoe RNAi) disrupts Pins polarity and spindle orientation in MDs 1, 3 and 5. Low dose CytoD injections (which should disrupt force transmission) also result in defective Pins polarity and spindle orientations. Finally, mechanical isolation by laser ablation also disrupts spindle orienttion. The authors find that preventing mesoderm invagination by snail dsRNA disrupts Pins polarity and spindle orientation in the head.

Major

1. Is there a certain chirality in the rotation of the spindles? From Movie 1, it seems like in MDs 1 and 3 at least, a majority of spindles on the right side of the embryo rotate clockwise, while spindles on the left side rotate counter-clockwise? Is that so, and in that case, are there geometric/molecular considerations that could explain that chirality?
2. The authors are experts in mesoderm invagination, and understandably concentrate on the role that forces from that process may have in the orientation of head MD divisions. However, the cephalic furrow forms much closer to the head MDs, and in an orientation that might also explain the alignment of spindles in the head. Is cephalic furrow formation important for Pins polarity and spindle orientation in the head MDs?
3. Does expression of myristoylated Pins afect mesoderm invagination (or cephalic furrow formation)? From Table S1 it seems that a maternal Gal4 driver was used to express myristoylated Pins, which could affect other tissues in the embryo. So it is in principle possible that effects of myristoylated Pins on mesoderm internalization/cephalic furrow formation could affect cell division orientation much like sna loss of function does, but in a mechanism that does not depend on Pins polarity. There is definitely an effect on mesoderm invagination in alpha-catenin RNAi (but not in canoe RNAi) embryos, so I wonder if the effect could be consistently through defects in mesoderm invagination (or cephalic furrow formation), and Pins polarity is really dispensable for spindle orientation. Are there head-specific Gal4 drivers that could be used to drive myristoylated Pins exclusively in the head?
4. Related to the previous point, does mechanical isolation by laser ablation (Figure 6I-N) affect Pins polarity? This experiment could alleviate some of my concerns above, as it certainly does not (should not?) disrupt neither mesoderm invagination nor cephalic furrow formation.

Minor

1. Figure S5: I am a bit confused about the role of Toll 2, 6, 8 in orienting spindle orientation. In Figure S5D it seems that dsRNA treatment against these genes does not disrupt spindle orientation, but Figure S5F shows quite a significant (p=0.0057) effect in triple mutants. The authors favor the idea that Toll receptors do not affect spindle orientation, but the difference with the mutant should be addressed. Furthermore, what happens in MDs 3, 5 and 14 (if the germband extension defect does not affect those divisions)? Is there a difference between dsRNA and triple mutant embryos in these other MDs?
2. No statistical analysis is provided for any of the differences in polarity between Pins and Gap43, and this should be done to demonstrate the significance of the polarization of Pins. Also, particularly for MD14, they should compare anterior vs. posterior polarity, as based on the images in Figure 2H it is not clear that there is a difference between the anterior and posterior side of cells.
3. Figure 2A-D: the authors propose that Pins localizes preferentially to the posterior end of cells (instead of both anterior and posterior ends) in MDs 1, 3 and 14 (and anterior in MD 5). How is the asymmetry in the distribution of Pins along the AP axis accomplished, and is there any significance to it? This should be discussed in a bit more detail (currently no potential mechanisms provided in the discussion, just an acknowledgment of the question).

Typos

1. Line 49: "one daughter cells" should be "one daughter cell".
2. Line 193: "rotation. (Figure 3E-F)." should be "rotation (Figure 3E-F)."
3. Lines 232-237: please review.
4. Line 238: "epithelia cells" should be "epithelial cells".

Significance

This is the first study to my knowledge that demonstrates the role of mechanical forces in polarizing Pins, and provides a nice model to further investigate how mechanical forces generated in one tissue may affect cell division orientation in distant ones. The paper is clear, well written, and quantitative analysis is present for most results. I have some issues with the statistics (or lack thereof) for a couple of results, and potential alternative interpretations for some experiments that in my opinion should be addressed prior to publication. Specifically, it is not clear to me if Pins polarity is at all necessary for spindle orientation in any of the examined MDs.

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Reply to the reviewers

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

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Referee #3

Evidence, reproducibility and clarity

Hattori et al. assessed the role of astrocytic CD38 by generating astrocyte-specific conditional CD38 knockout mice and discovered defects in social memory, synapse, and spine density in the mPFC. They further showed that conditioned media from CD38-deficient astrocytes are defective in promoting synapse formation. A known astrocyte-derived synapse promoting protein, Sparcl1, is reduced in the conditioned medium from CD38 KO astrocytes and pharmacological experiments suggest that CD38 and calcium signaling regulates Sparcl1 secretion by astrocytes.

The discoveries are novel and important and will be of broad interest to readers. However, the following concerns need to be addressed to improve the manuscript.

Major comments:

1. It's unclear if experiments were conducted while the experimenters are blinded to the genotype of the mice. This is essential for behavior tests.
2. Hippocampus is also important for memory formation. Do synapse and spine densities change in the hippocampus?
3. The proposed model of CD38 inducing Ryr3-mediated calcium release from internal stores is interesting. However, the Barres database showed that Ryr3 is not expressed by mouse astrocytes. Could the authors demonstrate the presence of Ryr3? That's a key link in their model that hasn't been demonstrated to operate in astrocytes.
4. The authors demonstrated reduced synapse and spine density in mPFC. Interestingly, a battery of behavior tests showed no defect, except for the social memory test. Reducing synapses in mPFC should affect a range of behaviors. Why that is not the case here?
5. The authors only tested very short-term memory (30 minutes delay). Does CD38 regulate long-term memory? It would be important to know but I realize that a single paper cannot address all questions and therefore do not think addressing this point is a prerequisite for publication.

Minor comments:

1. Fig. 2F, multiple comparison adjustment is needed.
2. Fig. 3A, scale bar is 10 micrometers, not millimeters
3. Fig. 4C, D, it is unclear if the quantification is normalized to actin loading control. BDNF levels appear lower in KO, though not significantly different, raising the question of whether an equal amount of samples was loaded.
4. Need to validate whether CD38 levels are reduced in P42-46-injected adult knockout before concluding that CD38 is required only during development

Significance

Astrocytic contribution to social memory has not been reported. This study is thus the first report on the role of astrocytes in social memory. Their discovery of CD38-regulation of Sparcl1 release is also novel and important for synapse formation, although more evidence is needed to support this point (see major comments above). This study will be of broad interest to neuroscientists. I have expertise in cellular and molecular neurobiology and can evaluate all parts of the paper.

Referees cross-commenting

I agree with the issues that the other reviewers pointed out, especially the need for improving data reporting and consistency/accuracy. Overall, I think this manuscript has potential and the issues are addressable.

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Referee #2

Evidence, reproducibility and clarity

Summary

In their manuscript, Hattori et al., put forward evidence that the knock-out of CD38 expression in astrocytes at approximately post-natal day 10 (referred to as CD38 AS-cKO P10) leads to a specific deficit in social memory in adult mice, while other types of memory remain unaltered. Using immunohistochemistry (IHC), the authors found a reduced number of excitatory synapses in the medial prefrontal cortex (mPFC) of CD38 AS-cKO P10 mice. Switching to in vitro primary cell culture models, the authors identify the astrocyte secreted protein SPARCL1 as a relevant synaptogenic factor. Using pharmacological dissection of relevant signaling pathways, Hattori et al., propose that cADPR formation and calcium released from intracellular stores, is essential for SPARCL1 secretion from astrocytes. Finally, the authors analyzed the transcriptome of primary CD38 KO astrocytes using bulk mRNA sequencing, and found that genes related to calcium signaling were downregulated in these cells.

Major commments:

• Are the key conclusions convincing?
  1. From a global perspective, the multiple lines of evidence provided by the authors strongly suggest that expression of CD38 in astrocytes is important for synaptogenesis in the mPFC of P10 mice, with ablation of CD38 and reduced synapse formation leading to social memory deficits at P70. However, the data concerning the role of astrocyte-secreted SPARCL1 is not particularly strong: further experiments are needed to support this claim (see below).
• Are the claims preliminary or speculative?
  1. As it stands, there is no proof that the claimed astrocyte-specific deletion of CD38 is actually astrocyte specific. This evidence is crucial: without it the reported effects could be due to non-specific CD38 knock-out in other CNS cells. In this respect, the Western Blot in Supplementary Figure 1A does not provide information on astrocyte-specific deletion, merely that CD38 was globally reduced in the mPFC. Interestingly, the authors have previously published data (Hattori et al., 2017, 10.1002/glia.23139) showing that CD38 expression is mostly astrocyte-specific, peaking at p14, which coincides with the peak period of synaptogenesis. The degree of CD38 heterogeneity is also an issue that I think the authors need to consider. Do they information on this? Is CD38 expressed in every astrocyte of the CNS, or are there some astrocytes that are CD38 negative at P14? Is the mPFC a region specifically enriched in CD38 positive astrocytes and does this explain the observed behavioral deficit? I think if this is known, the authors should mention it in the "Introduction" or "Discussion". If this is not known, maybe the authors could provide data addressing the issue.
  2. I think the authors should take more caution in claiming that SPARCL1 is the main factor secreted through the CD38 signaling pathway and responsible for increased synaptogenesis. This is for several reasons, all centered on data displayed in Figure 4 and Supplementary Figure 6:
     • a) Western Blot (WB) data: The "Materials and Methods" section for WB does not indicate how protein loading and transfer efficiency were controlled for. Normalizing to β-Actin levels is an acceptable way to control for loading and transfer efficiency when using cell lysates. However, in the absence of such an abundant structural protein in conditioned media it is unclear how loading and transfer was controlled for under these conditions. Do the authors normalized the CD 38 KO AS ACM data by expressing protein levels relative to those from WT AS ACM? Is BDNF being used as a control, based on proteomics data? If so, why is proteomics data not given in the manuscript and why is this control not shown for all ACM blots? I realize that (quantitative) blotting using ACM is difficult, but I am also not convinced that the methodology used is sufficiently rigorous. Simple steps to give confidence would be Coomassie staining of gels both before and after membrane transfer, to show that i) the total protein amount loaded was the same in each lane of the gel and ii) the transfer to the nitrocellulose membrane was complete. In addition, Ponceau S staining of the nitrocellulose membrane should also have been performed and displayed, to show (roughly) equal amounts of protein were transferred for each lane. In summary, the WB data quantification needs to be better controlled. The values of the Y axis in these graphs (and throughout the manuscript) are simply too small to be read properly. Finally, I want to highlight the general lack of precision regarding the nature of the replication unit (the "n"). For example, the legend of Figure4C-D states "n = 6", but we have no idea if these are 6 independent primary cultures originating from 6 mice, 6 independent cultures from the same mouse, 6 repeats of the Western Blot using the same sample etc. This issue is valid for the whole manuscript: in my opinion, the authors should be more much careful when it comes to these crucial elements of scientific reporting.
     • b) While the data hint at an important role of SPARCL1 in synapse formation, when the authors tested if ACM from CD38 KO astrocytes supplemented with exogenous SPARCL1 could rescue synapse formation, the effect was incomplete, with only a trend to an increase in synapse number (Figure 4J-K). Perhaps the authors simply forgot to indicate the statistical significance of differences between the experimental groups (Figure 4K)? However, if there really were no statistically significant differences observed, the authors should reduce the strength of their conclusions regarding SPARCL1. This protein may well be pro-synaptogenic but, as it stands, other factors could well be in play. Perhaps the authors should have tried higher concentrations of SPARCL1 to further boost synaptogenesis? In this respect, the SPARCL1 knockdown (KD) experiment in Supplementary Figure 6B-D is an important addition, but should be supplemented by rescue with an siRNA-resistant recombinant SPARCL1? If SPARCL1 is a major player in synaptogenesis, the prediction is that synapse numbers would be close to wild type levels with this approach.
     • c) In my opinion, there are also issues with the data displayed in Figure 4H-I. The authors want to convince the reader that SPARCL1 is mostly an astrocytic protein using immunohistochemistry on mouse mPFC sections, co-labelled with antibodies against neuronal and astrocytic markers. In these panels, we are presented with images showing a few cells, in which it seems SPARCL1 is absent from NeuN positive cells, present in WT astrocytes and reduced in CD38 AS-cKO P10 astrocytes. However, the numbers of cell counted and lack of quantification severely impact on the strength of this conclusion. In my opinion, the authors should have quantified their IHC data by counting cells and establishing the ratios of SPARCL1 positive over NeuN or S100β positive cells, in both control and CD38 AS-cKO P10 animals. This experiment would provide critical information that the conditional gene targeting strategy is robust. The authors should also consider quantifying the intensity of the SPARCL1 signal in astrocytes. This is recommended as the image displayed in Figure 4I for the CD38 AS-cKO is problematic: are the authors really claiming that the reduction in SPARCL1 expression following cKO of CD38 in astrocytes is at best only partial? Is 11 days between the first tamoxifen injection and tissue fixation actually sufficient to allow for CD38 turnover? With low levels of protein turnover, the possibility exists that residual levels of CD38 are still sufficient to impact SPARCL1 levels. What would happen if there is a greater interval between tamoxifen administration and tissue recovery? Would levels of synaptogenesis be further reduced? Is this an issue of production versus secretion or a combination of factors?
  3. The heatmap (Figure 5E-F) is simply too small to interpret. The color choice is also not accessible for colorblind readers. The authors might consider displaying this heatmap in a separate figure. The authors should also provide a supplementary table where all the genes detected are listed along with their respective counts. Furthermore, it is surprising that the authors only found genes being downregulated in CD 38 KO astrocytes. Were they really no genes up-regulated? The authors might also want to indicate the genes belong to each of the ontological categories listed in Figure 5F. On p. 11, Figure 5E: The authors should indicate in the main text they performed bulk RNA-sequencing and not another type of RNA sequencing (like single cell RNA sequencing for instance). The authors indicate n = 2 but we have no indications of the nature of the replicate (also see earlier comments). Please amend.
• Are additional experiments necessary? I think supplementary experiments are essential to support the claims of the paper. Most are described in the section above, but to summarize:
  1. Show data to prove that the CD38 AS-cKOP10 model is astrocyte-specific and leads to a total loss of CD38 in these cells.
  2. WB data: The issue of protein loading and transfer efficiency should be dealt with. Quantifications should be revisited.
  3. The authors should quantitatively analyze the different IHC performed in Figure 4H-I.
  4. The authors should provide more information on their RNA sequencing data: list of genes detected with their FPKM values etc. The authors should display the RNA sequencing data in a separate figure, allowing the heatmap to be enlarged.
  5. LC-MS/MS data: the authors should provide the list of all the proteins they identified in their LC-MS/MS experiment. As a supplementary table for instance? The majority of these experiments should be able to be performed with pre-existing samples/tissue slices. If not, the experimental pipeline necessary exits and these supporting experiments should not be too burdensome.
• Data and methods presentation Methods: The authors need to work on this aspect of the manuscript. Most of the important details are already described, but some crucial ones are missing, while the phrasing used to describe methods is sometimes misleading. I will give some examples here, but this is not an exhaustive list. The fact that the manuscript is riddled with small mistakes, inconsistencies and/or oversights makes it difficult to read and creates a negative impression. The whole manuscript would benefit from a thorough proof-reading, preferably by a native speaker.
  1. in the "Immunohistochemistry and Synaptic Puncta Analysis" section on p. 21-22, we have no indication of which antibodies against "GFAP, NDRG2, VGlut1, PSD95, S100β, NenN(?) and SPARCL1" were used. It is standard practice to indicate the company, product number and lot number. The authors must also indicate the dilution at which they use these antibodies. On p.22, the authors write the cells were incubated with "Alexa- or Cy3-conjugated secondary antibodies". The excitation wavelengths of the Alexa dyes used need to be given.
  2. The authors need to provide more details on the microscope they used. Merely writing "using a 63× lens on a fluorescence microscope" (p.23) is insufficient.
  3. In the "LC-MS/MS" method the authors wrote: "Briefly, these proteins were reduced, alkylated, and digested by trypsin". I think that in the reduction and alkylation steps, chemicals other than trypsin were actually used. This sentence should be modified to reflect this.
  4. p.19: "uM" is written when the authors very likely mean "µM". Please check the whole manuscript for repeat examples. I know this is often lab "short-hand", but it should be avoided in scientific publications.
  5. The authors should be careful when describing their data to always indicate whether they referring to experiments performed using cultured astrocytes or not. As it stands, the text is confusing: for instance, when describing RNA-sequencing data in Figure 5, the main text appears to indicate that these astrocytes were acutely isolated from adult mice, when in fact they were obtained from primary cultures. Given concerns in the literature about potential differences between acutely isolated and cultured astrocytes (Foo et al., Neuron, 2011), this is essential. Data presentation: The figures appear to have been produced in a rush - and almost have a "screenshot" feel to them. This is not a scientific issue per se, but does impact on the overall impression given by the manuscript. The following is a non-exhaustive list of issues with the figures. I list the major ones that the authors should correct.
  6. Almost all Y axis labels are too small. The authors should comply to the basic journal requirements in terms of font sizes. Some axes do not end on a tick (e.g. Figure 3R). This is not dramatic, but should be corrected. Globally, the authors need to display bigger bar plots - most of them are extremely hard to read. Labeling should also be checked: Figure 4K, the Y axis label indicates values displayed are in %, when I think the axis graduation displays ratio values. Some of the IHC pictures are also too small to be easily interpreted.
  7. The heatmap in Figure 5E is impossible to read and, as such, has little or no value for the manuscript.
  8. Scale bars: where is the scale bar in Figure 2A? Figure 3A-H: Is the scale bar really representing 10 millimeters? Supplementary Figure 3A: scale bar is missing. Please check for similar issues throughout the manuscript.
  9. Figure Legends are problematic, and often contain incorrect or incomplete information. Examples include: Supplementary Figure 1: The description of panels J, L and N appears to be missing. Please also use the Greek letter beta and not 'b' for S100β. Supplementary Figure 5: I think the term "KO" is missing after CD 38 in the legend title. Figure 3: why state that nuclei were counterstained with DAPI in Figure 3P,Q, when this precision is not given for panels Figure 3A-H? Figure 3A-H: If the authors choose to explicitly state PSD95 is a post-synaptic marker, why not indicate that VGlut1 is a pre-synaptic marker? Same issue in Supplementary Figure 4.
  10. There are multiple instances of panels being wrongly referred to in the main text. On p.10, Figure 4H is referenced, when I think the authors mean Figure 4I; on p.10, Figure 4I-J are referred to when the authors clearly describe data found in Figure 4J-K. These types of mistakes are problematic and recur throughout the manuscript.
• Statistical analysis As mentioned above, the exact nature of the replicates is often not stated, when the "n" number is indicated. The authors must correct this issue and give the information either at the appropriate point in the main text or in the figure legend.

The authors should also be more consistent in the way they indicate which statistical tests were performed. This should also be indicated either at the appropriate point in the main text or in the figure legend. Furthermore, care should be taken to ensure statistics are presented in an appropriate manner: at the end of legend for Figure 4, it is indicated #p < 0.05 vs. CD38 KO ACM. This hashtag symbol is completely absent from the figure. In Figure 4F-G, the lack of statistical symbols seems to indicate no statistical tests were performed on these data, when the legend covering these panels states "*p < 0.05 versus P70", indicating some tests were done. We cannot interpret this panel without knowing which comparisons were done exactly and which were significant.

In the "Materials and Methods", the authors give no indication that the assumptions of the statistical test they used were met (normality of data distribution for t-tests, homogeneity of variances for ANOVA...). This needs to be checked, and if not met, appropriate non-parametric tests should be used instead.

Minor commments:

• Specific experimental issues that are easily addressable. Most of the experimental issues that need to be addressed are given in previous sections and should be easily addressable.
• Citation of previous studies? Adequate
• Clarity and accuracy of text and figures There are issues with the clarity and accuracy of text and figures - which are described above. The text is also often problematic in its phrasing and other, more fundamental aspects. For instance, the authors spent a considerable amount of time speaking about the role of oxytocin, when they only performed one measurement of oxytocin levels in mice.
• Suggestions to improve the presentation of data and conclusions? All my suggestions to improve the presentation of data can found in previous sections. As for improving the authors presentation of their conclusions, the authors should make a considerable re-drafting effort, particularly for the "Discussion", which lacks clarity in how supporting arguments are built and presented. For example, on p.13, I am confused with the argument made by the authors. Their data are focused on synapses onto pyramidal neurons of the mPFC, but here the discussion states that the behavioral phenotype they observed in CD38 AS-cKOP10 might be explained by a lack of mPFC neurons synapsing onto neurons in the Nucleus Accumbens (assuming that "NAc" really refers to this brain region, as the definition is missing from the text). I think the authors should make it clear if this is their interpretation of their own result, which essentially renders their focus on mPFC pointless, or a speculation on possible other mechanisms that could also explain their behavioral results. Personally, given the data shown, I believe the authors should focus on explaining how their data in mPFC might explain the behavioral output observed. The authors could also provide perspectives on how the hypothesis laid down in this paragraph would be tested. When the authors write on p.14 "We identified SPARCL1 as a potential molecule for synapse formation in cortical neurons" why use the word "potential"? Does this mean the authors consider their data on SPARCL1 (one of the key messages of the paper) invalid? If the authors themselves think the role of SPARCKL1 is ambiguous based on their own data, they should perform further experiments. P. 13, the authors write: "Moreover, many studies have shown that astrocyte-specific molecules, including extracellular molecules such as IL-6, are involved in memory function"; Interleukin 6 (Il-6, abbreviation not defined in the manuscript) is definitely not an astrocyte-specific molecule (see, for example, Erta et al., 2021 10.7150/ijbs.4679).

Significance

NATURE AND SIGNIFICANCE OF THE ADVANCE: I think that despite the issues described above, this manuscript, once revised, could have a strong impact in the field. It would fuel the current paradigm shift which puts astrocytes at the forefront of neuronal circuit wiring during development with links to adult behavior. By identifying clear molecular targets involved in astrocyte-driven synaptogenesis, this article could help the clinical field to find new druggable targets, which may help reverse aging-related cognitive decline.

COMPARISON TO EXISTING PUBLISHED KNOWLEGDE: This work adds new data in the specific and growing line of research that study how astrocytes control synaptogenesis. Recent reviews have summarized advances in this field (Shan et al., 2021, 10.3389/fcell.2021.680301; Baldwin et al., 2021, 10.1016/j.conb.2017.05.006).

AUDIENCE: Neuroscientists in general, clinicians interested in cellular and molecular causes of neurodevelopmental disorders leading to social dysfunctions.

REVIEWER EXPERTISE: Astrocyte biology; Astrocyte-neuron interactions and synapse assembly; Neuronal circuit formation and plasticity

Referees cross-commenting

After careful reading of the other comments, I feel that there is considerable agreement/overlap between the reviewers on the main issues with this manuscript. Perhaps the major difference relates to the amount of further work necessary for the manuscript to be publication ready.

As Reviewer 3 rightly points out, this is always a moot point: how much is it reasonable for reviewers to ask authors to do? While I agree with all of Reviewer 1's comments regarding the rigour of the mass-spec/western blot analysis, it seems to me that from a molecular/cell biological point of view, the key issue is whether Sparcl1 is a synaptogenic factor released from astrocytes following CD38/cADPR/calcium signaling (irrespective of whether other factors may be in play); and whether raising Sparcl1 levels is sufficient to recover spine morphology and synapse numbers. Of course, if these experiments were performed in vivo using AAV-mediated overexpression of Sparcl1, it is also reasonable to think that the deficit in social memory may be reversed on testing.

The issues of whether there is a difference in observable behavioral phenotypes between the astrocyte-specific and constitutive CD38 knock-outs is an interesting one, as is why there is only a deficit in social memory seen following astrocyte-specific CD38 ablation. These issues should at least be discussed.

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Referee #1

Evidence, reproducibility and clarity

Summary

In their work submitted for review, Hattori et al. identify an astrocyte enriched protein (CD38) as important for social memory tasks in mice. The authors developed a conditional KO model to remove CD38 specifically in astrocytes using the GLAST-CreERT2 line crossed to a CD38 floxed line. The investigators use a three-chamber social approach test to show that loss of CD38 leads to reduced interaction time with a novel social stimulus only when the animal is given a break between test periods. The authors test whether changes in neuronal morphology or synapses in the medial prefrontal cortex (mPFC), a region important for social memory, can account for their behavioral phenotype. The researchers found that mPFC neurons in their conditional CD38 KO (cKO) animals have significantly less mature spines than wild-type (WT) controls. The authors then claim that this reduction in mature spines correlates with a reduction in VGluT1 positive excitatory synapse density in mPFC of cKO vs. WT. Next, the investigators use mass spectrometry of astrocyte conditioned media and neuronal cultures treated with astrocyte conditioned media to test whether a known astrocyte secreted synaptogenic factor, Sparcl1/Hevin, could underlie their reported changes in synapse density in their cKO animals. Finally, the authors use pharmacological inhibitors against different components of the CD38 signaling pathway to test whether CD38 regulates Hevin secretion by astrocytes. While the reported behavioral phenotype is interesting, this reviewer has several major concerns with the data claiming that reduction in Sparcl1/Hevin is underlying synaptic phenotypes in the CD38 cKO. Therefore, the paper is not suitable for publication without addressing the concerns listed below.

Major Concerns:

Synapse analysis in vivo: For the analysis of VGluT1 excitatory synapses in mPFC, it is not clear how the statistical analysis was performed. From the plotted error bars, it seems that the investigators used individual z-projections as the n for a t-test. This is inappropriate for this analysis as it would overinflate the N and down the p-value. It would be more appropriate to plot and compare animal averages between conditions or use a test that can account for the fact that there are repeated measures taken from the same animal. Additionally, the authors note a decrease in VGlut1+ puncta in the global CD38 KO but no change in the protein levels in both the global and cKO.

Synapse analysis in vitro: The authors are missing key experimental controls for their analysis of synapse induction by astrocyte conditioned media. Firstly, the authors do not include a condition of neurons cultured alone without astrocytes or astrocyte conditioned media treatments. This is critical to this experiment because, without this control, it is impossible to assess the effectiveness of the astrocyte conditioned media or any recombinant protein treatments on synapse formation. Secondly, the authors give very few details and no supporting data about the purity of their neuronal cultures. This is critical to this experiment because any contaminating astrocytes in their cultures could severely skew the data for any given condition. Finally, the authors do not specify how they determined the doses for astrocyte conditioned media and Hevin treatments. The researchers give no details on how the astrocyte conditioned media was collected or treated before adding onto neurons. For this experiment to be viable, the researchers must collect the conditioned media in serum-free media, determine the protein concentration of their samples, and the dose-response to the astrocyte conditioned media must be performed to determine the optimal dose for each batch. When comparing between genotypes, this type of quality control is critical to assess whether there is, in fact, a difference in their synaptogenic capacity.

Western blots: All western blot quantification of astrocyte conditioned media should include total protein normalization. The authors do not describe how they normalize the astrocyte conditioned media blots, but without a total protein stain to normalize, it is impossible to be sure the same amount of protein was loaded into the gel for each lane. In Figure 3L, the western blot data showing the expression of VGluT1 and PSD95 should be improved, and a better representation is recommended. It is also strange that the CD38 cKO has no expression because CD38 is also expressed in endothelial cells. Why not isolate astrocytes from CD38 KO? Also, for VGluT1 and PSD95 western blots, it would be better to test mPFC lysates rather than whole cortical lysates. Astrocyte morphogenesis: Since the astrocyte-specific deletion of CD38 from P10 impairs postnatal development of astrocytes, the authors should investigate if the impaired synaptogenesis seen in later stages is due to impaired astrocyte morphogenesis or the defect in the secretion of synaptogenic proteins like Sparcl1/Hevin or thrombospondins.

Mass spectrometry: There is no information about how many samples were used for mass spectrometry. This reviewer is concerned that this experiment may be underpowered given that other published datasets have identified significantly more proteins in wild-type ACM (about double than what was identified here). There needs to be a quality assessment of the ACM to help ensure the production protocol can capture the full extent of proteins secreted by cultured astrocytes.

RNA sequencing: RNA sequencing results seem underpowered, and an accurate description of their collection methods is missing. It also seems to this reviewer that any prolonged culturing of the astrocytes would lead to additional transcriptional changes independent of their genetic manipulation. To avoid confounds due to culture artifacts, it might be cleaner to FACS sort astrocytes using a fluorescent reporter such as the Aldh1l1-eGFP line or RTM in their GLAST-creERT2 model. In the latter case, this could also provide data on the specificity of their recombination, which is lacking elsewhere in the manuscript.

Comparison between astrocyte-specific cKO and global KO: Considering the abundant expression of CD38 in astrocytes compared to other cell types in the brain, I am wondering whether the comparison between the current astrocyte-specific CD38 cKO and the previous constitutive CD38 KO mice would provide a different phenotype with respect to its importance in synaptic function in neural circuits that mediate social behaviors in various brain regions. The authors note the importance of CA1, CA2, and NAC in social memory, but they only assessed synapses in mPFC. Multiple studies, including one from the authors, have reported that constitutive CD38 KO mice exhibit impaired social behaviors. Expanding beyond what is already known would require better spatial and temporal regulation of CD38 expression than presented here.

Rescue experiments: The authors claim that reduced levels of Hevin secretion are responsible for reducing intracortical synapses in mPFC and the inability of their CD38 KO ACM to stimulate synapse formation. However, Hevin has primarily been linked to the formation of VGluT2+ synapses with only a transient effect on VGluT1+ synapses. Furthermore, Hevin's synaptogenic effect in astrocyte conditioned media is masked by its homolog Sparc. To claim that Hevin is responsible for reducing VGluT1+ synapses in mPFC the authors need to do a rescue experiment by expressing hevin in CD38 KO through AAVs brains or intracortical injections of recombinant Hevin.

Other synaptogenic factors: The authors focus on Sparcl1/Hevin; however, other synaptogenic factors have been reported to affect VGluT1+ excitatory synapse formation and development directly. Notably, thrombospondins have been shown to regulate the formation of this specific synapse type through their receptor a2d1. The authors do not report any investigation into this family of factors despite their clear link to VGluT1+ synapse development.

Effect of CD38 cKO on astrocyte numbers: The authors note that CD38 cKO alters GFAP expression; however, they also report a decrease in the number of GFAP+ and S100ꞵ+ cells without a change in NDRG2+ cells. The authors should address this discrepancy in astrocyte numbers with additional known markers such as Sox9.

MBP quantification: The authors previously reported changes in MBP expression and oligodendrocyte maturation in the global CD38 KO animals. However, there is no quantification of the MBP staining in the cKO in supplementary figure 1. It would be important to verify that white matter structures developed properly in their cKO model, especially in mPFC.

Minor Concerns:

1. SPARCL1 annotation should be Sparcl1.
2. Avoid repetition of the same sentences in multiple places. E.g., The sentence- "Social behavior is essential for the health, survival, and reproduction of animals" is repeated both in the abstract and introduction.
3. The introduction needs to be thoroughly revised. In the first paragraph, a description of various studies(Fmr/Mecp2) which indicated the importance of synaptic function in neural circuits that mediate social behaviors in various brain regions could be presented later part of the introduction in a very concise manner since the article doesn't cover anything related to these genes. This part can be presented along with the narration of CD38, where authors described its importance in social behavior. Introduce the importance of social behavior and their behavioral paradigm, especially what social memory is and what brain regions are important for it.
4. Introduction feels too short and abrupt.
5. In Figures 2 and 3, Are the spine numbers/density/synapses affected in the p42 ctrl/CD38 AS-cKO group compared to the p10 ctrl/CD38 AS-cKO group?
6. In Figure 2; The authors should compare both the behavioral phenotype seen in two different tamoxifen injection/time points with the respective constitutive CD38 KO mice data.
7. In Figures 3 and 4, the authors should analyze the spine numbers/density both in WT or CD38 KO ACM treated experiments and Sparcl1 KD/Sparcl1 treated rescue experiments?
8. The discussion section needs to be revised to reflect better the conclusions drawn from the data without overstatement.

Significance

Understanding the mechanisms underlying control of behaviors is important and linking non-neuronal cell types to behavioral processes is novel and timely. However, the study at its current state lacks important controls, and interpretations are overstated and often too targetted to a favorite mechanism. These concerns limit the impact of the study and reduces its significance.

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Reply to the reviewers

RC-2022-01245 Willemsen et al., 2022

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

Summary:

Willemsen et al studied the contribution of PSMB4 and PSMB8 on proteasomal activity in adipocyte tissue/cells. Mutations in PSMB4/8 have been associated with metabolic diseases that lead to inflammation and proteotoxicity. They used mice and murine cell lines to assess the abundance and activity of the proteasome as well as the stress response upon depletion of psmb4 and regulatory factors. The study is interesting and could provide new insight into the role of specific proteasomal subunits of the immunoproteasome on the metabolism and associated diseases. However, there are a number of issues on the presented data that should be addressed. See below.

RESPONSE: We thank the reviewer for her/his positive remarks.

Major comments:

1. Most of the presented data are secondary data (graphs) and the differences between the experimental conditions (e.g. siRNA) are sometimes only minor and statistical analyses lacking or only indicated with a letter (a, b,..), but its meaning not explained. RESPONSE: We apologize if some of the statistics were difficult to see. Using letter designations for groups of indifference declutters the figure compared to using asterisks to indicate significant differences between groups. We have now made sure the statistical analyses are emphasized in the methods and figure legends.

The authors should introduce all tested marker genes e.g. the genes that were analyzed in figures 2H/I, 3D-F, 4A/D. What was the hypothesis and do they represent all or a selected set of genes of the integrated stress response?

RESPONSE: We apologize if the relevance of these markers was left unclear. We have now introduced the marker genes in the text.

Figure 1A: RNA levels were analyzed yet not protein levels. Why not?

RESPONSE: As we performed loss of function experiments later anyway, we decided not to venture into more descriptive analyses. The fact that Psmb4 and Psmb8 are robustly expressed in adipocytes was enough for us to justify studying their function further.

Figure 2C: This figure is problematic. Apart from the smear on the right side, a loading control is missing. This is essential to quantify signal intensities. Moreover, on the left side, the intensities of lanes 1 and 2 are very different, yet are both controls and were used for the conclusion that proteasomal activity is reduced upon siPsmb4 (lanes 3 and 4 - that do not differ from lane 2). In addition: from which day were the data collected? This information is missing, but important as Figure 2E shows opposing proteasomal activity on day 3 and day 5.

RESPONSE: We agree with the reviewer that the duplicate of the scrambled control cells showed variation. Therefore, we have now repeated the experiment and replaced the figure (now figure 2A). The outcome is not affected, as knockdown reduces proteasomal activity and leads to abnormal proteasome formation in the native PAGE. Regarding the internal loading, it is common practice to display both in-gel activity and immunoblot on the membrane as is. For recent examples in the literature please see VerPlank et al., PNAS 2019 (10.1073/pnas.1809254116) or Yazgili et al., Cell Press STAR Protocols 2021 (10.1016/j.xpro.2021.100526). Obviously, the same amount of protein was loaded, and this is also seen in the immunoblot. As this is a native PAGE, there is no beta-tubulin or other commonly used loading controls for immunoblots. Furthermore, we apologize for the missing information, this experiment was performed using day 5 mature cells. This information is now included in the figure legend.

Figure 2D: tubulin was used as loading control, yet the signal of tubulin in lane 1 is by far weaker compared to the other lanes. How does that affect the quantification (missing) of Nfe2I1?

RESPONSE: We have now included the quantifications, which do not affect the outcome of the experiments and the conclusions drawn.

Figure 3C and Fig 2E (control vs siPsmb4) contradict each other. Please clarify.

RESPONSE: We respectfully point out that the reviewer might have overlooked that 2E shows the time course and 3C only shows day 5 data – both in 2E and 3C, day 5 total proteasomal activity is (insignificantly) increased. Hence, the panels do not contradict each other.

Figure 3B: Issues with the loading control: tubulin signals are in first 3 lanes much weaker. Where is the quantification for that data set that takes the fluctuations of the tubulin signals into account?

RESPONSE: We have now included the quantification, which does not affect the outcome of the experiments and the conclusions drawn. The quantifications can be found in figure S3.

