5,526 Matching Annotations
  1. Aug 2023
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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      This work combines in-vitro experiments and numerical modeling to study the dynamics of microtubules, driven by molecular motors. In this bottom-up approach, molecular motors are immobilized on the surface and microtubule filaments are anchored to the surface from one end. The dynamics results in "beating like" motion of the anchored microtubules. The authors establish a phase diagram of the different dynamical patterns of "beating like" motions by varying the molecular motor density and the length of the microtubule anchored to the surface. They use a numerical framework that captures the observed patterns.

      Major comments:

      Overall the experiments and results are well described and claims are supported by the data. Both experimental and numerical methods are presented in a way that they can be reproduced.

      Minor comments:

      A key feature of beating cilia is the asymmetry of the beat pattern (fast stroke and slow recovery). It might be interesting to use the kymographs or the Phy vs time analysis to see whether or not this feature exists in this simplified experimental model.

      Also, the beating frequency is very low (mHz) compared to real cilia/flagella (~Hz). Would it be possible to use the model to predict which parameter would need to be tuned to reach more physiologically relevant beating frequencies ?

      Significance

      This study is part of the field of in-vitro reconstitution, from a minimal set of components, that aims to reproduce a biological function to identify and understand the minimal physical/biophysical mechanisms underlying a function. This study might be of interest for the people who address questions of the self-organization of cytoskeletal elements in minimal systems.

      The main limitation of this study relies on the claim of reproducing a flagella-like motion. Indeed, the frequency of the described oscillations is in the mHz range while the frequency of cilia is in the range of few Hz to tens of Hz. This suggests that the mechanism at play in such a reconstituted system is not the one that drives beating in real cilia/flagella. Yet, this limitation also applies to other studies in the field (Vilfan et al. 1999, Guido et al. 2022 ...).

      My second concern is that the added value with regards to state of art is not clearly explicit. I'm thinking about the work of the Isabelle Guido's team where they have more complex reconstituted systems (a pair of 2 microtubules); or the work of Pascal Martin's lab where the design of the system allows to capture more complex mechanisms such as myosin density waves, which result in frequency beat of 0.1Hz.

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      Referee #3

      Evidence, reproducibility and clarity

      In this manuscript, Parker et al present a nice exploration of the evolutionary and mechanistic relationships between 5′ splice site consensus sequences, intron numbers and METTL16/SNRNP27K. By performing inter-species association mapping in Saccharomycotina species, they found that a T in position +4 is strongly associated with the absence of METTL16 (and/or in some cases SNRNP27K or mutations in it). They also provide solid structural modeling data in support of this association.

      In general, I think this is a very nice manuscript. I only have a few comments, which could be addressed by rewording specific parts and/or improving the current figures.

      1. As the authors acknowledge, a key issue that cannot be fully resolved in this study is causality between the different events investigated. Overall, the authors are careful about this, but there are some exceptions that should be corrected. Probably the most important is in the abstract, where they write: "We conclude that variation in concerted processes of 5' splice site selection by U6 snRNA is crucial to evolutionary change in splicing complexity". I suggest they write something more open (and correct), such as: "We conclude that variation in concerted processes of 5' splice site selection by U6 snRNA is associated with evolutionary changes in splicing complexity". Similarly, other plausible scenarios should be discussed in the corresponding Discussion section.
      2. I do not agree with the statement that "The extent of alternative splicing is the best genomic predictor of developmental complexity". To start with, there are many ways to quantify "extent of alternative splicing" and there are also different types of alternative splicing that might have different prevalence and biological impact. Then, this claim is usually related with exon skipping, which is tightly linked with intron length, and that is likely a better prediction of complexity (yet clearly not causative). My concern is: to what extent has this claim been formally and properly assessed by comparing splicing prevalence with other genomic features, such as intergenic region length, intron length, or average distance between enhancer-promoter interactions (arguably the most relevant predictor, in light of many other studies)? Moreover, I found it a bit misleading to frame the work presented in this study as directly related with developmental (or even splicing) complexity. The work is very interesting on its own, and I doubt their findings on +4 position preference in Saccharomycotina has anything to do with developmental complexity (as the Abstract and Introduction seem to imply).
      3. I found Figure 2 and its associated supplementary figure very difficult to follow. I suggest the authors try to improve it and make it clearer. Also, other trees summarizing the results might be helpful.
      4. I also found the Results section corresponding to Figure 5B a bit confusing. I would argue (as I think the authors do) that there are two main patterns here: below 500 introns, there is no association, while above 500 introns there is an increasingly negative association (correlation). I think it would help to more explicitly distinguishing these two patterns. Then, for the intron-poor species: is the correlation (or lack of) for species with a T or an A in position +4 different?

      Significance

      This is a very interesting study that sheds light on an intriguing evolutionary pattern: the change in consensus sequence at position +4 of the 5' splice site. This topic is relevant since it is closely associated with intron loss and splicing efficiency and evolution.

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      Referee #2

      Evidence, reproducibility and clarity

      In their manuscript, Parker et al. investigate the evolutionary patterns of splice site preference, focusing on the A/U ratio at position A+4 on the 5´ splice site. Building upon prior studies in S. pombe and A. thaliana, the authors establish a strong correlation between this preference and the co-evolution of the METTL16 U6 snRNA methyltransferase. Furthermore, through inter-species association mapping, they identify the involvement of the splicing factor SNRNP27K in altered A/U ratios and highlight the significance of the residue Met-141 in SNRNP27K for this function. Overall, the paper effectively presents impactful new findings on the evolution of METTL16, U6 snRNA, and splicing.

      The computational analyses employed in this study are situated outside our field of expertise, preventing us from offering a comprehensive evaluation of the methodology's appropriateness and rigor. Nonetheless, the identification of METTL16 through the authors' methods, which aligns with previous research in S. pombe and A. thaliana, lends support to the validity of their approach. Notably, the close proximity between SNRNP27K and the methylated A43 residue in U6 snRNA within the spliceosome, particularly near Met-141, is an impressive finding. Previous studies have shown that a mutation at position M141T affects splicing at +4A introns, thus providing robust validation for their methods.

      The data presented in this study furnish crucial insights into the role of METTL16, U6 snRNA methylation, and splice site recognition. The authors expand upon recent observations that the "vertebrate conserved region" exists in non-vertebrates, despite the absence of primary sequence homology. These results will serve as a valuable guide for future molecular investigations into U6 snRNA methylation and its mechanisms in splicing. Furthermore, the implications of this paper extend to human evolution, as the plasticity in splicing is an essential factor in the evolution of developmental complexity.

      Minor suggestions for improvement:

      1. Given the significance of the interaction between U6 snRNA and the intron for understanding the data, it would be beneficial to include a figure illustrating the RNA-RNA base-pairing interactions between U6 snRNA and the 5´ splice site. This addition is particularly important if the paper is intended for publication in a journal with a general readership.
      2. Similarly, the section on U1 snRNA would be more comprehensible with the inclusion of U1 RNA-RNA intron diagrams and improved descriptions of both the figures and the assay. Despite being negative data in the supplement, clarifying this section is essential. As currently written, it is challenging to follow.
      3. Whenever possible, consider increasing the figure and font sizes to enhance readability for readers.
      4. In the text, there is no reference to Figure 1C.
      5. In Figure 5B, the y-axis in the top panel is labeled "species," but the legend only mentions U5/6p as the y-axis. Please revise the legend to include the appropriate information.

      Significance

      The data presented in this study furnish crucial insights into the role of METTL16, U6 snRNA methylation, and splice site recognition. The authors expand upon recent observations that the "vertebrate conserved region" exists in non-vertebrates, despite the absence of primary sequence homology. These results will serve as a valuable guide for future molecular investigations into U6 snRNA methylation and its mechanisms in splicing. Furthermore, the implications of this paper extend to human evolution, as the plasticity in splicing is an essential factor in the evolution of developmental complexity.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      The manuscript by Parker et al addresses the important question of how different organisms have evolved pre-messenger RNA systems that are either more or less complex. This question underlies the evolution of complex organisms and the genome adaptation of simple organisms to their specific environments, so is an important question to answer. This manuscript now provides the underlying molecular mechanisms of how 5' splice site sequence preference may have evolved which is both an interesting and exciting advance for the field.

      Major comments

      This manuscript builds on the previous work from this group where they identified the role of adenosine N6 methylation (m6A) of the U6 small nuclear RNA (snRNA) of the spliceosome by METTL16 as being important for 5' splice site selection. This work led to the speculation that loss of a METTL16 ortholog, or potentially other splicing factors, in some species could contribute to an evolutionary change in 5' splice site sequence preference. Here the authors now use the power of phylogenetics, interspecies association mapping and the available spliceosome structures to provide convincing conclusions that 5' splice site sequence preferences in the extensive number of organisms examined correlate with the presence of the U6 snRNA methyltransferase METTL16 and the splicing factor SNRNP27K.

      An analysis of METTL16 conservation was first carried out by comparing the METTL16 methyltransferase domain (MTD) in 29 diverse eukaryotic species. All the METTL16 orthologs were found to have either one or two globular domains. Three domain types were identified and compared in detail. What was not clear from this analysis was the functional significance of orthologs having either one or two domains. In addition, while this analysis provides important new information on the domain structure of METTL16 orthologs, especially where these domains had not been identified previously, the link between this section of the results and the following sections is not that apparent.

      Next novel bioinformatics pipelines were developed to compare both introns and orthologous groupings of protein coding genes between 227 Sacchromycotina genomes as well as 13 well-annotated eukaryote genomes. First, the 5' splice site sequence preference was compared and clearly indicates that the +4 position has the greatest variation in preferences within the Sacchromycotina. The ability to now compare a large number of genomes has provided novel information on the evolution of the 5' splice site sequence and the conclusion that there is more complexity to the 5' splice site in fungi that previously recognized. While it is apparent why only the 5' splice site signal was investigated here, with its relationship to the U6 snRNA and METTL16, it seems a shame the other splice site sequences were not analyzed using this novel pipeline. In any case, the complexity of the 5' splice site +4 position now allows, for the first time, interesting interspecies association studies.

      With the 5' splice site +4 variation identified, the next step was to determine the underlying molecular mechanisms that dictate the evolution of the various sequence preferences. Some obvious players here are the U1 and U6 snRNAs which directly interact with the 5' splice site during splicing. However, no association was found between these snRNAs and the 5' splice site +4 sequence.

      The powerful interspecies association mapping was then used to determine whether the presence or absence of METTL16 ortholog or a splicing factor correlated with the 5' splice site +4 sequence variation. Interestingly, a clear association was found between METTL16 and the 5' splice site +4 position; METTL16 presence was associated with +4A at the 5' splice site and METTL16 absence was associated with +4U at the 5' splice site. This is an exciting and significant finding.

      Interestingly, the next most significant association with the 5' splice site +4 position was with SNRNP27K. This result makes sense as in the cryo-EM structure of the pre-B spliceosome complex the C-terminal domain of SNRNP27K is found near the region of the U6 snRNA that will interact with the 5' splices site. Absence of SNRNP27K was associated with an increased preference for +4U at the 5' splice site. Now the real power of the interspecies association mapping was demonstrated by investigating whether any association could be determined specifically within the C-terminus of SNRNP27K. Significantly, the methionine 141 position in SNRNP27K was found to be associated with the +4 position of the 5' splice site. This finding fits nicely with previous studies where mutation of M141 caused a shift in 5' splice site selection away from +4A 5' splice sites, to 5' splice sites without +4A. What is not clear is whether M141 is conserved or invariant between all the species that were compared? Overall, this result reveals the power of the interspecies association approach and provides interesting and exciting information on the molecular determinants of 5' splice site evolution.

      The final analysis was to investigate the interaction potentials of the U5 and U6 snRNAs with the 5' splice site in the Sacchromycotina genomes and try to relate this to species with fewer introns and less alternative splicing. Species with low intron numbers and low splicing complexity were revealed to have weaker U5 and U6 anti-correlation potentials and favor +4U at the 5' splice site. On the other hand, species with high intron number and presumably higher splicing complexity featured anti-correlated U5 and U6 snRNA interaction potentials and favored +4A 5' splice sites. This extensive analysis provides novel information on the interactions and splice site properties of species with simple and complex splicing. Again, I see why there is emphasis on the 5' splice site here but a similar analysis with the U2 snRNA and the branch site could also be informative.

      Minor comments

      Should the Title include SNRNP27K?

      Should the title specify that it is the evolution of only the 5' splice site sequence preference being studied here?

      Include information on intron number and 5' splice site interaction potential of U5 and U6 snRNA in the Summary?

      Figure 1C is not referred to in the text?

      Page 8, line 5 - better to say "splicing signal phenotypes".

      What are the other points on Figure 3B? What is the next point below SNRNP27K? Is it U2A'?

      The second paragraph of the Discussion is vague and lacks a reference. "we could also identify an association with a methionine residue in the conserved C-terminal domain of SNRNP27K orthologs." There are a few methionines in the C-terminus, which one? Please reference the statement "transcriptome analysis of C. elegans SNRP-27 M141T mutants.."

      Significance

      Overall, this is a well written and clearly presented study that provides some key molecular information on the splicing factors involved in the evolution of 5' splice sites and shows the power of interspecies association studies. Some important conceptual principles have now been defined for the field going forward.

      The question remains as to whether METTL16 and SNRNP27K are the sole determinants of 5' splice site preference evolution at +4? One splicing factor that immediately comes to mind is Prp8 where there is extensive evidence for involvement in splice site selection and is clearly in the right location throughout splicing to be involved. This question should at least be discussed but Prp8 would also be a very interesting candidate for the interspecies association mapping.

      Also, as mentioned previously, only the 5' splice site was investigated here and the manuscript could become a more substantial piece of work if the other splice sites were included in some way.

      The obvious audience here are those directly in the splicing field but the overall principles are relevant for evolutionary biologists and those studying organismal complexity.

      My expertise is in yeast and human splicing mechanisms. I do not have the expertise to critically evaluate the bioinformatic pipelines but they were clearly explained and presented.

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      Reply to the reviewers

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

      The manuscript describes that simultaneous inhibition of LOXL2 and BRD4 reduces proliferation of TNBC in vitro and reduces growth in vivo.

      This observation is followed by extensive mechanistic studies that suggest physical interaction between LOXL2 and short isoform of BRD4-MED1. Inferences from Chip-seq analyses suggest that this interaction is involved in regulation of multiple transcriptional programs. Authors focus on differential activation of DREAM complex, to claim that this interaction "is fundamental for proliferation of TNBC". The manuscript is very well written and mechanistic inferences are based on a set of sophisticated epigenetic analyses and bioinformatical inferences. The phenotypic effects from LoxL2 inhibition by itself, or in combination with BRD4 inhibition are relatively modest. These modest effects, as well as many of the reported changes in gene expression are clearly inconsistent with the frequently used adjectives as "dramatic", "fundamental", "deeply affected", "drastically hampered" etc. Given the modest phenotypic effects, many of the key claims and conclusions are not supported by the data.