Minor comments:

Why did the authors not use human adipocyte cells and performed all experiments in murine cells?

RESPONSE: The advantage of the cell lines used lies in the ability to study both brown and white features as well studying aspects of adipogenesis and thermogenesis simultaneously. Based on this comment and the comment of reviewer #2, we have reproduced our findings in 3T3-L1 adipocytes, in the hope of strengthening our study. These data are shown in the new Supplementary figure 4.

In which cell/tissue is Psmb4 expressed?

RESPONSE: Thank you for this question, we have now measured Psmb4 expression in a panel of mouse tissues. As shown in figure S1, Psmb4 is ubiquitously expressed in all tissues measured with the highest levels in kidney and liver, followed by brown fat.

Figure 4G: information on the different colors is missing.

RESPONSE: Thank you for bringing this to our attention. We have now included a legend.

The result section appears to have been restructured as sections do not build up on each other well. This should be corrected.

RESPONSE: We appreciate this critical feedback. We have now improved the flow for an enhanced reading experience.

There are a number of grammatical errors or doubling of words/phrases e.g. bottom of page 1: "In addition, PRAAS patients display suffer from..." or on page 2: "Adapting proteasomal activity to the needs of the UPS..." This statement does not make sense. Maybe the authors mean "proteolytic demands"?

RESPONSE: Thank you, we have fixed the remaining typos.

Although, the UPS is a proteostasis node, the authors should avoid statements such as "We show that proteostasis and lipid metabolism are intricately linked..." Better is "UPS activity and lipid metabolism..." Or the authors should expand their analysis to protein synthesis, folding and additional clearance pathways.

RESPONSE: Thank you, we have specified our statement regarding proteostasis and UPS.

Reviewer #1 (Significance (Required)):

This study links clinical research with basic science and if the authors address the above mentioned issues this work will provide new insight into the role of the UPS in the lipid metabolism.

target audience: clinical scientist on lipid metabolism and basic researchers on the UPS and associated pathologies

my expertise: UPS

RESPONSE: We are very thankful for these positive concluding remarks.

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

The molecular mechanism by which proteasome mutations cause lipodystrophy in PRAAS, which is caused by proteasome dysfunctions, has not been well understood. However, it was shown that Psmb4 (β4), a component without enzymatic activity, is required for the formation and maintenance of adipocyte function. In the proteasome dysfunction state of Psmb4 deficiency, the expression of Nfe2l1 was enhanced for proteostasis, but it could not complement the adipocyte formation defect. We showed that repression of Arf3, which is associated with stress response and is markedly expressed in this situation, resulted in the recovery of inflammation and adipogenesis.

Major comments

1. The Graphical abstract seems to indicate that Loss of PSMB4 activates NFE2L1 and ATF3, resulting in the suppression of proteotoxicity and Inflammation. In the case of NFE2L1, this is correct, but in the case of ATF3, as shown in Fig. 4D, ATF3 acts in a promotive manner on Inflammation, causing misleading to the reader. RESPONSE: Thank you, we agree that this aspect of the graphical abstract was partially misleading. We hope the new version now makes more sense.

Fig. 2 shows the rise of Nfe2l1 and the restoration of proteasomal capacity on Day 5 (Fig. 2E). [Nfe2l1 is cleaved, and initiates the transcription of proteasome subunits, which results in restoration or heightening of proteasomal capacity (16,17).] It is known that the brown adipocyte mount an adaptive response to overcome UPS dysfunction, and the transcription of proteasome subunits was increased in this experimental system. However, there are no results showing that the transcription of proteasome subunits is actually increased in this experimental system.

RESPONSE: Thank you for pointing this out. In Psmb4 KD cells, we see an increase in Psmd2 protein levels (Fig. 3B). In addition, we see a small increase in expression levels of various proteasome subunits. We have now included a graph showing these expression levels (Fig 3C).

Are Psmb4KO mice available? If yes, are there any symptoms? Is there any change in proteasome activity, etc.?

RESPONSE: We do not have a Psmb4 KO mouse model, yet, and to the best of our knowledge, none is available. We agree with the reviewer that it would be insightful to study Psmb4 in an in vivo model, but in this project, we have used a cell model to study the cell intrinsic mechanisms of Psmb4.

4A. In PRAAS patients, most of the lipodystrophy occurs in white adipocytes, but if Psmb4 deficiency is induced in white adipocytes, do they show the same dynamics?

RESPONSE: Thank you for your stimulating question. We have repeated our Psmb4 KD experiments in 3T3-L1 cells, to study the dynamics in a white adipocyte model. We found that also in 3T3-L1 cells, Psmb4 knockdown disrupts adipogenesis. The results can be found in Supplemental figure 4.

4B. The first mention of heat production was made, but it was not clear how much the patient's cyclic fever symptoms were related to changes in brown adipocyte function.

RESPONSE: Our data suggest that aberrant brown adipose tissue does not contribute to cyclic fevers in PRAAS patients. We elaborate on this in the Discussion.

minor points

fig2; The numbering of the figure is not correct.

RESPONSE: Thank you, we have corrected the error.

Fig. 2: (day 5) in the figure legend of (E) is unnecessary.

RESPONSE: Thank you but based on the other reviewers’ questions we think it is important to indicate the stage of the differentiation.

Fig. 4 The figure legend in (A) and (B) are switched.

RESPONSE: Thank you, we have corrected the error.

In Fig. 4 (G), there are n = 8 and n = 6 in the figure legend, which is difficult to understand.

RESPONSE: Thank you, we have corrected the error.

Reviewer #2 (Significance (Required)):

It has been thought that the accumulation of defective proteins caused by proteasome dysfunction stresses cell metabolism and leads to lipodystrophy, but the detailed mechanism has not been understood. In this paper, we have clarified a part of the mechanism that links the accumulation of ubiquitinated proteins caused by proteasome dysfunction to the disruption of proteostasis, inflammation and adipogenesis. The results of the study, which showed the relationship between intracellular proteostasis, inflammation and lipid metabolism, will help us understand not only PRAAS patients but also abnormal lipid metabolism, obesity, induction of inflammation, and chronic inflammation with persistent inflammation.

RESPONSE: We are very thankful for these positive concluding remarks.

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

In this study Willemsen et al. investigated the role of proteasome subunit beta 4 and 8 (PSMB4/8) in immortalized brown (pre)adipocytes regarding adipogenesis ability, inflammation, function, and proteostasis. The group showed that Psmb4/8 are expressed in brown adipose tissue and adipocytes but that they are differently regulated. The loss of PSMB8 had no effect on brown adipose tissue/adipocyte function. In contrast, knock-down of PSMB4 altered proteostasis, which was partially compensated by NFE2L1, as well as reduced adipocyte differentiation, lipid accumulation, and beta adrenergic-stimulated glycerol release in immortalized brown adipocytes. The group further demonstrated that the effect of PSMB4 knock-down on impaired adipogenesis was mediated via Atf3 activation.

The manuscript is well-written with clearly structured text and figures. The data and methods are presented in a way that makes it easy to reproduce the experiments. The statistical analysis is adequate.

Some suggestions:

1. Since you stated in 3.1. Result section that Psmb4/8 are robustly expressed in BAT, it would be interesting to directly compare the expression of Psmb4/8 in BAT. RESPONSE: We thank the reviewer for this interesting suggestion. The comparison is now included in figure S3.

Please normalize the glycerol release to protein content (Fig 2J, 4F). It would be also interesting to show whether the reduced glycerol release is due to reduced TG content and/or lipolytic activity. Therefore, you should determine the expression of lipases (e.g. Atgl, Hsl) in adipocytes.

RESPONSE: We thank the reviewer for this interesting suggestion. We have looked at the expression of lipases. Psmb4 knockdown did not alter the expression of lipases, which indicates that the reduced glycerol release is rather due to reduced TG content than due to the absence of lipases. The comparison is now included as Figure 4G. For the glycerol assays, we have normalized glycerol release to protein content. Comparing an undifferentiated (in this case the cells with silencing of Psmb4) vs differentiated cells (in this case the scrambled siRNA control cells) will result in many fundamental differences. Specifically, the lipid to protein ratio is very different, much higher in mature adipocytes, obviously. This obscures some if the differences in lipolysis when glycerol release is normalized to protein levels. Therefore, we have included a figure, in which we show the fold change. Interestingly, this way in the Psmb4 knockdown cells, it is evident that they become refractory to norepinephrine stimulation, and this is rescued when Atf3 is silencing, too.

Please define early/late transfection - on which day of differentiation was Psmb8 silenced? (Fig S2)

RESPONSE: Psmb8 was silenced on day(-1). We have now added this information.

Reviewer #3 (Significance (Required)):

This study clearly demonstrated that proteasome dysfunction via impaired PSMB4 action modulates brown adipocytes differentiation, function, and health. In this study a novel link between dysfunctional proteostasis and impaired lipid metabolism was identified.

RESPONSE: We are very thankful for these positive concluding remarks.

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Referee #3

Evidence, reproducibility and clarity

In this study Willemsen et al. investigated the role of proteasome subunit beta 4 and 8 (PSMB4/8) in immortalized brown (pre)adipocytes regarding adipogenesis ability, inflammation, function, and proteostasis. The group showed that Psmb4/8 are expressed in brown adipose tissue and adipocytes but that they are differently regulated. The loss of PSMB8 had no effect on brown adipose tissue/adipocyte function. In contrast, knock-down of PSMB4 altered proteostasis, which was partially compensated by NFE2L1, as well as reduced adipocyte differentiation, lipid accumulation, and beta adrenergic-stimulated glycerol release in immortalized brown adipocytes. The group further demonstrated that the effect of PSMB4 knock-down on impaired adipogenesis was mediated via Atf3 activation.

The manuscript is well-written with clearly structured text and figures. The data and methods are presented in a way that makes it easy to reproduce the experiments. The statistical analysis is adequate.

Some suggestions:

Since you stated in 3.1. Result section that Psmb4/8 are robustly expressed in BAT, it would be interesting to directly compare the expression of Psmb4/8 in BAT. Please normalize the glycerol release to protein content (Fig 2J, 4F). It would be also interesting to show whether the reduced glycerol release is due to reduced TG content and/or lipolytic activity. Therefore, you should determine the expression of lipases (e.g. Atgl, Hsl) in adipocytes.

Please define early/late transfection - on which day of differentiation was Psmb8 silenced? (Fig S2)

Significance

This study clearly demonstrated that proteasome dysfunction via impaired PSMB4 action modulates brown adipocytes differentiation, function, and health. In this study a novel link between dysfunctional proteostasis and impaired lipid metabolism was identified.

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Referee #2

Evidence, reproducibility and clarity

The molecular mechanism by which proteasome mutations cause lipodystrophy in PRAAS, which is caused by proteasome dysfunctions, has not been well understood. However, it was shown that Psmb4 (β4), a component without enzymatic activity, is required for the formation and maintenance of adipocyte function. In the proteasome dysfunction state of Psmb4 deficiency, the expression of Nfe2l1 was enhanced for proteostasis, but it could not complement the adipocyte formation defect. We showed that repression of Arf3, which is associated with stress response and is markedly expressed in this situation, resulted in the recovery of inflammation and adipogenesis.

Major comments

1. The Graphical abstract seems to indicate that Loss of PSMB4 activates NFE2L1 and ATF3, resulting in the suppression of proteotoxicity and Inflammation. In the case of NFE2L1, this is correct, but in the case of ATF3, as shown in Fig. 4D, ATF3 acts in a promotive manner on Inflammation, causing misleading to the reader.
2. Fig. 2 shows the rise of Nfe2l1 and the restoration of proteasomal capacity on Day 5 (Fig. 2E). [Nfe2l1 is cleaved・・・・, and initiates the transcription of proteasome subunits, which results in restoration or heightening of proteasomal capacity (16,17).] It is known that the brown adipocyte mount an adaptive response to overcome UPS dysfunction, and the transcription of proteasome subunits was increased in this experimental system. However, there are no results showing that the transcription of proteasome subunits is actually increased in this experimental system.
3. Are Psmb4KO mice available? If yes, are there any symptoms? Is there any change in proteasome activity, etc.? In PRAAS patients, most of the lipodystrophy occurs in white adipocytes, but if Psmb4 deficiency is induced in white adipocytes, do they show the same dynamics? The first mention of heat production was made, but it was not clear how much the patient's cyclic fever symptoms were related to changes in brown adipocyte function.

Minor points

fig2; The numbering of the figure is not correct.

Fig. 2: (day 5) in the figure legend of (E) is unnecessary.

Fig. 4 The figure legend in (A) and (B) are switched.

In Fig. 4 (G), there are n = 8 and n = 6 in the figure legend, which is difficult to understand.

Significance

It has been thought that the accumulation of defective proteins caused by proteasome dysfunction stresses cell metabolism and leads to lipodystrophy, but the detailed mechanism has not been understood. In this paper, we have clarified a part of the mechanism that links the accumulation of ubiquitinated proteins caused by proteasome dysfunction to the disruption of proteostasis, inflammation and adipogenesis. The results of the study, which showed the relationship between intracellular proteostasis, inflammation and lipid metabolism, will help us understand not only PRAAS patients but also abnormal lipid metabolism, obesity, induction of inflammation, and chronic inflammation with persistent inflammation.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Willemsen et al studied the contribution of PSMB4 and PSMB8 on proteasomal activity in adipocyte tissue/cells. Mutations in PSMB4/8 have been associated with metabolic diseases that lead to inflammation and proteotoxicity. They used mice and murine cell lines to assess the abundance and activity of the proteasome as well as the stress response upon depletion of psmb4 and regulatory factors. The study is interesting and could provide new insight into the role of specific proteasomal subunits of the immunoproteasome on the metabolism and associated diseases. However, there are a number of issues on the presented data that should be addressed. See below.

Major comments:

• Most of the presented data are secondary data (graphs) and the differences between the experimental conditions (e.g. siRNA) are sometimes only minor and statistical analyses lacking or only indicated with a letter (a, b,..), but its meaning not explained.
• The authors should introduce all tested marker genes e.g. the genes that were analyzed in figures 2H/I, 3D-F, 4A/D. What was the hypothesis and do they represent all or a selected set of genes of the integrated stress response?
• Figure 1A: RNA levels were analyzed yet not protein levels. Why not?
• Figure 2C: This figure is problematic. Apart from the smear on the right side, a loading control is missing. This is essential to quantify signal intensities. Moreover, on the left side, the intensities of lanes 1 and 2 are very different, yet are both controls and were used for the conclusion that proteasomal activity is reduced upon siPsmb4 (lanes 3 and 4 - that do not differ from lane 2). In addition: from which day were the data collected? This information is missing, but important as Figure 2E shows opposing proteasomal activity on day 3 and day 5.
• Figure 2D: tubulin was used as loading control, yet the signal of tubulin in lane 1 is by far weaker compared to the other lanes. How does that affect the quantification (missing) of Nfe2I1?
• Figure 3C and Fig 2E (control vs siPsmb4) contradict each other. Please clarify.
• Figure 3B: Issues with the loading control: tubulin signals are in first 3 lanes much weaker. Where is the quantification for that data set that takes the fluctuations of the tubulin signals into account?

Minor comments:

• Why did the authors not use human adipocyte cells and performed all experiments in murine cells?
• In which cell/tissue is Psmb4 expressed?
• Figure 4G: information on the different colors is missing.
• The result section appears to have been restructured as sections do not build up on each other well. This should be corrected.
• There are a number of grammatical errors or doubling of words/phrases e.g. bottom of page 1: "In addition, PRAAS patients display suffer from..." or on page 2: "Adapting proteasomal activity to the needs of the UPS..." This statement does not make sense. Maybe the authors mean "proteolytic demands"?
• Although, the UPS is a proteostasis node, the authors should avoid statements such as "We show that proteostasis and lipid metabolism are intricately linked..." Better is "UPS activity and lipid metabolism..." Or the authors should expand their analysis to protein synthesis, folding and additional clearance pathways.

Significance

This study links clinical research with basic science and if the authors address the above mentioned issues this work will provide new insight into the role of the UPS in the lipid metabolism.

target audience: clinical scientist on lipid metabolism and basic researchers on the UPS and associated pathologies

my expertise: UPS

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Reply to the reviewers

Manuscript number: RC- 2021-01102

Corresponding author(s): Rita Tewari; Mohammad Zeeshan

1. General Statements [optional]

We wish to thank the reviewers and the Editor for their constructive comments and valuable suggestions to improve our manuscript. We have addressed as far as possible all comments and concerns and we hope that this revised manuscript, with additional new data, will be acceptable for publication. Please find below detailed responses (in italicized red text) to all specific points raised by the reviewers.

2. Point-by-point description of the revisions

This section is mandatory. Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript.

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

The study led by Dr. Zeeshan analyzes nine mouse Plasmodium parasites kinesin by determining their expression pattern and subcellular location in various stages of the parasites in the mammalian and mosquito host. The genetic and phenotypic analyses of all nine kinesins indicate that most are critical for parasite development in the mosquito host, except for Kinesin 13 being the only kinesin essential during the intraerythrocytic development cycle. The authors presented an in-depth analysis on kinesin 13 and 20, using an impressive pallet of molecular techniques such as promotor swapping, chromatin immunoprecipitation, and global transcriptomic analysis using RNAseq, as well as numerous microscopy techniques such as live fluorescence imaging, expansion microscopy, and electron microscopy. This comprehensive study provides an outstanding amount of data on Kinesins in Plasmodium parasites that would be best showcased with a rethinking of the manuscript structure and a more insightful discussion section that directed most of my comments in the review the manuscript. I believe no additional experiments are needed assuming that the authors will link Kinesin 13 and or 20 to the IMC formation in future work.

Authors’ response: We are pleased that the reviewer likes this comprehensive study and believes that no additional data are required. We have now reorganized the manuscript with more focus on kinesin 13 and kinesin 20.

**Major Comments:**

•The current manuscript shows the " Location and function of Plasmodium kinesins" as the title suggests; however, I strongly recommend the authors consider alternative storytelling focusing on Kinesin 13 and 20.

Authors’ response: We thank the reviewer for this recommendation. We have changed both the organization of the text and the title of the manuscript to focus on kinesin-13 and -20. The new title of the manuscript is “Key roles for kinesin-13 and kinesin-20 in malaria parasite proliferation, polarity, and transmission revealed by genome-wide functional analysis”.

The author provides in-depth phenotypical analysis resulting in the most innovating and exciting data. In addition, the discussion section from lines 592 to 634 was fascinating compared to the following section (see details comments for Discussion section below). Authors’ response: We are very pleased that the reviewer considers the phenotypic analysis to be the most innovative and exciting data, and that they appreciated the discussion section of lines 592 to 634.

The following significant comments are related to figures where I believe a restructuration is most needed to bring clarity to the paper.”

•Figure 1. I suggest the authors move Figure 4A to figure 1; Figure1C should move to supplementary information

Authors’ response: As suggested by the reviewer, old Figure 4A is now moved to Figure 1(as Figure 1D) and old Figure 1C is moved to supplementary information (as S3 Fig)

except for Kinesin 13 and 20 data to center the paper's focus on these two proteins

Authors’ response: The kinesin-13 and -20 data are now given prominence, as Figures 3 and 4 (kinesin-20) and Figures 5, 6, and 7 (kinesin-13).

I would also present the kinesin data in the current Figure4A not by numeric order but by biological relevance. All the "normal" together and so on

Authors’ response: We thank the reviewer for this suggestion; in Figure 1D (previously 4A) the data are now presented in the order of biological relevance.

•Figure 2: Kinesin 5 and 8X have the same results. I suggest the authors present only one in the same manuscript and place the other one in Supplementary information.

Authors’ response: kinesin-5 data are now part of S6 Fig in supplementary information and only kinesin-8X data are retained as part of Figure 2.

I would recommend adding the little schematic used in Figure1C to help the reader quickly identify the parasite stages presented in the figures

Authors’ response: A schematic is included in Figure 2C for clarification, as suggested.

•Figure4: Panels B to E should be a supplementary information

Authors’ response: These panels have now been moved to supplementary information as S5 Fig.

•Figure 5: Panels H to J should be supplementary information

Authors’ response: Panels H to J have been moved to supplementary information as part of S8 Fig.

and I strongly recommend the authors to present data by stages; therefore, I would remove panels F and G and replace them with Figure 6A, the expansion microscopy represents the data in Figure 4B, C, D, and E beautifully

Authors’ response: we thank the reviewer for this suggestion; expansion microscopy data are now incorporated into the new Figure 3, and the old panels F and G are now part of S8 Fig in the supplementary information.

•Figure 6B: It is challenging to identify the layout between WT and delta-kinesin 20. All annotations on the EM data cover the data itself. I recommend drawing a representative schematic to guide the reader for identification of ultrastructure

Authors’ response: we have now included a schematic diagram as Figure 4B, to highlight the key ultrastructural features and facilitate their identification by the reader.

•Figure 8: Panel C and D should be supplementary information and replaced by the accurate colocalization data of Kinesin 13 presented in Supp figure 5

Authors’ response: the kinesin-13 colocalization data are now in Figure 5, and the previous Figure 8 Panels C and D have been moved to supplementary information.

In addition, comment line 442 is also actual for the ookinete. The true colocalization is with tubulin in male gamete and gametocytes in figure 5A/B

Authors’ response: We agree with the reviewer; the colocalization data with tubulin in male gamete and gametocytes are now presented in Figures 6A and B.

Figure 9: Panel F to J go to supplementary information and replace with the data in figure 10

Authors’ response: We understand the reviewer’s concerns, however, we would like to include these data in the main figure because they provide important information on the differential regulation of transcripts involved in axoneme biogenesis and chromosome dynamics.

Figure 10: Could be a great abstract figure in the current state. As a model figure, I would recommend incorporating more details

Authors’ response: We have removed this figure (and therefore there are now seven rather than ten figures in the revised manuscript). We would, of course, be happy to use it as part of an abstract if required.

**Minor Comments:**

I will address my following minor comment by Line number rather than section:

Figure 1C: It is unclear if the black square is an actual picture or a black square. I would suggest the authors present the absence of data by a white square or a bar

Authors’ response: For this figure (now S3 Fig, previously Figure 1C), we have added the scale bars on the dark squares to indicate that these are actual pictures that show the absence of signal.

Line 96: " a final synchronized round of S-phase" The classical mitotic terminology is poorly used in the field of Plasmodium mitosis due to the absence of canonical cell cycle checkpoint. I would recommend the authors rephrase as " a final synchronized round of DNA replication."

Authors’ response: We thank the reviewer for this suggestion. We have now deleted this sentence as part of an effort to make the introduction more concise.* *

Line 149-151: Could the authors indicate what stage of the life cycle the work was done?

Authors’ response: We now indicate the stages in line number 127.

Line 161: Missing space between the word "parasite and cell"

Authors’ response: We have deleted this sentence while revising the introduction to make it more concise.

Line 163: " These findings will inform a strategy ..." Could the authors explain in greater detail how the study is informative for targeting MT motors for therapeutic. I would argue with the authors that it is an overstatement since the paper did not provide structural data on kinesin as a foundation for drug discovery.

Authors’ response: The sentence is now modified to remove this overstatement, in lines number 134-136.

Line 368: What was the reasoning for examining whether other kinesin genes' expression is misregulated in delta Kinesin 20?

Authors’ response: The main reason was the expression of other kinesins expressed in the cytoplasmic compartment of ookinete stages, such as kinesin-X3 and kinesin-13; and kinesin-13 that has a key role in MT organization during ookinete development. Therefore, we expected that the expression of other kinesins including kinesin-13 may be coordinated with that of kinesin-20 and modulated in the kinesin-20 knockout. We have added a sentence for clarification, lines 332-336.

Line 515: Could the authors define what is a nuclear pole?

Authors’ response: Nuclear pole is a synonym for spindle pole, which is in general usage with reference to electron micrographs. It serves as a microtubule-organizing center (MTOC) for mitotic spindles.* *

Line: 576 - 579: The authors mention the absence of the IFT component for flagellum assembly due to the assembly of the axoneme in the cytoplasm. It is known that kinesin-2 is required for the anterograde transport in organism building cilia and flagella using IFT. In the current study, kinesin 2 is not part of the nine kinesins; therefore, it is unclear why the authors made these comments and did not reflect on them. I would suggest removing it or comment it.

Authors’ response: it is well-established that axoneme assembly in Plasmodium gametocytes occurs in the cytoplasm, which does not require IFT, and the absence of a kinesin-2 gene is consistent with that process. In contrast, the location of kinesin-8B, kinesin-X4, and kinesin-13 suggests that they are involved in this non-canonical axoneme assembly. For clarification, we have added a sentence at line numbers 521-522.

Line 546-560: this entire section of discussion would be best in a review paper. It is a well-written summary of the current literature with no discussion related to the data on the present study; therefore, I suggest the authors remove it from the discussion.

Authors’ response: This section is now largely removed from the discussion except for a few relevant sentences at lines 509-515.

Line 561 – 571: Great summary of the Kinesin-13 work without discussion.

Authors’ response: This part (now at line numbers 595-602) has now been modified so that it is more relevant to the discussion.

Line 572: What do the authors mean by " these findings"?

Authors’ response: We have explained the meaning of “these findings” (line number 603).

Line 573 – 589 (assuming 673-689): The authors miss the opportunity to elaborate on how the depletion of kinesin protein could impact the global transcriptome. Are we looking at downstream effects? I strongly recommend the authors resolve the lack of discussion related to the RNAseq data in the study.

Authors’ response: we have now improved the discussion of the transcriptome data (line numbers 610-613).

Reviewer #1 (Significance (Required)):

This study is a tremendous amount of work done rigorously and will advance our knowledge in the biology of Plasmodium parasites. We are in urgent need to develop innovative ways to block the replication and transmission of Plasmodium spp. and it can happen only through advancing our knowledge in the basic biology of the parasite.

Authors’ response: We thank the reviewer for their positive and encouraging comments.

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

**Summary:** In this study, Zeeshan et al used live-cell imaging, ultrastructure expansion microscopy, and electron microscopy, gene deletion, genetic knockdown, RNA-seq, ChIP-seq analyses, and matrigel substrate to examine the subcellular localization and the function of Plasmodium kinesins throughout the P. berghei life cycle. They find that Kinesin-13 is the only kinesin essential for both asexual blood stages and sexual stages.

This manuscript represents a lot of work by the authors. The data appear rigorous and well-executed. The data are clearly presented and the writing is clear. I have only minor comments that may improve the reader's comprehension.

Authors’ response: We thanks the reviewer for their positive appreciation of our work.

**Major comments:**

Figure 2C:

The ChIP-seq experiments examined the kinesin-5 and -8x binding site at the chromosome at 6 mpa. Did the authors do any tests at other time points post-activation?

Authors’ response: We sampled only at 6 mpa because at this time point the expression of kinesin-5 and -8X is high, facilitating the ChIP-seq analysis using anti-GFP antibodies. We now include additional ChIP-seq data in S6 Fig.* *

Figure 4:

The authors conclude that kinesin-x3 and kinesin-x4 are non-essential for the P.berghei life cycle. Does deletion of kinesin-x4 affect the length of the flagella?

Authors’ response: We observed no obvious change in the length of flagella after these deletions.

Oocyte size: To the non-specialist, it is difficult to reconcile the images in panel E with the conclusions in panel A. Based on the images, it looks like only knocking out of kinesin-8x seems to affect oocyst size. Can the authors clarify and provide graphs of the quantification of oocyte size?

Authors’ response: We agree with the reviewer that only the knockout of kinesin-8X affects oocyst size. Similar data, obtained using live-cell fluorescence imaging and electron microscopy, have been described and discussed earlier in Zeeshan et al, 2019 PLOS Pathogens (PMID: 31600347).

**Minor comments:**

line 190: typo, kinesin-x4

Authors’ response: This typo has now been corrected (line 162).

Figure 3: what do the arrows mean?

Authors’ response: Arrows indicate the pellicle and axonemes that are mentioned in the figure legend (current Figures 2A and B).

Figure 4F:

1. Typo, scale bar, um.

Authors’ response: We have corrected this (please see the legend for current S5D Fig).* *

1. Does deletion of kinesin-5 show a significant difference?

Authors’ response: Yes, the number of infective sporozoites in salivary glands is significantly reduced following kinesin-5 deletion (as published previously in the manuscript of Zeeshan et al 2020 [PMID: 33154955]).

Reviewer #2 (Significance (Required)):

The study provides comprehensive information on the diverse subcellular location and functions of P. berghei kinesins throughout the P. berghei life cycle. That is useful to exploit the therapeutic targets against malaria.

The main findings are that kinesin-13 genetic knockdown affected MT dynamics during spindle formation and axoneme assembly in male gametocytes and subpellicular MT organization in ookinetes. In addition, Kinesin-13 shows different binding to kinetochores during the gametogenesis and ookinete development, suggesting other proteins may regulate kinesin-13 binding to kinetochores at various stages. The underlying mechanism will help to better understand the role of kinesin-13 in the parasite life cycle.

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

In this manuscript the authors show a huge ambition to catalog biological functions of Plasmodium kinesins. This was done by generating transgenic cell lines where kinesins were deleted and/or tagged with GFP that served as a tool to gather as much biological information on each kinesin isoform. On one side I find this manuscript highly impressive in terms of the amounts of data and information. In particular, the cell biology and microscopy results are of high quality and certainly provide useful information to the research community. I am fairly convinced that most results genuinely represents the individual biological aspects of the kinesins in the best possible way. Unfortunately, I have major reservations about the presentation of these results in the compiled manuscript. In my view the authors were overambitious about the volume and diversity of data that they wished to present, which opened a lot of questions about the depth and quality of each of the experimental effort. There is 10 figures which is highly nonstandard for a scientific publication to start with and yet there is, in my view, major gaps in some results descriptions, data presentations e.t.c. Perhaps, because of this huge ambition the data are presented in a highly superficial manner often lacking negative and positive controls. Unfortunately that creates many doubts about the overall quality of the results and as such the interpretations. In my view the authors might be well advised to separate this large body of work into several publications each focusing on more tangible biological problem in the more in-depth manner. This would give the reader (me) better confidence about the validity of the statements made in this manuscript.

I can give few examples of such discrepancies but cannot account for all.

1.The authors created GFP-tagged transgenic cell lines for each of the 9 kinesins and generated life cell images for each of the line across multiple stages of the entire plasmodium life cycle. This is an impressive amount of work and data. It is certainly useful to see that in life cell imaging the different kinesins isoforms can be detected in different sets of developmental stages some diffused in the cytoplasm and some associated with the nucleus. Even though these results are impressive, there are based solely on life cell imaging that rely on a certain level of detection limit and GFP visibility. One can imagine that a kinesin may still be expressed in a developmental stage and not detected by life cell imaging.

Authors’ response: We agree with the reviewer that live-cell imaging has a detection limit for the signal and we cannot rule out the possibility of expression below this limit. We also used immunofluorescence assays (IFAs) to confirm the presence or absence of the proteins at least in the asexual blood- and gametocyte stages. However, our focus was to examine expression by live-cell imaging during the transmission stages, and hence only those data were given in the manuscript.

I believe that some other detection methods such a western blot, immunoprecipitation e.t.c. should be provided to truly demonstrate that an individual isoform of a kinesin IS of ISNOT expressed. Without that the Figure 1B is overstated.

Authors’ response: Some western blots to confirm expression of the intact kinesin-GFP fusion protein has been published previously: for kinesin-8X (PMID: 31600347), kinesin-5 (PMID: 33154955), and kinesin-8B (PMID: 31600347). We now provide immunoprecipitation data for six kinesin-GFP fusion proteins, performed using GFP-trap antibody and with identification by mass spectrometry. These results (S2 Fig) clearly show the presence of the respective kinesins fused to GFP in the immunoprecipitates (S2 fig).

Moreover, the authors claim that the punctuate signal in the nucleus corresponds to spindle. I do not see any supporting evidence for this in this figure.

Authors’ response: We have previously provided IFA data using anti-tubulin antibodies (for detection of spindle MT) that clearly show co-localization with nuclear kinesins (kinesin-5 and kinesin-8X). For more detailed information please see Zeeshan et al., 2019, PLOS Pathogen (kinesin-8X; PMID: 31600347) and Zeeshan et al., 2020 Front. Cell. Infect. Microbiol (kinesin-5; PMID: 33154955).

2.For the analysis of kinesin 5 and 8x the authors note two types of experiments. First they created a "cross" between the two cells lines. Second, the authors carry out ChIP-Seq to show that the proteins localize to the centromere. This could be an impressive result unfortunately there is very little if any information about it. Genetic crosses in Plasmodium are not standard techniques that one can assume works all the time. I believe there should be more evidence that the presented images come from a true genetic cross.

Authors’ response: We now provide images obtained using both single and dual fluorescence in in the same panel, which show the signal of individual kinesins in different cells as well as both signals in one cell (please see S6A Fig). The two lines, one expressing a GFP-tagged protein and the other a mCherry-tagged protein are crossed by feeding gametocytes together to the mosquito where fertilization and genetic recombination takes place. This genetic cross follows Mendelian rules, producing parental single and two recombinant lines (1:2:1 ratio). The lines are not pure clones but contain parasites that express either both or single fluorescence signals.

The least the authors could show that the florescence signal for both channels come from genuine integrations of the GFP proteins to their target kinesins by PCR or genome sequencing.

Authors’ response: We have also confirmed the presence of genes for each tagged protein by integration PCR in these crossed lines, and by live-cell imaging, as shown in S6A Fig.

Similarly for the chip-seq, there is a need to provide much detailed information about the entire results with a particular clarity about the position of the peaks in respect to projected centromeres. In addition the ChIP-Seq analysis should be supported by data along with positive and negative controls to truly show the kinesins associations with the centromeres.

Authors’ response: We provide the positive and negative controls for the ChIP-seq data (please see S7 Fig). The raw data and further details are deposited in the NCBI Sequence Read Archive with accession number: PRJNA731497.

1. In the middle part, the author present rater impressive analyses of several kinesin deletion trains and their effect on the development of the mosquito stages. In particular, they demonstrate the effect of kinesin 20 on ookinete development. Yet in the next paragraph they present RNA seq analysis of the kinesin 20 deletion on gametocyte induction, in which kinesis 20 should not have any effect; judging from the presented phenotypic assay. This experiment seems out of context as it is unclear why this assay was done and what is the outcome. The authors identified a small group of differentially expressed genes seemingly unrelated to neither kinesin function nor gametocyte induction. This experiment does not make sense to me in the context of the rest of the paper.

Authors’ response: We agree with the reviewer about the effect of kinesin-20 deletion on ookinete development and the RNAseq analysis of the gametocyte stage. This is because kinesin-20 expression starts in female gametocytes and continues into later stages including ookinetes. We know that in Plasmodium there is translational repression in female gametocytes, which de-repress only in the zygote after fertilization, leading to translation of many proteins in the zygote. We wished to see whether there was a role of kinesin-20 in translational de-repression. Our transcriptomic data showed no role of kinesin-20 in this process. We have added a sentence for clarification in lines 338 -342.

Reviewer #3 (Significance (Required)):

As mentioned above, these three examples represent some of the discrepancies not necessarily about the data quality and fidelity but rather a confusing character of the entire study. From this perspective I have two types of problems with this manuscript. First, while reading this manuscript, lacking key controls and detailed description of some of the analyses, made me loose interest as well as confidence in other parts of the studies which may or may not be solid. Second, I struggle to see the key purpose of the presentation. Instead the manuscript seems to be a compilation of very diverse data some of which are interesting but other out of context, confusing and not connected to the rest of the study.

Authors’ response: We are sorry for the confusion, but we have now streamlined the data presented, to focus in the manuscript mainly on two kinesins, kinesin-13 and kinesin-20. We provide the relevant controls and a detailed description of the analyses. All experiments were repeated at least three times, and, where appropriate, figure legends provide information on the number of samples and repeats. We hope the reviewer finds the manuscript clearer now.

Overall I wish to reiterate that I believe that there are a lot of very good experimental results in this study but unfortunately many of these get lost in the overall presentation that is often superficial or out of context. My general impression is that the authors are trying to show too much, "too fast" and as such many of the presented results remain questionable. The author are likely able to correct all these discrepancies but this might not be possible to do in the manuscript.