      We thank the reviewer for appreciating our work, defining the manuscript as well-written, and saying that it comprises extensive mechanistic studies as well as sophisticated epigenetic analysis.

      We apologize if some of our statements seemed exaggerated. In this revised version, we revisited some of our conclusion to moderate them.

      Moreover, we took the reviewer's criticism as an opportunity to strengthen our findings. In the revised version of the manuscript, we included an additional TNBC PDX (PDX-127), and results from this experiment clearly reinforce our claims (Fig. 6D and Fig. EV9E-F). In this new in vivo experiment, we selected a PDX model in which the expression of BRD4L is not detectable, while BRD4S is clearly expressed. Therefore, the treatment with JQ1 would specifically affect the activity of BRD4S, making the treatment selective. Additionally, we reduced by half the dose of JQ1 administrated to limit the effect of BRD4S inhibition alone on tumor growth. The combinatorial treatment (JQ1+PXS) induced a clear superior effect in this setting as compared with single-agent treatments. In addition to this, we discarded that the observed growth reduction is not the result of the sole inhibition of LOXL2, which could affect FAK/Src activity or extracellular Collagen crosslinking. In conclusion, our data show that the combinatorial inhibition of LOXL2 and BRD4S is effective in reducing tumor proliferation in TNBC in vivo models, independently of the inhibition of BRD4S and of other pathways known to be regulated by LOXL2.

      Specifically:

      1) It is unclear why authors generalize their conclusions to TNBC. Figure 1B demonstrates synergy for 1/3 cell lines, which is chosen for the follow up study. Even for MDA231, the synergy is confined to low concentrations of BRD4i (S1c). While MDA231 cell line is frequently used in experimental studies of TNBC, it is quite dissimilar to majority of clinical TNBC, and contains mutant RAS, which is rare in this disease.

      The synergistic effect is observed in MDA-MB-231 cells because only this cell line expresses both BRD4S and LOXL2. Indeed, in Fig. 1C we show that MDA-MB-468 cells do not express LOXL2, while BT549 only express minimal BRD4 levels.

      To corroborate this hypothesis, in the revised version of the manuscript we added:

      1. A new cell line (Cal51) expressing the same LOXL2 and BRD4 levels (Fig. EV8C) but showing greater resistance to JQ1 than MDA-MB-231 (Fig. EV8D). Also, in this cell line, we could show that the combinatorial treatment had a superior effect on cell viability than the single agents’ treatment (Fig. EV8E).
      2. A western blot panel of different TNBC PDXs shows that the majority of them express medium to high levels of both BRD4S and LOXL2 proteins, as is the case of MDA-MB-231 (Fig. EV9E) and Cal51 (Fig. EV8C). This result suggests that the combinatorial treatment could be used in the majority of TNBC patients as they are expected to express both BRD4S and LOXL2.
      3. Finally, as explained above, we performed another in vivo choosing a PDX that expresses BRD4S (but not BRD4L) and LOXL2 (PDX-127) (Fig. 6D and Fig. EV9E-F). Also, in this new model, we could observe that the combinatorial inhibition had a superior effect than single treatments.

        2) In vivo, the effect appears to be modest even in the MDA231 model, selected for evidence of synergy in vitro. In vivo, the combination appears to have an additive effect. Tumor growth rates are reduced, but no shrinkage is occurring. In the PDX model, LOXL2i does not have an effect as a monotherapy, while modestly enhancing the impact of BRD4i. These results are at odds with the claim of the interaction being fundamental for proliferation.

      We agree with the reviewer that the combinatorial inhibition appears to have an additive effect in vivo using the MDA-MB-231 model.

      1. For that reason, we have now performed the in vivo PDX experiment mentioned above (PDX-127; Fig. 6D and Fig. EV9E-F) in which we decreased the dose of JQ1 by half to avoid strong tumor growth effect due to BRD4 inhibition alone. In this new experiment, the synergistic effect is evident. While single-agent treatment showed a very moderate effect (0% or 20% tumor growth reduction for LOXL2 and JQ1, respectively), the combinatorial treatment showed a 50% reduction in tumor volume, further supporting our conclusions.
      2. We also performed either BRD4 or MED1 pull-down experiments in the presence of PXS and JQ1. We show that upon PXS treatment, the interaction between LOXL2 and BRD4S is maintained while the interaction with MED1 is reduced (Fig. 5A-C). However, in the presence of JQ1, the interaction between LOXL2 and MED1 is maintained while BRD4S-LOXL2 and BRD4S-MED1 interactions are impaired (Fig. 5D-F). These new results explain why monotherapy does not have a sufficient effect in vivo and set the rationale for the use of the combinatorial treatment. We believe that these new results corroborate our initial findings and we hope to have been able to satisfy the reviewer comments.

      3) No analysis of cell proliferation was shown in vivo. Authors should have performed BrdU or KI67 staining to support the claim. For in vitro analyses, authors also used indirect assays for proliferation. PI staining by itself does not have sufficient resolution to clearly capture modest effects that authors demonstrate. BrdU-PI double staining would have been much more useful.

      We appreciate the reviewer’s comment. In the revised manuscript we have added Ki67 and H3S10p staining in the tumor samples for the new in vivo PDX experiment (Fig. 6E and Fig. EV10A-C). We show that the combinatorial treatment significantly induces a reduction of both proliferation markers, which is in agreement with a reduced tumor volume. Regarding the in vitro analysis, we did not only use PI staining to show a reduced proliferation state but also H3S10p staining (Fig. 4B) and an SLBP1 fluorescent reporter MDA-MB-231 cell line (Fig. 4D, Fig. EV6B, E, and Movie EV). In the revised version of the manuscript, we included a new FACS-PI analysis (Fig. 4A, C) to better represent the effects we see on the cell cycle.

      Minor points:

      Dose dependent decrease in phosphorylated H3 is not at all obvious from eyeballing the data in S1A; the only effect that I see is a modest reduction at the highest concentration of the inhibitor. Authors need to quantify the results to support the claim.

      We agree with the reviewer and we apologize for the misinterpretation. We have changed the revised manuscript as follows: “The selective LOXL2 inhibitor PXS-538224 (hereafter, PXS) efficiently reduced the levels of oxidized histone H3 (H3K4ox) in MDA-MB-231 cells at 40 μM (Fig. EV6C), indicating an efficient inhibition of LOXL2 catalytic activity in the nucleus.”

      Most of breast cancer cell lines are derived from metastatic disease, including pleural effusion, thus the point that because MDA231 cell line is derived from pleural effusion, it is metastatic does not have sufficient logical foundation.

      Many publications have shown the high metastatic capacity of MDA-MB-231 (e.g. https://doi.org/10.1016/j.bbabio.2011.04.015, doi: 10.1038/s41467-017-01829-1), which are therefore used as TNBC metastatic model. The scope of the analysis reported in Fig. 6C was just to show whether any of the used treatments could reduce the metastatic capacity of this cell line. We believe we do not overstate the results but just report them as they are.

      How is loss of cell-cell junction in vitro consistent with LOXL2 role in modulating ECM? There is no evidence of ECM production in MDA231 in vitro. On the other hand, this loss is associated with EMT.

      We thank the reviewer for identifying this mistake. In the revised manuscript we changed the text as follows: “Gene set enrichment analysis (GSEA) revealed that LOXL2 KD induced upregulation of processes involved in cell morphology, secretion, membrane trafficking, and cell differentiation, with cell-cell junction being one of the most significantly affected pathways (Fig. EV5E). These results agree with the role of LOXL2 in regulating epithelial-to-mesenchymal transition, corroborating the high quality of our dataset.”

      Reviewer #1 (Significance (Required)):

      Discovery and characterization of LOXL2-BRD4 interaction is advancing the ever-deepening understanding of molecular mechanisms of regulation of gene expression. The studies and analyses appear to be sufficiently rigorous and reported with clarity, and the claimed discovery of the biological interaction between LOXL2 and BRD4 is well supported. However, given the magnitude of the reported (rather than claimed) effects of this interaction, and concerns about generalizability of authors conclusions, it is not clear how these results are promising for the development of new therapies in TNBC. Moreover, in contrast to luminal BC, there is no clear evidence for utility of cytostatic drugs in constraining TNBC. Therefore, biological and clinical significance of the authors discovery is unclear and claims in this regard appear to be overblown

      We thank the reviewer for stating that our analysis is rigorous and reported with clarity. We really took the criticisms as an opportunity to strengthen our findings, as explained above.

      For the newly presented in vivo PDX model, we performed immunohistochemistry of Ki67, H3S10p and Cleaved Caspase 3 to check whether the reduction of tumor volume observed in the combinatorial treatment was a result of a cytotoxic and/or a cytostatic effect (Fig. 6E and Fig. EV10A-C). As shown in the figure, the combination of the two inhibitors induced a superior decrease of Ki67, H3S10p, and a clear increase of Cleaved Caspase 3. Therefore, these new data indicate that the combinatorial treatment does not only have a cytostatic effect but also cytotoxic, suggesting a clinical exploitability for the treatment of TNBC patients.

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

      In their study, Pascual-Reguant et al. show that combined inhibition of BRD4 and LOXL2 can synergize to restrict triple-negative breast cancer (TNBC) proliferation. BRD4 and LOXL2 are transcription regulators that can read and write epigenetic information, respectively. The authors employ three distinct breast cancer cell lines and mouse models with cell line-derived xenografts, and they show that combined inhibition of BRD4 and LOXL2 can be superior to single BRD4/LOXL2 inhibition in these model systems. In an attempt to identify a connection between BRD4 and LOXL2, the authors find that the two proteins can bind to each other. The authors performed most of the experiments in the breast cancer cell line MDA-MB-231. To assess the impact of LOXL2-inhibition on transcription, the authors assessed changes of the transcriptome in MDA-MB-231 cells following LOXL2 knockdown. They found that genes related to cell differentiation and morphology were upregulated, while genes related to the cell cycle were downregulated. ChIP-seq data of BRD4 showed that BRD4 can bind to cell cycle gene promoters and that this binding was enhanced upon loss of LOXL2. The authors found that LOXL2 and BRD4 interacted with the transcriptional cell cycle regulators B-MYB, FOXM1, and LIN9, which are components of the MYB-MuvB-FOXM1 (MMB-FOXM1) complex that is known to promote the expression of late cell cycle genes with important functions during mitosis. The authors conclude that LOXL2/BRD4 interact with each other and with the MMB-FOXM1 complex to drive the expression of cell cycle genes and cell proliferations. Vice versa, they conclude that inhibition of LOXL2/BRD4 reduces cell proliferation through inhibiting the expression of cell cycle genes.

      Major:

      • The data and methods are presented well. The experiments are adequately replicated and analyzed. However, except for the first section, all experiments were performed using only one cell line. It is important to validate key findings in at least a second cell line.

      We thank the reviewer for valuing our work.

      To address the reviewer’s comment, in the revised manuscript we added an additional cell line (Cal-51), that expresses similar levels of LOXL2 and BRD4 as compared to MDA-MB-231 (Fig. EV8C). Even though this cell line is clearly more resistant to JQ1 than the MDA-MB-231 cell line (Fig. EV8D), the combinatorial treatment is significantly more effective as compared with single agents’ treatment (Fig. EV8E).

      Moreover, we have also performed an additional in vivo experiment using another TNBC PDX (PDX-127) that expresses LOXL2 and BRD4S, but not BRD4L. Given that JQ1 can inhibit both BRD4 isoforms, this in vivo system allowed us to demonstrate that the tumor antiproliferative capacity of the combinatorial treatment is due to the simultaneous inhibition of LOXL2 and BRD4S (rather than BRD4S and L) (Fig. 6D and Fig. EV9E-F).

      • There appears to be a misunderstanding of the concept of cell cycle-dependent gene regulation by the DREAM complex and its related factors. Early (G1/S) cell cycle genes contain E2F promoter motifs, while late (G2/M) cell cycle genes contain CHR promoter motifs. The DREAM complex can bind both, while RB-E2F and MuvB recognize only E2F and CHR motifs, respectively. B-MYB and FOXM1 bind to MuvB and regulate late cell cycle genes, but they do not bind to early cell cycle genes. Given this concept, the authors' rationale to connect BRD4/LOXL2 through MuvB/B-MYB/FOXM1 with E2F promoter sequences and early cell cycle genes and the subsequent conclusions must be corrected.

      We thank the reviewer for their expert explanation. We corrected our conclusion in the revised version of the manuscript following the reviewer’s comment.

      • I felt that the suggested functional connection between LOXL2/BRD4 and DREAM is not strongly supported by the authors' data. Figure S6E: A similarity score of Fig. EV6E: We agree with the reviewer that a similarity score of Fig. 4E: We thank the reviewer for this comment. The performed pulldown showed that BRD4S, LOXL2, and MED1 interact with Lin9 and B-Myb, but not with FOXM1, thus FOXM1 itself is an internal negative control of the pulldown. Additionally, BRD4L does not show the same interaction pattern as BRD4S, LOXL2, and MED1, again acting as an internal negative control. We, therefore, believe that the pulldown is properly controlled and that the observed interaction is trustful. We furthermore agree with the reviewer that it would be interesting to characterize the interactions between the DREAM complex and BRD4S, LOXL2, and MED1. However, we believe that the dissection of these interactions at the mechanistic levels would require a deeper study, which can be a project in itself that we aim to explore in the future. For example, it would be interesting to investigate whether either the inhibition or the downregulation of LOXL2 and/or BRD4S specifically impairs the formation of the DREAM complex or the recruitment of specific DREAM complex subunits, as well as how these effects impair the DREAM complex chromatin binding. We are afraid that the suggested pulldowns would not be sufficient to answer these questions, which would require extensive cross-interaction studies in either BRD4/LOXL2 and BRD4+LOXL2 inhibition or downregulation followed by ChIP-seq and transcriptomics for all the conditions. We believe that the provided data, together with the functional characterization (both, in vitro and in vivo), of the phenotypes triggered by BRD4S and LOXL2 inhibition make a strong case for our manuscript and leave out of scope the suggested experiments. We hope the reviewer will understand our explanation and will appreciate that we are planning to pursue this further in the future.