Authors’ response: We are thankful to the reviewer for finding a lot of very good experimental data and hope that the revised manuscript, with more focus on two kinesins, will give more confidence in our work to the reviewer.

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Referee #3

Evidence, reproducibility and clarity

In this manuscript the authors show a huge ambition to catalog biological functions of Plasmodium kinesins. This was done by generating transgenic cell lines where kinesins were deleted and/or tagged with GFP that served as a tool to gather as much biological information on each kinesin isoform. On one side I find this manuscript highly impressive in terms of the amounts of data and information. In particular, the cell biology and microscopy results are of high quality and certainly provide useful information to the research community. I am fairly convinced that most results genuinely represents the individual biological aspects of the kinesins in the best possible way. Unfortunately, I have major reservations about the presentation of these results in the compiled manuscript. In my view the authors were overambitious about the volume and diversity of data that they wished to present, which opened a lot of questions about the depth and quality of each of the experimental effort. There is 10 figures which is highly nonstandard for a scientific publication to start with and yet there is, in my view, major gaps in some results descriptions, data presentations e.t.c. Perhaps, because of this huge ambition the data are presented in a highly superficial manner often lacking negative and positive controls. Unfortunately that creates many doubts about the overall quality of the results and as such the interpretations. In my view the authors might be well advised to separate this large body of work into several publications each focusing on more tangible biological problem in the more in-depth manner. This would give the reader (me) better confidence about the validity of the statements made in this manuscript.

I can give few examples of such discrepancies but cannot account for all.

1.The authors created GFP-tagged transgenic cell lines for each of the 9 kinesins and generated life cell images for each of the line across multiple stages of the entire plasmodium life cycle. This is an impressive amount of work and data. It is certainly useful to see that in life cell imaging the different kinesins isoforms can be detected in different sets of developmental stages some diffused in the cytoplasm and some associated with the nucleus. Even though these results are impressive, there are based solely on life cell imaging that rely on a certain level of detection limit and GFP visibility. One can imagine that a kinesin may still be expressed in a developmental stage and not detected by life cell imaging. I believe that some other detection methods such a western blot, immunoprecipitation e.t.c. should be provided to truly demonstrate that an individual isoform of a kinesin IS of ISNOT expressed. Without that the Figure 1B is overstated. Moreover, the authors claim that the punctuate signal in the nucleus corresponds to spindle. I do not see any supporting evidence for this in this figure.

2.For the analysis of kinesin 5 and 8x the authors note two types of experiments. First they created a "cross" between the two cells lines. Second, the authors carry out ChIP-Seq to show that the proteins localize to the centromere. This could be an impressive result unfortunately there is very little if any information about it. Genetic crosses in Plasmodium are not standard techniques that one can assume works all the time. I believe there should be more evidence that the presented images come from a true genetic cross. The least the authors could show that the florescence signal for both channels come from genuine integrations of the GFP proteins to their target kinesins by PCR or genome sequencing. Similarly for the chip-seq, there is a need to provide much detailed information about the entire results with a particular clarity about the position of the peaks in respect to projected centromeres. In addition the ChIP-Seq analysis should be supported by data along with positive and negative controls to truly show the kinesins associations with the centromeres.

3.In the middle part, the author present rater impressive analyses of several kinesin deletion trains and their effect on the development of the mosquito stages. In particular, they demonstrate the effect of kinesin 20 on ookinete development. Yet in the next paragraph they present RNA seq analysis of the kinesin 20 deletion on gametocyte induction, in which kinesis 20 should not have any effect; judging from the presented phenotypic assay. This experiment seems out of context as it is unclear why this assay was done and what is the outcome. The authors identified a small group of differentially expressed genes seemingly unrelated to neither kinesin function nor gametocyte induction. This experiment does not make sense to me in the context of the rest of the paper.

Significance

As mentioned above, these three examples represent some of the discrepancies not necessarily about the data quality and fidelity but rather a confusing character of the entire study. From this perspective I have two types of problems with this manuscript. First, while reading this manuscript, lacking key controls and detailed description of some of the analyses, made me loose interest as well as confidence in other parts of the studies which may or may not be solid. Second, I struggle to see the key purpose of the presentation. Instead the manuscript seems to be a compilation of very diverse data some of which are interesting but other out of context, confusing and not connected to the rest of the study.

Overall I wish to reiterate that I believe that there are a lot of very good experimental results in this study but unfortunately many of these get lost in the overall presentation that is often superficial or out of context. My general impression is that the authors are trying to show too much, "too fast" and as such many of the presented results remain questionable. The author are likely able to correct all these discrepancies but this might not be possible to do in ne manuscript.

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Referee #2

Evidence, reproducibility and clarity

Summary: In this study, Zeeshan et al used live-cell imaging, ultrastructure expansion microscopy, and electron microscopy, gene deletion, genetic knockdown, RNA-seq, ChIP-seq analyses, and matrigel substrate to examine the subcellular localization and the function of Plasmodium kinesins throughout the P. berghei life cycle. They find that Kinesin-13 is the only kinesin essential for both asexual blood stages and sexual stages.

This manuscript represents a lot of work by the authors. The data appear rigorous and well-executed. The data are clearly presented and the writing is clear. I have only minor comments that may improve the reader's comprehension.

Major comments:

Figure 2C:

The ChIP-seq experiments examined the kinesin-5 and -8x binding site at the chromosome at 6 mpa. Did the authors do any tests at other time points post-activation?

Figure 4:

The authors conclude that kinesin-x3 and kinesin-x4 are non-essential for the P.berghei life cycle. Does deletion of kinesin-x4 affect the length of the flagella?

Oocyte size: To the non-specialist, it is difficult to reconcile the images in panel E with the conclusions in panel A. Based on the images, it looks like only knocking out of kinesin-8x seems to affect oocyst size. Can the authors clarify and provide graphs of the quantification of oocyte size?

Minor comments:

line 190: typo, kinesin-x4

Figure 3: what do the arrows mean?

Figure 4F:

1. typo, scale bar, um.

2. Does deletion of kinesin-5 show a significant difference?

Significance

The study provides comprehensive information on the diverse subcellular location and functions of P. berghei kinesins throughout the P. berghei life cycle. That is useful to exploit the therapeutic targets against malaria.

The main findings are that kinesin-13 genetic knockdown affected MT dynamics during spindle formation and axoneme assembly in male gametocytes and subpellicular MT organization in ookinetes. In addition, Kinesin-13 shows different binding to kinetochores during the gametogenesis and ookinete development, suggesting other proteins may regulate kinesin-13 binding to kinetochores at various stages. The underlying mechanism will help to better understand the role of kinesin-13 in the parasite life cycle.

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Referee #1

Evidence, reproducibility and clarity

The study led by Dr. Zeeshan analyzes nine mouse Plasmodium parasites kinesin by determining their expression pattern and subcellular location in various stages of the parasites in the mammalian and mosquito host. The genetic and phenotypic analyses of all nine kinesins indicate that most are critical for parasite development in the mosquito host, except for Kinesin 13 being the only kinesin essential during the intraerythrocytic development cycle. The authors presented an in-depth analysis on kinesin 13 and 20, using an impressive pallet of molecular techniques such as promotor swapping, chromatin immunoprecipitation, and global transcriptomic analysis using RNAseq, as well as numerous microscopy techniques such as live fluorescence imaging, expansion microscopy, and electron microscopy. This comprehensive study provides an outstanding amount of data on Kinesins in Plasmodium parasites that would be best showcased with a rethinking of the manuscript structure and a more insightful discussion section that directed most of my comments in the review the manuscript. I believe no additional experiments are needed assuming that the authors will link Kinesin 13 and or 20 to the IMC formation in future work.

Major Comments:

•The current manuscript shows the " Location and function of Plasmodium kinesins" as the title suggests; however, I strongly recommend the authors consider alternative storytelling focusing on Kinesin 13 and 20. The author provides in-depth phenotypical analysis resulting in the most innovating and exciting data. In addition, the discussion section from lines 592 to 634 was fascinating compared to the following section (see details comments for Discussion section below).

•The following significant comments are related to figures where I believe a restructuration is most needed to bring clarity to the paper."

•Figure 1. I suggest the authors move Figure 4A to figure 1; Figure1C should move to supplementary information except for Kinesin 13 and 20 data to center the paper's focus on these two proteins. I would also present the kinesin data in the current Figure4A not by numeric order but by biological relevance. All the "normal" together and so on

•Figure 2: Kinesin 5 and 8X have the same results. I suggest the authors present only one in the same manuscript and place the other one in Supplementary information. I would recommend adding the little schematic used in Figure1C to help the reader quickly identify the parasite stages presented in the figures.

•Figure4: Panels B to E should be a supplementary information

•Figure 5: Panels H to J should be supplementary information, and I strongly recommend the authors to present data by stages; therefore, I would remove panels F and G and replace them with Figure 6A, the expansion microscopy represents the data in Figure 4B, C, D, and E beautifully.

•Figure 6B: It is challenging to identify the layout between WT and delta-kinesin 20. All annotations on the EM data cover the data itself. I recommend drawing a representative schematic to guide the reader for identification of ultrastructure.

•Figure 8: Panel C and D should be supplementary information and replaced by the accurate colocalization data of Kinesin 13 presented in Supp figure 5. In addition, comment line 442 is also actual for the ookinete. The true colocalization is with tubulin in male gamete and gametocytes in figure 5A/B.

•Figure 9: Panel F to J go to supplementary information and replace with the data in figure 10.

•Figure 10: Could be a great abstract figure in the current state. As a model figure, I would recommend incorporating more details

Minor Comments:

I will address my following minor comment by Line number rather than section:

Figure 1C: It is unclear if the black square is an actual picture or a black square. I would suggest the authors present the absence of data by a white square or a bar.

Line 96: " a final synchronized round of S-phase" The classical mitotic terminology is poorly used in the field of Plasmodium mitosis due to the absence of canonical cell cycle checkpoint. I would recommend the authors rephrase as " a final synchronized round of DNA replication."

Line 149-151: Could the authors indicate what stage of the life cycle the work was done?

Line 161: Missing space between the word "parasite and cell"

Line 163: " These findings will inform a strategy ..." Could the authors explain in greater detail how the study is informative for targeting MT motors for therapeutic. I would argue with the authors that it is an overstatement since the paper did not provide structural data on kinesin as a foundation for drug discovery.

Line 368: What was the reasoning for examining whether other kinesin genes' expression is misregulated in deltaKinesin 20?

Line 515: Could the authors define what is a nuclear pole?

Line: 576 - 579: The authors mention the absence of the IFT component for flagellum assembly due to the assembly of the axoneme in the cytoplasm. It is known that kinesin-2 is required for the anterograde transport in organism building cilia and flagella using IFT. In the current study, kinesin 2 is not part of the nine kinesins; therefore, it is unclear why the authors made these comments and did not reflect on them. I would suggest removing it or comment it.

Line 546-560: this entire section of discussion would be best in a review paper. It is a well-written summary of the current literature with no discussion related to the data on the present study; therefore, I suggest the authors remove it from the discussion.

Line 561 - 571: Great summary of the Kinesin-13 work without discussion.

Line 572: What do the authors mean by " these findings"?

Line 573 - 589: The authors miss the opportunity to elaborate on how the depletion of kinesin protein could impact the global transcriptome. Are we looking at downstream effects? I strongly recommend the authors resolve the lack of discussion related to the RNAseq data in the study.

Significance

This study is a tremendous amount of work done rigorously and will advance our knowledge in the biology of Plasmodium parasites. We are in urgent need to develop innovative ways to block the replication and transmission of Plasmodium spp. and it can happen only through advancing our knowledge in the basic biology of the parasite.

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Reply to the reviewers

Manuscript number: RC-2021-01015

Corresponding author(s): Jordan, Raff

1. General Statements [optional]

We thank the reviewers for their thoughtful and constructive comments and have now revised our manuscript accordingly. We apologise that it has taken so long to send in these revisions, but this is in part because both first authors have now left the lab.

2. Point-by-point description of the revisions

Reviewer #1

This reviewer was generally supportive. They note that it is unfortunate that our data suggests the CP110/Cep97 complex does not play a major part in controlling daughter centriole growth—although we believe that this is an important negative result—but feel that other aspects of our data are interesting. They requested no further experiments, but did comment that it would be interesting to determine when g-tubulin is incorporated into growing centrioles. Unfortunately, we cannot test this as the centrioles in these embryos recruit large amounts of g-tubulin to their PCM, so we cannot specifically assay the small amount of protein in the centriolar fraction.

Reviewer #2

Major Points:

__Figure 1: The reviewer notes that Sas-4 and CP110 have antagonistic roles in promoting/repressing centriole growth and asks if Sas-4 is involved in promoting centriole elongation and whether it also oscillates. __It is unclear if Sas-4 directly promotes centriole elongation in flies. We have previously shown that centriolar Sas-4 levels do oscillate during S-phase, but with a timing that is distinct from CP110/Cep97 (Novak et al., Curr. Biol., 2014). These observations do not shed much light on the potential antagonistic relationship between CP110/Cep97 and Sas-4, so we do not comment on this here.

Figure S1B: The reviewer requests that we image the centrioles with greater laser intensity to test whether some residual CP110 or Cep97 protein can be recruited in the absence of the other protein. The quantification of this data suggests that some residual CP110 or Cep97 can still be recruited to centrioles in the absence of the other (Graphs, Figure S1B,C), so we do not think it necessary to repeat this experiment at higher laser intensity to further test this point. We now state that the centriolar recruitment of one protein may not be completely dependent of the other (p6, para.2).

Figure 3: The reviewer questions whether the reduction in CP110/Cep97 levels at the mother centriole that we observe during S-phase could be due to photobleaching. This is an interesting point that we now analyse in more detail (p8, para.2). We do not think the decrease in mother centriolar CP110/Cep97 levels is due to photobleaching as our new analysis (which includes more data points during mitosis) strongly suggests that centriolar levels on the mother rise again at the start of the next cycle (New Figure 3C,D).

The reviewer asks whether the CP110/Cep97 oscillations occur at the tip of the growing centriole, and whether we can use super-resolution imaging to address this. A large body of evidence indicates that CP110/Cep97 are highly concentrated at centriole distal tips, and all our experiments suggest that it is this fraction that is oscillating. In Figure 3, for example, we use Airy-scan super-resolution imaging to follow the oscillation on Mother and Daughter centrioles in living embryos. Although the resolution in these experiments is not as high as we can achieve using 3D-SIM in fixed cells, it seems reasonable to assume that the dots of fluorescence we observe oscillating on these centrioles (Fig. 3) are the same fluorescent dots we observe localised at the distal tips of the mother and daughter using 3D-SIM in fixed cells (Fig. 1A).

The reviewer requests additional quantification of the western blots shown in Figure S1 that we use to judge relative expression levels. As we now describe in more detail in the M&M, these ECL blots are very sensitive, but highly non-linear, so we usually estimate relative expression levels by comparing serial dilutions of the different fractions (see, for example, Figure 1B, Franz et al., JCB, 2013). As we now clarify, the key point is not precisely by how much these proteins are over- or under-expressed, but that we observe a similar oscillatory behaviour when they are either over- or under-expressed.

__The reviewer points out that our statement that the CP110/Cep97 oscillation is entrained by the Cdk/Cyclin oscillator (CCO) is too strong as it is based only on a correlation. __We agree and apologise for this overstatement. To address this, we have now perturbed the CCO by halving the dose of Cyclin B (New Figure 5E—H). This extends S-phase length and we now show that the period of the CP110/Cep97 oscillation is also extended. This suggests that the CCO directly influences the period of the CP110/Cep97 oscillation.

The reviewer notes that our conclusion that the centriole cartwheels are longer or shorter when CP110 or Cep97 are absent or overexpressed, respectively, is based only on Sas-6-GFP fluorescence intensity. They ask if this fluorescence intensity perfectly reflects cartwheel length, and if we can confirm these conclusions using EM. Sas-6 is the main structural component of the cartwheel, so the amount of Sas-6 at the centriole should be proportional to cartwheel length, and we have published two papers that support this conclusion and that use the incorporation of Sas-6 as a proxy to measure cartwheel length (Aydogan et al., JCB, 2018; Aydogan et al., Cell, 2020). Importantly, our previous EM studies support our conclusions about the relationship between cartwheel length and CP110/Cep97 levels: the centrioles in wing-disc cells are slightly longer in the absence of CP110 and slightly shorter when CP110 is overexpressed (Franz et al., JCB, 2013). The new findings reported here provide a potential explanation for this EM data, which was puzzling at the time.

Minor Points:

Figure 1C: The reviewer noted that our schematic illustrations in this Figure could be misleading____. We agree and have now redrawn them.

Reviewer #3

Major points:

The reviewer requested that we clarify our use of the term oscillation, pointing out that oscillations are repetitive variations in levels/activity over time, whereas the “oscillations” we describe here occur during each round of centriole assembly. This is a fair point, and one that is often debated in the oscillation field, with many believing that too many biological processes are termed “oscillations”, when they are not truly driven by the passage of time. To avoid any ambiguity, we now no longer describe the behaviour of CP110/Cep97 as an oscillation (although, for ease of discussion, we still use the term in this letter).

The reviewer thought that the data we show in Figure 1 was not relevant as we largely analyse centrioles in living embryos whereas the data in Figure 1 is derived from fixed wing-disc cells—and similar fixed-cell data has been shown in previous studies. The reviewer suggests we use super-resolution methods to analyse Cp110/Cep97 dynamics in the syncytial embryo, and show this relative to Sas-6 and Plk4. They ask if Plk4 and CP110/Cep97 colocalise at any time. While CP110/Cep97 localisation has been analysed by super-resolution microscopy previously (e.g. Yang et al., Nat. Comm., 2018; LeGuennec et al., Sci. Adv., 2020), CP110/Cep97 was a minor part of these studies and our data is the first to show that this complex sits as a ring on top of the centriole MTs in fly centrioles (that lack the complex distal and sub-distal appendages present in the previously analysed systems). As this localisation is important in thinking about how CP110/Cep97 might influence centriole MT growth, we would like to include it. We cannot show this detail in living embryos as the movement of the centrioles reduces resolution and we cannot observe the ring structure.

Although we do use Airy-scan super-resolution microscopy to study CP110/Cep97 dynamics in living embryos (Figure 3), we cannot do this in two colours (to compare these dynamics to Sas-6 or Plk4 dynamics) as red-fluorescent proteins bleach too quickly. We now show the relative dynamics of CP110/Cep97 and Plk4 recruitment using standard resolution microscopy (New Figure S2). While it is well established that Plk4 and CP110/Cep97 are concentrated at opposite ends of centrioles, they are all recruited to the nascent site of daughter centriole assembly, effectively “colocalising” at this timepoint. This could provide an opportunity for the crosstalk we observe here, and we now mention this possibility (p17, para.1).

The Reviewer questioned whether the loading of Sas-6-GFP onto centrioles can be used as a proxy for cartwheel length, pointing out that Sas-6 could load into centrioles in a way that does not change the cartwheel structure, and that EM is required to test this. As described in our response to Reviewer #2, Sas-6 is the main structural component of the cartwheel, and we have published two papers that use the incorporation of Sas-6 into the cartwheel as a proxy to measure cartwheel length (Aydogan et al., JCB, 2018; Aydogan et al., Cell, 2020). While we cannot exclude that Sas-6 might also associate with the cartwheel in a way that does not involve its incorporation into the cartwheel, it is not clear how EM might address this question. Moreover, even if such a fraction existed, it should not affect our conclusions—as long as Sas-6 is binding to the cartwheel in some way, then the amount bound should remain proportional to the length of the cartwheel. Perhaps the reviewer is suggesting that we perform an EM time course of cartwheel growth to back up our conclusions from the Sas-6 incorporation assay? If so, we think this impractical. The changes in cartwheel length shown in Figure 6 are revealed from analysing several thousand images of centrioles compared at precise relative time points. Such an analysis cannot be done in fixed embryos by EM.

Similar to the point above, the reviewer notes that we use the length of the cartwheel to infer centriole MT length, but we never directly measure MT length. They suggest we perform either an EM analysis or use MT markers to directly measure the kinetics of centriole MT growth. In flies (and many other organisms), the centriole MTs grow to the same length as the centriole cartwheel (Gonzalez, JCS, 1998), so we can be confident that the final length of the cartwheel reflects the final length of the centriole MTs. Moreover, we previously measured the distance between the mother centriole and the GFP-Cep97 cap that sits at the distal tip of the centriole MTs as a proxy for centriole MT length, and found that the inferred kinetics of MT growth were similar to the kinetics of cartwheel growth (inferred from Sas-6 incorporation) (Aydogan et al., 2018). This manual analysis was very time consuming, and we have tried to implement computational analysis methods, but so far without success. For similar reasons to those described in the point above, it is not feasible to accurately measure centriole MT growth kinetics by EM (nobody has been able to do this). Moreover, the centrosomes in these embryos are associated with too much tubulin and the centriole MTs are not yet modified (e.g. by acetylation) as the cycles are so fast—so we cannot directly stain the centriole MTs in fixed embryos. We have now toned down our conclusions about MT length throughout the paper, and we make it clear that we cannot directly measure this.

All of the experiments shown here are performed in the presence of endogenous untagged proteins, and the reviewer wonders if recruitment dynamics might be influenced by competition for binding from the endogenous protein. We have compared the behaviour of many centriole and centrosome proteins in the presence and absence of the untagged WT protein. In all cases, less tagged-protein binds to centrioles/centrosomes in the presence of untagged protein, presumably due to competition. Apart from this, however, we usually observe no real difference in overall dynamics and in Reviewer Figure 1 (see below) we show that CP110-GFP and GFP-Cep97 both oscillate even in the absence of any endogenous protein. As we feel this result is not very surprising, we do not show it in the manuscript.

The reviewer correctly noted that our data was not strong enough to conclude that the CP110/Cep97 oscillation is influenced by the CCO. This was also raised by Reviewer #2 and, as described above (p2, para.3 above), we have now performed additional experiments to more directly demonstrate this point (new Figure 5G—H).

The reviewer requests more discussion of why our conclusion that CP110/Cep97 levels oscillate on the growing daughter centrioles during S-phase is different to that reached by Dobbelaere et al, (Curr. Biol., 2020), who conclude that Cep97-GFP only starts to incorporate into the new daughter centrioles late in S-phase when the daughters are fully grown. We have discussed this discrepancy with these authors and they kindly shared their reagents with us (so our endogenous Cep97-GFP oscillation data comes from the same line they used in their experiments), but we have not come to a clear conclusion on this point. We have shown robust oscillations for CP110 and Cep97 by quantifying many hundreds of centrioles using multiple transgenes (both over- and under-expressed) in multiple backgrounds. Cep97 dynamics were a very minor part of the Dobbelaere et al., study, and they analysed a much smaller number of centrioles. We now briefly mention this discrepancy (p9, para.1), but do not discuss it in detail as we have no definitive explanation for it.

The reviewer requests more experiments or more discussion to address the mechanism(s) of crosstalk between CP110/Cep97 and Plk4, and they suggest several avenues for further investigations. These are excellent ideas, and we are working hard on these approaches. These are all long-term experiments, however, and we feel it is important that the field be made aware of these surprising findings as soon as possible, as others may be better-placed to provide mechanistic insight into how this system ultimately works. We now briefly mention some of the future directions the reviewer highlights in the Discussion.

The reviewer thought we should highlight the previous publications showing that Plk4-induced centriole amplification requires CP110 and that Plk4 can phosphorylate CP110. These studies (Kleylein-Sohn et al, Dev. Cell, 2007; Lee et al., Cell Cycle, 2017) were mentioned, but we now discuss them more prominently (p17, para.2).

Minor Points:

The reviewer raised a number of minor concerns that we have now addressed: (1) We discuss the model the reviewer suggests; (2) we no longer state that the crosstalk between CP110/Cep97 and Plk4 is unexpected; (3) We have clarified our description of the shift in timing of the peak levels of CP110/Cep97, which we no longer refer to as an oscillation; (4) We define mNG as monomeric Neon Green; (5) We have changed our schematics in Figure 1 as suggested by the reviewer; (6) We have corrected the mistake in the legend to Figure 8.

Reviewer #4

Major points:

1. The reviewer noted that the amplitude of the CP110/Cep97 oscillations depended on protein expression levels, so the oscillations might not reflect the behaviour of the endogenous proteins. They requested that we either repeat our experiments with CRISPR knock-in alleles, or conduct experiments with the lines driven by the endogenous promotors but in their respective mutant backgrounds. We have not generated CRISPR knock-ins for CP110/Cep97, but have done so for many other centriole/centrosome proteins (>8) and found that most such lines are expressed at higher or lower levels than the endogenous allele (and sometimes very significantly so). This is also true for our standard transgenic lines, where genes are expressed from their endogenous promoters, but are randomly integrated into the genome. The blots in Figure 4 show that CP110-GFP and GFP-Cep97 expressed from a ubiquitin (u) promoter or from their endogenous promoters (e) are expressed at ~2-5X higher or ~2-5X lower levels than the endogenous proteins, respectively. As we observe CP110/Cep97 oscillations in all cases, it seems unnecessary to generate new CRISPR knock-ins (that are also likely to be somewhat over- or under-expressed) to show this again. As the reviewer asks, we show that Cep97-GFP and CP110-GFP still oscillate in in the absence of the endogenous proteins (Reviewer Figure 1). As this does not seem a surprising result, we do not show this in the main manuscript. In the same point the reviewer requests that we use antibody staining in fixed embryos to show that the untagged proteins also oscillate. Analysing protein dynamics is much harder in fixed embryos, as the levels of fluorescent staining are more variable and we can only approximately infer relative timing, rather than precisely measuring it (as we can in living embryos). Moreover, as both proteins in the CP110/Cep97 complex exhibit a very similar oscillatory behaviour when tagged with either GFP or RFP (e.g. Figure 2C), and this behaviour is distinct to that observed with several other GFP- or RFP-tagged centriole proteins (e.g. Novak et al., Curr. Biol., 2014; Conduit et al., eLife, 2015; Aydogan et al., JCB, 2018; Aydogan et al., Cell, 2020) it seems very unlikely that this behaviour is induced by the GFP (or RFP) tag.

The reviewer also suggests that we show the data with the endogenous promoter before we show the data with the ubiquitin promoter. As we now explain better (and show in Figure 4), this seems unnecessary as the proteins expressed from the ubiquitin promotor are probably actually expressed at levels that are more similar to the endogenous protein.

The reviewer questions whether the oscillations we observe might be due to the centrioles simply moving up and down in the embryo during the cell cycle, and they suggest we monitor Asl behaviour to rule this out. We have previously shown that Asl-GFP levels do not oscillate; they remain constant throughout the cell cycle on old-mother centrioles, and grow approximately linearly throughout S-phase on new-mother centrioles (see Figure 1D in Novak et al., Curr. Biol., 2014).

We were not sure we understood this point properly, so we copy the reviewers comment in full here: ____The authors mention (for instance on p. 3) that the inner cartwheel and the surrounding microtubules assemble at opposite ends of the daughter centriole. However, my understanding is that the short centrioles present in the fly embryo have an inner cartwheel that extends throughout the organelle, such that it might be moot to make a distinction between the two ends in this case. Moreover, it is also my understanding that this inner cartwheel is itself surrounded by microtubules, so that microtubule assembly might not be expected to occur strictly at the distal end no matter what. The reviewer is correct that Drosophila centrioles are short (~150nm) and that the cartwheel extends throughout the centriole. We think the reviewer is suggesting that it may not be relevant therefore whether the cartwheel and centriole MTs grow from opposite ends—as the activities that govern their growth may not be spatially separated? However, because cartwheels grow preferentially from the proximal-end (Aydogan et al., JCB 2018) while centriole MTs are assumed to grow preferentially from the distal (plus) end, there is an intrinsic problem in ensuring they grow to the same size—no matter how short or long the centrioles are. The reviewer is correct that one possible solution to this problem is that the centriole MTs actually grow from their minus ends, but this is not widely accepted (or even proposed). We have tried to explain this issue more clearly throughout the revised manuscript.

The reviewer points out that the schematic illustrations in Figure 1A and 1C are inaccurate and unhelpful. We agree and have now redrawn these.

The reviewer asks that we provide information about the eccentricities of the centrioles in the different datasets used to calculate the protein distributions shown in Figure 1, particularly as the data for Sas-4-GFP and Sas-6-GFP were obtained previously using a different microscope modality, making comparisons complicated. The point that comparing distance measurements across different datasets is difficult is an important one, and we now state that such comparisons should be treated with caution. However, we have not provided information on the distribution of centriole eccentricities in the different experiments as it wasn’t clear to us how this information could be used to make such comparisons more accurate (presumably the reviewer is suggesting we could apply a correction factor to each dataset?). The very tight overlap in the positioning of CP110/Cep97 fusions (Figure 1C) strongly suggests that any difference in the average centriole eccentricities of the different populations of centrioles analysed, which are already tightly selected for their en-face orientation (i.e. eccentricity

The reviewer requested that we show the “noisy data” we obtained during mitosis that we excluded from our analysis in Figure 3. As we now explain in more detail (p8, para.2), there are two reasons why the data for mitosis in this experiment is “noisy”: (1) The protein levels on the centrioles are low in mitosis and the centrioles are more mobile, so they are hard to track; (2) The Asl-mCherry marker used to identify the mother centriole starts to incorporate into the daughter (now new mother) centriole during mitosis, making it difficult to unambiguously distinguish mothers and daughters. As a result, we cannot track and assign mother/daughter identity to very many centrioles during mitosis—although we now include some extra data-points during mitosis for the centrioles where we could do this (revised Figure 3C,D). Importantly, it is clear that this “noisy” data hides no surprises: one can see (Figure 3C,D) that the signal on the centrioles is simply low during mitosis and then starts to rise again as the embryos enter the next cycle. This is confirmed in the normal resolution data (Figure 2B,C; Movies S1 and S2) where we can track many more centrioles due to the wider field of view and because we do not have to discard centrioles in mitosis that we cannot unambiguously assign as mothers or daughters.

The reviewer requests that we conduct a super-resolution Airy-scan analysis of CP110/Cep97 driven from their endogenous promoters (eCP110 or eCep97) to ensure that the oscillations we see with these lines (shown in Figure 4C,D) are also occurring at the daughter centriole—as we already show for the oscillations observed with the uCP110 and uCep97 lines (shown in Figure 4C,D, and analysed at super-resolution on the Airy-scan in Figure 3). This is technically very challenging as super-resolution techniques require a lot of light and the centriole signal in the eCP110/Cep97 embryos is very dim compared to uCP110/Cep97 embryos (Figure 4C,D). We have managed to do this for eCep97-GFP and confirmed that—even in these embryos that express Cep97-GFP at much lower levels than the endogenous protein (Figure 4A)—the “oscillation” is primarily on the daughter (Reviewer Figure 2). As this data is very noisy, and as the ubiquitin uCP110/Cep97 lines express these fusions at levels that are closer to endogenous levels (Figure 4A,B), we do not show this data in the main text.

The reviewer also asks for clarification as to why we use the Airy-scan for some experiments and 3D-SIM for others. As we now explain (p8, para.1), 3D-SIM has better resolution than the Airy-scan, but it takes more time and requires more light—so we cannot use it to follow these proteins in living embryos. Thus, for tracking CP110/Cep97 throughout S-phase in living embryos we had to use the Airy-scan.

The reviewer questions why in some experiments we analyse the behaviour of 100s of centrioles, whereas in others the numbers are much smaller (1-14 in Figure 3—note, the reviewer quoted this number as coming from Figure 4, but it actually comes from Figure 3, so we have assumed they mean Figure 3). We apologise for not explaining this properly. The super-resolution experiments in Figure 3 are performed on a Zeiss Airy-scan system, which has a much smaller field of view than the conventional systems we use in other experiments. Thus, we inherently analyse a much smaller number of centrioles in these experiments. In addition, as explained in point 6 above, in these experiments we need to analyse mother and daughter centrioles independently, and in many cases we cannot unambiguously make this assignment, so these centrioles have to be excluded from our analysis.

The reviewer questions why we selected the 10 brightest centrioles for the analysis shown in Figure S1B,C (note, the reviewer states Figure S2 here, but it is the data shown in Figure S1B,C that is selected from the 10 brightest centrioles, so we assume this is the relevant Figure). We apologise for not explaining this properly. In these mutant embryos very little CP110-GFP localises to centrioles in the absence of Cep97, and vice versa, so we cannot track centrioles using our usual pipeline and instead have to select centrioles using the Asl-mCherry signal. As the difference between the WT and mutant embryos is so striking, we simply selected the brightest 10 centrioles (based on Asl-mCherry levels) in both the WT and mutant embryos for quantification. We could select more centrioles, or select centrioles based on different criteria, but our main conclusion—that the centriolar localisation of one protein is largely dependent on the other—would not change.

The reviewer also questioned why we performed the analysis shown in Figure S2 (new Figure S3) during S-phase of nuclear cycle 14, when the rest of the manuscript focuses on nuclear cycles 11-13. We apologise for not explaining this properly. In cycles 11-13 centriolar CP110/Cep97 levels rise and fall during S-phase, whereas both proteins reach a sustained plateau during the extended S-phase (~1hr) of nuclear cycle 14—making it easier to analyse CP110/Cep97 levels in embryos when their centriole levels are maximal. We now explain this.

The reviewer requests that we quantify the western blots shown in Figure 4 in the same way we do in figure 8. To do this we would need to perform multiple repeats of these blots and we did not perform these because the blots shown in Figure 4 largely recapitulate already published data (Franz et al., JCB, 2013; Dobbelaere et al., Curr. Biol., 2020). Moreover, as described in our response to Reviewer #2, these ECL blots are very sensitive, but highly non-linear, so we always compare multiple serial dilutions of the different extracts to try to estimate relative levels of protein expression. We now explain this in the M&M.

The reviewer suggests the data shown in Figure 8 is a “straw man”: we really want to test whether modulating CP110/Cep97 levels modulates centriolar Plk4 levels, but instead we test how they modulate cytoplasmic Plk4 levels. The language here is harsh, as it suggests that our intention was to mislead readers into thinking that we have addressed a relevant question by addressing a different, irrelevant, one. We apologise if we have missed something, but we believe we do perform exactly the experiment that the reviewer thinks we should be doing—quantifying how centriolar Plk4 levels change when we modulate the levels of CP110 or Cep97 (Figure 7). It is clear from this data that modulating the levels of CP110/Cep97 does indeed modulate the centriolar levels of Plk4. In Figure 8 we seek to address whether this change in centriolar Plk4 levels occurs because global Plk4 levels in the embryo are affected—a very reasonable hypothesis, which this experiment addresses quite convincingly (although negatively).

Minor Points:

The reviewer highlights a small number of mistakes and omissions, all of which have been corrected.

Finally, we would like to thank the reviewers again for their detailed comments and suggestions. We hope that you and they will agree that the changes we have made in response to these comments have substantially improved that manuscript and that it is suitable for publication in The Journal of Cell Science.

Sincerely,

Jordan Raff

__Reviewer Figure 1. CP110/Cep97 dynamics remain cyclical even when Cep97-GFP and CP110-GFP are expressed from their endogenous promotors in the absence of any endogenous protein. __Graphs show how the levels (Mean±SEM) of centriolar CP110/Cep97-GFP change during nuclear cycle 12 in (A) Cep97-/- embryos expressing eCep97-GFP or (B) CP110-/- embryos expressing eCP110-GFP. CS=Centrosome Separation, NEB=Nuclear Envelope Breakdown. N≥11 embryos per group, average of n≥15 centrioles per embryo.

__Reviewer Figure 2. ____The cyclical recruitment of Cep97-GFP expressed from its endogenous promoter occurs largely at the growing daughter centriole. __The graph quantifies the fluorescence intensity (Mean±SD) acquired using Airy-scan microscopy of eCep97-GFP on mother (dark green) and daughter (light green) centrioles in individual embryos over Cycle 12. CS = Centrosome Separation, NEB = Nuclear Envelope Breakdown. Data was averaged from 3 embryos as the number of centriole pairs that could be measured was relatively low (total of 2-8 daughter and mother centrioles per time point; in part due to the much dimmer signal of eCep97-GFP in comparison to uGFP-Cep97).