      Fig. 3: We thank the reviewer for this important comment. The ChIP-seq technique very often does not provide exhaustive results due to sequencing depth limits and antibody performance. We believe that the fraction of DREAM target genes found in our dataset as bound by BRD4S is not exhaustive and that the analysis proposed by the reviewer would not lead to clear conclusive results. However, we understand the importance of verifying that DREAM target genes whose promoter is bound by BRD4 are indeed downregulated when LOXL2 is inhibited. To give an answer to this question, in the revised manuscript we added gene expression analysis of selected DREAM target genes upon treatment with JQ1, PXS their combination. We could successfully show that both JQ1 and PXS treatment impairs the transcription of the selected DREAM target genes, however, the combinatorial treatment almost shut down their expression, in agreement with our hypothesis (Fig. 5J).

      • The authors state that it is surprising to find that LOXL2 can promote target gene transcription because it is rather known as a transcriptional repressor. To this point, the authors should perform standard analyses using their RNA-seq and ChIP-seq data. Compare differential expression of genes that are bound by BRD4S/L/S+L and genes not bound by BRD4. Perform motif search and enrichment analyses for transcription factor and co-factor binding data (public ChIP-seq repositories). Such analyses may suggest what gene sets are up- and downregulated by LOXL2 through BRD4S/L and what other factors could be involved in LOXL2-dependent up- and downregulation of gene transcription.

      We thank the reviewer for this valuable comment that certainly provides the rationale for a follow-up project. However, we believe that the proposed study goes beyond the scope of our work at this moment.

      Minor:

      • I felt that background information on the BRD4 isoforms was missing. The short and long isoforms of BRD4 should be introduced briefly.

      We agree with the reviewer. In the revised manuscript, we addressed this by presenting BRD4 isoforms in the introduction part of the manuscript.

      • Given that BRD4 inhibition is known to activate p53 (e.g., PMID 23317504 and 33431824) and p21 (PMID 31265875), the authors should discuss the p53 status of their cell lines (largely mutant). In general, I felt that the authors could better cite and discuss the current literature on BRD4 and LOXL2.

      We appreciate the comment of the reviewer regarding p53. Given the fact that p53 is mutant in MDA-MB-231, we believe that the proliferation defect observed with the combinatorial treatment may be due to the activation of alternative cytostatic or cytotoxic signaling cascades, independently of P53 activation. We have now briefly mentioned this point in the manuscript discussion.

      • It was unclear to me why the authors did not actually test experimentally whether their predicted interaction models 2 or 4 are likely true (Figure 2E+G).

      We understand the reviewer’s comment. The fact that JQ1 treatment almost abrogates the interaction between LOXL2 and BRD4S strongly suggests that models 1 and 3 are likely wrong, therefore pointing towards models 2 and 4 as the correct ones. To test whether models 2 and 4 are indeed the correct models we are now performing extensive mutagenesis studies, which are producing preliminary results suggesting indeed that models 2 and 4 are correct. The reason why we did not include this study in the current manuscript, is that we started a parallel line of investigation aimed at identifying residues fundamental for the interaction that can be exploited in compound screening campaigns to identify molecules able to block the described interaction and thus cancer proliferation. Publishing these preliminary results at this stage could jeopardize the drug discovery campaign and we hope that the reviewer will understand our constraints.

      • The transcription of cell cycle genes depends on the cell cycle (i.e., reduced cell cycle entry correlates with reduced cell cycle gene expression). Given that the authors showed LOXL2 inhibition reduce MDA-MB-231 cell proliferation, they should note that reduced expression of cell cycle-related genes is expected upon LOXL2 knockdown.

      We understand the reviewer’s comment. We believe that we provide sufficient data supporting our hypothesis that LOXL2 controls the expression of cell cycle genes at the transcriptional level together with BRD4S. In addition, the sole inhibition of LOXL2 has practically no effect on tumor proliferation in vivo but largely enhances the antiproliferative effect of low-dose JQ1 (Fig. 6D). We hope these clarifications would satisfy the reviewer.

      • The authors specify in their discussion that their data show a function of LOXL2/BRD4 in the cell cycle interphase, while there were no experiments that support that specific conclusion. At least it is unclear to me why the authors rule out a function in mitosis?

      We thank the reviewer for this comment. We referred to interphase genes because these are the early cell cycle genes, while mitotic genes are the late ones. We do not discard a possible function for BRD4S and LOX2 regulating mitotic progression, however, we believe this would be a consequence of dysregulated G1-S-G2 gene expression, rather than a direct transcriptional effect. This conclusion derives from the fact that while we observe interactions between LOXL2, BRD4S, and MED1 with Lin9 and B-Myb, these are not fully conserved with FOXM1, which is typically required for the transcription of mitotic genes. To avoid confusion, we have now anyway removed the word “interphase” from the text.

      • I felt that the first part of the manuscript (combination of BRD4 and LOXL2 inhibitors in TNBC) was a bit uncoupled from the functional studies on LOXL2 and its connection to BRD4. The transition between these parts and the final discussion on why the joint control of cell cycle genes by LOXL2/BRD4 may be important for the synergistic effect of LOXL2/BRD4 inhibitors. To this point, the authors' model was not clear to me.

      We really appreciate the reviewer’s comment. To better connect the functional studies with the clinical significance of the proposed combinatorial treatment, we restructured the manuscript. In the revised version, the use of the combinatorial treatment is shown in Figure 6. Moreover, to better explain why we focused all the studies on BRD4 and LOXL2, we also included data from the Cancer Cell Line Encyclopedia (CCLE)-associated chemotherapeutics sensitivity (Fig. 1A and Fig. EV1) showing that LOXL2 expression levels can predict the response to BRD4 inhibition, suggesting a functional interaction between BRD4 and LOXL2 and the possibility to exploit it for therapeutical purposes. We believe that these data set the rationale to further explore the connection between LOXL2 and BRD4, both at the mechanistic and functional levels.

      Reviewer #2 (Significance (Required)):

      The study by Pascual-Reguant et al. shows that inhibitors of BRD4 and LOXL2 can be combined to achieve better efficacy in reducing proliferation of breast cancer cell lines and breast tumor growth in xenograft models. They provide strong evidence for a functional interaction between LOXL2 and BRD4 and investigate their common transcriptional targets. Intriguingly, some evidence points towards a direct regulation of the DREAM complex and its cell cycle gene targets.

      The findings are novel and can be the basis for further research on TNBC combination therapy using BRD4 and LOXL2 inhibitors. The link to the DREAM complex is preliminary.

      The study is of interest for a basic research audience with some translational aspects.

      I reviewed this manuscript as a researcher in gene regulatory mechanisms, with cell cycle genes as one focus area. I have no expertise in the computational modeling of protein-protein interactions and I am no expert for breast cancer.

      We thank the reviewer for the positive comments. We also would really like to thank the reviewer for their criticism, which, we believe, contributed to a new and improved manuscript version.

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

      Summary:

      In this manuscript, Laura Pascual-Reguant et al. identified a novel role of the LOXL2 oxidase in sustaining cell cycle progression through a so far uncharacterized gene-activating function is mediated by the BRD4S epigenetic reader and exerted on key DREAM-target genes in TNBC. Moreover, the authors showed that combinatorial treatment of TNBC with LOXL2- and BRD4-specific inhibitors result in a tremendous anti-tumorigenic effect. For all findings, they leveraged in vitro and in vivo settings as well as high-throughput sequencing approaches. However, the following points should be addressed and explained.

      Major points:

      -The authors on their working hypothesis propose that dual inhibition of BRD4 and LOXL2 is a novel strategy for curing TNBC. For my taste, just because both targets are quite promising for TNBC, the jump to this combinatorial treatment is kind of abrupt. Knowing the difficulty and time-/financial- investment, authors could optionally perform a mass spectrometry analysis on nuclei lysates with LOXL2 pull down to identify physical interactors. Due to the augmented resources and analysis of raw data, authors may necessitate a generous revision period (approx. 4 months for starters). By that, this can provide a more unbiased approached to look at nucleus-specific gene-regulatory functions and particularly at epigenetic readers. It would be also interesting to see if LOXL2 interacts with other members of the BRD family. Selecting BRD4 and no other members of the bromodomain family cannot be the only choice given that other BRD members can also interact with several of these mediator subunits.

      We thank the reviewer for the suggestion and we agree with the fact that the rationale for combining BRD4 and LOXL2 inhibitors was not sufficiently argued in the first version of the manuscript. For that reason, in the revised manuscript, we added new data to explain why we explored this topic. In particular, to better explain why we focused all the studies on BRD4 and LOXL2, we included data from the Cancer Cell Line Encyclopedia (CCLE)-associated chemotherapeutics sensitivity (Fig. 1A and Fig. EV1) showing that LOXL2 expression levels can predict the response to BRD4 inhibition (but not to other approved chemotherapeutic drug), suggesting a functional interaction between BRD4 and LOXL2 and the possibility to exploit it for therapeutical purposes. Moreover, we restructured the manuscript to make the story more linear, explaining first the functionality of BRD4S-LOXL2 interaction at the molecular and cellular levels, and then presenting the in vivo systems in the last part of the manuscript.

      We agree with the reviewer that it may be interesting to explore whether LOXL2 interacts with other BRD family members. However, given the prominent role of BRD4 in promoting cancer proliferation, we believe that understanding the relevance of BRD4S-LOXL2 interaction in TNBC is, per se, of great interest and provide a novel mechanistic understanding of how TNBC proliferation is controlled at the transcription level. In the specific case of TNBC, it has been shown that BRD4S has an oncogenic effect, while BRD4L is an oncosuppressor. In the manuscript, we now showed that LOXL2 downregulation sensitizes cells to JQ1 treatment (Fig. 1D). Additionally, while the downregulation of BRD4L does not have any additional effect on cell treated with PXS, the downregulation of BRD4S sensitize them to LOXL2 inhibition (Fig. EV8B). These results, once again, indicate the relevance of studying the functional interaction between BRD4S and LOXL2.

      -LOXL enzymes have been shown to promote collagen and fibronectin assembly, thereby sustaining the pro-survival effect of the ITG5A/FN1/FAK/SRC signaling cascade and shielding TNBC cells against chemotherapy treatment (32415208). Did authors observe if LOXL2 loss or inhibition decreased the active status of FAK and SRC, which are well known to promote G1-S transition (25381661)?

      Probably the cell cycle defects upon LOXL2 loss may also partially arise from the impairment of this cascade.

      We really appreciate the reviewer’s suggestions. In the revised version of the manuscript, we checked FAK and Src activation status in tumor samples from one of our in vivo experiments (Fig. EV10D). We did not observe any difference in phospho-FAK or phospho-Src upon treatment either with PXS, JQ1, or their combinations, suggesting that alterations in the activity of these factors were not driving the observed proliferation defects.

      -Authors exclusively use JQ1 as a BRD4 inhibitor. As JQ1 may have an unspecific effect on BRD2 as well, authors should consider reproducing key experiments with siControl- and siBRD4-treated cells and increasing doses of PSX as well as repeating the JQ1 dose response assay in Figure 1B using siRNA-mediated silencing of LOXL2. Given that both players are part of the same complex, silencing of one and inhibition of the other should sensitize cells compared to their control counterparts.

      We agree with the reviewer and we addressed this comment in the revised manuscript. In particular, we have added two additional experiments:

      • We transduced MDA-MB-231 cells with isoform-specific shBRD4s (shBRD4L and shBRD4S) (Fig. EV5H) and checked cell sensitivity to PXS treatment (Fig. EV8B). As explained also above, we observed that only when the short isoform of BRD4 was downregulated cells displayed higher sensitivity to PXS treatment. This result corroborates that BRD4S and LOXL2 are required for TNBC proliferation.

      • We transduced MDA-MB-231 cells with shLOXL2 and assessed JQ1 sensitivity (Fig. 1D). We showed that upon LOXL2 downregulation, cells became more sensitive to JQ1 treatment, again corroborating the fact that TNBC proliferation requires BRD4S and LOXL2.

      -Moreover, in Figures 1G and S3D the differential sensitivity of low and high LOXL2 cell lines is unclear. Do authors know if any of these growth kinetic lines represent one of the tested cell lines in Figure 1A-B? Authors should provide respective legends. In addition, authors should take advantage of their homemade data given that they have already selected a panel of TNBC cell lines with various LOXL2 expression at basal state (Figure 1A) for which dose response assays have been performed (Figure 1B). Therefore, I would perform an IC50 graph for JQ1 (without PSX treatment) using the existing data from Figure 1B.

      We apologize if our representation was confusing. In the revised manuscript we have changed the sensitivity plots (Fig. 1A and Fig. EV1) to make them easier to grasp. Additionally, in Figure 1A we included the analysis of CCLE cell lines stratified based on their LOXL2 expression levels. This analysis showed that LOXL2 expression levels could overall predict the response to BETi treatment. As suggested by the reviewer, we also plotted the IC50 of the 3 cell lines tested. However, their JQ1 sensitivity curves did not show any difference that could be attributed to their different LOXL2 levels. Our speculation is that only 3 cell lines do not provide a sufficient size to reach a meaningful conclusion, which, in contrast, can be achieved by comparing the CCLE BETi sensitivity.

      -In Figure 2D, the pull-down assay is inconclusive, as the molecular weight for each construct is not mentioned. I would probably add this information also in all performed western blots. Also, the overexpression of the BD1/BD2-mutated and especially the BD1/BD2-lacking construct is unclear if it still interacts with LOXL2, probably because of the lack of molecular weight reference of each band. Therefore, the authors should make this pull-down assay more descriptive regarding the size of the bands. Also, BD1 mutagenesis at N140 was shown to dislodge the binding of JQ1 to BRD4 (24497639), which implies that BD1 mutagenesis or overexpression of the BD1-deficient construct should abrogate the interaction of LOXL2 with BRD4, reminiscent to the abrogated interaction of BRD4/LOXL2 upon JQ1 that binds to both BDs (Figure 2F). And, what happens if a BD2-deficient construct is expressed?

      We thank the reviewer for spotting this distraction. We apologize for this and in the revised version of the manuscript we included molecular weights for all western blots.

      We acknowledge that BD1 mutagenesis displaces JQ1 binding, however, we respectfully disagree that because of this BD1-N140 mutant should not bind to LOXL2. Our docking analysis indeed showed that none of the poses is impaired either by BD1 or BD2 mutagenesis (Fig. EV4D). The fact that JQ1 disrupts the interaction between BRD4S and LOXL2 (Fig. 2F, G) is not due to the fact that they compete for the same binding residue, but rather for the space occupied by JQ1 inside the AcK binding pocket of either BD1 or BD2, which impedes proper binding to LOXL2. Our pulldown data indeed showed that mutant BD1 and BD2 retain the ability to bind to LOXL2 (Fig. 2C), as predicted by the docking.