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Referee #4

Evidence, reproducibility and clarity

The authors report that CP110 and Cep97 localize near the distal end of centrioles in Drosophila embryos. CP110 and Cep97 tagged with GFP exhibit an oscillatory distribution, with levels on the daughter centriole being maximal in mid S-phase. These oscillations correlate with cell cycle progression. The authors also show that modulating CP110 or Cep97 levels changes the rate at which Sas6-GFP incorporates in the daughter centriole, as well as aspects of the previously reported oscillatory behavior of Plk4.

These results could be of potential interest if the stated conclusions were backed up by more convincing data than that which is provided at present. The issues delineated hereafter must be addressed in full before further consideration of the manuscript.

Major points

1) The oscillatory amplitude of CP110/Cep97 tagged with GFP is much smaller when expression is driven by the endogenous promoters than upon overexpression (see Figure 4), raising the possibility that oscillation might not reflect, or only reflect in part, the behavior of the endogenous proteins. To address this issue, the authors could GFP tag the endogenous loci using CRISPR/Cas9. If this is too demanding, they should at the minimum conduct experiments with the extant lines driven by the endogenous promoters, but in the background of the available CP110 or Cep97 null mutants. Moreover, the authors should stain staged wild-type embryos with antibodies against CP110 and Cep97 to ensure that the mild oscillations reported in Figure 4 do not merely reflect the behavior of the tagged proteins, for example due to the presence of GFP. Related to this point, the authors should considering showing first the data with CP110-GFP GFP-Cep97 driven from the endogenous promoters (current Figure 4), perhaps relegating the results upon overexpression (current Figure 2) to a Supplementary Figure.

2) In repeating the above experiments, the authors must ensure that potential mild oscillations do not simply reflect the fact that centrioles are located at a slightly different distance from the coverslip as a function of cell cycle stage. This could be addressed for example by simultaneously imaging a mother centriole marker such as Asl-mCherry.

Other important points

3) The authors mention (for instance on p. 3) that the inner cartwheel and the surrounding microtubules assemble at opposite ends of the daughter centriole. However, my understanding is that the short centrioles present in the fly embryo have an inner cartwheel that extends throughout the organelle, such that it might be moot to make a distinction between the two ends in this case. Moreover, it is also my understanding that this inner cartwheel is itself surrounded by microtubules, so that microtubule assembly might not be expected to occur strictly at the distal end no matter what.

4) Partially related to the point above, the schematic representations in Figure 1 are somewhat confusing. The schematic in Figure 1A represents CP110/Cep97 strictly at the distal end of the centriole, yet the actual immunofluorescence data on the left suggests that CP110/Cep97 are in fact present very close to Asl-mCherry. This apparent difference must be resolved. Moreover, Figure 1C seems to indicate that all the depicted proteins are present throughout the centriole, which I guess is not what the authors wanted to convey.

5) For the quantification of the data reported in Figure 1, the authors considered only centrioles for which CP110/Cep97 ring eccentricity was less than 1.2, to ensure that only near top views are considered (see p. 23). This is entirely reasonable, but the authors should report the distribution of eccentricities in the data set for the two proteins, and compare them to those of the Sas6-GFP and Sas4-GFP data set, all the more since the latter two were obtained previously with a different microscope modality, potentially complicating thorough comparisons. A slight difference in the fraction of centrioles with a slight tilt could easily skew the data when dealing with such small dimensions.

6) In Figure 3, the authors chose not to report the "Noisy data" observed during mitosis. While it is understandable that the data is noisier at this stage, it must nevertheless be reported, as this may have bearing on assessing oscillations between cycles 12 and 13.

7) The authors should conduct Airy-scan analyses of CP110/Cep97 oscillations driven from the endogenous promoters, to ensure that the variations across the cell cycle reported in Figure 4 reflect changes in the daughter centriole. Moreover, it was not clear why the authors used the Airy-scan for some super-resolution experiments and 3D-SIM for others.

8) Why are solely 1-14 centrioles per embryo considered in the experiments reported in Figure 4 as compared to over 100 per embryo in Figure 2? And how were these centrioles chosen? This needs to be explained, justified and, potentially, rectified.

9) Likewise, why are only the 10 brightest centriole pairs in each embryo retained for the analysis reported in Figure S2? And would the conclusion differ if more centrioles than that were included? Moreover, S phase of cycle 14 is analyzed in Figure S2 for Sas6-GFP, whereas the remainder of the manuscript analyzes CP110/Cep97 during cycles 11 through 13 (with an emphasis on cycle 12). This must be resolved.

10) The Western blots in Figure 4A, 4B, as well as in Figure S1A, should be quantified in the same manner as those in Figure 8C, to achieve a better assessment of the differences in protein levels between conditions.

11) The set up for the experiment reported in Figure 8 comes across as a straw man. What one would really like to find out is whether levels of Plk4 at centrioles are modulated by CP110/Cep97 levels, as the authors themselves acknowledge. Since this does not appear to be feasible, the authors set out to test whether cytoplasmic levels of Plk4 differ, finding that this is not the case. Since this experiment does not address what should be tested, it could be reported as a Supplementary Figure, not as the last main figure of the manuscript.

Minor points

• The authors forgot to mention the Tang et al. paper (doi: 10.1038/ncb1889) when referring to Sas-4/CPAP (for instance on p. 4).
• On p. 9, the authors conclude that the "recruitment of CP110/Cep97 to centrioles is regulated by the CCO". Figure 5 shows that the two correlate, not that the latter regulates the former. A related comment holds for the discussion (bottom of p. 13).
• It is not clear why the authors sometimes report SDs (Figure 7) and sometimes SEMs (Figure 3), or fail to report what is being shown (Figure 2). This needs to be clarified.
• The legend of Figure 8A mentions Pie charts and other things that are not featured in the current rendition of the figure.

Significance

These results could be of potential interest if the stated conclusions were backed up by more convincing data than that which is provided at present.

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Referee #3

Evidence, reproducibility and clarity

SUMMARY

This study uses nuclear cycles 11-13 of Drosophila embryos to show the dynamics of the distal centriole localizing CP110/Cep97 complex during the predicted time of MT assembly during new centriole assembly. Continuing from prior work from this group, the authors find that the increase and decrease in CP110/Cep97 at new centrioles correlates with the timing of Cdk/Cyclin oscillations (CCO). The authors find that increased or decreased levels of CP110/Cep97 changes the dynamics of SAS6 and Plk4 levels. The authors suggest that there is crosstalk between the distal localizing CP110/Cep97 complex and the proximal localizing Plk4 and SAS6 proteins required for early centriole assembly.

MAJOR

Overall, the results are potentially interesting but I believe that there a number of instances in this manuscript where the conclusions need to either be strengthened with further experiments or toned down to reveal exactly what is shown in the manuscript.

CP110/Cep97 OSCILLATIONS

Because oscillations are repetitive variation in levels/activity with time, I think the manuscript needs to either use other terms that accurately describe what is measuring here or it should be defined what the authors are calling an oscillation. CP110/Cep97 only increases and then decreases during a single new centriole assembly and maturation event and I think that this should be clearly describe it this way.

LOCALIZATION OF CP110/Cep97 TO DISTAL END OF CENTRIOLES

Based on the existing published studies, it is clear that CP110/Cep97 localizes to the distal end of centrioles. Figure 1 does not show distal centriole localization in daughter centrioles of the syncytium that are the subject of this manuscript though. Its shows radial localization in the mother centriole of the fly wing. Figure 1 therefore has not relevance to the rest of the manuscript and has already been shown in prior studies.

My suggestion would be that this figure should study the dynamic localization of CP110/Cep97 at daughter centrioles during new centriole assembly in the syncytium. Moreover, this should localize these proteins relative to SAS6 and Plk4 that are the subject of the manuscript. Are there localization dynamic changes during the oscillation? Are there times when these proteins do co-localize?

SAS6 AND CW CONCLUSIONS

The current manuscript routinely equates SAS6 levels to cartwheel growth. This is overstated and EM is required to understand whether this is truly impacting the actual cartwheel structure. Loading more sas6 protein doesn't necessarily mean the cartwheel structure changed.

CONNECTION BETWEEN OSCILLATIONS AND MT GROWTH?

Much as above, the manuscript infers MT growth without ever showing it. How does all of this relate to centriole length and growth dynamics.? Page 8 refers to prior work but it seems like this is necessary with EM or MT markers. Having this comparison seems important to the conclusion that MTs do not stop growing when CP110/Cep97 levels reach a threshold level at the distal end.

The following statement is overstated when the data for MT growth are not even presented in this study. "...our findings essentially rule out the possibility that centriole MTs stop growing when a threshold level of CP110/Cep97 accumulates at the centriole distal end." To make such arguments in this study the manuscript would need to include EM and / or MT staining.

ENDOGENOUS UNTAGGED PROTEIN AFFECTING DYNAMICS?

The manuscript shows protein dynamics under conditions of both overexpressed and expression under the endogenous promoter. However, I believe that both of these conditions are also in the presence of untagged protein expression.(?). If so, is it possible that the dynamics represent competition for binding relative to the endogenous, untagged protein? I think this point should at least be discussed.

CP110/Cep97 "INFLUENCED" BY CCO

While I agree that it is likely to be the case that CP110/Cep97 rise and fall at the daughter centriole correlates with CCO, this study does not directly test if CCO changes impact CP110/Cep97 dynamics. Stating that "CP110/Cep97 oscillation is strongly influenced by the activity of the core Cdk/Cyclin cell cycle oscillator (CCO)" is overstated. Is does correlate though.

DISTINCTION FROM PRIOR STUDIES

Dobbelaere 2020 argue that CP110/Cep97 gets to the centriole distal end in late S phase. How could this be considering the data presented in this study? Need discussion of this point. Could Dobbelaere be following the dynamics of the core / basal levels and missed the dynamics that are found in this study? I think a discussion of the Cep97 functions needs to be provided.

MECHANISM OF CROSS TALK

How two apparently spatially separated complexes influence each other should be more mechanistically addressed through either or both experimentation and / or discussion. Obviously the impact of this study would greatly benefit by showing how they are associated and influence each other. CP110 is a phospho target of Plk4. Does this occur in the fly syncytium? Do these interact? What is the timing of the interaction and phosphorylation? Are the changes to SAS6 levels actually the result of Plk4 changes? At this point, these concepts are not tested.

BACKGROUND

In its current form the prior results that 1) Plk4-induced centriole amplification requires CP110 and 2) Plk4 phosphorylates CP110 is important for centriole assembly in some systems is not highlighted in this manuscript as further support for the model of interplay between CP110/Cep97, Plk4 and SAS6.

REPRODUCTION OF DATA

I believe that the data and methods are of high quality and described in such a way that they can be reproduced.

MINOR

ALTERNATIVE MODEL

Because CP110 is a target of Plk4, I wonder if the elevated expression of CP110 sequesters Plk4 away from its cartwheel functions (Ana2/STIL/SAS5 phosphorylation) and this is therefore affecting SAS6 levels?

OVERSTATED CROSSTALK

The text states a "...reveals an unexpected crosstalk between proteins that are usually thought to influence the proximal end of the CW and the distal end of centriole MTs." This is true but there are enough data in the literature to suggest that CP110/Cep97 influence centriole assembly that would indicate that this is not "unexpected".

PAGE 11 - SHIFT IN PEAK

I could not find the data clearly showing that there was a shift in "the Plk4 oscillation to later in S-phase". Are the authors referring to the plateau in levels? Please explain further.

WHAT IS "Plk4-NG"?

I assume Neon Green but I don't see the definition.

FIGURE 2

A schematic of the system used for image averaging would help the reader to understand that these "oscillations" represent the mother and daughter centriole together and that each "oscillation" represents one event of the daughter centriole only increasing in CP110/CEP97 levels and then decreasing after peak intensity.

FIGURE 5 and 8

I think these could be supplemental images. I was unable to figure this out but something is wrong with the legend in Figure 8. (A) is referencing items that I cannot find in the figure.

Significance

This study's advance is an expansion of the authors' prior work showing that during the fly nuclear cycles centriole assembly proteins increase and then reduce in what the authors call an oscillation. Here they show that the CP110/Cep97 complex also oscillates and somehow influences the levels of Plk4 and SAS4 that typically reside at the proximal end of the centriole. This is consistent with prior work indicating that, in some systems, CP110/Cep97 influence centriole duplication and assembly.

I believe that with additional experiments to strengthen the conclusions and toned down concluding statements this will be of interest to the centriole, centrosome, and cilia community. My research expertise is also in this community but I am not a Drosophila researcher. I do appreciate the beauty of this system that the authors use.

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Referee #2

Evidence, reproducibility and clarity

In this study, Aydogan, Hankins, and colleagues, present an interesting work that follows up on their article "An Autonomous Oscillation Times and Executes Centriole Biogenesis" published last year in Cell. In this new study, they analyzed the distal complex consisting of CP110/Cep97 in the centriole of Drosophila embryos. They first demonstrated their oscillatory recruitment at the distal tip of the daughter centriole and they proposed that this protein complex is implicated in the control of centriole growth timing. They also demonstrated the importance of the crosstalk between CP110/Cep97 and Plk4 and its impact on cartwheel growth. This paper proposes a compelling model explaining how centriole growth is regulated. This manuscript is very well written and the data is of high quality. However, some point needs to be clarified before publication:

Major points:

• Figure 1: Since SAS-4 and CP110/CEP97 are only 5nm apart, SAS-4/CPAP is thought to have an antagonistic function to CP110 in the regulation of centriolar growth, and Plk4 can phosphorylate CPAP (DOI: 10.1038/emboj.2010.118), do the authors think that SAS-4 might also be involved in cartwheel/centriole elongation? Does SAS-4 oscillate?
• Figure S1B: The reduction in the intensity of CP110 in Cep97-/- and of Cep97 in CP110-/- is very obvious, nevertheless it is surprising that the cytoplasmic background, even reduced, is not visible, the images are completely dark. Would it be possible to image with a higher laser power or boost the intensity to see if a small amount is present at centrioles?
• Figure 3: The authors indicate that "uGFP-CP110 or uGFP-CEP97 levels remained relatively constant on the mother". However, the intensity clearly decreases over time. Can the authors explain this result, is it due to photobleaching?
• Do the oscillations of CP110 and Cep97 occur at or around the tip of the growing centriole? Would it be possible to use super-resolution at different stages of the S-phase to answer this question?
• The authors indicate that the level of overexpression of CP110-GFP and Cep97-GFP is 2.5X compared to their endogenous proteins (based on the western blot in Figure S1). Nevertheless, it seems that the overexpression of CP110 is more important. Quantification is necessary here.
• The authors proposed that "The CP110/Cep97 oscillation is entrained by the Cdk/Cyclin cell cycle" because they observed a strong and significant correlation between the timing of the CP110/Cep97 peak and S-phase length for both uGFP-Cep97 and uCP110-GFP at all nuclear cycles. It seems to me that this correlation is not sufficient for this statement. If it is not possible to inhibit the CCO to check its impact on CP110/Cep97, this statement should be mitigated.
• Figure 6: According to your results, cartwheels are longer in absence of CP110 or CEP97 and opposite in overexpression situations. Does the intensity perfectly reflect the length of the cartwheel? is the centriole longer? Could you confirm your observation on cartwheel/centriole length using electron microscopy?

Minor points:

• Figure 1C: as the authors show that CP110 and Cep97 are localizing at the distal end of the centriole, I suggest that they place CP110 and Cep97 distally and not at the level of the cartwheel, this representation can be misleading and suggest that CP110 and Cep97 are part of the cartwheel/MT connection.

Significance

The results presented are new and quite unexpected. This work allows a better understanding of phenotypes previously observed. I believe that this work will have an important impact in the field as it brings a whole new vision on the regulation of centriole growth. This article is primarily aimed at centriole/centrosome/cilia fields but may be of interest to a broader cell biology audience.

My field of expertise is centriole/cilia biology

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Referee #1

Evidence, reproducibility and clarity

This manuscript is a continuation of the previous articles of the authors (Aydogan et al., JCB 217:1233, 2018; Aydogan et al., Cell 181:1566, 2020). They reported that Plk4 initiates and times the growth of the cartwheel at the proximal end during early divisions of the Drosophila embryos. In this manuscript, they investigated roles of the CP110/Cep97 complex in the centriole growth control at the distal end of the centriole. The daughter centriole levels of the CP110/Cep97 complex oscillate in S phase in a similar manner to those of Plk4. The CP110/Cep97 oscillation is entrained by the core Cdk/Cyclin cell cycle oscillator but not by Plk4. Rather, the centriolar levels of Plk4 increased in the CP110 and Cep97 deletion embryos. The experiments seem to be carefully carried out, data are nicely presented, and manuscript is clearly written.

Significance

I agree with their interpretation that the CP110/Cep97 oscillation does not appear to play a major part in determining the period of daughter centriole growth during early divisions of the Drosophila embryos. The CP110/Cep97 complex seems to have a limited role in the centriole length control. The CP110/Cep97 complex may be important to prevent centrioles from over-elongating after the initial growth of centrioles.

As suggested in the manuscript, phosphorylation may be a regulatory mechanism for CP110 behaviors at the centrioles. It was previously reported that CP110 is a substrate of the cell cycle kinases, such as Cdk2 (Chen et al., Dev Cell 3:339, 2002) and Plk4 (Lee et al., Cell Cycle 16:1225, 2017). Phosphorylation may be required for recruitment or removal of CP110 at the centrioles. Nonetheless, it is hard to interpret the functional significance of the S phase oscillation of the CP110/Cep97 complex with the data in the manuscript.

It is unfortunate to conclude that the CP110/Cep97 complex may not be a major player for controlling the centriole growth. However, the manuscript includes other interesting observations. For example, they presented data supporting that the SAS6 protein is added at the proximal side of the centrioles, which is opposite to the microtubule growth. Microtubules in the daughter centrioles may assemble at the minus end rather than the plus end. It would be interesting to determine when γ-tubulins are recruited to the growing centrioles.

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Reply to the reviewers

This manuscript was evaluated at Review Commons by four individual reviewers. There was a consensus amongst reviewers that the localization behavior of altORF peptide to the Golgi is a compelling observation and that, with some additional characterization, would provide an effective cell biological tool for use in labeling and studying the Golgi. Our primary goal for this paper was to explore this surprising alternative protein hidden within the sequence of a centromere component and to establish this peptide as a cell biological tool that can be used to study the Golgi. However, the reviewers also highlighted some interesting open questions regarding the nature of this peptide. Below we summarize these core comments our current changes and plans

• Where within the Golgi does the peptide localize? In the work currently included in the paper, we demonstrate that the altORF peptide robustly colocalizes with markers for the Golgi (GM130/TGN46), but not with markers for the Endoplasmic Reticulum (KDEL). However, the resolution at which we imaged the localization of the peptide was not sufficient to determine in which compartment of the Golgi the peptide resides. To address reviewer comments on the specificity of the peptide’s localization within the Golgi, we will attempt to use higher resolution imaging such as confocal or spinning disk microscopy to attempt to better resolve this.
• How does the peptide target to the Golgi? In this manuscript, we show that the localization of the altORF peptide relies on a Cysteine residue present within in a minimal 10 amino acid sequence. Through treatment with 2-Bromopalmitate (2-BP; a palymityltransferase inhibitor) to disrupt its localization, our work suggests that the peptide is palmitoylated. In addition to this observation, the reviewers asked for an additional demonstration that this peptide is palmitoylated in cells. To test this, we have attempted to identify this modification using mass spectrometry of the isolated (IP) GFP tagged peptide from cells. However, we were unable to identify peptides that coincide with the modified peptide cysteine residue. Secondly, we have attempted to identify the modification using Click-chemistry labeling strategy, but this has proved to be technically challenging and infeasible. As an alternative approach for the revised version, we will attempt to perform hydroxylamine treatment followed by SDS-PAGE analysis to determine whether this results in a shift in migration of the GFP tagged altORF, as suggested by a reviewer, to provide additional evidence that the peptide is modified.
• Can this peptide be used to ectopically target proteins to the Golgi? The reviewers asked whether the altORF peptide can be used to ectopically target proteins to the Golgi. In this manuscript, we demonstrate that the peptide sequence is sufficient to target both GFP and the Halo tag (two very different proteins) to the Golgi, and can be tagged at either terminus of the peptide, suggesting that it can be used as a powerful strategy to recruit other proteins to the outer surface of the Golgi. We have emphasized this point in the updated version that is included in this revision.
• Does this peptide alter Golgi structure? For this peptide to provide a useful cell biological marker, it would be preferential for it not to alter cellular physiology. Our work demonstrates that expression of the altORF peptide does not affect the growth of cultured cells. For this updated version, we have performed additional analysis to test whether induced expression of the altORF peptide alters the structure of the Golgi or the localization of other Golgi-associated proteins. Based on a qualitative analysis of these cells, we do not detect any obvious changes in Golgi organization or morphology. This is now included as Supplemental Figure 2D.
• • Is this peptide expressed in human cells? *We have analyzed published ribosome profiling data that suggests that this altORF can be translated, although it is produced to a much lower degree than the full-length CENP-R protein. The short length of the peptide as well as the nature of the amino acid sequence makes this peptide highly challenging to identify via mass spec. It is also possible that this peptide would be expressed in different cell types in the human body, but not robustly expressed in cultured cells. We believe that these are beyond the scope of this paper. However, we now comment on these important points in the updated version.
• • Is this peptide “functional”? *Based on our deliberate analysis of the evolutionary conservation of this altORF within the CENPR transcript, it is clear that this peptide acquired the ability to localize to the Golgi only recently during evolution (only old world primates have this capacity). We believe that this peptide represents a great example of evolution in action, with minor sequence changes resulting in the acquisition of a new capacity and trait. However, as this peptide is not broadly conserved across mammals, it is unlikely to facilitate a core biological function that can be analyzed in cell culture. It is certainly possible that this peptide would contribute to a feature of human biology on the organismal level, but it is not feasible to test this experimentally. The functional nature of this peptide, and particularly the recent evolutionary acquisition of this novel trait are interesting points that we have now commented on in the updated manuscript (text changes in blue).

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Referee #4

Evidence, reproducibility and clarity

The manuscript reports the characterisation of a 37 amino acid alternative open reading frame (altORF) within the RNA of the centromere protein, CENP-R. The resulting peptide, when expressed in different cell lines fused to GFP, localises on the Golgi complex, exposed on the cytosolic face of Golgi membranes. It remains associated with the Golgi complex under conditions inducing fragmentation or dispersal of the Golgi complex such as mitosis and BFA. The authors identify in aa 5-14 the minimal Golgi targeting motif and in cysteine 11 a key aa for the targeting. They suppose that palmitoylation may be involved in Golgi targeting as palmitoylation inhibitors prevent its Golgi targeting. The data are clearly presented and sustain the conclusions.

Significance

Though the identification of a Golgi targeting motif is of potential interest, the manuscript appears to be at a preliminary stage as it fails to provide any data on the possible function of the altORF of CENP-R palmitoylation or even evidence for its existence in the cells used in the manuscript. The authors appear to be aware of the limits of their study as they conclude their study led to the identification of an "easy-to-use Golgi labeling construct". Also in this scenario, however, some key information are missing: the actual sub-Golgi localisation of the probe, its possible impact on Golgi structure and function, and the formal proof that it is palmitoylated.

Referess cross-commenting

I see all the reviewers agree that the manuscript has major limits. Overcoming these limits wold require years if one had to provide proofs for the existence and for the physiological relevance of this alternative ORF, and months to provide the missing information that have been highlighted by the reviewers to consider "just" the technical aspect of this altORF as a possible Golgi reporter/targeting sequence.

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Referee #3

Evidence, reproducibility and clarity

Summary:

In this study, the authors characterized a potential alternative open reading frame close to the CENP-R open reading frame that had previously been found by ribosome profiling. Its 37 amino acid peptide sequence was included in a proteomic database and is conserved in primates. Transfection of different cell lines with the GFP-tagged peptide was used for immunofluorescence and proteolytic cleavage by a cytosolic protease was used to show that it localizes to the cytoplasmic face of Golgi membranes throughout the cell cycle and Brefeldin treatment had no influence on fragmentation or reformation of the Golgi stacks. The specific localization could be confirmed using different cell lines. The use of numerous truncation mutants allowed to narrow down the minimal Golgi recognition sequence to a 10 amino acid stretch including a species-specific conserved cysteine that required palmitoylation. From these data and comparison with similar sequences in other species the authors determined a consensus sequence for this Golgi targeting sequence in primates.

Major comments:

1. Without ultrastructural analysis it is always difficult to judge whether a localization is limited to just one organelle. Immunofluorescence alone gives no clear answer in particular when organelles differ in size and form from cell to cell. In particular when the authors claim that the peptide may serve as a marker. For example when you are working on secretion it is important to distinguish membranes derived from ER exit sites (ERES), the ER-Golgi intermediate compartment (ERGIC), the Golgi itself and Golgi-derived vesicles. I therefore recommend to add a subcellular fractionation by which numerous fractions can be analyzed by a gel in parallel using markers for all the above mentioned different membrane origins.
2. Is it possible to confirm the in vivo existence of this peptide? There are probably no specific antibodies available, but it should be possible to detect the peptide in enriched Golgi membrane fractions by mass spectrometry.
3. It would be interesting to reveal the potential in vivo role of this peptide, when it exists. The authors failed to identify potential interaction partners by IP-MS, so I wonder whether its role may be different by controlling the Golgi association of other well known Golgi interactors like GM130, Golgin or GORASP proteins. Is their Golgi association altered in the presence of the peptide?
4. Finally the authors determined a consensus sequence which they claim to be a Golgi targeting sequence, however when this is true one would expect that there are other proteins in the cell that use this consensus sequence as targeting sequence. The authors only show that the consensus is conserved among the same alternative open reading frame in primates, but to serve as a Golgi targeting sequence it should be possible to identify unrelated other proteins using this consensus by bioinformatics. What happens when an otherwise differently addressed protein is attached to this Golgi sequence, is it mislocalized?

Minor comments:

There are a couple of typos and smaller issues - In the Introduction line 2 the citation is missing and skip the "a" in line 7. - In the Results and Discussion section page 5, line 5 "In our ongoing work, we..." - In the same section close to the end in the second from the last paragraphs Figure 5B should be Figure 5C - In the Methods section check the temperature specifications: 4{degree sign}C or 37{degree sign}C, not 4C or 37C - Also in the Methods: there are no secondary antibodies recognizing complete animals (antiMouse or antiRabbit)! The antibodies are directed either against IgG or IgM (e.g. anti-Mouse IgG) - Some subscripts are missing: MgCl2 not MgCl2, NaN3 not NaN3 - Also on the last page of the Methods section the antibody is specific for TGN46, not TGN146 - Last paragraph: for concentrations use μM not uM (also in the Fig.4 legend) - The end of the second from the last sentence is missing. - In the References, is the citation for the Samandi et al. manuscript correct, just one number? - Legend to Suppl. Fig 3: Golgi (capital letter), (~) is missing in figure - Suppl. Fig1B use Courier also for peptide sequence, this will omit alignment problems

Significance

Overall, this study is interesting and may provide a helpful tool for cell biologists working on trafficking projects (like myself) in particular because a general Golgi targeting sequence is missing. For techniques like RUSH (Retention using specific hooks) which can be used to synchronize secretory protein traffic reliable and highly specific targeting sequences are required. I am supportive of this study, however, to be useful for the audience the authors need to provide more examples demonstrating the targeting efficiency.

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Referee #2

Evidence, reproducibility and clarity

The authors have identified a alternately translated region with in the mRNA of CENP-R that encodes a small 37 aa peptide that localizes to the perinuclear Golgi region. The main premise here is the this peptide can be used as a novel Golgi marker. The peptide seems to localize peripherally to membranes and interacts with cis and TGN elements based on light microscopy. Mutational analysis indicates that a cysteine residue within a 10 aa region is critical and defines a minimal consensus sequence required for Golgi localization. Evidence is presented based on inhibition by 2-bromopalmitate that C11 is palmitoylated.

Significance

If this peptide probe is to be used as a Golgi-specific marker, there are several major issues that have not been addressed. The first is whether it actually binds to Golgi elements and if so, what are the specific elements? The light microscopy images are not of high enough resolution to determine if the peptide interacts with cis or TGN Golgi. The BFA experiments suggest it interacts with the TGN or some other associated vesicular compartment since staining fragments into vesicles and does not get integrated into the ER (Fig. 2B). The authors would have to use higher resolution confocal imaging or, more preferably, immuno-EM to identify exactly where the peptide is located.

The second issue is the conclusion that the peptide is palmitoylated, which is only based on partial loss of 'Golgi' staining after 2-BP treatment (Fig. 4D). More conclusive evidence is required such as incorporation of radiolabelled or click-palmitate probes into peptide, or band shift after hydroxylamine treatment. In regard to the last point, the protein seems to migrate as a doublet on SDS-PAGE (Fig. 2D) suggesting some type of modification or cleavage that is not commented on.

Lastly, I would be unlikely to use this as a Golgi probe for the reasons described above, as well as the fact that there is nothing known about the biological function of the peptide (this is potentially the most interesting aspect that is seemingly ignored). If you express the peptide what impact does it have on Golgi structure and function? I could envision that its binding to a Golgi element(s) could affect one of myriad functions that rely on Golgi activity.

Referees cross-commenting

This is more of a technical report that does not address the function of the peptide within the Golgi complex. Without this information, and identification of the compartments involved, I don't see the advantage of the probe compared to other methods. As one reviewer mentioned, this seems to be a preliminary study that is difficult to assess given the limited and ambiguous results.

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Referee #1

Evidence, reproducibility and clarity

The authors note that we currently lack a robust targeting signal to direct proteins to the cytoplasmic face of Golgi membranes. The presented work clearly identifies a novel Golgi targeting sequence rich in aromatic/hydrophobic/basic residues and with a key critical cysteine (C11). One can imagine a situation where the non-cysteine residues provide an underlying affinity for cell membranes and thereby allow access to membrane-associated zDHHC S-acyltransferases. I guess the unknown question is whether Golgi specificity comes from the amino acid sequence per se (mediating specific interaction with components of Golgi membranes) or instead by specific recognition of the cysteine by Golgi-localised zDHHC enzymes. It might be worth discussing this in the paper although this should not detract from the main focus/message of the paper- the identification of a Golgi targeting peptide. Data is compelling and support the conclusions of the paper. Although much of the data is not quantified, the data provided is convincing.

Significance

Interesting advance for researchers in the general membrane trafficking area and S-acylation field. Provides new information that can be used to target proteins of interest to the Golgi. I note that restriction of an S-acylated peptide at the Golgi is unusual as S-acylation is usually followed by trafficking to the plasma membrane. My expertise is in S-acylation and protein trafficking

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Reply to the reviewers

1. General Statements

*The reviewers are enthusiastic. They agree with the claims made and comment favorably with regard to the impact as well on the short- and long-term potential for translation. All three go out of their way to emphasize positive aspects. A variety of questions were raised and we submit a complete revision with point-by-point replies that addresses all of these. This includes addressing tumor organoid (tumoroid) plasticity (reviewer #1) and composition/heterogeneity (reviewer #3) by incorporating single cell data as well as other analyses. We thank the reviewers for the thorough feedback. The additional data, analyses and clarifications strengthen the study. *

To keep the rebuttal as short as possible we have only copied the reviewers’ concerns/questions, not the favorable comments. The copied remarks are in highlighted. Our replies are in italics. Each question is accompanied by a reply and a brief description of changes made in response.

2. Point-by-point description of the revisions

__Reviewer #1, Major Comment #1: “__The authors provided a foundational validation of their organoids through various methods, and their protocol stands to impact the field of RMS biology. To validate the organoids as recapitulating the primary human tumors, the authors perform analysis on the bulk organoid and bulk human primary tumors. The authors showed through sequencing efforts that the bulk mutational profile and transcriptional profiles do not dramatically change between the parent tumor and organoids. This analysis was done well; however, the authors fail to rigorously illustrate that the organoids maintain tumor cell heterogeneity of the primary human tumors. To rigorously validate the organoid system, the authors should illustrate the organoid culture conditions do not alter the heterogeneity of cells (cell plasticity) compared to that of the primary tumor. A formal assessment of the cellular plasticity in the organoids to the primary tumor would determine how the organoid system either maintains or shifts the cancer cell plasticity because of changes in microenvironment (Oncogene, 2020, 39: 2055-2068). The addition scRNA-seq would illustrate whether the organoids maintain the same populations as the primary tumor or bias for the propagation of specific cell populations at a single cell level and provide more rigorous information about every cell type present.”

Reply: The reviewer’s question is whether the tumor cells in the tumoroid culture have the same degree of plasticity and are therefore as heterogeneous in culture as they are in the tumors that they are derived from. We agree that evaluating the heterogeneity of tumor cells in the tumoroid culture is desirable. This would ensure that the procedure has not simply selected for a single type of tumor cell. We have therefore generated single-cell RNA sequencing (scRNA-seq) data of tumoroid cells as suggested. It is important to point out that a complete inventory of RMS tumor cell heterogeneity by scRNA-seq has not been published as yet. Such an undertaking, i.e., scRNA-seq of a large cohort of RMS tumors, is an entire study in itself and lies outside the scope of this study. It would also not be feasible due to limited sample material for many of the tumors used here. Nevertheless, as is being alluded to by the reviewer, there is ample evidence of tumor cell heterogeneity in primary RMS tumors based on previous studies using immunohistochemistry (for example the well-known heterogeneity in expression of RMS marker proteins such as Myogenin, MyoD1 and Desmin). As shown in new Fig. 2D, when cultured as tumoroid models, examples from both of the main tumor types (FP-RMS sample RMS127 and FN-RMS sample RMS444) show a large degree of heterogeneity in expression of the known, heterogeneously expressed tumor cell markers Myogenin (MYOG gene), MyoD1 (MYOD1 gene) and Desmin (DES gene). Comparison with the cell cycle marker Ki-67 (MKI67 gene) shows that this heterogeneity is not due the cells being present in different cell cycle phases. Tumor cell heterogeneity in the tumoroid culture is further indicated by the heterogeneous CNV patterns derived from the tumoroid scRNA-seq data (new Suppl. Fig. 1B).

Both the CNV analysis and the scRNA-seq marker gene expression indicate that the tumoroid culture conditions neither stringently select for a single type of tumor cell, nor drive the tumor cells into a uniform expression pattern phenotype, consistent with maintaining plasticity, even after the 7 (RMS127) and 5 (RMS444) passages. These are good indications of retained plasticity/heterogeneity. Additionally, we make it clear in the revised version that a more exhaustive answer would benefit from having a complete cohort of tumor scRNA-seq data to first determine the degree of heterogeneity exhibited by RMS tumors for all genes.

The related question of tumoroid cellular composition, with regard to the presence of non-tumor cells, is addressed in response to reviewer #3, major comment #1.

Changes: Addition of a new Fig. 2D and a new Suppl. Fig. 1B with figure captions. Additional text in the Results and the Discussion sections. Additions to the Methods for the generation and analysis of the scRNA-seq data.

Reviewer #1, Major Comment #2: “The authors took great strides to show that the organoids respond to therapeutics similarly to primary tumors. However, Figure 5A could be more transparent with more data labelled in the graph instead of just in the app and the implications of the variable responses could have been explored in the discussion section. Furthermore, for this model to be clinically relevant for pharmacokinetic studies, propagation in mice needs to be shown.”