      We did not try to express constructs either lacking BD1 or BD2 and we cannot speculate what could happen to the BRD4S-LOXL2 interaction in this scenario. Even though this experiment could help dissect the interaction between LOXL2 and BRD4S, we decided to rather perform mutagenesis of specific residues that have been predicted to be important for the interaction. The reason why we did not include this study in the current manuscript, is that we started a parallel line of investigation aimed at identifying residues fundamental for the interaction that can be exploited in compound screening campaigns to identify molecules able to block the described interaction and thus cancer proliferation. Publishing these preliminary results at this stage could jeopardize the drug discovery campaign and we hope that the reviewer will understand our constraints.

      -If authors support that BRD4S is the predominant isoform driving the expression of DREAM-targets, this means that DREAM-targets are mainly bound by BRD4S, relying on Figure 3E-F. However, based on the author's ChIPseq tracks in Figure 3H, DREAM targets such as EZH2 and HMGB2 are co-occupied by both BRD4 isoforms at the basal state on their promoter region. Also, especially for EZH2 and PLK4, authors should set to 'group auto-scale' both conditions in a smaller scale range for ChIPseq- and RNAseq tracks, although I do not these two genes as good candidates representing your analysis. Therefore, authors should initially show all genes (e.g in a table format) that enrich the 'DREAM-targets' signature and select for a greater panel of genes (like for AURKB and HMGB2) demonstrating a preferential occupancy of the BRD4S at their promoter region. Finally, authors are recommended to perform a ChIP-qPCR on these genomic regions at basal state (no LOXL2 silencing) to validate the predominant occupancy of BRD4S and the low/absent occupancy of BRD4L at these genomic sites.

      We apologize for the confusion. To make the figure more understandable, we now scaled all the panels to the same scale and highlighted in grey the promoter region of each selected DREAM target gene. As the reviewer can appreciate, none of these genes is bound by BRD4L in basal conditions (Fig. 3F).

      To better characterize the differential binding, following the reviewer’s suggestion, we performed ChIP-qPCR using Ab2 (which recognizes both BRD4 isoforms), in cells either downregulated for BRD4L or BRD4S with isoform-specific shRNAs (Fig. EV5H). Results showed that only the downregulation of BRD4S reduced the binding of Ab2 to the promoter of the selected DREAM target genes (Fig. 3D), corroborating our hypothesis and validating our ChIPseq strategy.

      -Authors in Figure 3G should select an equal-sized population of randomly chosen non-DREAM-target genes, otherwise, the comparison of log2FC difference between these two gene cohorts is unreliable and difficult to make. Mann-Whitney test should also be performed.

      We thank the reviewer for this suggestion, which was added to the revised version of the manuscript (Fig. 3E, lower panel).

      -Authors should repeat the cell cycle analysis (Figure 4A) as the number of cells subjected to flow cytometry is quite discrepant between the conditions. Also, it is not clear if the experiment was performed in at least biological triplicates (although in the respective legend, it is stated so). If performed in biological triplicates, authors should make a new graph where each cell cycle phase cell population differs between the two conditions. Moreover, the difference in cell cycle defects in LOXL2-inhibited cells (Figure 4C) is indifferent compared to their control counterpart. Therefore, authors should address these inconsistencies.

      We thank the reviewer for the suggestion. In the revised version of the manuscript, we represent the cell cycle also as a bar plot with statistical analysis (Fig. 4A, C). Even though the number of cells was the same across conditions, the sub-G1 population of the LOXL2 KD cells may have distorted the profile of the cell cycle. To avoid misinterpretations, we repeated the analysis in the revised version of the manuscript. Statistical analysis supports that LOXL2 inhibition or downregulation has a significant effect on cell cycle progression (Fig. 4A, C, right panel).

      -Furthermore, authors should explain what was the rational selecting a mediator subunit and specifically MED1 as a possible interacting partner of LOXL2 and BRD4s since MED12 and MED24 were also highly essential (Figure 4F).

      We selected MED1 as a Mediator Complex proxy. In our essentiality analysis MED 1, 9, 10, 12, 15, 16, 19, 23, 24, 25 score as significant, suggesting a functional interaction between LOXL2 and the Mediator Complex, rather than a specific subunit. MED1 has been previously described as a BRD4 partner and it is often used in immunofluorescence to visualize transcriptional foci, which made it the best candidate for follow-up study in our project.

      -Moreover, do authors also observe this functional relationship of LOXL2 and BRD4S in cell cycle progression in other breast cancer subtypes presenting a high proliferation index e.g HER2+?

      Presumably, the author's proposed mechanism applies to a wide panel of breast cancer entities, for which, only key experiments could be performed.

      We thank the reviewer for the suggestion. We hypothesized that other cancer types expressing LOXL2 and BRD4S could also benefit from the combinatorial treatment. Indeed, the CCLE drug sensitivity panel in Fig. 1A comprises cancer cell lines of different origins, not just TNBC, and corroborates that the relationship between LOXL2 expression levels and BRD4 sensitivity exist also beyond TNBC. Even though it is important to experimentally verify this hypothesis, we decided to pursue it in the future to broaden the applicability of the proposed strategy in preclinical settings.

      -Authors in Figure 5H represent LOXL2 and BRD4s as integral chromatin looping factors together with MED1 at promoter and enhancer regions. However, this illustration is an overrepresentation of their finding because authors did not address the differential occupancy of BRD4S upon LOXL2 loss in DREAM-target-specific enhancer regions. If they wish to do so, they may use the RANK ORDERING OF SUPER-ENHANCERS (ROSE) package to call for super-enhancer regions in the proximity of DREAM-targets and confirm similar results as for their TSS-proximal sites.

      We thank the reviewer for the useful suggestion. In the new version of the manuscript, we have simplified the representation, which now does not show super-enhancers. However, following the reviewer’s suggestion, we performed super enhancer analysis using ROSE. Results showed that BRD4S binds to super-enhancers more than BRD4L, including DREAM target gene super-enhancers. Additionally, while LOXL2 KD did not alter the binding of LOXL2 to DREAM target gene super-enhancers, it decreased the binding of BRD4S to them (Fig. EV7D, E). Overall, these data are in agreement with our hypothesis that BRD4S together with LOXL2 controls the expression of DREAM target genes.

      -In the current manuscript, authors did not address the translational relevance of their proposed mechanism in the context of conventional therapies. Knowing that several BRD-specific compounds currently undergo clinical trials, authors should address if LOXL2 low (MDAMB468) and high (BT549) cells demonstrate a differential sensitivity to increasing doses of chemotherapy, in the presence or absence of BRD4. By doing that, LOXL2 apart from being a therapeutic target could be also used as a prognostic marker to stratify patients and achieve better response to standard therapies.

      We really appreciate the reviewer’s suggestion and we think this is a fundamental point. In the new version of the manuscript, we have performed further analysis using a greater panel of chemotherapeutic agents from the CCLE sensitivity database. We now show that LOXL2 low-expressing cells show significantly more sensitivity to BETi treatments, but not to conventional chemotherapeutic agents (e.g. doxorubicin, Olaparib, 5-fluorouracil, paclitaxel, etc.) (Fig. 1A and Fig. EV1), which set the rationale to further explore the functional relationship between BRD4 and LOXL2.

      Minor points:

      -In Figure 1D, the authors should convert the y-axis to a logarithmic scale to better represent the differences between JQ1, PXS, and combo. Also, One-way Anova should be performed between JQ1, PXS and combo.

      We don’t understand the reviewer’s suggestion since Fig. 1D (Fig. 6B, right panel in the revised version) is a tumor picture for which the y-axis cannot be converted to a logarithmic scale.

      -In Figure S6F, authors did not show the sensitivity of LOXL2 low and high cell lines for BRD4 KO. If LOXL2-proficient cells are less sensitive to JQ1, based on Figure 1B, authors should consider showing something similar from the gene essentiality database.

      We agree with the reviewer and we apologize for this mistake. We have included the sensitivity of LOXL2 low and high cell lines for BRD4 KO and also for MYC KO (Fig. EV6G).

      -Authors failed to discuss the work from Ozge Saatci et al (PMID: 32415208) regarding LOXL2 in TNBC and ECM reorganization as well as in other cancer entities (PMID: 35428659) in the context of ECM remodeling. Authors should realize that these published works and the current ones are not conflicting but complement each other.

      We thank the reviewer for the suggestion. In the revised version of the manuscript, we discussed this work.

      Reviewer #3 (Significance (Required)):

      SIGNIFICANCE

      The conception and findings are of enlightening significance for TNBC therapy, especially given the lack of targeted therapies in this particularly aggressive breast cancer subtype. Hence, I posit this work as highly relevant for the cancer epigenetics research community interested in characterizing unknown factors that facilitate the gene-activating function of epigenetic readers in health and disease.

      My field of expertise is to uncover epigenetic vulnerabilities responsible for transcriptional plasticity driving drug tolerance in aggressive forms of breast cancer.

      We would like to take the opportunity to thank the reviewer for the relevant suggestions. We strongly believe the revised version of the manuscript has been substantially improved by addressing the comments the reviewer made.

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

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      In this manuscript, Laura Pascual-Reguant et al. identified a novel role of the LOXL2 oxidase in sustaining cell cycle progression through a so far uncharacterized gene-activating function is mediated by the BRD4S epigenetic reader and exerted on key DREAM-target genes in TNBC. Moreover, the authors showed that combinatorial treatment of TNBC with LOXL2- and BRD4-specific inhibitors result in a tremendous anti-tumorigenic effect. For all findings, they leveraged in vitro and in vivo settings as well as high-throughput sequencing approaches. However, the following points should be addressed and explained.

      Major points:

      • The authors on their working hypothesis propose that dual inhibition of BRD4 and LOXL2 is a novel strategy for curing TNBC. For my taste, just because both targets are quite promising for TNBC, the jump to this combinatorial treatment is kind of abrupt. Knowing the difficulty and time-/financial- investment, authors could optionally perform a mass spectrometry analysis on nuclei lysates with LOXL2 pull down to identify physical interactors. Due to the augmented resources and analysis of raw data, authors may necessitate a generous revision period (approx. 4 months for starters). By that, this can provide a more unbiased approached to look at nucleus-specific gene-regulatory functions and particularly at epigenetic readers. It would be also interesting to see if LOXL2 interacts with other members of the BRD family. Selecting BRD4 and no other members of the bromodomain family cannot be the only choice given that other BRD members can also interact with several of these mediator subunits.
      • LOXL enzymes have been shown to promote collagen and fibronectin assembly, thereby sustaining the pro-survival effect of the ITG5A/FN1/FAK/SRC signaling cascade and shielding TNBC cells against chemotherapy treatment (32415208). Did authors observe if LOXL2 loss or inhibition decreased the active status of FAK and SRC, which are well known to promote G1-S transition (25381661)? Probably the cell cycle defects upon LOXL2 loss may also partially arise from the impairment of this cascade.
      • Authors exclusively use JQ1 as a BRD4 inhibitor. As JQ1 may have an unspecific effect on BRD2 as well, authors should consider reproducing key experiments with siControl- and siBRD4-treated cells and increasing doses of PSX as well as repeating the JQ1 dose response assay in Figure 1B using siRNA-mediated silencing of LOXL2. Given that both players are part of the same complex, silencing of one and inhibition of the other should sensitize cells compared to their control counterparts.
      • Moreover, in Figures 1G and S3D the differential sensitivity of low and high LOXL2 cell lines is unclear. Do authors know if any of these growth kinetic lines represent one of the tested cell lines in Figure 1A-B? Authors should provide respective legends. In addition, authors should take advantage of their homemade data given that they have already selected a panel of TNBC cell lines with various LOXL2 expression at basal state (Figure 1A) for which dose response assays have been performed (Figure 1B). Therefore, I would perform an IC50 graph for JQ1 (without PSX treatment) using the existing data from Figure 1B.
      • In Figure 2D, the pull-down assay is inconclusive, as the molecular weight for each construct is not mentioned. I would probably add this information also in all performed western blots. Also, the overexpression of the BD1/BD2-mutated and especially the BD1/BD2-lacking construct is unclear if it still interacts with LOXL2, probably because of the lack of molecular weight reference of each band. Therefore, the authors should make this pull-down assay more descriptive regarding the size of the bands. Also, BD1 mutagenesis at N140 was shown to dislodge the binding of JQ1 to BRD4 (24497639), which implies that BD1 mutagenesis or overexpression of the BD1-deficient construct should abrogate the interaction of LOXL2 with BRD4, reminiscent to the abrogated interaction of BRD4/LOXL2 upon JQ1 that binds to both BDs (Figure 2F). And, what happens if a BD2-deficient construct is expressed?
      • If authors support that BRD4S is the predominant isoform driving the expression of DREAM-targets, this means that DREAM-targets are mainly bound by BRD4S, relying on Figure 3E-F. However, based on the author's ChIPseq tracks in Figure 3H, DREAM targets such as EZH2 and HMGB2 are co-occupied by both BRD4 isoforms at the basal state on their promoter region. Also, especially for EZH2 and PLK4, authors should set to 'group auto-scale' both conditions in a smaller scale range for ChIPseq- and RNAseq tracks, although I do not these two genes as good candidates representing your analysis. Therefore, authors should initially show all genes (e.g in a table format) that enrich the 'DREAM-targets' signature and select for a greater panel of genes (like for AURKB and HMGB2) demonstrating a preferential occupancy of the BRD4S at their promoter region. Finally, authors are recommended to perform a ChIP-qPCR on these genomic regions at basal state (no LOXL2 silencing) to validate the predominant occupancy of BRD4S and the low/absent occupancy of BRD4L at these genomic sites.
      • Authors in Figure 3G should select an equal-sized population of randomly chosen non-DREAM-target genes, otherwise, the comparison of log2FC difference between these two gene cohorts is unreliable and difficult to make. Mann-Whitney test should also be performed.
      • Authors should repeat the cell cycle analysis (Figure 4A) as the number of cells subjected to flow cytometry is quite discrepant between the conditions. Also, it is not clear if the experiment was performed in at least biological triplicates (although in the respective legend, it is stated so). If performed in biological triplicates, authors should make a new graph where each cell cycle phase cell population differs between the two conditions. Moreover, the difference in cell cycle defects in LOXL2-inhibited cells (Figure 4C) is indifferent compared to their control counterpart. Therefore, authors should address these inconsistencies.
      • Furthermore, authors should explain what was the rational selecting a mediator subunit and specifically MED1 as a possible interacting partner of LOXL2 and BRD4s since MED12 and MED24 were also highly essential (Figure 4F).
      • Moreover, do authors also observe this functional relationship of LOXL2 and BRD4S in cell cycle progression in other breast cancer subtypes presenting a high proliferation index e.g HER2+? Presumably, the author's proposed mechanism applies to a wide panel of breast cancer entities, for which, only key experiments could be performed.
      • Authors in Figure 5H represent LOXL2 and BRD4s as integral chromatin looping factors together with MED1 at promoter and enhancer regions. However, this illustration is an overrepresentation of their finding because authors did not address the differential occupancy of BRD4S upon LOXL2 loss in DREAM-target-specific enhancer regions. If they wish to do so, they may use the RANK ORDERING OF SUPER-ENHANCERS (ROSE) package to call for super-enhancer regions in the proximity of DREAM-targets and confirm similar results as for their TSS-proximal sites.
      • In the current manuscript, authors did not address the translational relevance of their proposed mechanism in the context of conventional therapies. Knowing that several BRD-specific compounds currently undergo clinical trials, authors should address if LOXL2 low (MDAMB468) and high (BT549) cells demonstrate a differential sensitivity to increasing doses of chemotherapy, in the presence or absence of BRD4. By doing that, LOXL2 apart from being a therapeutic target could be also used as a prognostic marker to stratify patients and achieve better response to standard therapies.