Reply:

• We have made Fig. 5A more transparent by adding the drug names.
• The different response between FN-RMS and FP-RMS subtypes for certain drugs is known and the implication that the models reflect this is discussed more thoroughly now as suggested.
• We agree that animal experiments are imperative for pharmacokinetic studies of new drugs. However, most of the drugs that were included here, especially the ones highlighted, have already been evaluated in early phase clinical trials in adults and/or children. The pharmacokinetic data for humans is therefore already available for these drugs, making additional animal studies for pharmacokinetics of these drugs redundant. For future studies, various types of animal studies are likely to be required and we make this clear in the Discussion, also emphasizing that in general, tumoroid models do propagate in mice. To address this specifically for RMS, we have started a collaboration to generate PDX mouse models derived from RMS tumor and tumoroid samples in parallel. Anecdotally we can state here that at least 50% propagate. However, since we wish to investigate this systematically and with a complete set of tumoroid models, it is not prudent to wait for these results before publishing the current study. This would delay making the protocols, findings and tumoroid models available to the scientific community and as our (and many other groups’) work exemplifies, tumoroid models can yield important findings on their own. Changes: Drug names added to rows in Fig. 5A. The Discussion has been expanded to include the differential response of tumor subtypes and tumoroids to different drugs and to include the uses (including pharmacokinetics) of different types of models has been expanded.

Reviewer #1, Major Comment #3: “Figure 1 is well put together to graphically demonstrate the process by which organoids were obtained and manipulated. Figure 1B, however, as a graphical summary is a little confusing, and the information would be greatly enhanced by the addition of a comprehensive table. Furthermore, additional information could be added to the table to make it a more inclusive and impactful addition to the paper.”

Reply: We agree.

Changes: A new Table 1 has been added as a separate file with a corresponding revised legend in the main document.

Reviewer #1, Major Comment #4: “It is quite impactful that the authors were able to actively engineer the organoids with CRISPR/Cas9 and accurately delete TRP53, but controls were not represented in the figure. The experiment should have included a sgRNA targeting a pan-essential gene as a positive control and a non-targeting sgRNA as a negative control. We recommend addition of both controls to the experiment outlined in Figure 6 to increase the validity and rigor of the data presented.”

Reply:* We respectfully note that all appropriate controls were done. This included a non-targeting sgRNA as negative control (see Methods lines 1137 to 1140). As also explained in Fig. 6A, the strategy for generating a P53 knock-out involved selection through nutlin-3 exposure, whereby cells wildtype for P53 are selected against. As described (Methods lines 1144 to 1146), cells transfected with the non-targeting sgRNA plasmid indeed died upon nutlin-3 exposure. A sgRNA against a pan-essential gene was not included in this strategy since the nutlin-3 already kills all cells with a wildtype P53. Finally, we draw attention to the fact that the success of the approach was assessed by Western Blotting (Fig. 6B) and Sanger sequencing (Suppl. Fig. 6A). *

Changes: None.

Reviewer #1, Major Comment #5: “Although the authors provide an insight into a useful preclinical RMS model, the paper lacks mechanistic insight besides cursory description of the model.”

Reply: Insight into a wide variety of different molecular and cellular mechanisms will be exciting to explore in future studies. This publication is indeed focused on describing an approach that works for RMS, and therefore showing for the first time that this works systematically for mesenchymal-derived tumors. In addition, the study describes key characteristics of the tumoroid models that are important to establish their validity as models and that are essential to demonstrate before making the tumoroid models available to the wider scientific community in order to perform the further mechanistic analyses. The word cursory is in contrast to the many positive comments made by this reviewer and the other two reviewers with regard to the extensive characterization.

Changes: None.

Reviewer #1, Minor Comment #1: “Figure 3C and 4B are not transparent in their labels and could be altered so that every line has an associated gene in the publication. Furthermore, there are sample specific differences that could be explored in the discussion.”

Reply: We agree.

Changes: Gene names have been added for every row in both figures. The Discussion now incorporates the observed differences.

Reviewer #1, Minor Comment #2: “In Supplementary Figure 1, higher magnification inserts are needed to get a closer look at the IHC. Furthermore, the white balance is not the same in all the images and needs to be corrected prior to publication. The difference in white balance can clearly be seen in the last panels depicting IHC for RMS335, where the MYOD1 staining has a yellow background whereas the H&E staining has a white background.”

Reply: We agree.

Changes: Higher magnification inserts have now been provided throughout Suppl. Fig. 1A. The white balance has been corrected.

Reviewer #1, Minor Comment #3____: “The authors mentioned in line 202 that some of their organoids contain the novel fusion of PAX3 and WWTR1, but this fusion is not indeed novel as it has previously been seen in biphenotypic sinonasal sarcoma (Am J Surg Pathol 2019, 43:747-754).”

Reply: We rephrased this to clarify that this is the first report of such a fusion in RMS, rather than in general.

Changes: The corresponding sentence has been rephrased.

Reviewer #1, Comment within the Significance Statement: “The authors state that this is the first system to use organoids but should recognize the advances demonstrated by Manzella et al. (Nat Commun, 2020, 11:4629). Additionally, the authors state that this is the first demonstration of pre-clinical models harboring FGFR4V550L mutations; this fails to recognize the prior reported work by several groups (Chen et al., Cancer Cell, 2013, 24:710-24; Manzella et al., Nat Commun, 2020, 11:4629; McKinnon et al., Oncogene, 2018, 37:2630-2644).”

Reply:* We had in fact already recognized the advances described by Manzella et al. which was referenced in two places in the original submission (current lines 100 and 388). We thank the reviewer for pointing out the previous work done on an RMS cell line that harbored an FGFR4 p.V550L mutation. *

Changes: We rephrased the corresponding passages concerning the FGFR4 mutation.

We thank reviewer #1 for all the comments. This has resulted in many improvements.

Reviewer #2: W____e thank reviewer #2 for the positive comments. There are no major/minor queries to address.

Reviewer #3, Major Comment #1: “The authors describe the models derived as organoids/tumoroids implying that multiple cell types are represented potentially recreating the tumor microenvironment. Can the authors comment more specifically and demonstrate the extent to which cell types in addition to the tumor cells are represented, viable and are organized through analyses of the original and tumoroid sections (extend fig 2C/supplementary fig) and via analyses of the RNAseq data?”

Reply: We use the term tumor organoid or tumoroids as coined by the field in general. This indeed indicates a degree of self-organization such as the three-dimensional growth in spheres and the propagation of a heterogeneous population of tumor cells (see comment #1, reviewer 1) for example. In general, however, tumoroids do not include growth of a non-tumor cell microenvironment inter-woven with the (different types of) tumor cells. Exceptions to this are very early passage tumoroids that are not yet stable and which may still contain non-tumor cells, or specialized co-culture conditions that are currently being actively sought to allow for co-culture of tumor cells within a non tumor cell microenvironment. It is therefore not anticipated that late passage tumoroid models will have non-tumor cells. The basis of the technology is that the defined set of growth factors in the medium mimics the tumor stimulating conditions of the non-tumor cell microenvironment. Since the mixed presence of tumor and non-tumor cells generally gives rise to one (frequently the non-tumour cell) outgrowing the other, it is often considered the hallmark of an unsuccessful tumoroid.

The reviewer therefore raises an important point that we have failed to make clear. We have addressed this in two ways. We emphasize that the scRNA-seq data that are now included in response to reviewer #1, comment #1 do not indicate the presence of any non-tumor cells (as expected). In addition, this aspect of tumor organoid technology is explained better in the Introduction.

Changes: The results section has been expanded with the description of the scRNA-seq data emphasizing the expected lack of non-tumor cells and the introductory section on tumor organoid technology has been improved to make it clear that currently this generally involves growth of different types of tumor cells only.

Reviewer #3, Major Comment #2: “Does the quantification from the RT-qPCR analyses for the MYOD1, MYOG and Desmin of the models match that in the samples from which they were derived? Does the RNAseq that was performed on tumor and the culture at the time of the drug screen tie in with this?”

Reply: The answer is yes. The figure below shows tumor and tumoroid bulk RNA seq of those genes also analyzed by RT-qPCR (i.e., DES, MYOG, and MYOD1). Note that this is also the same stage as for the drug screening. As can be appreciated, the expression of these markers is generally very comparable between tumors and the derived tumoroid models. Note that this also constitutes a nice independent (albeit indirect) verification of the similar degree of heterogeneity issue raised by Reviewer #1 (comment #1). Expression of the markers was lower in the tumoroid models of RMS000HQC and RMS000ETY compared to the primary tumor. In line with this, expression of these genes was also already lower in the early passages of the culture as determined by RT-qPCR (Fig. 2A). Nevertheless, copy-number analysis inferred from whole-genome sequencing showed that the resulting tumoroid models are indeed tumor cells (Suppl. Fig. 2A top panel and Suppl. Fig. 2B lower panel).

We therefore conclude that the expression of probed marker genes is generally comparable between tumor and tumoroid and that early passage RT-qPCR based expression analysis of these markers can be reflective of the expression in the fully established model.

*- Rebuttal letter includes corresponding figure here - *

Changes: None. The expression data are already available within the interactive browser-based Shiny App.

Reviewer #3, Major Comment #3: “How do the frequencies of SNVs compare with recent studies? Or are the numbers in the risk groups not appropriately represented?”

Reply: The SNV frequencies are quite comparable to recent studies, with similar differences between risk groups, all as depicted in the new Suppl. Fig 2E. The SNV frequency was calculated from our WGS data following the procedure from the most recent report in pediatric cancer (https://www.biorxiv.org/content/10.1101/2021.09.28.462210v1). Across tumor and tumoroid models we found a somatic mutation frequency of SNVs with a VAF of >0.3 ranging from 0.03 to 1.92 mut/MB (median 0.70 mut/MB) which is comparable to the reported somatic mutation frequency in the afore-mentioned study (median 0.9 mut/MB in RMS). Concerning the risk groups, a recent study (https://pubmed.ncbi.nlm.nih.gov/31699828/) found a significant difference in the tumor mutational burden between fusion-negative (FN) and fusion-positive (FP) RMS (2.6 mut/MB vs. 1.0 mut/MB, respectively) with a higher mutational burden associated with poorer outcome. In our study, the FN-RMS tumoroid models also show a higher mutation frequency compared to the FP-RMS tumoroid models (FN 4 vs. FP 15, p = 0.02, Wilcoxon). Such a difference is also found between the original tumors but without statistical difference (FN 4 vs. FP 15, p = 0.15, Wilcoxon) likely related to the small sample sizes. This underscores the representative nature of the tumoroid models and is of obvious interest to include. We have made the appropriate changes.

Changes: To include these analyses in the manuscript, we added a new Suppl. Fig. 2E with corresponding Suppl. Fig. legend and a new paragraph in the main text.

Reviewer #3, Minor Comment #1: “The number of models and success rates would be useful to indicate in the abstract.”

Reply: We agree.

Changes: We added this information to the abstract.

Reviewer #3, Minor Comment #2: “It would be helpful to define the SBS1, 5,and 18 in the figure legends. Do the age related signatures in any way correlate with patient age or aggressivness of tumors?”

Reply:

• Agreed. The definitions of SBS1, 5, and 18 have now been included the legends of Fig. 3B and 4A.
• The age-related signatures SBS1 (but not SBS5) shows a weak albeit significant correlation with patient age only in RMS tumoroid models but not in RMS tumors. Furthermore, concerning aggressiveness, FP-RMS tumors and tumoroid models show a significantly higher contribution of SBS1 (but not SBS5) to their overall somatic mutation frequency compared to FN-RMS tumors and tumoroid models. However, since FP-RMS tumor samples were obtained from older patients (median 14 years versus median 6 years in FN-RMS tumor samples), this observation could also be related to the patient-age and not primarily to the fusion-status. The heterogeneity of samples (e.g., primary therapy-naïve samples versus relapse and therefore pre-treated samples) and the relatively low sample number could be explanations for the lack of a stronger correlation in general. Changes: Added definitions of SBS1, 5, and 18 in the legends of Fig. 3B and 4A. Added text in the Results section to indicate the observed correlations.

Reviewer #3, Minor Comment #3: “Page 13 line 300 just because the RH30 cell line has TP53 mutation doesn't mean that it was acquired in culture - unless there is specific evidence that supports this.”

Reply:* We thank the reviewer for this rectification. To our knowledge, there is indeed no specific evidence that this cell line acquired the TP53 mutation during culturing or whether the mutation was already present in the primary tumor the cell line was derived from. *

Changes: The corresponding statement has been removed.

We thank reviewer #3 for all the comments. This has resulted in many improvements.

Besides the changes described above, additional minor changes were made:

*We have moved the interactive, browser-based Shiny app to a server that is managed by our institute instead of having it hosted on shinyapps.io. We include the new URL in line 556. *

The data upload to the European Genome-Phenome Archive (EGA) of the data from the initial submission has been completed and the raw sequencing data can now be accessed. The data upload of the scRNA-seq data generated for the revision is currently ongoing. We have therefore adapted and renamed the “Bulk sequencing data availability” section in the Methods in the manuscript (lines 1043 to 1050).

We updated the code available at https://github.com/teresouza/rms2018-009* following the additional analyses performed for the revision. *

Supplementary Table 1: The values for row “RMS000FLV” for columns “sample_body_site” and “primary_site_specific” were corrected as this tumor was located in the upper leg and not the upper arm of the patient. Furthermore, we added patient numbers as in the new Table 1 and corrected spelling errors. This does not change any of the conclusions in the manuscript.

Figure 6A: The protein “P53” was spelled without capital “P” in the initial version. We corrected this.

We included the recently described Zebrafish RMS PDX models (https://pubmed.ncbi.nlm.nih.gov/31031007/) in the Discussion of RMS models. See lines 507 to 510.

With the addition of Fig. 2D, the figure legends of Fig. 2A and 2B were moved to the side (Fig. 2A) or below (Fig. 2B) the figure. With the addition of the single-cell copy-number plots, Suppl. Fig. 1 was divided in Suppl. Fig. 1A and 1B.

Some of the original scale bars in Fig. 2C and Suppl. Fig. 1A were incorrectly labelled and this has now been corrected. This does not change any of the conclusions.

Minor corrections in the sections Affiliations, Financial support, Author contributions and Conflict of Interests.

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Referee #3

Evidence, reproducibility and clarity

Meister et al., describe their methodology in establishing what they term organoid or tumoroid 2D/3D cultures derived from samples of rhabdomyosarcoma (RMS) patient tumours. They have success to varying degrees across the subtypes with greater success in those more clinically aggressive. Their analyses of markers, the somatic genetics and gene expression profiles suggest that they are largely representative of RMS and the tumor samples from which they were derived. Their utility in drug screening and manipulation by knocking out TP53 by CRISP/Cas9 is also demonstrated. The conclusion is that this represents a useful approach for generating patient derived models and a unique resource for preclinical analyses and other research into RMS.

This is a major piece of work that is well written and presented. The link to interrogate the data worked. I have only a few comments.

Major comments

The authors describe the models derived as organoids/tumoroids implying that multiple cell types are represented potentially recreating the tumor microenvironment. Can the authors comment more specifically and demonstrate the extent to which cell types in addition to the tumor cells are represented, viable and are organized through analyses of the original and tumoroid sections (extend fig 2C/supplementary fig) and via analyses of the RNAseq data?

Does the quantification from the RT-qPCR analyses for the MYOD1, MYOG and Desmin of the models match that in the samples from which they were derived? Does the RNAseq that was performed on tumour and the culture at the time of the drug screen tie in with this?

How do the frequencies of SNVs compare with recent studies? Or are the numbers in the risk groups not appropriately represented?

Minor comments

The number of models and success rates would be useful to indicate in the abstract.

It would be helpful to define the SBS1, 5,and 18 in the figure legends. Do the age related signatures in any way correlate with patient age or aggressivness of tumors?

Page 13 line 300 just because the RH30 cell line has TP53 mutation doesn't mean that it was acquired in culture - unless there is specific evidence that supports this.

Significance

The significance of this study is in describing how a relatively large number of models of RMS were established plus increasing awareness of the biobank resource and associated data that has been created. The approach, although used in more ad hoc reports of smaller numbers of RMS, represents a useful development for mesenchymal tumors versus the more established development of such models in epithelial cancers. Although a lower success rate than xenografts, it is a useful and practical cost-effective alternative for preclinical testing and research. Likely interest to a speciaclist audience for those involved in the RMS, sarcoma and pediatric cancer field.

Referees cross-commenting

OK with the balance of comments for the authors to address. I think the extent to which they are prepared to address the heterogeneity issue, and the results of this for the models, is likely to affect the impact of their study.

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Referee #2

Evidence, reproducibility and clarity

This manuscript describes the possibility to generate a collection of pediatric rhabdomyosarcoma (RMS) tumor organoid models comprising broad spectrum subtypes from highly aggressive to extremely rare. The authors were able very successfully establish 19 RMS models from 46 pediatric RMS patient samples with 41 % efficiency. All RMS tumoroid models were thoroughly characterized and retained the molecular characteristics of the tumor they are derived from as well as they displayed genetic stability over time. Most of the tested tumors showed long-term propagation potential, reaching passage 40 and remaining stable. Though, establishing time for RMS tumoroid models varied with a median time from acquisition of the tumor sample to successful drug screening being 81 days, highly aggressive tumors were established in as little as 27 days. Also, authors shown us in elegant manner the suitability of RMS tumoroid models for research in two specific ways: via drug screening and CRISPER/Cas9 genome editing.

Significance

In summary, the author's work made significant progress in 3D culture and tumor organoid models of mesenchymal origin, being the first collection of tumoroid models from mesenchymal malignant tumors and the second thoroughly characterized tumoroid collection specific for pediatric cancers. Without doubt, biobanked collection of RMS tumoroids will be useful for drug screening as well as molecular editing. Also, these models will be a useful resource for future research and in preclinical and clinical testing therapeutics for RMS. In the future, organoids generated from patients with RMS may lead to precise and personalized treatment.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Meister et al. set out to develop a new organoid preclinical model of rhabdomyosarcoma (RMS). The authors comment that this system would be beneficial for preclinical modeling because it has the ability to maintain the tumor's molecular characteristics. The authors then proved that organoids derived from multiple RMS subtypes resembled their parent tumors using RT-qPCR for characteristic markers, histopathology, copy number profiles, mutational signature analyses, and transcriptional profiling. Importantly, the authors performed long term studies to show that the organoids remain stable over multiple passages and do not change their mutational landscape dramatically. Finally, the authors tested their organoids with known RMS therapeutics and for their ability to be engineered with the CRISPR/Cas9 system. Not surprisingly, the authors found their organoids sensitive to known RMS therapeutics and were successfully able to generate TP53-/- organoids with CRISPR/Cas9, underscoring this organoid system in translatable use. This report nicely describes a method for the establishment of human RMS organoid culture systems that can be leveraged for preclinical testing.

Major Comments:

1. The authors provided a foundational validation of their organoids through various methods, and their protocol stands to impact the field of RMS biology. To validate the organoids as recapitulating the primary human tumors, the authors perform analysis on the bulk organoid and bulk human primary tumors. The authors showed through sequencing efforts that the bulk mutational profile and transcriptional profiles do not dramatically change between the parent tumor and organoids. This analysis was done well; however, the authors fail to rigorously illustrate that the organoids maintain tumor cell heterogeneity of the primary human tumors. To rigorously validate the organoid system, the authors should illustrate the organoid culture conditions do not alter the heterogeneity of cells (cell plasticity) compared to that of the primary tumor. A formal assessment of the cellular plasticity in the organoids to the primary tumor would determine how the organoid system either maintains or shifts the cancer cell plasticity because of changes in microenvironment (Oncogene, 2020, 39: 2055-2068). The addition scRNA-seq would illustrate whether the organoids maintain the same populations as the primary tumor or bias for the propagation of specific cell populations at a single cell level and provide more rigorous information about every cell type present.
2. The authors took great strides to show that the organoids respond to therapeutics similarly to primary tumors. However, Figure 5A could be more transparent with more data labelled in the graph instead of just in the app and the implications of the variable responses could have been explored in the discussion section. Furthermore, for this model to be clinically relevant for pharmacokinetic studies, propagation in mice needs to be shown.
3. Figure 1 is well put together to graphically demonstrate the process by which organoids were obtained and manipulated. Figure 1B, however, as a graphical summary is a little confusing, and the information would be greatly enhanced by the addition of a comprehensive table. Furthermore, additional information could be added to the table to make it a more inclusive and impactful addition to the paper.
4. It is quite impactful that the authors were able to actively engineer the organoids with CRISPR/Cas9 and accurately delete TRP53, but controls were not represented in the figure. The experiment should have included a sgRNA targeting a pan-essential gene as a positive control and a non-targeting sgRNA as a negative control. We recommend addition of both controls to the experiment outlined in Figure 6 to increase the validity and rigor of the data presented.
5. Although the authors provide an insight into a useful preclinical RMS model, the paper lacks mechanistic insight besides cursory description of the model.

Minor Comments

1. Figure 3C and 4B are not transparent in their labels and could be altered so that every line has an associated gene in the publication. Furthermore, there are sample specific differences that could be explored in the discussion.
2. In Supplementary Figure 1, higher magnification inserts are needed to get a closer look at the IHC. Furthermore, the white balance is not the same in all the images and needs to be corrected prior to publication. The difference in white balance can clearly be seen in the last panels depicting IHC for RMS335, where the MYOD1 staining has a yellow background whereas the H&E staining has a white background.
3. The authors mentioned in line 202 that some of their organoids contain the novel fusion of PAX3 and WWTR1, but this fusion is not indeed novel as it has previously been seen in biphenotypic sinonasal sarcoma (Am J Surg Pathol 2019, 43:747-754).

Significance

As has been mentioned previously, this research is impactful to the field of RMS biology because the authors were successfully able to use organoid technology, which has not previously been reported. The authors do a great job of listing current RMS modelling techniques and explaining how their system addresses the pitfalls of the others. Furthermore, this protocol could be expanded to the development of other organoid systems for other sarcomas. The rhabdomyosarcoma field and larger sarcoma community would be keenly interested in this work. It is clear that this system has the potential for use in pre-clinical settings as well as in high-throughput screens, but further validation and increased rigor is required on both fronts.

It is astounding and the authors should be complimented that they were able to show a median time from patient to drug screen was 81 days! This has enormous potential such as rapid translation of therapies and personalized medicine. That said, the authors must first refine the heterogeneity of the organoids and demonstrate how the organoids reflect the phenotypic and cellular plasticity of the parent tumors. Furthermore, the authors ought to be careful when making priority claims. The authors state that this is the first system to use organoids but should recognize the advances demonstrated by Manzella et al. (Nat Commun, 2020, 11:4629). Additionally, the authors state that this is the first demonstration of pre-clinical models harboring FGFR4V550L mutations; this fails to recognize the prior reported work by several groups (Chen et al., Cancer Cell, 2013, 24:710-24; Manzella et al., Nat Commun, 2020, 11:4629; McKinnon et al., Oncogene, 2018, 37:2630-2644).

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Reply to the reviewers

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

In this manuscript by Wang and colleagues, the authors analyse single-cell RNA-seq (scRNAseq) data by applying transition path theory to infer gene regulatory network (GRN) changes along the transition (reaction coordinate, trajectory) between free energy stable states (i.e. cell types). The work aims to understand how stable cell types, and their regulatory programs (combination of active and repressed genes) switches during differentiation/reprogramming/response (i.e. cell phenotypic transition/CPT). The premise of the work is to assess whether genes within GRNs undergo step-wise repression, state-change and activation (& vice-versa; analogous to SN1) or concurrently regulate gene expression (analogous to SN2). The GRNs are inferred based on highly variable genes and their expression dynamics from RNA velocity over CPT, across 3 scRNA-seq datasets.

The authors first analyse public scRNA-seq dataset of 3003 human A549 adenocarcinomic basal epithelial cells treated with TGF-b for 0hrs, 8hrs, 1 day and 3 days (4 timepoints). The authors select two stable states (Day0-untreated; Epithelial and Day 3-treatment; Mesenchymal) using local kernel densities and set transition paths using Dijkstra shortest path, dividing state space into Voronoi cells (i.e. reaction coordinate value), and constructed single-cell GRNs based on RNA velocity differences (n=500 genes) and a linear model (from Qiu et al). This GRN is based on expression and velocity estimates, and does not distinguish direct from indirect regulation. Calculating interaction frequency (edges) across two stable states over 4 louvain clusters, the authors find global increase in effective edges that correlates with increased active genes; but with variable trend within inter-cluster edges. To quantify the concerted GRN changes between clusters, the authors utilise a "frustration" score (from Tripathi et al 2020). The average frustration score increases and peaks at day 1 treatment, followed by a decline over terminal stable state (day 3-treatment); similar to interaction frequency trends. The author also separately measure network heterogeneity and repeat analysis using alternative transition matrix. The authors conclude that EMT proceeds through concerted regulation of multiple genes first with an increase in inter-cluster edges, frustration and heterogeneity followed by a decrease into final stable state. The authors apply the analysis to scRNA-seq data from (i) pancreatic endocrine differentiation where Ngn3-low progenitors give rise to Ngn3-high, then Fev-high and into glucagon producing a-endocrine cells; (ii) dendate gyrus; radial glial cell differentiation into nIPCs, neuroblast, immature granule and mature granule cells. In both cases, the authors observe concerted regulation with initial increase in inter-community edges, heterogeneity during differentiation followed by decrease towards final stable state. **

The study and ideas in the manuscript are interesting and the methods would be potentially be useful. However, there are a few specific and general comments stated below, which the authors should try to address.

1 • P4: "RC increases first and reaches a peak when cells were treated with TGF-β for about one day, then decreases (Fig. 1G)". It would be better to label the figure with the treatment information. *

Reply: Thanks for your advice. In the revised manuscript, we analyzed two additional datasets, and moved the EMT result in the supplemental Fig. EV8. In the new Fig. 1d, we marked the cell types along the reaction coordinate.

*2 • Fig. 1G and EV1D: Why are the trends different? *

Reply: In the original figures, ____Fig____.1g is the frustration score and EV1D shows the variation of pseudo-Hamiltonian along the reaction coordinate. The frustration score is the focus of this work. We also calculated the pseudo-Hamiltonian since it has been used in the literature. However, we realized that showing both of the results might lead to confusion, so we deleted all pseudo-Hamiltonian results in the revised manuscript.

* 3 • How is the appropriate community/cluster/Louvain resolution selected? This can have a major impact on number of cell states, types and transition path from initial to final state. *

Reply: The number of cell states, types and transition path from initial to final state____ are not determined from the community/cluster/Louvain analyses. For the EMT data, we assume most cells in the initial treatment time are epithelial cells, and those in the final time point are mesenchymal cells. For other datasets, we followed the original publications to assign cell types based on known marker expression.

The Louvain method was applied to coarse grain the gene regulation network, and it does not affect the number of cell states, types and transition path, which were determined separately. To address the reviewer’s question, we also use the Leiden method to adjust the resolution ____(1)____. The resolution does not affect the result. The results are added to Fig. EV12. We tried three different resolution values 0.8,1.0 and 1.2. The number of inter-community edges consistently shows the trend that it increases first then decreases.

Figure EV12 Cell-specific variation of the number of effective inter-community edges between communities calculated with different resolution parameter values for dentate gyrus neurogenesis (a), pancreatic endocrinogenesis (b), and bone marrow marrow hematopoiesis (c). Each dot represents a cell and the color represents the number of inter-community edges____.

• * What effect does the Louvain resolution have on e.g. frustration scores? * Reply: The resolution of community division algorithm doesn’t affect the frustration scores, since the frustration score is based on the gene-gene interactions instead of community assignment.

• * The authors match resolution to samples/timepoints/known prior cell types i.e. 3-4 communities. However it is unclear whether this is enough to describe entire differentiation/transition process. * Reply: This is a good question. In one above reply we have explained how the cell types were determined____. We also agree with the reviewer that these coarse-grained communities cannot reflect the overall heterogeneity and dynamics of the whole process. Notice in most of our analyses (e.g., reaction coordinate and transition paths), we treated the transition as continuous and the distribution of single cell data points in all datasets cover the whole space that involved in cell phenotype transition. The coarse-grained analyses are for further mechanistic insights on how gene regulatory networks are reorganized during the transition process.

• * Gene selection: The selection based on minimum 20 counts as highly expressed genes is arbitrary and dependent on sequencing depth. Perhaps the authors could show distribution of gene counts for the datasets and have a data-driven filtering criteria * Reply: Thanks for the advice. The number 20 is a default value suggested in the package (scVelo) we use, and in another package dynamo the default number is 30. Following the reviewer’s suggestion (together with the next question on the influence of all highly variable genes), we looked for a data-drive filtering criterion. The method has been described in different tools ____(2-4)____. We first grouped the genes into 20 bins by their mean expression values, and____ scaled their dispersions by subtracting the mean of dispersions and dividing standard deviation of dispersions____. Figure EV9 shows the distribution of the minimum shared counts. ____As one can see, most genes counts are larger than 10, and using a smaller value causes error in the following velocity analysis. Therefore we set the minimum shared counts as 10 in the new results.

Figure EV9 Shared counts distribution of the datasets. (a) Dentate gyrus neurogenesis; (b) Pancreatic endocrinogenesis; (c) Bone marrow hematopoiesis.

• * The choice of 500 variable genes (for human A549 cells) is also quite arbitrary. Perhaps, the authors could compare how additional genes (all highly variable genes) affects their analysis and interpretation. * Reply: ____Thanks. Following previous question on shared counts and ____data-driven filtering criteria____,____ we take all the highly variable genes into consideration. The details of gene selection and binarization are given in the Materialss and Methods (Materials and Methods 2) section.

• * How are other factors (sequencing depth, genes detected, #of cell types, multiple branches) affects the connectivity between communities at different phases of transition/development? * Reply: This is a good question. The A549 EMT dataset has a sequence depth of 40000-50000. The ____dentate gyrus neurogenesis dataset____ has a sequence depth of 56,700 reads. A saturation depth would be close to 1,000,000, but there is a compromise between cell number and depth. There are genes that are not detected even under the saturation reads setting. That is why the preprocessing is needed. On the other hand, the network we inferred include both direct and indirect interaction, so the influence of sequence depth and gene number detected can be reduced to a certain extent. We used a random subset of the selected gene and performed the same analyses. The results are consistent with what we obtained using all the genes (Fig. EV11b). With the new gene selection criteria (Materials and Method 2), our analyses are not related with the number of cell types.

We did analysis on another beta branch of pancreatic endocrinogenesis data. The other branches show the same results (Fig. EV4). There are two additional branches in the pancreatic endocrinogenesis dataset. It has been reported that the RNA velocity estimation for the epsilon branch is incorrect ____(3)____. There are too few cells in the delta branch for reliable analyses. Therefore we didn’t present results for these two branches.

Figure EV4 Analyses on the branch of glucagon producing β-cells in pancreatic endocrinogenesis.

(a) Transition graph based on RNA velocity.

(b) The RCs and corresponding Voronoi cells. The large colored dots represent the RC points (start from blue and ends in red). The small dots represent cells with color as cell type.

(c) Frustration score along the RCs.

(d) Cell-specific variation of effective intercommunity regulation. Each dot represents a cell. Color represents the number of effective intercommunity edges within each cell in the GRN.

• • Are the velocity graph, transition matrix and further shortest path estimation derived in a reduced latent space, and if so, how much (nPCs) and what impact does it have. Presumably, the density estimation is not performed in expression space. Reply: Yes. ____The calculation of transition matrix is based on neighbor information. The calculation of neighbors was in the reduced latent space in scVelo and Dynamo. We performed the same analysis by varying number of principal components. The results are similar because the first several components account for large proportion of variance. Figure R1 shows the results of dentate gyrus neurogenesis with the number of principal components being 10, 20 and 30, respectively. In the revised manuscript, we delete the step of using density estimation constrain to simplify the procedure. __Figure R1 Frustration scorer along RCs (left) and cell specific variation of number of effective intercommunity edges (Each dot represents a cell and color represents the number of effective intercommunity edges) in the GRN within each cell (right) when using different number of PCs in analyses (dentate gyrus neurogenesis): (a) number of PCs is 10.*__

(b) number of PCs is 20. (c) number of PCs is 30

* - The figure legends and labels were hard to read. These should be improved for better readability. *

Reply: Thanks. We modified the figure legends and labels.

* - A suggestion would be move the initial results section to methods and highlight the biological interpretation. *

Reply: Thanks for your advice. We moved large part of this section to the Materials and Methods.

*The authors could highly which GRN and representative genes/edge pairs are highest ranked within inter-community and to overall final stable states. *

Reply: Thanks. We list some representative gene pairs in the Table. EV 2&EV 3 &EV 4 for different datasets. And we performed gene enrichment analysis for each community.

* - How does the GRN inference compare to current state-of-the-art GRN inference scRNA-seq methods? *

Reply: we used the method GRISLI to perform the same analysis ____(5)____. The results are similar to what obtained with our current method (Figure EV6). We want to emphasize that the focus of this work is not on another GRN inference method, but discussing some general principles of GRN reorganization during a cell phenotypic transition process.

Figure EV6 Analyses of datasets of dentate gyrus neurogenesis (a), pancreatic endocrinogenesis (b), and hematopoiesis (c) based on GRN inferred with GRISLI.

(a) Frustration score along the RCs of dentate gyrus neurogenesis (left) and cell-specific variation of the number of inter-community edges (right). Each dot represents a cell and color represents the number of inter-community edges in GRN within each cell.

(b) Same as in panel (a), except for pancreatic endocrinogenesis.

(c) Same as in panel (a), except for hematopoiesis.

* - How do extremely noisy/stochastic genes vary in metrics between final stable states? How are the metrics affected by number of cells and stochasticity of expression within a given cluster/community. *

Reply: To address this question, we selected two genes, Id2 and Cdkn1c, with high variance and compare their distributions in the initial and final states. ____The gene distributions show significant shift between the Ngn3 low EP cells and Alpha cells (Fig. R2 a &b left).____ Then we randomly selected a subset (half) of cells and compared the distributions of these high-variance genes in the sub-population (Fig. R2 a&b right). The results are similar to the full-set results.

Fig. R2 Comparison of gene distribution in the initial and final states in pancreatic endocrinogenesis. (a) Comparison of the distribution of gene Id2 at the initial and final states (left), and in the randomly selected sub-population at the initial and final states (right). (b) Comparison of the distribution of Cdkn1c at the initial and final states (left), and in the randomly selected sub-population at the initial and final states (right).

* - Given that the author's approach includes both direct and indirect genes effects, the authors could further prune genes based on existing TF databases or protein-protein validated networks. *Reply: This is a good suggestion. We will work on this idea in future work. As we mentioned, due to constrains of data quality, only tens of transcription factors can be analyzed in these dataset. We list some regulations of transcription factors inferred with current method in Table EV1.

• *It is unclear which GRNs are already known and which ones are novel and biologically relevant * Reply: We compare some regulations inferred with the method and compare these interactions w____ith some references in Table. EV1____.

* - It would be good for authors to comment when there are multiple bifurcations instead of A-B transitions. Particularly in datasets with multiple discrete stable states. *Reply: This is a good question.____ In our analysis, we focus on the transition from one stable state to another stable state. For transition process with multiple bifurcations like____ the pancreatic endocrinogenesis, the results are similar across different branches. For the transition that goes through multiple discrete stable states, for example, a transition from state A____à____B____à____C, we expect to observe two peaks in the frustration score and the number of inter-community edges. We added some discussions in the Discussion section.

• *Another suggestion would be to highlight gene expression of selected markers based on f-regression and mi over the trajectory * Reply: As we modified the criteria of gene selection, we plotted trajectories of some high-variance genes versus the reaction coordinate obtained with different datasets in Fig. EV10 based on current criteria.

Figure EV10 ____Typical trajectories of high variance genes versus RCs of dentate gyrus neurogenesis (a), pancreatic endocrinogenesis (b) and bone marrow ____hematopoiesis ____(c).

* - If possible, a proof of principle could be re-analysis of a perturbation scRNA-seq dataset (e.g. where one path/transition path is stalled) *

Reply: Thanks. This is a really a good suggestion. We will perform more systematic studies in future work.

* Reviewer #1 (Significance (Required)): Nature and significance of advance: The study and ideas in the manuscript are interesting and the methods would be potentially be useful to community. Compare to existing published knowledge: *

*Audience: Predominantly computational audience *

*Your Expertise: PI with background in experimental, computational biology and expertise in single-cell genomic tools and developmental biology *

*

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

Understanding the cellular and molecular basis of cell type or cell state transitions occurring during development or reprogramming is a fundamental challenge. scRNA-seq has provided a window into gene expression programs across thousands of cells undergoing such transitions. Wang and colleagues leverage scRNA-seq and develop an approach to reverse engineer gene regulatory network underlying cells along a path from one cell type/state to another, and characterize community-level properties of this network associated with various stages of the cell phenotype transition. The study is innovative and rigorous, and their results point to how intercommunity interactions increase and then decrease, indicating a concerted regulatory rewiring that orchestrates transitions. Application of their approach to three different datasets also shows that this trend is consistent across three different transitions and maybe a general trend. However, there are some major and minor concerns that need to be addressed.