      Minor points:

      • In Figure 1D, the authors should convert the y-axis to a logarithmic scale to better represent the differences between JQ1, PXS, and combo. Also, One-way Anova should be performed between JQ1, PXS and combo.
      • In Figure S6F, authors did not show the sensitivity of LOXL2 low and high cell lines for BRD4 KO. If LOXL2-proficient cells are less sensitive to JQ1, based on Figure 1B, authors should consider showing something similar from the gene essentiality database.
      • Authors failed to discuss the work from Ozge Saatci et al (PMID: 32415208) regarding LOXL2 in TNBC and ECM reorganization as well as in other cancer entities (PMID: 35428659) in the context of ECM remodeling. Authors should realize that these published works and the current ones are not conflicting but complement each other.

      Significance

      Minor points:

      • In Figure 1D, the authors should convert the y-axis to a logarithmic scale to better represent the differences between JQ1, PXS, and combo. Also, One-way Anova should be performed between JQ1, PXS, and combo.
      • In Figure S6F, the authors did not show the sensitivity of LOXL2 low and high cell lines for BRD4 KO. If LOXL2-proficient cells are less sensitive to JQ1, based on Figure 1B, authors should consider showing something similar from the gene essentiality database.
      • Authors failed to discuss the work from Ozge Saatci et al (PMID: 32415208) regarding LOXL2 in TNBC and ECM reorganization as well as in other cancer entities (PMID: 35428659) in the context of ECM remodeling. Authors should realize that these published works and the current ones are not conflicting but complement each other.

      Significance

      The conception and findings are of enlightening significance for TNBC therapy, especially given the lack of targeted therapies in this particularly aggressive breast cancer subtype. Hence, I posit this work as highly relevant for the cancer epigenetics research community interested in characterizing unknown factors that facilitate the gene-activating function of epigenetic readers in health and disease.

      My field of expertise is to uncover epigenetic vulnerabilities responsible for transcriptional plasticity driving drug tolerance in aggressive forms of breast cancer.

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      Referee #2

      Evidence, reproducibility and clarity

      In their study, Pascual-Reguant et al. show that combined inhibition of BRD4 and LOXL2 can synergize to restrict triple-negative breast cancer (TNBC) proliferation. BRD4 and LOXL2 are transcription regulators that can read and write epigenetic information, respectively. The authors employ three distinct breast cancer cell lines and mouse models with cell line-derived xenografts, and they show that combined inhibition of BRD4 and LOXL2 can be superior to single BRD4/LOXL2 inhibition in these model systems. In an attempt to identify a connection between BRD4 and LOXL2, the authors find that the two proteins can bind to each other. The authors performed most of the experiments in the breast cancer cell line MDA-MB-231. To assess the impact of LOXL2-inhibition on transcription, the authors assessed changes of the transcriptome in MDA-MB-231 cells following LOXL2 knockdown. They found that genes related to cell differentiation and morphology were upregulated, while genes related to the cell cycle were downregulated. ChIP-seq data of BRD4 showed that BRD4 can bind to cell cycle gene promoters and that this binding was enhanced upon loss of LOXL2. The authors found that LOXL2 and BRD4 interacted with the transcriptional cell cycle regulators B-MYB, FOXM1, and LIN9, which are components of the MYB-MuvB-FOXM1 (MMB-FOXM1) complex that is known to promote the expression of late cell cycle genes with important functions during mitosis. The authors conclude that LOXL2/BRD4 interact with each other and with the MMB-FOXM1 complex to drive the expression of cell cycle genes and cell proliferations. Vice versa, they conclude that inhibition of LOXL2/BRD4 reduces cell proliferation through inhibiting the expression of cell cycle genes.

      Major:

      • The data and methods are presented well. The experiments are adequately replicated and analyzed. However, except for the first section, all experiments were performed using only one cell line. It is important to validate key findings in at least a second cell line.
      • There appears to be a misunderstanding of the concept of cell cycle-dependent gene regulation by the DREAM complex and its related factors. Early (G1/S) cell cycle genes contain E2F promoter motifs, while late (G2/M) cell cycle genes contain CHR promoter motifs. The DREAM complex can bind both, while RB-E2F and MuvB recognize only E2F and CHR motifs, respectively. B-MYB and FOXM1 bind to MuvB and regulate late cell cycle genes, but they do not bind to early cell cycle genes. Given this concept, the authors' rationale to connect BRD4/LOXL2 through MuvB/B-MYB/FOXM1 with E2F promoter sequences and early cell cycle genes and the subsequent conclusions must be corrected.
      • I felt that the suggested functional connection between LOXL2/BRD4 and DREAM is not strongly supported by the authors' data. Figure S6E: A similarity score of <0.7 is poor support for a 'consensus E2F sequence' and indicates very limited specificity. Figure 4E: IP with BRD4 and LOXL2 is missing as important control. A chromatin-binding control is missing that does not bind to DREAM/LOXL2/BRD4. To test for binding to the actual DREAM complex, the authors should include E2F4 and p130 in their IPs and western blots, perhaps following LOXL2 inhibition/knockdown. Figure 3: The authors' ChIP-seq data indicate that only a fraction of DREAM targets is bound by BRD4. To provide more evidence that LOXL2/BRD4 may be directly involved in regulating DREAM targets, the authors should compare the differential regulation of BRD4-bound DREAM targets upon LOXL2 knockdown with DREAM targets which are not bound by BRD4. If LOXL2/BRD4 acted in a direct manner on those targets, one would expect that loss of LOXL2 affected their transcription more strongly than the other DREAM targets which are affected only indirectly. Such an analysis can be performed readily using the available data.
      • The authors state that it is surprising to find that LOXL2 can promote target gene transcription because it is rather known as a transcriptional repressor. To this point, the authors should perform standard analyses using their RNA-seq and ChIP-seq data. Compare differential expression of genes that are bound by BRD4S/L/S+L and genes not bound by BRD4. Perform motif search and enrichment analyses for transcription factor and co-factor binding data (public ChIP-seq repositories). Such analyses may suggest what gene sets are up- and downregulated by LOXL2 through BRD4S/L and what other factors could be involved in LOXL2-dependent up- and downregulation of gene transcription.

      Minor:

      • I felt that background information on the BRD4 isoforms was missing. The short and long isoforms of BRD4 should be introduced briefly.
      • Given that BRD4 inhibition is known to activate p53 (e.g., PMID 23317504 and 33431824) and p21 (PMID 31265875), the authors should discuss the p53 status of their cell lines (largely mutant). In general, I felt that the authors could better cite and discuss the current literature on BRD4 and LOXL2.
      • It was unclear to me why the authors did not actually test experimentally whether their predicted interaction models 2 or 4 are likely true (Figure 2E+G).
      • The transcription of cell cycle genes depends on the cell cycle (i.e., reduced cell cycle entry correlates with reduced cell cycle gene expression). Given that the authors showed LOXL2 inhibition reduce MDA-MB-231 cell proliferation, they should note that reduced expression of cell cycle-related genes is expected upon LOXL2 knockdown.
      • The authors specify in their discussion that their data show a function of LOXL2/BRD4 in the cell cycle interphase, while there were no experiments that support that specific conclusion. At least it is unclear to me why the authors rule out a function in mitosis?
      • I felt that the first part of the manuscript (combination of BRD4 and LOXL2 inhibitors in TNBC) was a bit uncoupled from the functional studies on LOXL2 and its connection to BRD4. The transition between these parts and the final discussion on why the joint control of cell cycle genes by LOXL2/BRD4 may be important for the synergistic effect of LOXL2/BRD4 inhibitors. To this point, the authors' model was not clear to me.

      Significance

      The study by Pascual-Reguant et al. shows that inhibitors of BRD4 and LOXL2 can be combined to achieve better efficacy in reducing proliferation of breast cancer cell lines and breast tumor growth in xenograft models. They provide strong evidence for a functional interaction between LOXL2 and BRD4 and investigate their common transcriptional targets. Intriguingly, some evidence points towards a direct regulation of the DREAM complex and its cell cycle gene targets.

      The findings are novel and can be the basis for further research on TNBC combination therapy using BRD4 and LOXL2 inhibitors. The link to the DREAM complex is preliminary.

      The study is of interest for a basic research audience with some translational aspects.

      I reviewed this manuscript as a researcher in gene regulatory mechanisms, with cell cycle genes as one focus area. I have no expertise in the computational modeling of protein-protein interactions and I am no expert for breast cancer.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript describes that simultaneous inhibition of LOXL2 and BRD4 reduces proliferation of TNBC in vitro and reduces growth in vivo. This observation is followed by extensive mechanistic studies that suggest physical interaction between LOXL2 and short isoform of BRD4-MED1. Inferences from Chip-seq analyses suggest that this interaction is involved in regulation of multiple transcriptional programs. Authors focus on differential activation of DREAM complex, to claim that this interaction "is fundamental for proliferation of TNBC". The manuscript is very well written and mechanistic inferences are based on a set of sophisticated epigenetic analyses and bioinformatical inferences. The phenotypic effects from LoxL2 inhibition by itself, or in combination with BRD4 inhibition are relatively modest. These modest effects, as well as many of the reported changes in gene expression are clearly inconsistent with the frequently used adjectives as "dramatic", "fundamental", "deeply affected", "drastically hampered" etc. Given the modest phenotypic effects, many of the key claims and conclusions are not supported by the data.

      Specifically:

      1. It is unclear why authors generalize their conclusions to TNBC. Figure 1B demonstrates synergy for 1/3 cell lines, which is chosen for the follow up study. Even for MDA231, the synergy is confined to low concentrations of BRD4i (S1c). While MDA231 cell line is frequently used in experimental studies of TNBC, it is quite dissimilar to majority of clinical TNBC, and contains mutant RAS, which is rare in this disease.
      2. In vivo, the effect appears to be modest even in the MDA231 model, selected for evidence of synergy in vitro. In vivo, the combination appears to have an additive effect. Tumor growth rates are reduced, but no shrinkage is occurring. In the PDX model, LOXL2i does not have an effect as a monotherapy, while modestly enhancing the impact of BRD4i. These results are at odds with the claim of the interaction being fundamental for proliferation.
      3. No analysis of cell proliferation was shown in vivo. Authors should have performed BrdU or KI67 staining to support the claim. For in vitro analyses, authors also used indirect assays for proliferation. PI staining by itself does not have sufficient resolution to clearly capture modest effects that authors demonstrate. BrdU-PI double staining would have been much more useful.

      Minor points:

      1. Dose dependent decrease in phosphorylated H3 is not at all obvious from eyeballing the data in S1A; the only effect that I see is a modest reduction at the highest concentration of the inhibitor. Authors need to quantify the results to support the claim.
      2. Most of breast cancer cell lines are derived from metastatic disease, including pleural effusion, thus the point that because MDA231 cell line is derived from pleural effusion, it is metastatic does not have sufficient logical foundation.
      3. How is loss of cell-cell junction in vitro consistent with LOXL2 role in modulating ECM? There is no evidence of ECM production in MDA231 in vitro. On the other hand, this loss is associated with EMT.

      Significance

      Discovery and characterization of LOXL2-BRD4 interaction is advancing the ever-deepening understanding of molecular mechanisms of regulation of gene expression. The studies and analyses appear to be sufficiently rigorous and reported with clarity, and the claimed discovery of the biological interaction between LOXL2 and BRD4 is well supported. However, given the magnitude of the reported (rather than claimed) effects of this interaction, and concerns about generalizability of authors conclusions, it is not clear how these results are promising for the development of new therapies in TNBC. Moreover, in contrast to luminal BC, there is no clear evidence for utility of cytostatic drugs in constraining TNBC. Therefore, biological and clinical significance of the authors discovery is unclear and claims in this regard appear to be overblown.

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      Reply to the reviewers

      The authors show expression of the thyroid hormone transporter MCT8 in the human placenta. The MCT8-inhibiting compound sylchristine reduces the transfer of T4 from the maternal to the fetal side and accordingly reduces T4 degradation by DIO3. Since maternal thyroid hormones are relevant for neurodevelopment before the onset of fetal thyroid gland function, disruption of MCT8 transport, as occurs in the Allan-Herdon-Dudley syndrome, may contribute to the neurodevelopment failure present in these patients.

      The results of the transfer experiments are clear and support the authors' conclusions.

      We thank the reviewer for this positive statement.

      Minor comments:

      1. Line 71: please provide some references on the immaturity of the blood.brain barrier before 18 months. The endothelial cells may have tight junctions when the vessels sprout in the CNS. "Maturity" implies the full complement of the neurovascular unit, i.e., pericytes and astrocytes. So, please clarify this point, even if it does not contradict the experimental results showing the role of placental MCT8.

      We acknowledge that there is debate when the human blood-brain barrier is regarded as mature. Tight junctions of endothelial cells are functional from week 14 in fetal development (Saili et al, DOI: 10.1002/bdr2.1180) and reach functionality comparable with adult blood-brain barrier from 18 weeks onwards (DOI: 10.1016/j.placenta.2016.12.005). A fully functional blood-brain barrier requires interaction with a range of cells, including pericytes, astrocytes, microglia and neurons (DOI: 10.1016/j.placenta.2016.12.005), which matures during the entire pregnancy (e.g. cortical astrocytes start to appear from 30 weeks onwards).

      In our manuscript, we do not intend to overstate the relevance of our findings. Hence, in the revised manuscript, we changed the wording in lines 70-71 from ‘’mature’’ to ‘’functional’’. This avoids the discussion on when the human blood-brain barrier is mature, while conveying the message that the placental barrier is key in determining bioavailability of thyroid hormone for the fetal brain.