**Major comments and questions**

1. The analogy to SN1 and SN2 mechanisms of chemical bond formation is very nice.
2. What is the basis for the two statements made in paragraph 3 of Introduction (beginning with "A question arises ...") about transitions being sequential or concurrent? Please *Reply: Thanks. We added references in this paragraph.

* 2.1. Provide references to previous experimental and computational studies that have investigated developmental and reprogramming gene expression programs. *

Reply: Thanks. We added a paragraph in the Introduction.

*

2.2. Describe specific examples of findings that support the two possible transitions highlighted here. Why couldn't transitions happen through an entirely gradual process involving changes to overlapping subsets of genes. *

Reply: Thanks. In the review paper of Naomi Moris et. al., they proposed the hypothesis that cell phenotype transition is similar to a chemical reaction ____(6)____. Thus we extrapolate this hypothesis and test it in our study. For the example of SN1 mechanism, ____Kalkan et al. showed that mouse embryonic stem cells can exit from ____naïve pluripotency____ but remain uncommitted ____(7)____.

Just like the SN1 and SN2 mechanisms are two extremes in chemical reactions and there are cases lie in between, for cell phenotypic transitions we agree with the reviewer that such gradual process may exist. Actually the result in Fig. EV4d shows that the frustration score remains flat for the Fev+ ____à____ Beta transition, suggesting a possible gradual process. With the analyses provided in this work, such as the reaction coordinate, frustration score, heterogeneity, and inter-/intra- community edges, one may perform more systematic studies on a larger number of datasets and enumerate/classify possible patterns of transitions.

• Please make plots of the number of effective intra-community edges vs. number of active genes to support the statement that these two numbers are correlated. *

Reply: We plotted the corresponding intra-community active genes and calculated its correlation coefficient with the number of effective intra-community edges in dentate gyrus neurogenesis (Fig. EV1d). ____The correlation coefficients are 0.91,0.96, 0.99 and 0.96 for community 0, 1, 2 and 3 separately.

* A bunch of notations are not clear:

4.1. What is the "r" in "strongest intercommunity interactions at r = 10 (Fig. 1F)"? Is it the same as the "r" mentioned in the Methods section? *

Reply: r____ is the index number of the discretized reaction coordinate. We added it when we define the reaction coordinate. We modified the conflict usage of r in Materials and Method 4.

4.2. What is "s_i" in "cell-specific effective matrix, Fbar_ij = (2*s_i - 1)*F_ij"? Also, that description of F_ij, f_ij, and H should be moved to the Methods section, and a more high-level, intuitive description should instead be included in this Results paragraph. Reply: represent the binarized gene expression state. is 0 for when gene is in low expression level (silence) and is 1 when gene is in high express level (active). We modified this part following your advice.

* How were the h_f and h_m thresholds chosen? *

Reply: and are based on the distribution of each dataset. Following suggestions from another reviewer, we modified this part. All the highly variable genes were selected and the genes were binarized with the Silverman’s bandwidth method and ____K____means (Materials and Methods 2).

* What is the "density of each single cell" ("⍴_t")? The formulation of the penalty of the distance between cells i and j (the expression with -logP_ij...) is unclear. What is the intuition behind it? What is r? How were the values of r (0.5 and 0.8) chosen? *

Reply: The probability density of cells in the expression space is based on the kernel density estimation. Intuitively, a region in the expression space with more cells is more likely passed by more cell trajectories. The values are based on the distribution of kernel density estimation in different datasets.

In the modified manuscript, we used trajectory simulation and deleted this assumption for simplification.

* One of the reasons the authors state to justify the choice of PLSR is "In the scRNA dataset, the number of genes is often comparable to or larger than the number of cells." This is not true most of the time. In nearly all recent studies, the number of cells is way larger than the number of genes measured. *

Reply: The PLSR method definitely can be used for the data whose number of cells is larger than the number of genes. Also the PLSR method was applied on cells that are the k nearest neighbors of each reaction coordinate, which are a subset of the whole dataset (Materials and Methods 5). While we mainly presented results with the PLSR method, in this revised manuscript we also added results with another method of GRISLI (Materials and Methods 9). The results are similar with what we obtained with PLSR.

* There is a fleeting reference to a nice previous finding that supports their observations: "several lines of evidence support that EMT proceeds through a concerted mechanism. Indeed, both in vivo and in vitro studies have identified intermediate states of EMT that have co-expressed epithelial and mesenchymal genes (Pastushenko et al, 2018; Zhang et al, 2014)". The authors should thoroughly survey the literature related to EMT transition, development of pancreatic endocrine cells, and development of the granule cell lineage in dentate gyrus, to find more previously identified molecular/cellular features relevant to cell state/type transitions, compared and contrasted with findings from this study. *

Reply: Thanks. We added references on these cell phenotype transitions and modified the corresponding part. We do want to point out that the main focus of this work is that all these processes share a common feature of transient increase of intercommunity interactions.

* What is the "dynamo" package, which is supposed to contain a Python notebook? As of now, the code and data have not been made available. Both need to be released along with thorough documentation on how to run the code to reproduce the analyses described here. *Reply: Thanks. Dynamo is a python package accompanying our recent publication ____(8)____. We uploaded the code on Github and added the link of Dynamo.

* **Minor comments and questions**

1. Replace "confliction" throughout the manuscript with "conflict" or "conflicting" as appropriate. *

Reply: Thanks. We modified them.

* Paragraph two of the Introduction (beginning with "Another example of transitions ...") is missing multiple references, esp. for the last four sentences. *

Reply: Thanks. We added references.

* There are direct quotes from previous papers like "predicts the future state of individual cells on a timescale of hours". The authors are highly encouraged to check for usage of exact phrasing using available text software such as iThenticate. *

Reply____: ____Thanks a lot for pointing out this severe mistake. We re-edited the manuscript and checked with iThenticate. *

*

• "Each community contains both E and M genes": what does this mean? *

Reply: The E (M) genes are defined as those genes that are active or have high expression levels in epithelial (mesenchymal) state or sample. As we reorganized the manuscript, we add this explanation for all datasets in the caption of Fig.1i.*

*

• Reference to Qui 2021 is missing in the "Path analysis" subsection under Methods. *

Reply: We added it in the Methods.

* Fix: "transition between the cells that their sample time points are successive" in Methods. *

Reply: Thanks. ____We modified it.

* In Methods, under "Network inference", it is "partial least square regression" (not *least* s square). *

Reply: Thanks. We modified it.

* Figure 1: The cyan, magenta, and lime in 1C are very hard to see and, perhaps, the grey of the points can be made lighter. Also, change the red and green colors for the arrows in 1I to something else. These colors are not colorblind-friendly. *

Reply: Thanks. We re-plotted the figures and changed the colormap.*

*

• Periods and commas are missing at several places. Reply: Thanks. We modify these and re-edit the manuscript.

Reviewer #2 (Significance (Required)):

The study uses RNA-velocity calculated from scRNA-seq data in an inventive way to characterize paths that reflect cell phenotype transitions. Then, a sparse gene regulatory network is reverse engineered from the data and the community structure within this network is examined at various stages along the transition to make observations about inter- and intra-community regulation and network "frustration". However, the study lacks the context of existing literature in terms of previous work studying cell transitions both experimentally and computationally. Adding this context (as suggested in the comments) will considerably improve the utility and significance of the findings. Overall, this study will be of broad interest to researchers interested in development and reprogramming as well as computational scientists developing and applying methods for scRNA-seq data analysis, trajectory inference, and network reconstruction. All the comments and questions raised here are based on my background and expertise in omics data (including scRNA-seq) analysis and network biology.

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

The authors analyze three datasets of Single cell RNA velocity measured during phenotypic transition. They infer the gene regulatory network in each case and characterize the transition between the initial and final expression states (in which different sets of genes are expressed). Their motivating question was to find whether during such transitions first genes characterizing the initial state are no longer expressed and only then the genes associated with the final state start expressing or alternatively there is gradual transition through an intermediate state in which subsets of both initial and final state genes are transiently expressed.

They define a measure of regulatory frustration representing the mismatch between regulatory signals a gene receives and its current expression state. They conclude that phenotypic transitions involve transient interactions between otherwise non-interacting gene modules and a temporary increase of gene frustration, which is relaxed once the final expression state is reached.

The study uses of advanced inference and machine learning methods.

I find the question studied in this manuscript interesting, opening avenue to further questions and studies and relevant to different scientific communities. Personally I think that the focus of the paper should be the exposition of the methods used this manuscript would benefit from a longer format, but that depends of course on the journal they are aiming at. *

*

Statistical analysis is missing. Especially since the authors mention the potential of over-fitting due to large number of genes (on the order of the number of cells) - I think the authors should provide a sensitivity analysis testing how sensitive are the conclusions to the choice of cells or genes by applying the methods to subsets of the cells / genes. *

Reply: Thanks. For the subset of cells, we randomly selected cells from the dataset and performed the analyses (Fig. EV11a). For the subset of genes, we selected a subset of genes randomly and performed the analyses (Fig. EV 11b). We found the results are not affected. We also perform another statistical analysis by varying the value of resolution in community detection algorithm. And we found that the conclusion on variation of inter-community edges is not affected (Fig. EV12).

Figure EV11 Statistical analyses of dentate gyrus neurogenesis. Each dot represents a cell and color represents the number of inter-community edges.

(a) Frustration score along the RCs (left) and cell-specific variation of the number of inter-community edges (right) of a randomly selected sub-population of 2000 cells (from a total of 3184 cells);

(b) Frustration score along the RCs (left) and cell-specific variation of the number of inter-community edges) (right) of cells on the space of 400 randomly selected genes (from a total of 678 genes).

*What is the meaning of the distribution in the frustration plots? *

Reply: For each cell we calculated a frustration score. Therefore for cells in each Voronoi cell (which is a geometric cell, don’t be confused with the biological “cells”) along the reaction coordinate (Fig.1d, Fig. 2b &2g), we obtained a distribution of the frustration scores.*

In general, the conclusions are well-justified, but I think some statements in the discussion are inaccurate: "intercommunity interactions of a GRN are indeed minimized' - are they minimal or are they only lower at the stable states? There are two stable states - for which of them is intercommunity interaction lower? *

Reply: Thank. We agree with the reviewer and modified the writing. Comparing with the transition state, the number of intercommunity interactions is less for the stable states. ____The datasets' quality are not high enough for us to investigate whether ____"intercommunity interactions of a GRN are indeed minimized”.*

It is written in the discussion that 'for all three datasets frustration decreases with differentiation', but then Fig. 1g shows the opposite (final state is more frustrated than initial state). It is interesting to discuss the differences between the datasets analyzed in that respect and what could cause transition to a more frustrated state. I suggest that the authors also refer in the discussion to related questions and possible follow-up studies, such as: what determines the duration of the phenotypic transition? A relevant number is the switching time of a single gene. *

Reply: Good suggestion. Compared to other datasets, we found that the result of EMT shows larger variances. The relative difference of the frustration score is also affected by the GRN inference algorithm. For example, the difference between initial and final frustration scores of the pancreatic endocrinogenesis is more significant when using the GRISLI method (Figure EV6b). Given these, the trend that the frustration scores in the transition states transiently increase keep consistent.

Our conclusion is limited by the quality of the data. So we delete this part of discussion in the manuscript.

Qiu et al. have shown that splicing-based ____RNA velocities are relative, while metabolic-labeling-based RNA velocities are more quantitative and accurate____(8)____. We will re-analyze this problem if data with metabolic labeling becomes available.

* The authors mention at the end that the networks can often reach multiple final states from a common initial states. Do such transitions share some of their path (and in particular the intermediate frustrated state)? Given the intermediate connected state, it would be interesting to characterize the network stability to perturbations. *

Reply: This is a very important question. To reliably address these questions, we need higher quality data. We plan to characterize the network stability to perturbations in future studies, while in our recent paper using a full nonlinear modeling framework____(8)____, we performed in silico perturbations.

* While interesting, the manuscript itself is unfortunately hard to read and would benefit from major editing, including better exposition of the science and language editing. *

Reply: Thanks. We revised the manuscript extensively.*

Methods: Description of PCA and 'revised finite temperature string method' are missing in the Methods section. *

Reply:____ Thanks. PCA is used in RNA velocity analysis for dimension reduction. We added this in Materials and Methods 3. The revised string method is in Materials and Methods ____4.

*

Some examples:

Figure captions are very short and often non-informative. Some variables are not defined (or only defined later on) and the reader then needs to guess their meaning: it took me a while to understand what is 'r' in Fig. 1f and what 'r=10' (p. 4) means. *

Reply: Thanks. ____r____ represents the index number of reaction coordinates. We added this in the manuscript where we define reaction coordinates.*

p. 4: what are 'f' (as opposed to F) and 's_ij' and 's_j' (expression states?) Or is fs_ij one variable? What does a Hamiltonian of a cell mean (p. 4, bottom)? *

Reply: is the regulation of gene ____j on gene i, and is the expression state of gene i (0 for silence, and 1 for active expression). is the frustration value of regulation from gene j to gene i.

The pseudo Hamiltonian value is proposed in the literature as an analogy of ____the magnetic systems following the work of Boolean model in EMT ____(9)____. A high Hamiltonian value indicates that the cell is in an unstable state. In the original manuscript we included this quantity since it has been discussed in the literature. However we found it causes confusion and is not necessary for our discussions, so we removed the pseudo-Hamiltonian results in the revised manuscript. * P. 4: how are 'E and M genes' defined? *

Reply: The E (M) genes are defined as those genes that are active or have high expression levels at the epithelial (mesenchymal) state or sample. We explained our general strategy in the caption of Fig.1i . * What does 'network heterogeneity' (p. 5) mean? *

Reply: Network heterogeneity measures how homogenously the connections are distributed among the genes____(10)____. A high heterogeneity ____means that some genes have high degree of connectivity (the so-called hubs), while some have low degree of connectivity.

*

Fig. 1 is too tiny and hard to read and details are missing. *

Reply: Thanks. We modified this figure and caption.*

A glossary for all the acronyms used would be very helpful. *

Reply: Thanks. We added glossary in the manuscript.*

Language (some examples):

p. 5 bottom: Another system is on development... invitro -> in vitro

p. 6: 'measure on developmental potential' -> measure of... *

Reply: Thanks. We modified these and re-edited the whole manuscript.*

Reviewer #3 (Significance (Required)):

This study presents a methodological advance in demonstrating the application of data analysis methods to study developmental phenotypic transitions. High throughput measurements and computation power available today enable putting to test theoretical conjectures, as made by Waddington. I think this is a promising line of research, which could be used to further develop the computational methods as well as to further our understanding of developmental transitions and potentially develop associated mathematical modeling frameworks.

This study should be of interest to a diverse readership composed of developmental biologists as well as to quantitative biologists and CS researchers applying optimization techniques and data analysis methods to high-throughput biological data.

I am not an expert on the computational methods applied in this manuscript and hence cannot assess their correct use and statistical analysis.

*

1. Traag VA, Waltman L, & van Eck NJ (2019) From Louvain to Leiden: guaranteeing well-connected communities. Scientific Reports 9(1):5233.
2. Stuart T, et al. (2019) Comprehensive Integration of Single-Cell Data. Cell 177(7):1888-1902.e1821.
3. Bergen V, Lange M, Peidli S, Wolf FA, & Theis FJ (2020) Generalizing RNA velocity to transient cell states through dynamical modeling. Nature Biotechnology 38(12):1408-1414.
4. Wolf FA, Angerer P, & Theis FJ (2018) SCANPY: large-scale single-cell gene expression data analysis. Genome Biology 19(1):15.
5. Aubin-Frankowski P-C & Vert J-P (2020) Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference. Bioinformatics (Oxford, England) 36(18):4774-4780.
6. Moris N, Pina C, & Arias AM (2016) Transition states and cell fate decisions in epigenetic landscapes. Nature reviews. Genetics 17(11):693-703.
7. Kalkan T, et al. (2017) Tracking the embryonic stem cell transition from ground state pluripotency. Development 144(7):1221-1234.
8. Qiu X, et al. (2022) Mapping Transcriptomic Vector Fields of Single Cells. Cell 185(4):690-711.
9. Font-Clos F, Zapperi S, & La Porta CAM (2018) Topography of epithelial–mesenchymal plasticity. Proceedings of the National Academy of Sciences 115(23):5902-5907.
10. Gao J, Barzel B, & Barabási A-L (2016) Universal resilience patterns in complex networks. Nature 530(7590):307-312.

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Referee #3

Evidence, reproducibility and clarity

The authors analyze three datasets of Single cell RNA velocity measured during phenotypic transition. They infer the gene regulatory network in each case and characterize the transition between the initial and final expression states (in which different sets of genes are expressed). Their motivating question was to find whether during such transitions first genes characterizing the initial state are no longer expressed and only then the genes associated with the final state start expressing or alternatively there is gradual transition through an intermediate state in which subsets of both initial and final state genes are transiently expressed.

They define a measure of regulatory frustration representing the mismatch between regulatory signals a gene receives and its current expression state. They conclude that phenotypic transitions involve transient interactions between otherwise non-interacting gene modules and a temporary increase of gene frustration, which is relaxed once the final expression state is reached.

The study uses of advanced inference and machine learning methods.

I find the question studied in this manuscript interesting, opening avenue to further questions and studies and relevant to different scientific communities. Personally I think that the focus of the paper should be the exposition of the methods used this manuscript would benefit from a longer format, but that depends of course on the journal they are aiming at.

Statistical analysis is missing. Especially since the authors mention the potential of over-fitting due to large number of genes (on the order of the number of cells) - I think the authors should provide a sensitivity analysis testing how sensitive are the conclusions to the choice of cells or genes by applying the methods to subsets of the cells / genes.

What is the meaning of the distribution in the frustration plots?

In general, the conclusions are well-justified, but I think some statements in the discussion are inaccurate: "intercommunity interactions of a GRN are indeed minimized' - are they minimal or are they only lower at the stable states? There are two stable states - for which of them is intercommunity interaction lower?

It is written in the discussion that 'for all three datasets frustration decreases with differentiation', but then Fig. 1g shows the opposite (final state is more frustrated than initial state). It is interesting to discuss the differences between the datasets analyzed in that respect and what could cause transition to a more frustrated state. I suggest that the authors also refer in the discussion to related questions and possible follow-up studies, such as: what determines the duration of the phenotypic transition? A relevant number is the switching time of a single gene.

The authors mention at the end that the networks can often reach multiple final states from a common initial states. Do such transitions share some of their path (and in particular the intermediate frustrated state)? Given the intermediate connected state, it would be interesting to characterize the network stability to perturbations. While interesting, the manuscript itself is unfortunately hard to read and would benefit from major editing, including better exposition of the science and language editing.

Methods: Description of PCA and 'revised finite temperature string method' are missing in the Methods section.

Some examples:

Figure captions are very short and often non-informative. Some variables are not defined (or only defined later on) and the reader then needs to guess their meaning: it took me a while to understand what is 'r' in Fig. 1f and what 'r=10' (p. 4) means.

p. 4: what are 'f' (as opposed to F) and 's_ij' and 's_j' (expression states?) Or is fs_ij one variable? What does a Hamiltonian of a cell mean (p. 4, bottom)?

P. 4: how are 'E and M genes' defined?

What does 'network heterogeneity' (p. 5) mean?

Fig. 1 is too tiny and hard to read and details are missing.

A glossary for all the acronyms used would be very helpful.

Language (some examples):

p. 5 bottom: Another system is on development... invitro -> in vitro

p. 6: 'measure on developmental potential' -> measure of...

Significance

This study presents a methodological advance in demonstrating the application of data analysis methods to study developmental phenotypic transitions. High throughput measurements and computation power available today enable putting to test theoretical conjectures, as made by Waddington. I think this is a promising line of research, which could be used to further develop the computational methods as well as to further our understanding of developmental transitions and potentially develop associated mathematical modeling frameworks.

This study should be of interest to a diverse readership composed of developmental biologists as well as to quantitative biologists and CS researchers applying optimization techniques and data analysis methods to high-throughput biological data.

I am not an expert on the computational methods applied in this manuscript and hence cannot assess their correct use and statistical analysis.

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Referee #2

Evidence, reproducibility and clarity

Understanding the cellular and molecular basis of cell type or cell state transitions occurring during development or reprogramming is a fundamental challenge. scRNA-seq has provided a window into gene expression programs across thousands of cells undergoing such transitions. Wang and colleagues leverage scRNA-seq and develop an approach to reverse engineer gene regulatory network underlying cells along a path from one cell type/state to another, and characterize community-level properties of this network associated with various stages of the cell phenotype transition. The study is innovative and rigorous, and their results point to how intercommunity interactions increase and then decrease, indicating a concerted regulatory rewiring that orchestrates transitions. Application of their approach to three different datasets also shows that this trend is consistent across three different transitions and maybe a general trend. However, there are some major and minor concerns that need to be addressed.

Major comments and questions

1. The analogy to SN1 and SN2 mechanisms of chemical bond formation is very nice.
2. What is the basis for the two statements made in paragraph 3 of Introduction (beginning with "A question arises ...") about transitions being sequential or concurrent? Please

2.1. Provide references to previous experimental and computational studies that have investigated developmental and reprogramming gene expression programs.

2.2. Describe specific examples of findings that support the two possible transitions highlighted here. Why couldn't transitions happen through an entirely gradual process involving changes to overlapping subsets of genes.

1. Please make plots of the number of effective intra-community edges vs. number of active genes to support the statement that these two numbers are correlated.
2. A bunch of notations are not clear:

4.1. What is the "r" in "strongest intercommunity interactions at r = 10 (Fig. 1F)"? Is it the same as the "r" mentioned in the Methods section?

4.2. What is "s_i" in "cell-specific effective matrix, Fbar_ij = (2s_i - 1)F_ij"? Also, that description of F_ij, f_ij, and H should be moved to the Methods section, and a more high-level, intuitive description should instead be included in this Results paragraph.

1. How were the h_f and h_m thresholds chosen?
2. What is the "density of each single cell" ("⍴_t")? The formulation of the penalty of the distance between cells i and j (the expression with -logP_ij...) is unclear. What is the intuition behind it? What is r? How were the values of r (0.5 and 0.8) chosen?
3. One of the reasons the authors state to justify the choice of PLSR is "In the scRNA dataset, the number of genes is often comparable to or larger than the number of cells." This is not true most of the time. In nearly all recent studies, the number of cells is way larger than the number of genes measured.
4. There is a fleeting reference to a nice previous finding that supports their observations: "several lines of evidence support that EMT proceeds through a concerted mechanism. Indeed, both in vivo and in vitro studies have identified intermediate states of EMT that have co-expressed epithelial and mesenchymal genes (Pastushenko et al, 2018; Zhang et al, 2014)". The authors should thoroughly survey the literature related to EMT transition, development of pancreatic endocrine cells, and development of the granule cell lineage in dentate gyrus, to find more previously identified molecular/cellular features relevant to cell state/type transitions, compared and contrasted with findings from this study.
5. What is the "dynamo" package, which is supposed to contain a Python notebook? As of now, the code and data have not been made available. Both need to be released along with thorough documentation on how to run the code to reproduce the analyses described here.

Minor comments and questions

1. Replace "confliction" throughout the manuscript with "conflict" or "conflicting" as appropriate.
2. Paragraph two of the Introduction (beginning with "Another example of transitions ...") is missing multiple references, esp. for the last four sentences.
3. There are direct quotes from previous papers like "predicts the future state of individual cells on a timescale of hours". The authors are highly encouraged to check for usage of exact phrasing using available text software such as iThenticate.
4. "Each community contains both E and M genes": what does this mean?
5. Reference to Qui 2021 is missing in the "Path analysis" subsection under Methods.
6. Fix: "transition between the cells that their sample time points are successive" in Methods.
7. In Methods, under "Network inference", it is "partial least square regression" (not least s square).
8. Figure 1: The cyan, magenta, and lime in 1C are very hard to see and, perhaps, the grey of the points can be made lighter. Also, change the red and green colors for the arrows in 1I to something else. These colors are not colorblind-friendly.
9. Periods and commas are missing at several places.

Significance

The study uses RNA-velocity calculated from scRNA-seq data in an inventive way to characterize paths that reflect cell phenotype transitions. Then, a sparse gene regulatory network is reverse engineered from the data and the community structure within this network is examined at various stages along the transition to make observations about inter- and intra-community regulation and network "frustration". However, the study lacks the context of existing literature in terms of previous work studying cell transitions both experimentally and computationally. Adding this context (as suggested in the comments) will considerably improve the utility and significance of the findings. Overall, this study will be of broad interest to researchers interested in development and reprogramming as well as computational scientists developing and applying methods for scRNA-seq data analysis, trajectory inference, and network reconstruction. All the comments and questions raised here are based on my background and expertise in omics data (including scRNA-seq) analysis and network biology.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript by Wang and colleagues, the authors analyse single-cell RNA-seq (scRNAseq) data by applying transition path theory to infer gene regulatory network (GRN) changes along the transition (reaction coordinate, trajectory) between free energy stable states (i.e. cell types). The work aims to understand how stable cell types, and their regulatory programs (combination of active and repressed genes) switches during differentiation/reprogramming/response (i.e. cell phenotypic transition/CPT). The premise of the work is to assess whether genes within GRNs undergo step-wise repression, state-change and activation (& vice-versa; analogous to SN1) or concurrently regulate gene expression (analogous to SN2). The GRNs are inferred based on highly variable genes and their expression dynamics from RNA velocity over CPT, across 3 scRNA-seq datasets.

The authors first analyse public scRNA-seq dataset of 3003 human A549 adenocarcinomic basal epithelial cells treated with TGF- for 0hrs, 8hrs, 1 day and 3 days (4 timepoints). The authors select two stable states (Day0-untreated; Epithelial and Day 3-treatment; Mesenchymal) using local kernel densities and set transition paths using Dijkstra shortest path, dividing state space into Voronoi cells (i.e. reaction coordinate value), and constructed single-cell GRNs based on RNA velocity differences (n=500 genes) and a linear model (from Qiu et al). This GRN is based on expression and velocity estimates, and does not distinguish direct from indirect regulation. Calculating interaction frequency (edges) across two stable states over 4 louvain clusters, the authors find global increase in effective edges that correlates with increased active genes; but with variable trend within inter-cluster edges. To quantify the concerted GRN changes between clusters, the authors utilise a "frustration" score (from Tripathi et al 2020). The average frustration score increases and peaks at day 1 treatment, followed by a decline over terminal stable state (day 3-treatment); similar to interaction frequency trends. The author also separately measure network heterogeneity and repeat analysis using alternative transition matrix. The authors conclude that EMT proceeds through concerted regulation of multiple genes first with an increase in inter-cluster edges, frustration and heterogeneity followed by a decrease into final stable state. The authors apply the analysis to scRNA-seq data from (i) pancreatic endocrine differentiation where Ngn3-low progenitors give rise to Ngn3-high, then Fev-high and into glucagon producing -endocrine cells; (ii) dendate gyrus; radial glial cell differentiation into nIPCs, neuroblast, immature granule and mature granule cells. In both cases, the authors observe concerted regulation with initial increase in inter-community edges, heterogeneity during differentiation followed by decrease towards final stable state.

The study and ideas in the manuscript are interesting and the methods would be potentially be useful. However, there are a few specific and general comments stated below, which the authors should try to address.

• P4: "RC increases first and reaches a peak when cells were treated with TGF-β for about one day, then decreases (Fig. 1G)". It would be better to label the figure with the treatment information. • Fig. 1G and EV1D: Why are the trends different? • How is the appropriate community/cluster/Louvain resolution selected? This can have a major impact on number of cell states, types and transition path from initial to final state. • What effect does the Louvain resolution have on e.g. frustration scores? • The authors match resolution to samples/timepoints/known prior cell types i.e. 3-4 communities. However it is unclear whether this is enough to describe entire differentiation/transition process. • Gene selection: The selection based on minimum 20 counts as highly expressed genes is arbitrary and dependent on sequencing depth. Perhaps the authors could show distribution of gene counts for the datasets and have a data-driven filtering criteria • The choice of 500 variable genes (for human A549 cells) is also quite arbitrary. Perhaps, the authors could compare how additional genes (all highly variable genes) affects their analysis and interpretation. • How are other factors (sequencing depth, genes detected, #of cell types, multiple branches) affects the connectivity between communities at different phases of transition/development? • Are the velocity graph, transition matrix and further shortest path estimation derived in a reduced latent space, and if so, how much (nPCs) and what impact does it have. Presumably, the density estimation is not performed in expression space.

• The figure legends and labels were hard to read. These should be improved for better readability.
• A suggestion would be move the initial results section to methods and highlight the biological interpretation. The authors could highly which GRN and representative genes/edge pairs are highest ranked within inter-community and to overall final stable states.
• How does the GRN inference compare to current state-of-the-art GRN inference scRNA-seq methods?
• How do extremely noisy/stochastic genes vary in metrics between final stable states? How are the metrics affected by number of cells and stochasticity of expression within a given cluster/community.
• Given that the author's approach includes both direct and indirect genes effects, the authors could further prune genes based on existing TF databases or protein-protein validated networks.
• It is unclear which GRNs are already known and which ones are novel and biologically relevant
• It would be good for authors to comment when there are multiple bifurcations instead of A-B transitions. Particularly in datasets with multiple discrete stable states.
• Another suggestion would be to highlight gene expression of selected markers based on f-regression and mi over the trajectory
• If possible, a proof of principle could be re-analysis of a perturbation scRNA-seq dataset (e.g. where one path/transition path is stalled)

Significance

Nature and significance of advance: The study and ideas in the manuscript are interesting and the methods would be potentially be useful to community.

Compare to existing published knowledge: -

Audience: Predominantly computational audience

Your Expertise: PI with background in experimental, computational biology and expertise in single-cell genomic tools and developmental biology

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Reply to the reviewers

We are very grateful to the three referees for their constructive comments and suggestions which have helped improve the quality of our manuscript.

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

In the publication HAT-field: a very cheap, robust and quantitative point-of-care serological test for Covid-19 by Joly and Ribes the authors describe an adaption and an improved protocol to their previously published haemagglutination based test to detect antibodies to SARS-CoV-2 in patient blood (Towsend et al., 2021). In detail, they analyzed the effect of several adaptions including buffer optimization, plate coating, usage of patient whole blood instead of washed RBCs and plasma. Additionally they tested different temperatures and stability of the reagents, namely the nanobody-RBD construct IH4-RBD. For validation they compared their optimized HAT-field assay with Jurkat-S&R as a FACS-based assay.

Major comments:

Introduction: This section is rather short and could benefit from a broader overview of currently established methods and assays to detect appropriate immune responses against SARS-CoV-2. The author are advised to summarize the current literature in the field more comprehensively and not only focus on their own work.

Response: Hundreds of different tests to monitor immune responses against SARS-CoV-2 have been described to date, and the literature on these various tests is vast, with new articles coming out almost on a daily basis. We would not feel either that the introduction of our rather technical paper would benefit from being lengthened by such a review of the current literature, or even competent to carry out such a summary. Following the referee’s suggestion, we have, however, introduced a new sentence and given three references providing relatively recent overviews on the subject of immune-monitoring.

Cross-reactivity with IH4-RBD. In Figure 6, the authors highlight the samples in red and orange that showed cross-reactivity with IH4-RBD. In their discussion, however, the authors state that only 2 of 60 (3%) were cross-reactive. In making this statement, they ignore the proportion of cross-reactive samples that were also positive in the Jurkat S&R assay. Therefore, the authors should acknowledge in the discussion that the actual number of cross-reactive samples was higher.

Response*: The statement in the discussion about 2 cross reactive samples out of 60 concerns the results obtained after an incubation of one hour under normal gravity, and not the two red dots in each of the three graphs of figure 6, which correspond to the two negative samples which gave false-positive results in HAT plasma titrations after spinning (Figure 6C), for which we correctly state in the discussion that 12 samples showed cross-reactivity on IH4 alone. The data presented in Figure 6B corresponds to HAT-field after spinning, for which we correctly state in the discussion that 5 out of 60 showed cross-reactivity (4 orange dots + 1 red dot, the second red dot having a score of 0, in accordance with the fact that this sample showed no cross reaction on IH4 alone in HAT-field after spinning). *

*To try to prevent this possible confusion, we have now clarified what data we are referring to at the start of that paragraph in the discussion. *

Quantitative Assay. Since the HAT assay does not allow determination of the absolute number of antibodies reactive to SARS-CoV-2 in the blood samples, the authors should refrain from claiming that the HAT-field is a quantitative assay.

Response*: Since immune sera are inherently polyclonal, they contain a multitude of different types of antibodies of different affinities and avidities, and we are not aware of any technique that allows to determine the “absolute number” of antibodies directed against a given antigen in such samples. *

*For many serological tests, including ELISA and the initial protocol of HAT, serum or plasma titrations are used as a means to obtain what is widely considered as a quantitative evaluation of the amounts of antibodies in blood samples. Even FACS-based assays such as the Jurkat-S&R-flow test we have used, are commonly considered as quantitative but those only provide relative results and not absolute numbers. *

We perceive that the close correlations we find between the results of the HAT-field protocol and those of the Jurkat-S&R-flow test as well as with serum titrations using the standard HAT protocol warrants considering the results of HAT-field as being as quantitative as those obtained with all those other tests.

Morphological read out For field application, the morphological description of the observed deposits ("teardrop" vs. "button") could be problematic and might lead to bias depending on the user. Thus, the authors should provide a clearer description for phenotype classification.

Response: We have now introduced a specific paragraph detailing how to score HAT assays in the Methods section, as well as a new figure providing a graphic description of positive, partial and negative RBCs deposits.

Minor comments: Title: the authors should remove "very"

Response*: We have now removed the word ‘very’ from the title, and thank the referee for this helpful suggestion. *

By the way: What are the costs of IH4-RBD for a 96 well plate? Who will produce this reagent? Is the sequence of the IH4 fully disclosed?

Response*: As specified in our original paper (see Townsend et al. 2021), the plasmid coding for the IH4-RBD is available upon request from Alain Townsend (Oxford, UK). Furthermore, his laboratory funded the production of 1 gram of the IH4-RBD reagent by a commercial company, and professor Townsend has been graciously sending aliquots of 1 mg of this reagent, which suffice for several thousand tests, to all the laboratories that have requested it from him. *

*In its initial format, HAT only required 100 ng of IH4-RBD per well, corresponding to a cost of 0.0027 £ per well. For the HAT-field protocol, 5 times more reagent is needed, thus bringing the cost of the reagent to 1.5 cts per test, to which one would have to add a similar cost for the IH4-reagent alone. This would thus bring the cost of the two reagents to approximately 3 cts, which is still lower than the price of any of the cheap disposable plasticware necessary for the test (lancet, pipet, plastic tube and portion of a plate). *

The sequence of the IH4 nanobody is indeed fully disclosed (see figure 1 of Townsend et al. 2021), and has actually been protected by a patent ( US9879090B2 ). Whilst IH4 can be used freely for research purposes, licensing rights would have to be taken into consideration by any health authority wishing to use the technique broadly, or for any commercial distribution.

The usage of the CR3022 as positive control for neutralizing antibodies should be reconsidered since this antibody does not confer viral neutralization. Other well describe antibodies blocking the ACE2:RBD interface might be better suited.

Response*: CR3022 was the one that we had at our disposal, but other mAbs can certainly be used instead of as positive controls, and this is actually indicated in the detailed HAT-field protocol provided. Since the use of a positive control is only to ensure that the IH4-RBD has not been degraded and works as well as expected, and that any negative samples are not due to a very rare glycophorin mutation that could prevent IH4 from binding to it at the surface of RBCs, we are not sure why using a mAb with neutralizing activity would necessarily be better than the CR3022 mAb. *

Figure 2: Please state the concentration of IH4-RBD used. As stated in the figure legends for Figure 2 B, the authors should show the result all 4 replicates (incl. SD)

Response: The concentration of IH4-RBD was 1 m*g/ml, i.e. the normal concentration for standard HAT tests. This was already indicated in the Methods section, but has now been added to the legend of Figure 2. *

Whilst 4 experiments were indeed carried out, which all gave similar results, i.e. showed that using PBS-N3 or PBN did not hinder HAT performance, but could instead result in a slight increase in HAT sensitivity, those various experiments were not all exact replicates of the experiment shown on figure 2. Furthermore, performing of those various experiments was spread over a period of over a year, using different reagents, thus precluding numerical comparisons between the various results. We have clarified this issue by rewording the final statement to “Comparable results were obtained in four similar experiments.”

Figure 3: Although the authors showed stability of IH4-RBD at 2 µg/ml they do not provide data for the stabilities at higher dilutions. As the authors suggest to predistribute the IH4-RBD in plates they should at least discuss this issue.

We thank the referee for raising this valid point, which has now been discussed in the paragraph entitled “Practical considerations for performing HAT assays” in the Methods section: “One aspect that will have to be considered for the design and use of such individual strips of wells will be to ensure that, upon storage, the various dilutions of IH4-RBD are as stable in such strips as the working stocks of IH4-RBD (2 mg/ml) tested in Figure 3.”

Figure 6/Supplementary Figure 1 and 3 The presentation of the data is not accurate, as many of the points (samples) are obviously identically positioned in the graph. The authors should choose a different representation of their data. E.g. they could adjust the size of the points to the number of overlapping samples.

Response: We thank the referee for raising this issue, which was also pointed to by referee #2. This apparent inaccuracy is due to the fact that, on these plots, the scales for both x and Y axes used discrete values, which indeed results in multiple points overlapping on top of one another. This was resolved by adding numbers next to the positions where several dots overlapped

Wording / text length In the current manuscript the text is very long. Thus, the authors should shorten it to report the essential findings more appropriately. Additionally they should check for correct English wording.

Response*: We thank the referee for this remark, which helped us realize that the excessive length of the manuscript was mostly due to an extensive discussion of highly technical and practical points. The corresponding paragraphs were indeed out of place in the general discussion, and have not been deleted but have been moved to the Methods section since we feel that they contain very important information for people who would actually start to performing HAT assays. *

Reviewer #1 (Significance (Required)):

In summary, the authors describe the HAT-field test as a simple PoC test for the detection of SARS-CoV-2 antibodies in patients. Because of its ease of use and robustness, the test appears to be particularly well suited for use in countries with underdeveloped health care or limited testing facilities, as also reported previously. The value of this manuscript lies mainly in the detailed description of the protocol and its validation. In this context, the adaptations described are certainly useful and helpful from a practical point of view, but do not provide significant new scientific insights. In light of these considerations, we recommend that this work be submitted to an appropriate journal specializing in the publication of such methods

Expertise The reviewers have established and published different serological assays to monitor immune responses against SARS-CoV-2

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

In this paper, the authors developed a feasible protocol for an affordable point-of-care serological test for SARS-CoV-2. This method was adapted from the HAT plasma titration test that the authors previously published. Specifically, the test utilizes a 96-well plate pre-coated with the RBD of SARS-CoV-2 spike glycoprotein fusing to a red blood cell targeting nanobody (IH4). By adding microliters amount of the blood or plasma samples to the plate, it allows the detection of antibodies against RBD by measuring the level of hemagglutination. In the current upgraded protocol (so called HAT-field), the authors made major modifications including optimizations of buffer and experimental protocol and the use of pre-titrated IH4-RBD on the plate, which collectively helped to lower the sample consumptions, improved the stability and the sensitivity of detection, and made the test more user-friendly under non-clinical settings.

Major comments: My major concerns are related to the robustness and quantitative capability of this approach. Specifically: It seems that multiple variables may impact the results. These include volume of droplets, the presence/absence of serum IH4 or BSA cross-reactive antibodies, and the amount (%) of red blood cells which may vary substantially among samples. Could you find a way to normalize the results (e.g., the discrepancy shown in Figure 6) instead of only leaving them as false-positives or false-negative?

Response*: Regarding the volume of the droplets, in other words, the amount of blood collected and used in an assay, two sentences in the manuscript underline the fact that this is not a critical variable: *

In the Results section “the precise volume of blood collected is not critical; it may vary by as much as 30% with no detectable influence on the results.”

In the discussion: “On this subject, we have found that increasing the amount of whole blood per well (in other words using blood that is less dilute) has very little influence over the HAT-field results, and, if anything, adding more blood can sometimes reduce the sensitivity, albeit never by more than 1 dilution.”

Consequently the % of RBCs in samples seem unlikely to influence the HAT-field scores significantly. This is supported by the fact that, although men tend to have higher hematocrits than women, we have not noticed any detectable difference between men and women in the correlation of the HAT-field scores with those of the Jurkat-S&R-flow test.

We are not sure that we fully understand what discrepancy shown in Figure 6 the referee is pointing to, but if it is about the increase in the number of samples found to be cross reacting on IH4 alone when the sensitivity increases, in the discussion, we propose to perform tests using titrations of the IH4 nanobody alone simultaneously to using the IH4-RBD reagent, so as to minimize the number of samples that would be identified as false positives if only one concentration of IH4 alone was used as negative control. Comparing the titers obtained with IH4-RBD and IH4 alone will then provide some level of normalization for the samples cross reacting on IH4. As for the hypothetical presence of antibodies cross reacting on BSA alluded to by the referee, since such antibodies would not bind to RBCs, we do not think they would affect the HAT results.

Second, the score of the HAT-field ranges from 0 - 8. However, based on the current manuscript, it is not clear how the scoring and scaling works. How is the noise (non-specific antibody signal) defined here?

Response: We have now introduced a specific paragraph and a new figure detailing how to score HAT assays in the Methods section.

In addition, it is unclear how to translate the HAT-field score into a meaningful measure of protection by serum antibodies.

Response*: Documenting the correlation between HAT-field scores and levels of protection against SARS-CoV-2 infections and/or Covid-19 severity would indeed be extremely interesting. This would, however, require setting up a large scale clinical trial carried out over several months. This type of work could only be carried out by a large consortium including clinicians or even preferably a national health agency. This was, however, far beyond the reach of this initial project, which was based on the work of a single person on a shoestring budget. *

Can you provide more evidence to demonstrate that the test is quantitative? For example, performing additional orthogonal experiments to better validate the scoring and generate a correlation function?

Response*: Inasmuch as it would have been very interesting to perform additional serological tests from commercial sources on the samples of our cohort, such tests are all very expensive (e.g. ca. 500 € for one ELISA plate). This was in fact the main reason for developing the Jurkat-S&R-flow test in the first place, since it is much cheaper, more modular, and at least as sensitive as ELISA (see Maurel Ribes et al. 2021). The funds for this whole project came from a single 15 k€ grant obtained from the ANR, and we simply did not have access to the funds, or to the human resources to carry out such experiments based on commercial serological tests. *

Minor comments: Figure 6: are all results included? To me, it does not seem that all 60 samples data were included in the plot.

Response: We thank the referee for raising this issue, which was also pointed to by referee #1. This apparent inaccuracy is due to the fact that the scales for both x and Y axes used discrete values, which results in multiple points overlapping on top of one another. This was resolved by adding numbers next to the positions where several dots overlapped.

There are several redundant statements in the discussion and results section. Please make the text more concise.

Response: The discussion has now been shortened considerably, mostly by moving the paragraphs pertaining to technical considerations to the Methods section.

Reviewer #2 (Significance (Required)):

The current paper is built upon the improvement of previous published work. In addition, there are similar approaches that have been published. It was unclear if the current method is superior to other works.

Response: Whilst we have made no statement regarding whether the method we describe is superior to other methods, we are pretty confident that very few alternatives will be as frugal and simple as the HAT-field protocol described here. As alluded to in the final paragraph of the discussion, two recent reports have described that HAT could be performed on cards rather than in V-shaped wells, with semi-quantitative results being obtained in minutes. If such card-based approaches turn out to provide sensitivity and reliability comparable to those of the HAT-field protocol, they will certainly represent very interesting alternatives. As stated in our manuscript, we would be very interested if the comparative evaluation of the two approaches could be carried out by one or several independent third party.

My research involves the development of antiviral antibody therapeutics. This method may be used as a point-of-care tool for the measurement of serologic response to RBD in less developed countries. However, due to the high vaccination rate and large infected populations, the overall needs for such detection drastically decrease. The significance of the work and utilities of the test may expand with more experiments related to the variants.

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

This paper describes a low-cost robust and quantitative serological test based on haemgglutination, which could be used in resource limited settings for evaluating population-based and vaccine induced immunity. Neutralising antibodies to the receptor binding domain (RBD) on the SARS CoV-2 spike protein are an immunological correlate of protection. The HAT has a single reagent the RBD domain of SARS CoV-2 linked to a monomeric anti-erythrocyte single domain nanobody. When human polyclonal serum antibodies bind to the RBD they cross-link and agglutinate human red blood cells, resulting in haemagglutination which can be read visually.

This paper thoroughly evaluate the stability of the HAT reagents used to measure human and monoclonal antibodies examining the robustness of the HAT reagent. It provides a comprehensive protocol for conducting field based HAT with limited reagents. The test can evaluate is subjects have been infected using a simple finger prick to detect RBD specific antibodies. The field HAT can also be used to define people that can be susceptible to reinfection or in need of vaccination, With the use of RBDs from the variants of concern the test can be rapidly adapted to evaluate antibodies as new variants arise to evaluate surrogate correlates of protection to allow timely evaluation of vaccine effectiveness and predict the need for vaccine booster doses. The data are very comprehensively presented with good figures demonstrating the most appropriate buffer to store the IH4-RBD reagent and the robustness of the HAT over time at different temperatures. No additional experiments are needed and suitable numbers of replicates are included. All data, methods and reagents are comprehensively described.

Minor comments: The paper is well written but rather long in places and may have benefited from being more succinct.

Response: The excessive length of the manuscript was mostly due to an extensive discussion of highly technical and practical points. The discussion has now been shortened considerably, mostly by moving the paragraphs pertaining to technical considerations to the Methods section.

Panels in figures could be labelled as A, B, C etc to help in identifying the correct panel..

Response: We thank the referee for this helpful suggestion, which we have followed.

I would avoid the use of experiment and project and refer to next we confirmed... or in this paper or our results show Please make sure all abbreviation are defined upon first use. Perhaps include early in the paper that most of the work was conducted with the Wuhan RBD

Response: We thank the referee for these helpful suggestions, which we have followed to the best of our abilities. The abstract now contains a mention of the fact the work on optimizing the protocol was carried out with the IH4-RBD carrying the Wuhan version.

Figure 2: I would suggest placing either a solid line between the two halves of the plates to make it easier for the reader to differentiate between the two antibodies. It also would have been easier to read if the bottom PBS, PBS-N3 and PBN were at 45 degree angle. In B include the serum name (e.g. serum 197).

Response: We thank the referee for these helpful suggestions, which we have followed.

Legend to figure 4: please include the serum numbers after covid-19 patients. Perhaps include arrows to demonstrate the dilutions of serum and IH4-RBD in the figure.

Page 6 it might be easiest to use the same times as in figure 6 and use for example more than one year in the discussion

Response: We thank the referee for these helpful suggestions, which we have all followed.

Legend figure 6 perhaps replace dots with circles page 10 include the R values from figure 6 in the description of results.

Response: We are grateful to the referee for these helpful suggestions, but have not followed them since we do not feel that these changes would be real improvements.

Page 12 of note perhaps this can be moved to the methods ?

Response: This, and several other paragraphs of the Discussion, have now been moved to the Methods section.

Supplementary figure 2 A can be seen, is something missing here?

Response: An s was indeed missing : “A can be seen” corrected to “As can be seen “

*

Reviewer #3 (Significance (Required)):

This paper describes a simple rapid field test for evaluating antibodies to the receptor binding domain of the spike of SARS CoV-2 using the Wuhan and delta variant. Whilst high income countries can provide booster doses and extensive testing (either lateral flow or RT-PCR based) and contact racing to control the waves of the pandemic, low income countries have had limited access to Covid vaccine and the extent of previous waves of the pandemic in the populations are unknown.

This paper describes a robust and simple test for investigating human antibodies to SARS-CoV-2 which could be performed in resource limited settings providing a very useful tool for monitoring infection in the community and potentially for prioritising this scarce COVID-19 vaccines available.

This study builds upon the work conducted on the HAT and has extensively studied and optimised the test so that it could be used globally. This paper provides a comprehensive protocol and has simplified the test to ensure it could be used in LMICs.

This paper would be of great interest to a wide scientific audience who are interested in a rapid low-cost test to evaluate population based and vaccine induced immunity.

Reviewer: serological assays for use in virology and vaccinology. Suitable competence to review the whole paper *

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Referee #3

Evidence, reproducibility and clarity

This paper describes a low-cost robust and quantitative serological test based on haemgglutination, which could be used in resource limited settings for evaluating population-based and vaccine induced immunity. Neutralising antibodies to the receptor binding domain (RBD) on the SARS CoV-2 spike protein are an immunological correlate of protection. The HAT has a single reagent the RBD domain of SARS CoV-2 linked to a monomeric anti-erythrocyte single domain nanobody. When human polyclonal serum antibodies bind to the RBD they cross-link and agglutinate human red blood cells, resulting in haemagglutination which can be read visually.

This paper thoroughly evaluate the stability of the HAT reagents used to measure human and monoclonal antibodies examining the robustness of the HAT reagent. It provides a comprehensive protocol for conducting field based HAT with limited reagents. The test can evaluate is subjects have been infected using a simple finger prick to detect RBD specific antibodies. The field HAT can also be used to define people that can be susceptible to reinfection or in need of vaccination, With the use of RBDsfrom the variants of concern the test can be rapidly adapted to evaluate antibodies as new variants arise to evaluate surrogate correlates of protection to allow timely evaluation of vaccine effectiveness and predict the need for vaccine booster doses. The data are very comprehensively presented with good figures demonstrating the most appropriate buffer to store the IH4-RBD reagent and the robustness of the HAT over time at different temperatures. No additional experiments are needed and suitable numbers of replicates are included. All data, methods and reagents are comprehensively described.

Minor comments:

The paper is well written but rather long in places and may have benefited from being more succinct.

Panels in figures could be labelled as A, B, C etc to help in identifying the correct panel..

I would avoid the use of experiment and project and refer to next we confirmed... or in this paper or our results show

Please make sure all abbreviation are defined upon first use.

Perhaps include early in the paper that most of the work was conducted with the Wuhan RBD

Figure 2: I would suggest placing either a solid line between the two halves of the plates to make it easier for the reader to differentiate between the two antibodies. It also would have been easier to read if the bottom PBS, PBS-N3 and PBN were at 45 degree angle. In B include the serum name (e.g. serum 197).

Legend to figure 4: please include the serum numbers after covid-19 patients. Perhaps include arrows to demonstrate the dilutions of serum and IH4-RBD in the figure.

Page 6 it might be easiest to use the same times as in figure 6 and use for example more than one year in the discussion Legend figure 6 perhaps replace dots with circles page 10 include the R values from figure 6 in the description of results.

Page 12 of note preps this can be moved to the methods Supplementary figure 2 A can be seen, is something missing here?

Significance

This paper describes a simple rapid field test for evaluating antibodies to the receptor binding domain of the spike of SARS CoV-2 using the Wuhan and delta variant. Whilst high income countries can provide booster doses and extensive testing (either lateral flow or RT-PCR based) and contact racing to control the waves of the pandemic, low income countries have had limited access to Covid vaccine and the extent of previous waves of the pandemic in the populations are unknown.

This paper describes a robust and simple test for investigating human antibodies to SARS-CoV-2 which could be performed in resource limited settings providing a very useful tool for monitoring infection in the community and potentially for prioritising this scarce COVID-19 vaccines available.

This study builds upon the work conducted on the HAT and has extensively studied and optimised the test so that it could be used globally. This paper provides a comprehensive protocol and has simplified the test to ensure it could be used in LMICs.

This paper would be of great interest to a wide scientific audience who are interested in a rapid low-cost test to evaluate population based and vaccine induced immunity.

Reviewer: serological assays for use in virology and vaccinology. Suitable competence to review the whole paper

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Referee #2

Evidence, reproducibility and clarity

In this paper, the authors developed a feasible protocol for an affordable point-of-care serological test for SARS-CoV-2. This method was adapted from the HAT plasma titration test that the authors previously published. Specifically, the test utilizes a 96-well plate pre-coated with the RBD of SARS-CoV-2 spike glycoprotein fusing to a red blood cell targeting nanobody (IH4). By adding microliters amount of the blood or plasma samples to the plate, it allows the detection of antibodies against RBD by measuring the level of hemagglutination. In the current upgraded protocol (so called HAT-field), the authors made major modifications including optimizations of buffer and experimental protocol and the use of pre-titrated IH4-RBD on the plate, which collectively helped to lower the sample consumptions, improved the stability and the sensitivity of detection, and made the test more user-friendly under non-clinical settings.

Major comments:

My major concerns are related to the robustness and quantitative capability of this approach.

Specifically:

It seems that multiple variables may impact the results. These include volume of droplets, the presence/absence of serum IH4 or BSA cross-reactive antibodies, and the amount (%) of red blood cells which may vary substantially among samples. Could you find a way to normalize the results (e.g., the discrepancy shown in Figure 6) instead of only leaving them as false-positives or false-negative? Second, the score of the HAT-field ranges from 0 - 8. However, based on the current manuscript, it is not clear how the scoring and scaling works. How is the noise (non-specific antibody singal) defined here? In addition, it is unclear how to translate the HAT-field score into a meaningful measure of protection by serum antibodies. Can you provide more evidence to demonstrate that the test is quantitative? For example, performing additional orthogonal experiments to better validate the scoring and generate a correlation function?

Minor comments:

Figure 6: are all results included? To me, it does not seem that all 60 samples data were included in the plot.

There are several redundant statements in the discussion and results section. Please make the text more concise.

Significance

The current paper is built upon the improvement of previous published work. In addition, there are similar approaches that have been published. It was unclear if the current method is superior to other works. My research involves the development of antiviral antibody therapeutics. This method may be used as a point-of-care tool for the measurement of serologic response to RBD in less developed countries. However, due to the high vaccination rate and large infected populations, the overall needs for such detection drastically decrease. The significance of the work and utilities of the test may expand with more experiments related to the variants.

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Referee #1

Evidence, reproducibility and clarity

In the publication HAT-field: a very cheap, robust and quantitative point-of-care serological test for Covid-19 by Joly and Ribes the authors describe an adaption and an improved protocol to their previously published haemagglutination based test to detect antibodies to SARS-CoV-2 in patient blood (Towsend et al., 2021). In detail, they analyzed the effect of several adaptions including buffer optimization, plate coating, usage of patient whole blood instead of washed RBCs and plasma. Additionally they tested different temperatures and stability of the reagents, namely the nanobody-RBD construct IH4-RBD. For validation they compared their optimized HAT-field assay with Jurkat-S&R as a FACS-based assay.

Major comments:

Introduction: This section is rather short and could benefit from a broader overview of currently established methods and assays to detect appropriate immune responses against SARS-CoV-2. The author are advised to summarize the current literature in the field more comprehensively and not only focus on their own work.

Cross-reactivity with IH4-RBD. In Figure 6, the authors highlight the samples in red and orange that showed cross-reactivity with IH4-RBD. In their discussion, however, the authors state that only 2 of 60 (3%) were cross-reactive. In making this statement, they ignore the proportion of cross-reactive samples that were also positive in the Jurkat S&R assay. Therefore, the authors should acknowledge in the discussion that the actual number of cross-reactive samples was higher.

Quantitative Assay. Since the HAT assay does not allow determination of the absolute number of antibodies reactive to SARS-CoV-2 in the blood samples, the authors should refrain from claiming that the HAT-field is a quantitative assay.

Morphological read out For field application, the morphological description of the observed deposits ("teardrop" vs. "button") could be problematic and might lead to bias depending on the user. Thus, the authors should provide a clearer description for phenotype classification.

Minor comments:

Title: the authors should remove "very" By the way: What are the costs of IH4-RBD for a 96 well plate? Who will produce this reagent? Is the sequence of the IH4 fully disclosed?

The usage of the CR3022 as positive control for neutralizing antibodies should be reconsidered since this antibody does not confer viral neutralization. Other well describe antibodies blocking the ACE2:RBD interface might be better suited.

Figure 2: Please state the concentration of IH4-RBD used. As stated in the figure legends for Figure 2 B, the authors should show the result all 4 replicates (incl. SD)

Figure 3: Although the authors showed stability of IH4-RBD at 2 µg/ml they do not provide data for the stabilities at higher dilutions. As the authors suggest to predistribute the IH4-RBD in plates they should at discuss this issue.

Figure 6/Supplementary Figure 1 and 3 The presentation of the data is not accurate, as many of the points (samples) are obviously identically positioned in the graph. The authors should choose a different representation of their data. E.g. they could adjust the size of the points to the number of overlapping samples.

Wording / text length In the current manuscript the text is very long. Thus, the authors should shorten it to report the essential findings more appropriately. Additionally they should check for correct English wording.

Significance

In summary, the authors describe the HAT-field test as a simple PoC test for the detection of SARS-CoV-2 antibodies in patients. Because of its ease of use and robustness, the test appears to be particularly well suited for use in countries with underdeveloped health care or limited testing facilities, as also reported previously. The value of this manuscript lies mainly in the detailed description of the protocol and its validation. In this context, the adaptations described are certainly useful and helpful from a practical point of view, but do not provide significant new scientific insights. In light of these considerations, we recommend that this work be submitted to an appropriate journal specializing in the publication of such methods

Expertise The reviewers have established and published different serological assays to monitor immune responses against SARS-CoV-2

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Referee #3

Evidence, reproducibility and clarity

Summary:

Estrach and colleagues seek to identify the ECM components that are key to regulating hair follicle stem cell (HFSC) activation using the highly-characterized mouse hair follicle as a model. They first use a targeted approach to examine key ECM components expressed by HFSC and find that Fibronectin (FN) is highly expressed. Further, wholemount analysis of the hair follicle reveals a meshwork of FN enveloping the hair follicle. They hypothesize that FN is a fundamental regulator of hair follicle (HF) cycling and then proceed to carry out longterm studies required to examine hair follicle cycling and knockout FN with two different HFSC Cre lines (Lrig1 and Krt19), as well as integrin coreceptor SLC3A2. They clearly show that absence of Fibronectin (FN) and SLC3A2 is detrimental to hair follicle stem cell activation and cycling (FN) and hair follicle identity (SLC3A2).

Overall comments:

The authors use the tail hair follicles as a model similarly to the highly-characterized, synchronous back skin hair follicles. However, the tail hair follicles are asynchronous (Braun et al. 2003, PMID: 12954714), thus reporting the age of the mouse from which the tail whole mounts came from is not sufficient to claim a HF cycle disorder - HF should be imaged in an unbiased manner and subsequently quantified for phase. The manuscript would greatly benefit from including more information in the figure legends, such as age of mice, number of mice and HF quantified, as well as what the error bars represent. Further, in samples where many HF were counted per mouse, these should be averaged and then the average per mouse displayed; super plots would be great to use here.

Major comments:

1. In Figure 1, the use of tail whole mount images indeed provides striking display of the fibronectin meshwork that envelops the hair follicle. However, addition of a marker of the regenerative phase (e.g. proliferation) and resting phase would provide more convincing evidence that this is the particular phase of the hair cycle that you have captured, especially given my overall comment regarding the asynchronous nature of the tail HF cycle.
2. The authors show that FN is expressed in early-mid anagen and conclude that FN is a regenerative signal. This claim should be substantiated with FN staining on more time points across the HF cycle to substantiate the argument that it is a regeneration-specific signal, found only in the telogen-anagen transition.
3. Lrig1-cre and K19-cre-mediated FN knockout result in HF that are thinner at D158 - this is not immediately apparent from histological sections. Can you use your thick sections to give better perspective?
4. The authors measure the width of the infundibulum from lightsheet microscope images. It is a bit difficult to position whole tissues using this technique, and the images that are shown are not from the same perspective, and thus measurement of the width is not accurate from these images. I suggest either removing this analysis or using more comparable images. Further, if this is a true phenotype, can you speculate on what the thickened infundibulum might mean?
5. The authors then show mislocalization of Lrig1+ cells to the infundibulum in absence of FN. Are other stem markers localized to the infundibulum or outside of the bulge? Further, what might the mislocalization of Lrig1+ cells might mean?
6. Please explain your conclusion after Figure 3i and at the end of the manuscript that states that FN is required for stem cell anchorage. I think that a very plausible explanation is that FN is required for stem cell function and identity, but anchorage of the SC lacks sufficient evidence. Further, your only evidence to support the anchoring theory only comes from expression of Lrig1 in FN knockout and no other markers. Are they also mislocalized? Please either tone down this conclusion on SC anchorage or provide stainings for more SC markers to show mislocalization in absence of FN.
7. In Figure 3l-o, you examine proliferation on the control vs the conditional deletion of FN in D30 and D158. However, in D30, these tissues are not at all directly comparable since one is obviously in anagen and the knockout in telogen. You must compare the anagen knockout sample, although this occurs a bit later than the control. Further, how was the infundibulum distinguished from the bulb in these control images?
8. In Figure 3P, you carry out RT-qPCR on whole skin to detect HFSC markers. This should have been carried out on sorted epithelial cells as isolation of whole back skin introduces bias to the system in that the number of stem cells may artificially look different in skin that is in anagen vs skin that is in telogen as the anagen skin has a different proportion of SC to progenitor cells to dermal cells. This concern is also similar to point 9 - the control and FN knockout at D30 are not comparable given that they are in different phases of the hair cycle.
9. Figure 4a these images need to be of the whole mouse - it is not possible to determine what we are looking at or where - there is not even a scale bar.
10. After Figure 4, you argue that because fibronectin expression resolves from healing dermis is the reason that hair follicles do not form, and site Dekonick and Blanpain (PMID: 30602767) - however this review makes no mention of the dynamics of fibronectin in wound healing. Further, evidence from Driskell et al (2013, PMID: 30602767) would suggest that it is the fibroblast population that responds to the wound that determines whether HF regenerate. And further, very large wounds do regenerate HF (Ito et al PMID: 30602767). In addition, this would all be fibroblast-derived FN, as opposed to the current study which examines keratinocyte-derived FN. Please reconsider this argument.
11. The authors knockout SLC3A2, an integrin coreceptor that is localized to the plasma membrane. They show a very similar, yet more severe phenotype to the Lrig1- and K19- mediated knockout of FN. Given the bidirectional communication that SLC3A2 is responsible for, can you reconcile whether the defects in the HF cycle and the HFSC are a result of outside-in or inside-out signaling? Further, is it possible that integrin function regulated by SLC3A2 is necessary for more than FN assembly? This could be especially relevant given that your targeted screen also identified Col17A1, which is well known to be required for HFSC function (Matsumura et al., PMID: 26912707)
12. It is intriguing that in the absence of HFSC-derived SLC3A2 that no FN network forms. Is FN expressed or is the assembly perturbed in the absence of properly functioning integrins? The authors conclude that the signaling cascade flows from fibronectin to integrin to SLC3A2, but do not test where the FN phenotype arises in the SLC3A2 knockout - is it due to aberrant assembly of the FN meshwork or a change in transcriptional or translational levels?
13. In the grafting assay in Supplemental Figure 3, keratinocytes undergo a de novo hair follicle morphogenesis - is Lrig1 expression maintained in order to carry out cre-mediated deletion? Further, the fibroblasts in this assay may adopt a wound-like phenotype, expressing FN, which you earlier claim to be required for hair follicle production in wounds. Yet in the absence of epithelial FN, no HF form. Can the authors reconcile this?

Minor comments:

1. In Figure 1a, the two populations are Lgr5+ and basal; please define what the basal population is in this experiment.
2. Significative is not a word.
3. In Figure 4 figure legend, there is reference to a grafting experiment but no experiment shown.
4. The authors delete FN in Lrig1+ or K19+ cells starting D19 and harvest at D30, and conclude that the hair follicles do not enter anagen after the second telogen, can you please include the data supporting the statement that mutant HF did not reenter the hair cycle after D65.

Significance

The authors show for the first time that fibronectin is expressed during cutaneous homeostasis and that it is required for normal function of the hair follicle stem cells. This is significant conceptual advance for the field of skin biology because fibronectin is thought to only be present in wounds: derived first from infiltrating serum and second from fibroblasts to act as provisional dermal ECM to support epithelialization during wound-response, which is ultimately resolved upon the conclusion of wound healing (reviewed in: Singer and Clark, PMID: 10471461). Further, FN has also been characterized as an EMT marker during cancerous progression (Lamouille et al, PMID: 24556840). Estrach and colleagues show that fibronectin is actually expressed by hair follicle stem cell keratinocytes and then is assembled into a meshwork that envelops the hair follicle and is in fact necessary for hair follicle stem cell homeostasis. This work would be broadly interesting to the field of stem cell biology as well as those working on extra cellular matrix signaling. My field is epithelial stem cells and more specifically hair follicle development and cycling.

Referee Cross-commenting

I have no disagreement with any of the points raised by the other reviewers. In fact, we seem to agree on the majority of the concerns. This includes the use of the tail wholemount model, the use of Lrig1-cre, selection of timepoint vs phase of the hair cycle, the appropriateness of the link between Fibronectin and SLC3A2, and further significant issues related to display of data and their reproducibility. Further, all of the major comments raised need to be addressed in order to properly evaluate the conclusions that the authors make. In my opinion, none of the comments raised here are unreasonable.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, Estrach et al., investigated whether extracellular matrix component fibronectin function in hair follicle regeneration, using a range of approaches including FACS, RT-qPCR, immunofluorescent staining, and mouse genetics. They proposed that fibronectin in Lrig1+ cells was necessary for hair follicle stem cell maintenance and activation, and the fibronectin expression and assembly relayed on the integrin co-receptor SLC3A2.

Significance

Major points:

1. In Figure 1a, the author used Lrig1+GFP and a6 to isolate Lrig1+ cells in the infundibulum junction zone above the sebaceous gland at Day 28. However, in Figure 1f-h, the GFP expression was not only in infundibulum above SG, but also in some inner root sheath cells. Since the Lrig1+ cells do not include the hair follicle stem cells (CD34+ bulge cells), result in Figure 1a does not support fibronectin expression in HFSCs.
2. In Figure 1b-e, the author detected fibronectin expression by IF staining with tail skin whole mount and back skin section. The fibronectin is mainly detected in differentiated cells in the inner root sheath (IRS) in anagen (Figure 1b and 1d), upper IRS in catagen (Figure 1c), hair germ in telogen (Figure 1e), but not in the bulge region (Figure 2i-l). Again, these results do not support fibronectin enrichment in HFSCs either.
3. In Figure 2a-c, the author knocked out fibronectin in Lrig1+ cells with Lrig1-CreERT2, FN fl/fl mice, and then validated the knockout efficiency by IF staining. However, result shows the fibronectin expression was not only depleted in the GFP+ Lrig1+ cells, but also depleted in GFP- inner root sheath and matrix. Similarly in Figure 4n, fibronectin was only knocked out in Lrig1+ cells, however, result showed the fibronectin cannot be detected in any cell types in skin. The author should explain why fibronectin depletion in Lrig1+ cell lead to completely fibronectin depletion.
4. In Figure 2r, by Flow cytometry, the author found a significative reduction of a6+CD34+ SC population when fibronectin is conditional knocked out in Lrig1+ cells. As the Lrig1+ cells and a6+CD34+ HFSCs are two distinct cell populations, the author needs to explain how fibronectin depletion in Lrig1+ cells affect the number and activation of HFSCs population.
5. In Figure 3b-c and 2g-h, the author reported the HF thinning in Lrig1-CreERT cKO mice by back skin HE staining. However, by tail skin wholemount staining, the HF thinning was not observed in those cKO mice (Figure 3f-g; Figure 2k-l). The author needs to explain the discrepency. In addition to this, the low quality of HE staining and poor orientation of HF (Figure 2h and 3b), coupled with lack of quantification, made these results and conclusion unconvincing.

Mini Points:

1. Color information in each IF staining panel is only completely presented in Figure 4. It is incomplete or lost in other 4 Figures.
2. In Figure 1a, FACS profile and representative figures are needed to demonstrate the author isolated the correct population at correct hair follicle stage.

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Referee #1

Evidence, reproducibility and clarity

Summary

In the manuscript, Estrach et al addressed the role of skin epithelial stem cell-derived extracellular matrix in hair follicle regeneration. They found that Lrig1+ epithelial stem cells highly express fibronectin gene compared to other basal epithelial cells. Conditional deletion of fibronectin gene using Lrig1CreERt2-GFP or K19CreER (the latter is expressed in the bulge stem cells) resulted in hair follicle regeneration blockade, change in the expression pattern of Lrig1-GFP. Injection of fibronectin protein into the dermis of the conditional fibronectin mutants (Lrig1CreERt2-GFP) rescued the hair regeneration blockade phenotype. The authors also conditionally deleted SLC3A2, an integrin coreceptor, using the same CreER lines and found a decrease in fibronectin deposition and CD34+ bulge stem cell number. With the results from these mouse genetics and phenotype analysis, they conclude that fibronectin-SLC3A2 cascade finely tunes hair follicle stem cell fate and their tissue regenerative capacity.

Major comments

1 Immunostaining results of fibronectin in the tail epidermal wholemounts are not convincing enough and would require improvements. First, the tail epidermal wholemounts lack the mesenchymal matrix and the basement membrane (Fig. 1b, c, f-h; Fig. 2b, c; Fig. 5d-j; Fig. S1b, c) (Braun et al., 2003 Development (PMID: 12954714)). Fibronectin is localized mainly in the mesenchymal matrix and the basement membrane in the skin and other organs (Stenman and Vaheri, 1978 JEM (PMID: 650151); Couchman et al., 1979 Archives of Dermatological Research (PMID: 393184); Jahoda et al., 1992 J. Anat (PMID: 1294570)), thus this sample preparation method is not appropriate to assess fibronectin tissue distribution. The authors use thick back skin sections, which contain entire skin tissues, thus I would recommend this method. Furthermore, fibronectin antibody signals in the tail epidermal wholemounts are detected in the inner part of the hair follicle epithelium, where there is no expected ECM structure (see Couchman et al. and Jahoda et al. above). Consistently, fibronectin signals are localized inside the Lrig1-GFP+ epithelial basal cells (Fig. 1f-h). Thus the specificity of the fibronectin staining needs to be confirmed. The reviewer understands that the authors provide an image showing the great reduction of fibronectin staining in a D30 tail epidermal wholemount of 4-OHT-treated Lrig1CreERt2GFP,FNfl/fl mice (Fig. 2c). However, as the D65 tail epidermal wholemount from wildtype mice also show many hair follicles without fibronectin signals (Fig. 1c), rigorous assessments would be required.

2 Lrig1+ stem cells have been reported to maintain the upper pilosebaceuos unit, containing the infundibulum and sebaceous gland, but contribute to neither the hair follicle nor the interfollicular epidermis under normal homeostatic condition (Page et al., 2013 Cell Stem Cell (PMID: 23954751)). However, only 11 days after the first 4-OHT treatment on Lrig1CreERt2GFP;FNfl/fl mice, Estrach et al found the defects in hair cycle blockade, reduced cell proliferation in the hair bulb, and significant reduction in fibronectin deposition in entire hair follicle structure. Please explain how the deletion of fibronectin gene in Lrig1+ stem cells, which do not contribute to hair follicle lineages, lead to significant hair regeneration defects in a short period of time. Current data do not well explain a causal relationship between the genetic perturbation and the observed phenotypes.