      It is unclear if the partial effect of sylchristine on T4 transport means that MCT8 contribution is also partial and other transporters contribute.

      We thank the reviewer for raising this point. Our in vitro data (Figure Expanded View 2) showed that at 10 µM concentration silychristin fully inhibits MCT8, agreeing with previous data by others (Johannes et al, DOI: 10.1210/en.2015-1933). As MCT8 is expressed at the apical membrane of the syncytiotrophoblasts which is in direct contact of maternal circulation, it can be inferred that T4 entering the placenta via MCT8 is fully inhibited. In our manuscript, we show that the application of silychristin on the maternal circulation leads to a 60% reduction of T4 accumulation at the fetal side, with the remaining 40% of fetal T4 corresponding to an absolute concentration of ~ 4 nM T4. Of note, we previously showed in the same placenta model that there is ~4 nM T4 endogenously present in the placenta (see Figure 3, DOI: 10.1089/thy.2022.0406). This endogenous placental T4 can be transferred to the fetal circulation; this latter process is not blocked by silychristin, which is only present in the maternal circulation. As adding silychristin results in only ~ 4 nM T4 appearing at the fetal side, equal to the endogenous concentration, it is likely that the contribution by other transporters is minimal.

      To clarify this, we have added this in the Discussion of the revised manuscript (lines 117-120).

      Lines 113-114. I missed the controls measuring TRIAC transport without sylchristine, or do the authors have strong reasons to assume that sylchristine does not affect TRIAC transport? If so, it should be stated.

      We thank the reviewer for raising this question. No human TRIAC transporters have been published and, hence, we cannot exclude the possibility that silychristin may inhibit TRIAC transport. However, we previously showed that human MCT8 does not induce TRIAC uptake (Figure 7, DOI: 10.1089/thy.2019.0009), indicating that TRIAC transport is MCT8 independent. In a previous study, we tested the specificity of silychristin for the thyroid hormone transporters expressed in human term placenta (Figure 2 in DOI: 10.1089/thy.2021.0503)). Silychristin potently inhibited MCT8 with an IC50 of 0.12 µM and at a much higher concentration (10 µM) it also inhibited OATP1A2 by ~40% but none of the other transporters. However, OATP1A2 does not transport TRIAC (unpublished data). Therefore, we feel that silychristin is unlikely to have relevant effects on other placental thyroid hormone transporters that may facilitate TRIAC transport. Hence, we did not include experiments of TRIAC transport in the absence of silychristin.

      Our aim was to provide a proof-of-concept that TRIAC is very efficiently transported across human placenta when MCT8 is inhibited. Should the reviewer insist to perform TRIAC transport in the absence of silychristin, we would be happy to do so.

      In the revised manuscript, we have included this point as a limitation in our study (lines 146-151).

      Reviewer #1 (Significance (Required)):

      This study confirms the presence of MCT8 in the human placenta and adds additional data to demonstrate its functionality with experiments using placental perfusion.

      We are pleased to see that the reviewer agrees that our data are a relevant addition to the field.

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

      Short summary of findings:

      The authors used silychristin to selectively block the thyroid hormone transporter MCT8 in perfused human placenta. This was done to model the MCT8 deficient placenta in the rare genetic condition called Allan Herndon Dudley Syndrome. They then showed that the thyroid hormone analogue TRIAC can still cross the placenta and may be a potential treatment to prevent some of the effects of thyroid hormone deficiency in the affected fetus.

      Major comments:

      The absence of binding proteins in the maternal circulation is an issue since protein bound thyroid hormones can also be taken up by trophoblasts. Additionally, the placenta produces and secretes transthyretin and albumin into the maternal circulation - was this taken into account?

      We thank the reviewer for raising this point. We are aware of thyroid binding proteins such as transthyretin (TTR) (eg. DOI: 10.1016/j.placenta.2013.05.005; DOI: 10.1016/j.placenta.2012.01.006; DOI: 10.1210/jc.2009-0048). From such data, it has been established that the placenta secretes TTR. Also, it has been shown that the TTR-T4 complex can be internalized into Jeg3 cells. However, to our knowledge, there is no direct evidence showing that the TTR-T4 complex is transported across human placenta reaching the fetal circulation.

      As we have mentioned in the response to the comment 2 of Reviewer 1, adding silychristin results in only ~ 4 nM T4 appearing at the fetal side, equal to the endogenous concentration present in the placenta. Therefore it is likely that the contribution by other transporters or transport mechanisms such as TTR-T4 is minimal.

      A caveat to the abovementioned arguments is that our perfusion only lasted for 3 hours because longer perfusions will lead to loss of intactness of the placenta and, hence, less functionality. Therefore, it cannot be excluded that during longer exposures TTR might have a role.

      Following this reviewer’s comment, we added this as a limitation to our model in the revised manuscript (lines 151-153).

      Other studies have suggested that T4 cannot cross the placenta unless the type 3 deiodinase is blocked which differs from this study. This paper should be referenced and discussed.

      We are aware of the study by Mortimer et al (DOI: 10.1210/jcem.81.6.8964859) that showed T4 could transport across human term placenta only when D3 was blocked by iopanoic acid. In our previous study (DOI: 10.1089/thy.2022.0406), we confirmed their findings in perfusion experiments with and without iopanoic acid on maternal-to-fetal T4 transfer (Figure 1), which we discussed in the discussion of our previous publication.

      However, in that previous study we also found that transport of T4 in human term placenta is asymmetrical with fetal-to-maternal transfer being more rapid than maternal-to-fetal transfer. However, when adding albumin (BSA) to the fetal circulation (which was not done by Mortimer et al), we prevented re-uptake of T4 and were able to show fetal T4 accumulation in the absence of iopanoic acid. Therefore, we optimized the model by maintaining the physiological conditions in which the type 3 deiodinase is present.

      Following the reviewer’s suggestion, we discussed this paper between lines 144-146.

      How specific is the action of silychristin? Does it have effects on other thyroid hormone transporters that may facilitate TRIAC transport?

      In a previous study, we tested the specificity of silychristin for the thyroid hormone transporters expressed in human term placenta (Figure 2 in DOI: 10.1089/thy.2021.0503). Silychristin potently inhibited MCT8 with an IC50 of 0.12 µM and at a much higher concentration 10 µM it also inhibited OATP1A2 by ~40%. However, OATP1A2 does not transport TRIAC (unpublished data). Therefore, we feel that silychristin is unlikely to have relevant effects on other placental thyroid hormone transporters that may facilitate TRIAC transport.

      We have added discussion about this between lines 146-151.

      Although TRIAC is able to cross the placenta, it is likely that it still would not be able to cross into the fetal brain making its use somewhat limited. Additionally, it has been suggested that TRIAC exposure may also be a neurodevelopmental risk ((Barez-Lopez et al., 2016) and (Yamauchi et al. 2022 TRIAC disrupts cerebral thyroid hormone action via a negative feedback loop and heterogenous distribution among organs. BioRXiv). This should be discussed.

      We thank the reviewer for raising this important point. We would like to respectfully point out that TRIAC in different animal models for MCT8 deficiency has been able to restore abnormal brain development (DOI: 10.1210/me.2014-1135, DOI: 10.3390/ijms232415547, DOI: 10.1530/JOE-16-0323, DOI: 10.1242/dmm.027227).

      Specifically, TRIAC administration between postnatal day 1 and 12 restored T3-dependent neural differentiation in the cerebral and cerebellar cortex in Mct8/Oatp1c1 double knockout mice, which represents a relevant mouse model recapitulating the neurological phenotype in patients with MCT8 deficiency (doi: 10.1210/me.2014-1135).

      Barez-Lopez (2016) administered TRIAC to Mct8 single knock-out mice. As Oatp1c1 is a redundant T4 transporter in mice, brains of these animals are only mildly hypothyroid and do not recapitulate the severity of the phenotype seen in humans. Hence, we disagree with the conclusion of these authors as they utilized a non-optimal mouse model (DOI: 10.1210/jc.2012-3759). Yamauchi et al. (doi: 10.1016/j.isci.2023.107135) showed that TRIAC content in cerebral cortex did not increase after oral administration of TRIAC after postnatal day 21 in euthyroid and hypothyroid mice. Moreover, they utilized the same dose as T3 as comparison, while TRIAC should be dosed 10-times higher ( DOI: 10.1210/me.2014-1135). Using such a dose, it is very much understandable that TRIAC only affects the hypothalamus-pituitary-thyroid axis, but is insufficient to exert thyroid hormone action in the brain.

      We would like to emphasize that there is >70-year experience with TRIAC in humans for other conditions. Neurotoxicity has never been observed. Currently, TRIAC is being studied in high dosages in young children with MCT8 deficiency. The study protocols have been approved by different Ethics Committees as well has been discussed with regulatory authorities.

      In the revised manuscript, we have incorporated some information that there is sufficient data in different animal models showing that TRIAC is able to enter the brain.

      The data for the control group was used in a previous publication - were the data collected at the same time? Ideally, the control vs silichrystin treated placental cotyledons should be from the same placental samples.

      We agree with the reviewer that ideally the control and silychristin treated cotyledons should be matched from the same placenta. However, in practice, it is extremely difficult to realize this for many reasons. In our perfusion experiments, only ~30% of the perfusions succeeded as determined by the criteria of the quality controls (antipyrine and FITC-dextran). Moreover, using two cotyledons from one placenta is not feasible for different reasons (e.g. absence of two intact cotyledons due to damage during delivery; one cotyledon is intact during perfusion, whereas the other is not; one cotyledon has maternal antipyrine diffused to the fetal circulation whereas the other one is not. Therefore, due to practical obstacles and strict quality control criteria, it is not likely to obtain such data of these from different placentas.

      Minor comments

      T4 and TRIAC were prepared in 0.1N NaOH and silychristin in DMSO. Do NaOH or DMSO affect membrane transporters? Were vehicle controls used in the perfusion experiment?

      We dissolved T4 and TRIAC in 0.1 N NaOH and silychristin in DMSO and added them to the perfusion buffer at a 1000 times dilution. For NaOH, it is commonly used in transport assays. Such NaOH and DMSO dilutions did not affect thyroid hormone transport in COS1 cells; therefore we did not include vehicle control DMSO in perfusion experiments.

      Were the silichrystin vs control samples matched from the same placentas?

      The silychristin and control samples were not matched from the same placentas for the reasons mentioned above.

      In Figure 1C, why is there an increase in TRIAC on the maternal side between the first and second time points?

      As we sometimes observed in our perfusion experiments with other compounds, at t=0 min (the first time point), the buffer is not aerated and still heterogeneous, leading to differences in the measured concentrations of the TRIAC. Therefore we also included t=6 min (the second time point) to get a more accurate starting concentration.

      In figure 1C, TRIAC movement from maternal to fetal side in silichrystin treated placenta is shown but there is no data from untreated placenta? TRIAC transport may be reduced but we cannot tell without a control to compare it to. This should be included.

      We thank the reviewer for raising this question, which is similar to comment 3 of Reviewer 1. Hence, we would like to refer to our response to the comment 3 of Reviewer 1.

      Reviewer #2 (Significance (Required)):

      This study is interesting and adds to the current gaps in the knowledge of transplacental thyroid hormone transport. There is still very limited information around how thyroid hormones cross the placenta despite many groups working on this over the years. However, the manuscript would be more interesting if the placental transporter for TRIAC was identified. There are several clinical trials already underway looking at how TRIAC therapy may be useful in this condition and it may even be detrimental. If TRIAC can cross the placenta its use may still be problematic since others have shown that it cannot cross the into the MCT8 deficient brain from the circulation and must be delivered directly into the brain (Barez-Lopez et al., 2019). This is a small study and is fairly limited however it would be interesting to those, like me, with an interest in endocrinology, placental biology and pregnancy.

      Barez-Lopez, S., Grijota-Martinez, C., Liao, X.H., Refetoff, S. and Guadano-Ferraz, A., 2019. Intracerebroventricular administration of the thyroid hormone analog TRIAC increases its brain content in the absence of MCT8, PLoS One. 14, e0226017.

      Barez-Lopez, S., Obregon, M.J., Martinez-de-Mena, R., Bernal, J., Guadano-Ferraz, A. and Morte, B., 2016. Effect of Triiodothyroacetic Acid Treatment in Mct8 Deficiency: A Word of Caution, Thyroid. 26, 618-26.

      We are pleased to see that the reviewer agrees that our data are a relevant addition to the field. We have alluded to the discussion in the field in the revised manuscript (lines 129-132).

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      Referee #2

      Evidence, reproducibility and clarity

      Short summary of findings:

      The authors used silychristin to selectively block the thyroid hormone transporter MCT8 in perfused human placenta. This was done to model the MCT8 deficient placenta in the rare genetic condition called Allan Herndon Dudley Syndrome. They then showed that the thyroid hormone analogue TRIAC can still cross the placenta and may be a potential treatment to prevent some of the effects of thyroid hormone deficiency in the affected fetus.

      Major comments:

      The absence of binding proteins in the maternal circulation is an issue since protein bound thyroid hormones can also be taken up by trophoblasts. Additionally, the placenta produces and secretes transthyretin and albumin into the maternal circulation - was this taken into account?

      Other studies have suggested that T4 cannot cross the placenta unless the type 3 deiodinase is blocked which differs from this study. This paper should be referenced and discussed.

      How specific is the action of silychristin? Does it have effects on other thyroid hormone transporters that may facilitate TRIAC transport?

      Although TRIAC is able to cross the placenta, it is likely that it still would not be able to cross into the fetal brain making its use somewhat limited. Additionally, it has been suggested that TRIAC exposure may also be a neurodevelopmental risk ((Barez-Lopez et al., 2016) and (Yamauchi et al. 2022 TRIAC disrupts cerebral thyroid hormone action via a negative feedback loop and heterogenous distribution among organs. BioRXiv). This should be discussed. The data for the control group was used in a previous publication - were the data collected at the same time? Ideally, the control vs silichrystin treated placental cotyledons should be from the same placental samples.

      Minor comments

      T4 and TRIAC were prepared in 0.1N NaOH and silychristin in DMSO. Do NaOH or DMSO affect membrane transporters? Were vehicle controls used in the perfusion experiment?

      Were the silichrystin vs control samples matched from the same placentas?

      In Figure 1C, why is there an increase in TRIAC on the maternal side between the first and second time points? In figure 1C, TRIAC movement from maternal to fetal side in silichrystin treated placenta is shown but there is no data from untreated placenta? TRIAC transport may be reduced but we cannot tell without a control to compare it to. This should be included.

      Significance

      This study is interesting and adds to the current gaps in the knowledge of transplacental thyroid hormone transport. There is still very limited information around how thyroid hormones cross the placenta despite many groups working on this over the years. However, the manuscript would be more interesting if the placental transporter for TRIAC was identified. There are several clinical trials already underway looking at how TRIAC therapy may be useful in this condition and it may even be detrimental. If TRIAC can cross the placenta its use may still be problematic since others have shown that it cannot cross the into the MCT8 deficient brain from the circulation and must be delivered directly into the brain (Barez-Lopez et al., 2019). This is a small study and is fairly limited however it would be interesting to those, like me, with an interest in endocrinology, placental biology and pregnancy.

      Barez-Lopez, S., Grijota-Martinez, C., Liao, X.H., Refetoff, S. and Guadano-Ferraz, A., 2019. Intracerebroventricular administration of the thyroid hormone analog TRIAC increases its brain content in the absence of MCT8, PLoS One. 14, e0226017.

      Barez-Lopez, S., Obregon, M.J., Martinez-de-Mena, R., Bernal, J., Guadano-Ferraz, A. and Morte, B., 2016. Effect of Triiodothyroacetic Acid Treatment in Mct8 Deficiency: A Word of Caution, Thyroid. 26, 618-26.

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      Referee #1

      Evidence, reproducibility and clarity

      The authors show expression of the thyroid hormone transporter MCT8 in the human placenta. The MCT8-inhibiting compound sylchristine reduces the transfer of T4 from the maternal to the fetal side and accordingly reduces T4 degradation by DIO3. Since maternal thyroid hormones are relevant for neurodevelopment before the onset of fetal thyroid gland function, disruption of MCT8 transport, as occurs in the Allan-Herdon-Dudley syndrome, may contribute to the neurodevelopment failure present in these patients. The results of the transfer experiments are clear and support the authors' conclusions.

      Minor comments:

      1. Line 71: please provide some references on the immaturity of the blood.brain barrier before 18 months. The endothelial cells may have tight junctions when the vessels sprout in the CNS. "Maturity" implies the full complement of the neurovascular unit, i.e., pericytes and astrocytes. So, please clarify this point, even if it does not contradict the experimental results showing the role of placental MCT8.
      2. It is unclear if the partial effect of sylchristine on T4 transport means that MCT8 contribution is also partial and other transporters contribute.
      3. Lines 113-114. I missed the controls measuring TRIAC transport without sylchristine, or do the authors have strong reasons to assume that sylchristine does not affect TRIAC transport? If so, it should be stated.

      Significance

      This study confirms the presence of MCT8 in the human placenta and adds additional data to demonstrate its functionality with experiments using placental perfusion.

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      Reply to the reviewers

      We appreciate the positive feedback from both reviewers and their critical comments, which will help us to improve the manuscript. Below, we provide a point-by-point response and how we propose to address their queries and comments.

      As the laboratory is currently undergoing a major transition, we propose essential experiments that are realistic to perform under these circumstances. We are positive that we can address all the most critical points identified by the reviewers.

      Suggestions for minor changes to the figures are already included.

      We also include responses to questions the reviewers raise.

      Reviewer #1 (Evidence, reproducibility and clarity):

      Major comments:

      1 - The authors assessed acridine orange incorporation in BECs upon LiverZap and concluded that LiverZap triggers hepatocyte-specific cell death without a bystander effect in adjacent cells (Figure 1 D-E). What happened to endothelial cells, which could also be affected either directly by ROS production in hepatocytes or indirectly by gross morphological changes in tissue organization?

      Response:

      The reviewer raises two excellent points:

      (i) Bystander effect of hepatocyte-produced ROS on endothelial cells: the cell death analysis included in the manuscript, shows that Acridine Orange staining overlaps with hepatocyte _tg(fabp10a:dsRed) expression, but not with biliary tg(tp1:H2B-mCherry) indicating cell death is specific to hepatocytes. Moreover, singlet oxygen species as produced by LiverZap have been shown to have a very short half-life and short range of action, suggesting that neighbouring cells are unlikely affected (Liang et al., 2020).

      PLAN: Investigate potential bystander effect in endothelial cells, by activating the LiverZap tool in livers expressing transgenic tg(kdrl:mcherry) marking the vascular network followed by live staining with Acridine Orange at 8 hours post illumination.

      (ii) indirect effects of morphological tissue changes on endothelial cells: studying the tissue response of the vascular network to hepatocyte ablation would be very interesting. A separate and detailed study would be required to generate meaningful data and insights into said process. This could encompass for instance the use of the transgenic endothelial _tg(kdrl:mcherry) line for LiverZap experiments and parallel those in Figure 4A-S. Thus, it seems beyond the scope of this work.

      NO CHANGES PROPOSED.

      2 - The evaluation criteria for distinguishing mCherry and cells in imaging experiments should be clearly described in the methods section. The authors should also provide some quantitative data regarding the level of correlation between the mCherry hepatocytes and the BEC-derived hepatocytes strictly defined based on the TP1-H2B-EGFP lineage tracing, as the former was used as a surrogate marker for the latter in some experiments.

      Response:

      Here, we believe the reviewer refers to the Tp1:H2B-mCherry-based lineage tracing, since the tg(tp1:egfp) line has not been used for this purpose. Similar to previous studies in the regeneration field (e.g. Choi et al., 2014; He at al., 2014), we have used histone inheritance of Tp1:H2B-mCherry for short-term lineage tracing. Tp1:H2B-mCherry-based lineage tracing was assessed on the whole organ level, for which we will describe the quantification pipeline. Tp1:H2B-mCherrylow cells were identified as BEC-derived hepatocytes after severe hepatocyte ablation, as shown in Fig. 2A,C, correlating with hepatocyte marker tg(fabp10a:GFP) expression. Tp1high and Tp1low cell numbers were quantified for 12, 24, 48 and 72 hpi and can be added as supplementary information.

      PLAN: Update the material and methods section and produce a more detailed description. This would include the following information: Whole-mounted livers of tg(tp1:H2B-mCherry) fish were stained for mCherry and imaged using an Leica SP8 confocal microscope. Image processing was carried out using the Imaris software. All mCherry-expressing cells in the liver were masked using the “spots” function, which allows quantification of signal intensity of all cells, represented by a sphere. Tp1high and Tp1low cells were identified using an automatically generated intensity threshold. Due to intensity differences with increasing imaging depth/z-position, segmented Tp1high cells were manually curated.

      To showcase the analysis strategy, we propose to include an example showing original image data, semi-automated quantification at the surface and deep tissue levels, as well as the overall Tp1:H2B-mCherry intensities for all positive cells and specifically Tp1high cells for all z-positions of an entire liver (see data figure below). This example could be included as supplementary data. Likewise, cell number quantification for Tp1high and Tp1low across regeneration can be added to Fig. S2.

      Fig. Quantification of Tp1:H2B-mCherryhigh cells. (A,B) 10 µm maximum intensity projections from whole mount stained tg(LiverZap);tg(tp1:H2B-mCherry) livers at 48 hpi: at the surface (A-A’) and deep in the liver (B-B’). Tp1high cells are identified by fluorescence intensity of segmented nuclei, outlined in yellow (A’ and B’). Graphs showing distribution of all Tp1:H2B-mCherry nuclei (C) and Tp1high nuclei (D) by fluorescence intensity and z-position (C). The intensity of all mCherry+ nuclei decreases with increasing z-position (C-D). The dotted line outlines the liver in A-B’.

      3 - OPTIONAL: In the locally restricted ablation model, do hepatocytes located adjacent to the ROI proliferate and/or contribute to the regeneration of the injured region?

      Response:

      An important consideration, as highlighted by the reviewer, is whether neighbouring hepatocytes also contribute to regeneration following ROI ablation.

      PLAN: To address this point, LiverZap ROI ablation will be followed by cell proliferation analysis using an EdU incorporation assay at 24 and 72 hpi. These time points are selected based on the proliferation results following global LiverZap ablation; see Fig. 2D-F. The experiment will be performed in a tg(tp1:H2B-mcherry); _tg(fabp10a:gfp)_background to distinguish proliferating GFP-positive hepatocytes, which are H2B-mCherry-negative, from LPC-derived hepatocytes that have inherited H2B-mCherry (Tp1low). The resulting insights may help to refine hypotheses regarding the process(es) stimulating the formation of new hepatocytes adjacent to the ablated region.

      4 - OPTIONAL: Figure 4, A-S. It should be of significant interest if the authors could also analyze the BEC dynamics using the locally restricted hepatocyte ablation model, comparing those in the injured region (ROI) and the outside of the ROI.

      Response:

      We agree with the reviewer that this is the exciting next question, as it likely would provide insights into the cellular mechanism by which the biliary network is de- and re-constructed, as well as the mechanism by which BECs outside the ROI may initiate the LPC response to give rise to hepatocytes in a semi-systemic response. For this, the experimental set-up introduced in Fig.4J-P, in which BECs in the ROI are distinguished from adjacent ones by photoconversion, would be followed by extended live light-sheet microscopy of the regenerating liver. Due to the complexity, extent of the experiments and current unavailability of a light-sheet microscope, we would address this optional comment in future investigations.

      NO CHANGES PROPOSED.

      5A- Figure 4, T-V'. The data shown here for the changes in E-cadherin distribution is difficult to understand and interpret. The authors should provide magnified images and better description on how to distinguish the membranous (spotted signals?) and intracellular localization. Quantitative assessment should certainly be a plus, if possible.

      Response:

      We appreciate that it may be difficult to recognize the changes in E-Cadherin localisation, in particular at BEC membranes, given that there are intracellular puncta, and that E-Cadherin is expressed both in BECs and hepatocytes. We are convinced of the related data described in Figures 4 and S4, because the first experiment allowed quantification of the staining using both Tp1:H2B-mCherry to identify BECs and intestinal E-Cadherin for normalisation, which revealed a 51% E-Cadherin reduction at BEC cell membranes following injury. Unfortunately, the signal-to-noise ratio declined in consecutive experiments precluding further quantification although we could still observe a change in localisation. We tested alternative antibodies against E-Cadherin as well as optimized staining protocols, yet without success.

      5B - OPTIONAL: In relation to the above point, it is this reviewer's candid impression that the very last part regarding the possible role of E-cadherin dynamics in regulating the biliary network remodeling is still preliminary compared to the remaining parts, thereby rather depreciating the value of the entire manuscript. Perhaps this part could be published separately, together with more functional evidence regarding the causal relationship between them (e.g., showing the effect of Ecadherin knockdown in hepatocytes on the biliary remodeling and the induction of the BECdependent regeneration program)

      Response:

      PLAN: Following this reviewer’s and reviewer 2’s comments and suggestions, we agree to remove the data on E-Cadherin. Loss of adhesion as a mechanism for adopting an LPC-state remains very exciting, future investigations with novel tools to monitor and modulate E‑Cadherin expression in BECs would thus be needed.

      6 - Do zebrafish livers possess lobular structures with the portal-to-central vein axis and the metabolic zonation as typically observed in mammalian livers? As has been described in the manuscript, the "localized" injury patters in the mammalian livers usually occur at the sub-lobular structure levels (i.e., peri-portal region-restricted vs. peri-central region-restricted). Although the "localized" injury model described in this study using the zebrafish livers was indeed localized from the viewpoint of the entire organ (or the lobe), it still seemed much more "global" when considering those situations in the mammalian livers, so that the authors' claim that the former recapitulating the latter might be too exaggerated and somehow misleading. The authors should clarify and discuss this point in the manuscript.

      Response:

      The reviewer raises an important point, and it seems that our wording might not have been clear. In mammals, boundaries between injured and healthy tissue arise, because liver injuries frequently occur at the sub-lobular level. Although zebrafish livers are composed of metabolically diverse hepatocytes, a spatial arrangement comparable to mammalian zonation has so far not been identified (Morrison et al. 2022; Oderberg and Goessling, 2023). Yet, the liver lobes in the adult zebrafish have a central vein and periportal veins at the periphery of the organ, similar to the mammalian lobular organisation (Ota et al. 2022). Therefore, the scale of injury in the mammalian setting and the ROI-ablation model introduced in the current work differs. It, nevertheless, creates boundaries of healthy and injured liver tissue relevant for uncovering dynamic cellular processes mediating tissue repair in chronic liver disease. Importantly, with its suitability for advanced live imaging and optogenetic methods (e.g. photoconversion), LiverZap, complements mammalian models, in which this is still challenging. This offers therefore the powerful opportunity to employ LiverZap to screen for dynamic repair behaviours, which subsequently can be validated in a target approach in mammalian injury models.

      PLAN: To describe the relevance of our ROI ablation paradigm for elucidating repair processes at the interface of injured and healthy tissue more precisely. We will further edit and clarify text to place the ROI ablation into the context of hepatic injuries at the sub-lobular level throughout the mammalian liver.

      Minor comments:

      7 - Figure 4. Panels D and G should correspond to the same one image and the way of labeling be changed (as in Figure 1G). Likewise, in panel J, the bars shown separately as "M" and "S" at 12 dpi should correspond to the same data, so that they should be unified as one bar.

      Response:

      Thank you for pointing this out, this is changed in the updated figures; panels Fig. 4D and I.

      8 - Figure S3L. How was the ROI border defined? Perhaps the shape of the ROI should change significantly during regeneration due to dynamic tissue remodeling processes, thereby moving the position of the border as well.

      Response:

      The ROI border was defined as the interface between photoconverted and non-converted BECs. We concur with the reviewer’s notion that cell movement and rearrangement may occur during the regeneration process (see Fig. 4A-J), and the initially straight ROI border could consequently change during the regeneration process. Nevertheless, the border between photoconverted and non-converted BECs persists, serving as a landmark for the measurements shown in Figure S3L.

      Fig.: Quantification strategy for determining the region exhibiting an LPC-response outside the ROI ablation region. The dashed line of the ROI indicates morphogenetic changes of the interface between photoconverted and nonconverted cells over time due to repair-related cell rearrangement.

      PLAN: In the revised manuscript, we propose to include the below schematic as panel J to Figure S3. Moreover, we also suggest to change the solid line of the squares indicating the ROI area in figure panels 3C,G,O,P and S3D,H,K into a dashed line at the interface between photoconverted and non-converted tissue (see below figure as an example).

      9 - The authors should comment in the manuscript as to whether the system can be applicable for induction of more restricted areas (e.g., at a single hepatocyte level; in particular metabolic zones, if existing), as well as for ablation of other hepatic cell types such as BECs and endothelial cells.

      Response:

      Indeed, the optogenetic nature of the LiverZap system allows to induce hepatocyte death at the single cell level, as well as any defined region of interest that can be generated by the light source (e.g. confocal microscope software).