3 In some experiments (listed below), description about the methods, replication and statistics is not adequate, raising concerns about reproducibility. 3.1 Fig. 1a: data variation for basal cells should be presented. Biological replicate number should also be indicated in the figure legend. 3.2 Fig. 2g, h: hair follicle thinning is described here, but only one HE staining image with only one hair follicle is not enough to support this important claim. 3.3 Fig. 2r, 3i: flow cytometric data should be presented. 3.4 Fig. 4: No biological replicate and reproducibility information are provided. 3.5 Fig. 5j: how many biological replicates and hair follicles were analysed? The authors should also perform statistical tests. 3.6 Fig. S3g, h: information for biological replicates should be described. Statistical tests should be applied to Fig. S3h. 3.7 Fig. 5k-n: only one HE staining panel from each mouse line cannot provide rigorous evidence of the defects, which are not obvious from the HE staining.

4 In Fig. 3j, k, n, hair follicles in the control and 4-OHT treated skin are in different hair cycle phases. Therefore there is a possibility that the difference in their PCNA pattern simply reflects the difference in the cell proliferative activity between different hair cycle phases, but not indicates direct effects from the deletion of fibronectin gene in Lrig1+ cells.

5 To assess the expression levels of signaling-related genes (Fig. 3p, S2), the authors used mRNA extracted from whole skin tissues, which contain all epithelial and mesenchymal cell populations in different hair cycle phases. Thus, the time and spatial resolution of the analysis is low and it also cannot eliminate confounding factors derived from the difference in hair cycle phases between control and cKO.

6 In order to provide the characteristics and purity of the FACS isolated cell populations at D28 (Fig. 1a), their flow cytometry data and some marker gene expression data should be presented (see Page et al., 2013 Cell Stem Cell). This assessment is particularly important for the skin compared to other static organs, as it exhibits dynamic gene expression and tissue structural changes during the hair cycle. It is also important to check whether fibronectin protein accumulates around Lrig1+ stem cells in D28 dorsal skin, where upregulation of fibronectin gene expression was detected. The authors should not use tail epidermal wholemounts for the reason described above.

Minor comments

7 The increase in stem cell marker expressions shown in Fig. 3p contradicts to the reduction in the number of bulge stem cells shown in Fig. 2r and 3i. Please provide an explanation for this apparent discrepancy.

8 Although Fig. 5s-v show reduction of a6+CD34+ bulge cell population, the bulge tissue structure can be observed in Fig. 5p. Please explain how to interpret this apparent discrepancy. They just lost the expression of CD34?

9 Connectivity of the data in the fibronectin cKO with that of SLC3A2 cKO is weak. For example, it could be strengthened if the authors show colocalization of fibronectin and SLC3A2 in vivo.

10 Although the format of the manuscript is free in Review Commons, the Introduction and Discussion of this manuscript are too brief for us to understand the background and significance of this study. So I would recommend the authors to provide more detailed background information and discussion.

11 The authors use the term 'HFSC', but it is unclear which stem cell populations they mention; bulge, Lrig1+ or other stem cell populations?

12 Please provide details of fibronectin protein and antibody used in this study.

13 Due to the short for experimental information for Fig S3a, b, d, e, I cannot evaluate the data, thus several questions are raised. As the SLC3A2 level was significantly reduced in most cells in the plot, I assumed that Lrig1-GFP+ cells were gated before examining the expression level of SLC3A2. However, no information on the procedures for 4OHT treatment, isolation of cells and flow gating strategy is described. In the case of K19CreER mice (Fig. S3d, e), if the authors gated GFP+ cells before analysis, what GFP means in this case?

14 The manuscript wants to be checked for copyediting.

Significance

These findings might provide a conceptual advance into the role of epithelial stem cell-derived extracellular matrix in regulating stem cell behaviour and tissue regeneration. As fibronectin is upregulated in development, wound healing and cancers in many other organs, their findings may point to the importance of fibronectin in activating tissue progenitors and stem cells in these processes. Thus, this manuscript is likely to be of interest to a wide range of readers, not only in skin biology, but also in stem cell, regenerative and matrix biology. The contributions of this paper could be enhanced if the documentation were to be made stronger and more rigorous in a revised manuscript.

Referee Cross-commenting

I totally agree with Reviewer #3's comments in this consultation session. I have no disagreement with any of the points raised by other reviewers.

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Reply to the reviewers

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 are grateful to the reviewers for their honest opinion regarding this work and plan to address the majority of the comments in a revised version either through new analysis or revision to the text, as we believe these will improve the manuscript by making some of the details clearer. There were few suggestions that will lead to substantiative changes to the findings. Here, we address the most salient critiques, the primary one being related to novelty.

We respectfully disagree, as our detailed analysis of the DNA methylome in Octopus bimaculoides represents a significant advance to understanding how the epigenome is patterned in non-model invertebrates in general, and cephalopods in particular. We acknowledge that the previous report that the octopus methylome resembles the few other invertebrates where low DNA methylation has been found, the finding was part of a multi-organism study last year (de Mendoza et al., 2021), which lacked any detailed investigation. Our study provides the first in depth analysis on methylation patterning, the relationship with transposons and gene expression, and reports the finding of other key epigenetic marks in O. bimaculoides, and in other cephalopods.

In short, we believe our study to be highly novel and that it represent the first analysis of this kind in cephalopods and one of the few existing in non-model invertebrate organisms. In addition, we identify the conservation of the histone code in cephalopods. While this may be expected, this is the first experimental evidence in this class and represents an important step forward to understand the epigenetic regulation of genes and transposons in invertebrates. Finally, we plan to provide an updated transcriptome annotation for O. bimaculoides that will be available for the scientific community as a new valuable resource. We believe these features will make this study highly cited.

We believe that findings like ours will complement several recent studies that extend the epigenetics field out of the current narrow focus on model organisms to understand how epigenetic mechanisms function in diverse animals. This provides new insights regarding the epigenetic mechanism of gene regulation in an emerging invertebrate model.

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 raised the following points that we are planning to address:

*- It is unclear why the authors did not use the original gene models of O. bimaculoides or tried to improve them. By only relying on adult tissue (but the relatively late hatchling stage), they would have omitted most developmentally expressed genes, that are incidentally also the ones that are subjected to extensive spatiotemporal gene regulation (which is also a problem to assess the role of methylation). I think more comparisons with existing gene models and how the newly generated stringtie models should be provided. *

We agree that using as many tissues and developmental stages as possible will expand the octopus transcriptome.

We plan to:

• Add RNA-seq data from stage 15 embryos to improve this.
• Compare the gene model used in the original version of the manuscript (Stringtie model to use in Trinotate for improving the annotation of the genes) to the existing annotation model and report on which has superior performance for annotating the * bimaculoides* transcriptome.
• Extend the annotation of the transcriptome which we undertook in a focused fashion in the first iteration of this manuscript. Reviewer 2 raised the following points that we are planning to address:

*- It is not exactly clear to me why the authors look for expression clusters in the first part of the manuscript? This information, while interesting, does not seem to be used in the methylation analysis. It is also somewhat contradictory because the authors first claim that, based on their GO-term enrichment analysis, that different expression clusters are associated with "complex regulatory mechanisms, potentially based in the epigenome". Yet at the end they conclude that, due to the global and tissue-overarching nature of methylation, this "argues against this epigenetic modification as a player in the dynamic regulation of gene expression". *

We thank the reviewer for pointing out this issue and we plan to clarify the point through changing the text and additional analysis. Since we found that the methylation pattern was stable across tissues, and that it corresponded to gene expression levels regardless of tissues, we concluded that the methylation pattern is not likely relevant for the tissue-specific gene expression pattern reported in Figure 1.

We plan to:

• Ask whether there is a correlation between the gene clusters generated in Figure 1 and the DNA methylation patterns identified in Figure 4. *- At least for the trees that are shown in the main figures it would be great to show support values. *

We thank the reviewer for this request.

We plan to:

• Add full Supplementary information regarding the support values in Supplemental Files for all the trees present in the main Figures. Reviewer 3 raised the following points that we are planning to address:

*- It would be great to see more data on cephalopod TET and MBD structure. For example, it would be interesting to know whether octopus TETs have a CxxC domain or whether MBD proteins harbor functional 5mC - binding domains. *

We agree that it would be of interest to examine the conservation of TET genes to expand upon the initial analysis by Planques et al 2021 showing that O. bimaculoides have one TET homolog, one MBD4 homolog and one MBD1/2/3 homolog. Detailed analysis of MBD4 protein has been already performed in de Mendoza et al. 2021 by using the protein sequence of O. vulgaris, as the MBD4 gene in the O. bimaculoides genome appears truncated.

We plan to:

• add the PFAM domain analysis for TET proteins This will be added as a new figure panel.
• Update the text to include the reference to the identification of MBD4/MECP2 as the invertebrate homologs of vertebrate MBD4. *- Even though RRBS provides limited insight into DNA methylation patterns, the authors could have done more to explore read-level 5mC information. For example, by studying single reads, the authors could deduce the numbers of fully methylated, unmethylated or partially methylated reads. Such analyses might provide valuable insight into potentially different modes of epigenetic inheritance in different tissues i.e are there tissues that favor fully methylated or unmethylated stretches of DNA vs tissues that favor partial methylation? *

We think this is a really interesting point. This has been partially addressed in a previous work (de Mendoza et al., 2021) which found limited to no partially methylated reads in whole-genome bisulfite sequencing from O. bimaculoides brain.

We plan to:

• Use Proportional Discordant Reads on the RRBS data we generated from 30 dpf hatchlings to assess the presence of discordant methylated reads across different tissues to assess possible different modes of epigenetic inheritance. We will use “WSHPackage” in R: https://academic.oup.com/nar/article/48/8/e46/5760751?login=true https://github.com/MPIIComputationalEpigenetics/WSHPackage/blob/master/vignettes/WSH.md#x1-50003.1 This will be added as a new figure panel.

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.

Reviewer 1 raised the following points that we have already addressed:

We addressed all the comments raised by this Reviewer by revising the text, fixing references, typos and improving clarity.

Reviewer 2 raised the following points that we have already addressed:

We addressed all the minor Comments raised by this Reviewer regarding spelling errors and Supplementary Figures.

- The finding that less than 10% of all possible sites are methylated is surprising. I could not (easily) find statistics of RRBS experiment read mapping to the genome.

We have now provided this data and new Supplemental Table 1 (refereed in the text as Table S1).

*- It is very exciting to see methylation of gene bodies and some correlation to their expression levels, but the authors may need to include a disclaimer that the methylation of TEs may go undetected due to the gapness of the genome. In fact, the authors may try to map their data onto a somewhat closely related Octopus sinensis genome sequenced with long reads available at NCBI to confirm overall pattern. It is likely though that due the evolutionary distance only gene bodies will have mapping. *

The thank the reviewer for this suggestion and we included a sentence in the Result session indicating that methylation of TEs may go undetected due to the poor annotation of the octopus genome.

*- The statistical reasoning (and methodology) behind how clusters in Figures 1 and 4 were defined is unclear. In particular, in Figure 4, it seems that the authors had asked the program to give four clusters in total - why was this number chosen? It seems that using the same generic clustering approach as in Figure 1 may benefit or confirm the results in Figure 4. *

We clarified the rationale in the Material and Methods session to describe the bioinformatic analysis. We will put the full code used in the manuscript in our GitHub page (https://github.com/SadlerEdepli-NYUAD/) to have a more comprehensive understanding of the Method used.

Reviewer 3 raised the following points that we have already addressed:

We addressed all the minor comments in the text and figures raised by this reviewer regarding typos and clarity.

*- There is little info on the generated 5mC data. To bolster its value as a resource, the manuscript should have a link to the table describing RRBS metrics. This should include: non-conversion rates, numbers of sequenced and mapped reads, read length and other info that the authors deem useful. *

We have now provided this data in a new Supplemental Table 1 (refereed in the text as Table S1).

4. 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.

Reviewer 1 raised the following points that we are not planning to address:

*- The newly sequence RNA-seq samples are using a ribodepletion protocol (RiboZero) while the other ones are using a polyA selection. This might be a slight problem to compare them quantitatively. Actually in the Figure 1, all 4 newly generated samples group together in the hierarchical clustering. *

We acknowledge the reviewer’s point here and agree that heterogeneity in library prep and batch is a common issue when comparing public available with newly generated datasets. This could account for the clustering of the Ribosomal RNA depleted (i.e. RiboZero) from polyA selected RNA libraries. While this could potentially introduce bias, we do not believe that it substantially alters any of the main findings or the interpretations of this data. Our purpose for carrying out the cluster analysis of transcriptomic data from multiple tissues was to identify distinct gene patterns that defined different tissue types. This was accomplished regardless of the potential confounding variable introduced by different library preparations. In addition, we used TMP which seems to help in the comparison across different samples when used for qualitative analysis such as PCA and cluster analysis (Zhao et al. 2020; DOI: http://www.rnajournal.org/cgi/doi/10.1261/rna.074922.120). Therefore, even if not ideal we think that this approach is still valuable.

*- I am not so sure about the way the authors used z-score normalized logTPMs and applied hierarchical clusters, this most likely would not fully alleviate the impact of expression level on the outcome compared to more advanced form of normalization and clustering. *

We agree with the reviewer that applying z-score or a logTPMs normalization would not fully resolve the technical variance in the direct comparison of libraries generataed with different RNA selection methods. We did not apply z-score on logTPMs but these 2 methods were applied separately: z-score on TPMs in Figure 1B to define the gene clusters and log2(TPM+1) in Figure 4E. We have clarified the text to reflect this.

*- I am not convinced that differences in western blot for histone modification could really provide a clear insight into their regulatory role. *

We agree with the reviewer that Western blotting for histone modifications does not provide deep insight into their regulatory role. However, this is the first description of these marks in any cephalopod, and we believe that reporting a finding from experimental evidence is important, even if the result is aligned with the existing paradigm. Moreover, the marked difference in levels of distinct histone marks across tissues supports the hypothesis that they play a regulatory role. We observed this in mice where difference abundance in western blot correspond to different abundance and enrichment also by ChIP-seq (Zhang et al., 2021 DOI: https://doi.org/10.1038/s41467-021-24466-1). Considering the limited tools available in this species, we still consider this an important finding.

Reviewer 2 raised the following points that we are not planning to address:

*- The finding that less than 10% of all possible sites are methylated is surprising. I could not (easily) find statistics of RRBS experiment read mapping to the genome. I also wonder how much the gap-richness of the genome may affect the overall methylation estimate. If assembly permits, would it make sense to limit the sampled sites to areas where no flanking gaps are present (and sufficient scaffold length is available, maybe excluding very short scaffolds)? *

We added all the statistical values regarding the RRBS in a NEW Supplemental Table 1. We used a single base pair analysis approach (not tiling windows), so the data we extracted is not biased by the length of the scaffolds. This is confirmed by the fact that the DNA methylation value obtained in our RRBS data matches the findings observed in Whole Genome Bisulfite Sequencing (WGBS). Moreover, global DNA methylation values assessed by Slot blot analysis as a technique independent from genome assembly confirmed what observed with RRBS.

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Referee #3

Evidence, reproducibility and clarity

The manuscript by Macchi et al describes the epigenome and the transcriptome of Octopus bimaculoides. While the manuscript itself is well written and the data are properly analyzed, it is fair to say that the work itself offers little biological novelty. Nevertheless, I still believe that the datasets and some of the analyses could be useful to researchers studying invertebrate epigenomes and gene regulation.

1. It would be great to see more data on cephalopod TET and MBD structure. For example, it would be interesting to know whether octopus TETs have a CxxC domain or whether MBD proteins harbor functional 5mC - binding domains.
2. There is little info on the generated 5mC data. To bolster its value as a resource, the manuscript should have a link to the table describing RRBS metrics. This should include: non-conversion rates, numbers of sequenced and mapped reads, read length and other info that the authors deem useful.
3. Even though RRBS provides limited insight into DNA methylation patterns, the authors could have done more to explore read-level 5mC information. For example, by studying single reads, the authors could deduce the numbers of fully methylated, unmethylated or partially methylated reads. Such analyses might provide valuable insight into potentially different modes of epigenetic inheritance in different tissues i.e are there tissues that favor fully methylated or unmethylated stretches of DNA vs tissues that favor partial methylation?

Minor comments:

There are a few spelling errors throughout the manuscript. Please check for those: Figure 4F ("Trascrips" instead of transcripts), Schmedtea instead of Schmidtea. There are likely other errors as well.

Page 3 - "intergenome"sounds a bit weird.

The authors might consider citing Planques et al, 2021 (BMC Biol) alongside Mendoza et al when discussing unusually high 5mC levels in the sponge.

Significance

The main points of the paper are: i) a somewhat improved transcriptome, ii) DNA methylation data generated by RRBS that follows a canonical invertebrate pattern (low 5mCG levels present in GBs and absent from repeats), and iii) evolutionary analyses of epigenetic machinery components. While lacking biological novelty, the presented data have a resource value and could likely serve as a decent starting point for further exploration of cephalopod gene regulation. I therefore believe that with some revision the manuscript will merit publication in one of the Review Commons - associated journals.

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Referee #2

Evidence, reproducibility and clarity

The paper by Macchi et al studies DNA methylation patterns in Octopus bimaculoides, describing overall conservation of DNA methylation machinery and genome-wide methylation patterns and their effect on gene expression across broad tissue sampling. As such, the paper comrpises a key advancement in the emerging field of cephalopod (epi)genomics and gene regulation. Despite the difficulties relating to the genome assembly of O. bimaculoides, the authors have done a solid analysis of methylation patterns and the results look generally sound. I have a few points that may help the authors improve their manuscript:

• The finding that less than 10% of all possible sites are methylated is surprising. I could not (easily) find statistics of RRBS experiment read mapping to the genome. I also wonder how much the gap-richness of the genome may affect the overall methylation estimate. If assembly permits, would it make sense to limit the sampled sites to areas where no flanking gaps are present (and sufficient scaffold length is available, maybe excluding very short scaffolds)?
• It is not exactly clear to me why the authors look for expression clusters in the first part of the manuscript? This information, while interesting, does not seem to be used in the methylation analysis. It is also somewhat contradictory because the authors first claim that, based on their GO-term enrichment analysis, that different expression clusters are associated with "complex regulatory mechanisms, potentially based in the epigenome". Yet at the end they conclude that, due to the global and tissue-overarching nature of methylation, this "argues against this epigenetic modification as a player in the dynamic regulation of gene expression".
• It is very exciting to see methylation of gene bodies and some correlation to their expression levels, but the authors may need to include a disclaimer that the methylation of TEs may go undetected due to the gapness of the genome. In fact, the authors may try to map their data onto a somewhat closely related Octopus sinensis genome sequenced with long reads available at NCBI to confirm overall pattern. It is likely though that due the evolutionary distance only gene bodies will have mapping.
• At least for the trees that are shown in the main figures it would be great to show support values.
• The statistical reasoning (and methodology) behind how clusters in Figures 1 and 4 were defined is unclear. In particular, in Figure 4, it seems that the authors had asked the program to give four clusters in total - why was this number chosen? It seems that using the same generic clustering approach as in Figure 1 may benefit or confirm the results in Figure 4.
• In the discussion Scmidtea is misspelled.
• Some supplementary figures have to be exported as spell checker highlights are still present (e.g., in Suppl Fig 4).

Significance

This manuscript is an important step towards understanding the workings of gene regulation at the epi-genomic level in octopus and cephalopods in general

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Referee #1

Evidence, reproducibility and clarity

This manuscript focuses on the role of DNA methylation and histone modification in the gene regulation of cephalopods. It complements recently published RNA-seq and MethylSeq datasets with a few extra samples and generally confirms previous findings that DNA methylation does not play an active role in tissue or stage-specific regulation of gene expression in cephalopods (which is the general rule for most non-vertebrates). I don't see any methodological issue serious enough to preclude publication but some details should be strengthened.

• the newly sequence RNA-seq samples are using a ribodepletion protocol (RiboZero) while the other ones are using a polyA selection. This might be a slight problem to compare them quantitatively. Actually in the Figure 1, all 4 newly generated samples group together in the hierarchical clustering.
• It is unclear why the authors did not use the original gene models of O. bimaculoides or tried to improve them. By only relying on adult tissue (but the relatively late hatchling stage), they would have omitted most developmentally expressed genes, that are incidentally also the ones that are subjected to extensive spatiotemporal gene regulation (which is also a problem to assess the role of methylation). I think more comparisons with existing gene models and how the newly generated stringtie models should be provided.
• I am not so sure about the way the authors used z-score normalised logTPMs and applied hierarchical clusters, this most likely would not fully alleviate the impact of expression level on the outcome compared to more advanced form of normalisation and clustering.
• I am not convinced that differences in western blot for histone modification could really provide a clear insight into their regulatory role

Significance

This manuscript reports confirmatory results, partly reanalysing and confirming previous work. I would also like to stress that the methylation results have already been reported and discussed in a previous paper (de Mendoza et al. 2021). I don't have a fundamental problem with this but I also find the paper slightly overambitious and unspecific in its goals. I think it should benefit from being made slightly more concise. I find the part of histone marks is quite overstated. These marks are quite universal in eukaryotes and generally demonstrated to play a regulatory role, the fact that they can be detected in cephalopods by western blot is therefore not really a result.

Comments on the text (difficult without line numbers):

• Intro, first section: it would be good to have a few more references
• "While this has been extremely fruitful in elucidating detailed mechanisms of epigenome patterning, regulation and function, they do not provide a comprehensive understanding of the multiple and varied ways that the epigenome functions." -> sentence is quite confusing and without very clear meaning
• "In contrast, the most common invertebrate model organisms - Caenorhabditis elegans and Drosophila melanogaster - lack DNA methylation entirely. " -> could sound like this is the case for more invertebrates.
• P4 "This is the case in many animals, " -> give examples, it is unclear which examples of TE control by methylation outside vertebrates have been corroborated by data. The paper cited do not deal with methylation in squid
• Evolution has selected for variations in the canonical patterns of methylation -> such explanation could also be consistent with neutralistic explanations
• p19: Schmidtea (type) "such as the planarian Schmedtea mediterranea"

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Referee #2

Evidence, reproducibility and clarity

Summary:

The manuscript entitled "Lhx2 is a progenitor-intrinsic modulator of Sonic Hedgehog signaling during early retinal neurogenesis" by Li et al is a very interesting study in which the importance of Lhx2 is studied in conditional knock-out background to decipher the importance during retinal neurogenesis of developing embryo. The study reveal importance of co-receptors essential to Shh signalling. Data presented are clean and would add depth of knowledge to the literature. The study/manuscript do have some lacunae which are listed below which would be good to address before it is published

Major comments

1. Authors do not seem to have performed a rescue experiment with Lhx2 CKO. If this is absolutely not possible, a conditional overexpression could have given confirmatory clues on the Lhx CKO phenotype discussed. In any case some sort of rescue experiments are essential with respect to Lhx2 as done with Cdon and Gas1
2. Also, overexpression studies with the following genes, Cdon, and Gas1 is interesting in the CKO background. What about their over expression phenotype in Wild-type, Ptch1-CKO, and purmorphamine/Shh-N treated conditions. Alternatively, an ex-vivo or in vitro approach using cultured cells may also prove worthy to prove this point.
3. A logical explanation of Tamoxifen administration at a window E11.5 - E15.5 is good to have in the results section.
4. Authors say ".........that Lhx2-deficient RPCs can respond to recombinant Shh-N at more physiologically relevant concentrations, but their response is still attenuated compared to Lhx2-expressing RPCs." If the Shh-N dose is increased above physiological concentrations in Lhx-CKO conditions does the Gli1 read-out will restore to normalcy ? This would give insight on to the roles of co-receptors such as Cdon, Gas1.

Minor comments

1. In the 'Introduction' the authors write "Pathway activation is not achieved, however, by simple ligand binding to Patched, but also requires one of three co-receptors: Cell Adhesion, Oncogene Regulated (Cdon), Brother of Cdon (Boc), or Growth Arrest Specific 1 (Gas1)." Please give a citation of appropriate literature.
2. In the introduction authors write "In this case, an interaction with Notch signaling is partly responsible, through Lhx2-dependent expression of ligand (Notch1), receptors (Dll1, Dll3), and downstream transcriptional effectors (Hes1, Hes5)" I feel Dll1 and Dll3 are ligand and Notch is receptor. Please check.
3. Figure 1c: Western blots tubulin is less in CKO alongside Gli1. It would be better to do quantification for showing any significant changes.
4. Figure 2A and Result 2: Why harvesting at E14.5 specifically for qPCR/ChIP sequencing, while for RNA sequencing and in situ , it was done E15.5.
5. Figure 5D, E: It is not clear why there were more mCitrine+ cells in CKO explants at 96 hours.
6. Figure 6 C,D,E,F: Does Lhx2 CKO, cause cell death as the levels of vsx2 are low in vehicle in CKO (D,F) as compared to ctrl (C,E).
7. The levels of Gli1 are higher in CKO vehicle (D,F) than the control panel (C,E). This difference in Gli1 expression is more evident D versus C. Does it mean that CKO increases Gli1 expression in explants, which seems to be opposite of what shown in the first results (Figure 1D, E).
8. It is not clear why the increase in Gli1 expression with Shh-N (E,F) is not that much evident than with purmorphamine (C,D) in both control and CKO explants.
9. Figure 7 7-F: The changes in the levels of Cyclin D1 and Hes1 in Ctrl/Lhx2 CKO/ dCKO are not very clear from the data in present form. It would be better to show the changes in their mRNA levels by qPCR analysis. Further, it is not clear how the changes in CyclidD1 and Hes1 levels are proving that Lhx2 acts downstream of Shh signaling.
10. Supplementary table 4: In the page 3 have typo error "centrations at 72 hr"
11. Supplementary Figure 8 is labeled as Supplementary Figure 7, so there are two figures labeled as Supplementary Figure 7.

Significance

Study is significant, and adds more depth to existing knowledge in this science field. Developmental and cell biologists would benefit from this study.

Retina regeneration, Cellular signalling, Epigenetics, One knock outs/knockdowns, transgenics, RNAseq, Microarray, ChIPseq, Cell sorting

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Referee #1

Evidence, reproducibility and clarity

The manuscript by Li et al., entitled: "Lhx2 is a progenitor-intrinsic modulator of Sonic Hedgehog signaling during early retinal neurogenesis," focuses on an important topic in developmental biology-the regulatory interaction between transcription factors (TFs) and signaling pathways, namely how the TF confers cells' competence to respond to extrinsic cues. The study focuses specifically on Lhx2 regulation of Sonic Hedgehog (HH)-pathway genes in retinal development. The authors approach this complex topic through global transcriptomic analyses combined with elegant functional in-vivo studies, including a systematic examination of the pathway genes through the use of inhibitors and ligand on cKO retinal explants. The results reveal complex regulation by Lhx2 of several HH-pathway genes in the developing mouse retina, including Ptch, Gli1 and the co receptors Cdon and Gas1. The finding that a single TF controls several components of the signaling pathway is interesting. Nevertheless, probably due to the complex activity of Lhx2, which functions on additional targets, it remains unclear how regulation of HH-pathway genes by Lhx2 impacts the eventual phenotype of the Lhx2 cKO retinal progenitor cells (RPCs). In the following, I list the main findings and several comments that need to be addressed:

Fig. 1 presents the experimental system using inducible Hes1-CreERT to mutate Lhx2 on E11.5 and examine the expression of Gli1 and Sonic Hedgehog in the control and mutant.

• The authors should present the distribution of the Lhx2 protein in the control vs. mutant. Considering that the deletion is of only part of the gene (as shown in Fig. 2), it is important to present the loss of the protein as well as the efficiency of Cre activity. • On the figure, add a characterization of the cellular phenotype of the cKO retinas on E15.5 by presenting the expression of markers for ganglion cells and RPCs. Figs. 2 & 3 present the bulk RNA-seq analysis of the Lhx2 cKO retinas, including experimental design, validation and results. The integration of previously published ATAC-seq and ChIP-seq data for Lhx2 point to the direct targets and bound regulatory regions.<br /> • "4 biological repeats per genotype" - Specify if four eyes were sampled from two embryos or from four different embryos. Were the embryos from different litters? • Add GSEA analysis for the HH-pathway genes. Fig. 4 presents a published approach to quantifying the response to HH using a cellular reporter assay (Li et al., 2018), whereas in Fig. 5, availability of HH ligand is evaluated by elegantly implementing the cellular reporter assay. The results suggest that Lhx2 does not regulate ligand availability. • Fig. 4 presents a published approach and thus can be included in Fig. 5.<br /> Fig. 6 presents evidence that in the Lhx2 cKO, the Shh pathway is functional downstream of Smo, because the expression of Gli1 increases in cKO cells following Smo activation (with purmorphamine). Furthermore, the response to Shh-N is shown to be partly attenuated in the Lhx2 cKO retina. <br /> Figure 7 examines whether Ptch deletion can rescue aspects of the Lhx2 phenotype. This was done by comparing the phenotypes of cKOs of Lhx2, Ptch, or both Ptch and Lhx2. The results revealed partial rescue, in the Ptch and Lhx2 cKO, of the expression of Ptch1 and Gli1, but not of the proliferation and premature differentiation phenotypes based on expression of Cyclin D1, EDU, PCNA and Hes1. • Add images of the control to Fig. 7B,C. • Explain how the deletion of Ptch1 was examined. They next investigated regulation of the Ptch co - receptors Cdon and Gas1 by Lhx2 (Figs. 8, 9). Fig. 8 presents the developmental expression pattern of Cdon and Gas1 in the control, and their downregulation in the Lhx2 cKO (although Cdon is maintained in the dorsal optic cup). The results show that Cdon is the co-receptor that is normally expressed in RPCs. GAS1 seems to play a role in the peripheral progenitors destined to ciliary body and iris.<br /> Electroporation of both receptors into the Lhx2 cKO retinas resulted in increased pathway activity (based on Gli1 reporter). • Both Cdon and Gas1 were electroporated into the Lhx2 cKO retina, although Gas1 is not expressed in control RPCs (based on the analysis in previous panels). Explain why both were co-electroporated and the outcome of electroporating only Cdon. • The outcome of electroporation of the co-receptors into control retina should be presented. • It is important to include staining for Lhx2; it is possible that the cells that respond to the co-receptors are those that were not mutated (escapers). Presenting the loss of Lhx2 (or Cre activity through the use of a reporter) and comparing it to the outcome of electroporation into the control retina are therefore required.

Finally, the authors present evidence that Lhx2 cKO, on E13.5 when Cdon is no longer expressed in the RPCs, continues to compromise the HH - pathway genes. This further supports continued regulation of several HH-pathway genes in early and late RPCs.

• The finding that a Lhx2 controls several components of HH pathway could be relevant to Lhx2 activity in patterning of the cortex - I suggest to discuss the possible relevance of the findings to other organs.

Additional comments:

• Fig. 4E: Add explanation of the quantitative analysis. • Fig. 5: Explain how results were normalized based on retinal size (which is significantly smaller in cKO retinas). How many independent experiments were run here? How many different retinas were tested? Were retinas taken from the same mouse considered 'independent'?<br /> • Fig. 8B: Indicate the genotype of the presented tissue. • Fig. 8 A,B should be presented in one panel, in the same orientation. • Fig. 8D: Present the different channels, in addition to the merge image.

Significance

The study focuses on an important topic in developmental biology-the regulatory interaction between transcription factors (TFs) and signaling pathways, namely how the TF confers cells' competence to respond to extrinsic cues. The study focuses specifically on Lhx2 regulation of Sonic Hedgehog (HH)-pathway genes in retinal development. The results reveal complex regulation by Lhx2 of several HH-pathway genes in the developing mouse retina, including Ptch, Gli1 and the co receptors Cdon and Gas1. The finding that a single TF controls several components of the signaling pathway is interesting. Nevertheless, probably due to the complex activity of Lhx2, which functions on additional targets, it remains unclear how regulation of HH-pathway genes by Lhx2 impacts the eventual phenotype of the Lhx2 cKO retinal progenitor cells (RPCs).

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Reply to the reviewers

We plan to address the minor comments from both reviewers as here described:

Reviewer 1 – comments____:

1. Figure 1a + 1b: The pattern of H3K9me3 at SVA elements appears to be quite different between the two cell types tested - in iPSCs it appears to be more strongly centered on the SVA with some "spreading" occurring upstream of the element, however in NCCITs it appears to mark downstream of the element and is not clearly seen to occur on the SVA. Is this due to sequencing data from different labs or due to cell - type specific epigenetic marks? The authors should consider showing H3K9me3 marks at a comparable region for example repressed genes or another family of repressed TEs as an internal control as well as showing profile plots to more clearly show the spread of epigenetic marks over the SVAs. As recommended by the Reviewer, in Figure 1 we will show several examples of H3K9me3 marked SVAs in both cell types. We will propose explanations for discrepancies if needed, but acknowledge the possibility of a sequencing artifact, as suggested by the Reviewer.

Figure 1: Given that the data in this paper is mostly from NCCIT cells it would be advisable for conclusions in hiPSCs to be tempered accordingly.

We will temper the conclusions as recommended.

Figure 1c: Which SVAs have neither H3K9me3 nor H3K27ac, what might these represent? Do they have any other notable features, or are they of a specific age?

The (very few) SVAs that have neither of those markers are almost certainly an artifact of mappability issues. We will point this out in the manuscript, including both the text and the figure legend.

Line 128-129: Is the assumption that SVAs are enriched in these region as opposed to dictating the epigenetic landscape as a consequence of their own sequence, is this correct?

This is correct, and we will clarify this in the text.

Figure 2c: Are there any enriched motifs in the repressed SVAs?

We performed this analysis, and several motifs are enriched in the repressed SVAs. We will add this data in the revised manuscript and in Fig. 2C.

Figure 2c: How many of the de-repressed SVAs contain this motif? It would be interesting to know if the YY1/OCT4 binding sites exist within any of the repressed SVAs and then, later, whether they accordingly lack actual binding of the TFs due to the presence of H3K9me3.

We will perform this straightforward analysis using the MEME-suit or HOMER, and include it in the revised version.

Figure 3d: It would be interesting to assess how close the differentially expressed genes are to an LTR5H element, or however many of the SVA-proximal and differentially expressed genes are also LTR5H-proximal?

We will perform this straightforward analysis and include it in the revised version.

Figure 3e: Are genes close only to de-repressed SVAs considered here? Worth specifying in the text. Also, what are the 20% of genes which are upregulated?

Yes, the reviewer is correct, only genes close to de-repressed SVAs are considered. We will specify that in the manuscript, and also add a results paragraph on the 20% of genes that are upregulated.

Figure 4a: Are the binding motifs for YY1 present at both binding sites? Are they the same, is the surrounding sequence the same? Are either of the binding sites present in repressed SVAs/is there any detectable binding of SVA/OCT4 in repressed SVAs? Are the YY1/OCT4 bound SVAs also those marked with H3K27ac in the Wysocka Lab dataset?

The YY1-OCT4 motif is present only where both of the factors bind together. We will specify this in the manuscript (results section). As for the second question: yes these correspond to the regions marked by H3K27ac in the Wysocka dataset. We will clarify this in the manuscript.

Figure 4c: Example used is a SVA-D element, are the YY1/OCT4-bound SVAs within the de-repressed group of a specific age?

No, they are found in all SVA groups (SVA through F). We will specify this in the manuscript (results section).

Figure 4d: are there GO enrichment terms for the genes bound by either YY1 and / or OCT4 different?

We will perform this straightforward analysis using the Ingenuity Pathway Analysis toolkit, and include it in the revised version in the results section.

Figure 5: Concluding figure should address how the SVAs subset in terms of binding, H3K9me3, gene expression changes and TFBS/TF binding - there are a lot of parameters which are assessed within the de-repressed subclass and it would be useful to show somewhere graphically where and when they co-occur or not.

We will edit the model figure to show the mechanism highlighted by the reviewer, as requested.

Finally, it would be helpful to see a discussion about what dictates the absence of H3K9me3 / presence of H3K27ac? Is this due to the TFBS sequence within the element? Further, a discussion on how the TFBS is gained in newer elements / lost in older elements is lacking. While the authors begin by stating that they are going to address what dictates whether an element is co-opted and conclude that it is due to sequence and location, I would suggest that as no conclusion is drawn on how the sequence changes to permit co-option and how the location dictates co-option, it may be worth tempering down the introduction on this point.

We will edit the introduction and discussion, as requested.