      Likewise, the FAP-TAP system can be easily applied to BECs or endothelial cells, or any cell type for which a specific promoter has been identified to drive the genetic FAP component fluorogen-activating protein dL5**.

      Response:

      PLAN: Both points will be included in the discussion section of the manuscript.

      Reviewer #2 (Evidence, reproducibility and clarity):

      MAJOR COMMENTS:

      1 - The LiverZap is an elegant new tool to induce localized ablation of hepatocytes. It is not as claimed by the authors a real breakthrough: (1) While localized ablation is nice compared to NTR-MTZ model in zebrafish, mice model such as CCl4 chronic injury can also study the interaction between healthy and injured tissue. (2) Although not using MTZ, the system still requires injection or exposure to malachite green derivate dye MG-2I. A few searches suggest that this compound could induce toxicity. Can the authors study and compare the toxicity of malachite green derivate dye MG-2I to the toxicity of MTZ? This is important as this would be indeed a strong argument in favor of the presented tool.

      Response:

      Point 1 – studying interactions between healthy and injured liver tissue: The reviewer is of course correct that interactions between healthy and injured tissue can also be studied in the mouse. However, ROI ablation with the LiverZap system can be combined with live imaging, thereby enabling the observation of cellular responses of the same sample over time, at a resolution currently difficult to achieve in mammals. Moreover, the possibility to induce cell death in a defined ROI, also allows to simultaneously employ other genetic tools, including cell-type specific lineage tracing by photoconversion, which is difficult to achieve in mammalian systems. The finding that BECs beyond the ROI of hepatocyte ablation produce new hepatocytes by a LPC response, illustrates the power of this approach. The optogenetic LiverZap ablation system would therefore complement existing mammalian and zebrafish liver regeneration models.

      PLAN: to include a more detailed discussion of this point and the complementary knowledge that can be gained in the discussion section.

      Point 2 – MG-2I toxicity__: Indeed, as described in the manuscript, the FAP-TAP system, underlying LiverZap hepatocyte ablation, requires MG-2I incubation for the formation of the photosensitiser. Compared to the NTR/MTZ system, incubation with MG2I is short, requiring <3 hours in contrast to more than 24hours MTZ incubation. The system, including MG-2I has also been employed in cells, as well as in the zebrafish heart and nervous system without reported adverse effects (He et al., 2016; Xie et al., 2020). Consistently, we have not observed any apparent adverse effects between 0-72 hpi following 3-18 hour MG-2I incubation (unpublished). Nevertheless, toxicity studies evaluating survival upon MG-2I incubation have not yet been carried out and may be required for comparison with MTZ.

      PLAN: To perform toxicity studies for MG-2I, similar to those previously performed for MTZ (e.g. Mathias et al. 2014), in which larval survival after 3, 24 and 48 hour MG-2I exposure starting at 4 dpf will be assessed daily until 8 days post fertilisation.

      2 -The term ablation is choose because it is anticipated that it induces heaptospecific death. However, the consequences of cell death is not shown. In particular, the inflammatory immune response is not shown nor discussed.

      Response:

      The reviewer raises an interesting point, namely the inflammatory immune response, which is not the focus of this manuscript. Acridine Orange- and TUNEL-positive cells during the ablation process indicate that the reactive oxygen species produced by the FAP-TAP system cause hepatocyte apoptosis. We predict that this would recruit and be cleared by macrophages with little or no inflammatory response, like findings for the NTR-MTZ system (Stoddard et al., 2019). However, the role of neutrophils is unclear due to a possible direct effect of MTZ on this cell type.

      PLAN: We will include this point in the discussion.

      Future in-depth live imaging of transgenic reporters will be required for detailed studies of macrophage and neutrophil recruitment and their role in efferocytosis, including transcriptome analysis of specific gene signatures to detect an inflammatory response.

      3 - The difference between mild and severe ablation is hard to grasp. Can the authors explain more clearly the differences between mild and severe: what are the criteria as there is no difference in liver volume between mild and severe ablation? How do you achieve mild or severe ablation? It appears that the severity of the ablation is judged a posteriori and not decided per the experiment.

      Response:

      Concerning the first point, there must be a misunderstanding. Mild and severe hepatocyte ablation result in clearly different liver sizes, for instance at 30 hpi, the end of ablation, liver volumes are reduced by 23 % for mild or 64 % for severe cases (Fig. 1Q). This is supported by representative image data in Figs. 1F-P and S1A-C. Nevertheless, for consistency, we had represented the 12 hpi volume data as the same two data bars, although we cannot distinguish them yet at that timepoint of the experiment, as shown by images in Fig. 1F-G.

      PLAN: Adjust Fig.1Q and represent the 12 hpi liver volume data as a shared graph for mild/severe ablation, see included figure 1Q. We propose to similarly represent all 12 hpi quantifications, as represented in Figs. S1F, 2D-F and S2A.

      For the second point, the reviewer is correct that ablation severity is evaluated and determined between 24-30 hpi, at the end of hepatocyte ablation, given there is some variability in the response. Nonetheless, both length of 660nm illumination and oxygen availability can be used to shift the proportion of mild and severe ablation, depending on the desired outcome (Figs. 1Q, S1G-H).

      NO CHANGES PLANNED.

      4 - The work supports that biliary-driven regeneration also occurs when hepatocyte ablation happens in a small area of interest. Our knowledge is that you need a large defect in hepatocyte or a chronic liver injury ro activate the BDC-driven auxiliary process for regeneration. Could this be a specificity of the fish model?

      Response:

      Like the reviewer, our understanding is that severe hepatocyte loss, senescence or chronic liver injury activate BEC-derived regeneration in mammals and in zebrafish. All these cases are characterised by substantial reduction of local hepatocyte density or loss of function (in senescence). Given the overall hepatocyte loss is only 10-20% in the ROI model, the induction of the local LPC response was very surprising, on the other hand it corresponds to a near complete local hepatocyte depletion. The hepatobiliary architecture in zebrafish is similar to that of the mammalian ductular reaction, an adaptation of the biliary network to severe hepatocyte loss. In both cases, the majority of hepatocytes connect directly via their apical canaliculi to biliary ductules to ensure physiologic transport of hepatocyte products, often preceding the LPC response (Sato et al., 2019; Caviglia et al., 2022). Therefore, we propose that the LPC response following ROI hepatocyte ablation is not specific to the zebrafish model, but a common mechanism elicited across species and related to the severity of the injury and the configuration of the hepatobiliary network at the time of injury, such as the ductular reaction.

      PLAN: To edit the text and discuss this point clearly.

      5 - Pathways revealed to control liver regeneration or BEC-driven regeneration in fish have not be found to have a similar drastic predominance in rodents. This mitigate perhaps the use of fish for this type of research?

      Response:

      On the contrary, zebrafish has been established and validated as a model to investigate and elucidate developmental hepatic programs as well as regeneration (Goessling and Sadler, 2015; Wang et al., 2017). However, we acknowledge that more comparative studies are needed to understand the molecular pathways driving regeneration both in zebrafish and mammals and their similarity.

      Specifically, zebrafish and mammals display high conservation in the parenchymal and non-parenchymal cell types of the liver as well as their developmental programs (Goessling and Sadler, 2015; Wang et al., 2017). Using different injury paradigms in zebrafish, including ethanol, acetaminophen toxicity and the pharmacogenetic NTR-MTZ model, it has been shown that cellular responses to liver injury are also remarkably conserved with mammals where hepatocyte proliferation governs repair after mild injury while severe injury repair is driven by conversion of BECs into LPCs (So, et al., 2020; Forbes and Newsome, 2019). Major pathways, such as Wnt, FGF and BMP signaling show conserved functions in restorative hepatocyte proliferation (Goessling et al., 2008; Kan et al. 2009, Böhm et al 2010). At present, only very little is known about the molecular mechanisms controlling the BEC/LPC to hepatocyte conversion particularly in rodent models (Kim et al., 2023), while a number of zebrafish studies have started to elucidate the signals governing the different steps of this process (Kim et al., 2023), due to the relative ease of using the larval zebrafish model for this work. Notably, the Notch pathway plays multiple roles in both mouse and zebrafish LPC-mediated repair (Minnis-Lyons et al., 2021; Huang et al 2014; Russel et al.,2019), however further work will be necessary to determine the detailed corresponding functions. Therefore, future work in both rodents and zebrafish will be essential to uncover the molecular mechanisms of this repair process relevant for chronic injury. Given the large conservation of developmental and repair mechanism between mammals and zebrafish observed so far, it is highly likely that this will also apply to LPC-mediated repair. Studies promise to uncover even greater similarity between zebrafish and human (e.g. Fang et al 2011), underscoring the power of using complementary vertebrate models.

      PLAN: To edit the text in the introduction and discussion to clarify and highlight the similarities, differences, and opportunities the zebrafish model offers for understanding the mechanisms of vertebrate liver regeneration in general and in particular by using the LiverZap system.

      6 - The authors show that in the case of mild ablation, hepatocytes are responsible for replenishment of the parenchyma, but in the context of severe ablation, LPC-mediated regeneration takes control. However, when the authors perform localized and controlled ablation, which is small (around 10-20%) and, to my understanding, a mild / local ablation, however the authors show that LPC mediates the regeneration. Can the authors explain the discrepancy between their results?

      Response:

      We agree with the reviewer that the LPC response in the smaller, local ROI ablation was unexpected. However, it could be explained by the following: while such ROI hepatocyte ablation represents only a 10-20% ablation of the total hepatocyte population, by sheer numbers comparable to a mild global ablation, the near-complete local hepatocyte loss however makes it more similar to a severe or chronic global injury. Notably, the zebrafish hepatobiliary architecture in zebrafish is similar to that of the mammalian ductular reaction, an adaptation of the biliary network to severe hepatocyte loss. In both cases, the majority of hepatocytes connect directly via their apical canaliculi to biliary ductules to ensure physiologic transport of hepatocyte products, often preceding the LPC response (Sato et al., 2019; Caviglia et al., 2022). We hypothesize that if a similar local, near complete hepatocyte loss would be induced in a mammalian liver exhibiting a ductular reaction, it would similarly induce local LPC-mediated repair. Since this is, to our knowledge not possible, the LiverZap model represents a unique opportunity to induce the LPC-response in a controlled manner and in addition investigate the underlying cellular and molecular processes of injured and adjacent healthy tissues at high resolution in an in vivo context.

      PLAN: We will edit the discussion to clarify this important point.

      7 - The last part of the paper about E-Cadherin expression is not convincing. I am not sure about the quality of the IF stainings of E-Cadherin, and it is not helping proving the point of the authors. Can the authors provide better stainings for this figure?

      Response:

      (Same response as to point 5A+B of reviewer 1). We appreciate that it may be difficult to recognize the changes in E-Cadherin localisation, in particular at BEC membranes, given that there are intracellular puncta and that E-Cadherin is expressed both in BECs and hepatocytes. We are convinced of the related data described in Figures 4 and S4, because the first experiment allowed quantification of the staining using both Tp1:H2B-mCherry to identify BECs and intestinal E-Cadherin for normalisation, which revealed a 51 % E-Cadherin reduction at BEC cell membranes following injury. Unfortunately, the signal to noise ratio declined in consecutive experiments, while we could still observe a change in localisation, it challenged a meaningful quantification. We tested alternative antibodies against E-Cadherin, yet without success.

      PLAN: Following both reviewers’ comments and suggestions, we agree to remove the data on E-Cadherin.

      8 - Could the authors provide a bit more information on the live imaging. Exactly how do they achieve imaging for such a long time?

      Response:

      Thank you for pointing this out, the information was not very detailed. We used relatively standard mounting conditions (low-melting point agarose and Tricaine anaesthesia, see below for details), combined with light-sheet microscopy, which was the key to achieving the long imaging. We believe that in addition to the known gentle imaging condition, the mounting set-up is critical as the fish is completely suspended in a very low-percentage, low melting point agarose within a large volume of embryo medium.

      PLAN: Update the material and methods section with the following details: Long-term live imaging was performed using a LS1 Live light sheet microscopy system (Viventis Microscopy Sàrl). Larvae were with anesthetized with 0.4% Tricaine and mounted ventrally in 0.8% low melting point agarose in E3/PTU media supplemented with 0.16% tricaine. Once the agarose solidified, the chamber was filled with E3/PTU with 0.16% Tricaine to maintain anaesthesia. A 25X objective was used and acquisition was performed every 20 minutes.

      MINOR COMMENTS:

      9 - It is hard to imagine the full-size liver in Figure 1, bad contrast. Can the authors manually delineate it?

      Response:

      The livers in this figure are now outlined in the updated figures, see new Figure 1.

      10 - "This finding is very surprising, since current understanding in the field links the generation of new hepatocytes from BECs/LPCs with global hepatocyte death." This statement lacks references.

      Response:

      PLAN: To add the following primary references to the above sentence: (Choi et al., 2014; He et al., 2014; Manco et al., 2019; Raven et al., 2017) and recent review (Kim et al_._, 2023).

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      Manco R, Clerbaux LA, Verhulst S, Bou Nader M, Sempoux C, Ambroise J, Bearzatto B, Gala JL, Horsmans Y, van Grunsven L, Desdouets C, Leclercq I. Reactive cholangiocytes differentiate into proliferative hepatocytes with efficient DNA repair in mice with chronic liver injury. J Hepatol. 2019

      doi: 10.1016/j.jhep.2019.02.003.

      Mathias JR, Zhang Z, Saxena MT, Mumm JS. Enhanced cell-specific ablation in zebrafish using a triple mutant of Escherichia coli nitroreductase. Zebrafish. 2014 doi: 10.1089/zeb.2013.0937.

      Minnis-Lyons SE, Ferreira-González S, Aleksieva N, Man TY, Gadd VL, Williams MJ, Guest RV, Lu WY, Dwyer BJ, Jamieson T, Nixon C, Van Hul N, Lemaigre FP, McCafferty J, Leclercq IA, Sansom OJ, Boulter L, Forbes SJ. Notch-IGF1 signaling during liver regeneration drives biliary epithelial cell expansion and inhibits hepatocyte differentiation. Sci Signal. 2021 doi:10.1126/scisignal.aay9185.

      Morrison JK, DeRossi C, Alter IL, Nayar S, Giri M, Zhang C, Cho JH, Chu J. (2022) Single-cell transcriptomics reveals conserved cell identities and fibrogenic phenotypes in zebrafish and human liver. Hepatol Commun. doi: 10.1002/hep4.1930.

      Oderberg IM, Goessling W. (2023) Biliary epithelial cells are facultative liver stem cells during liver regeneration in adult zebrafish. JCI Insight. doi: 10.1172/jci.insight.163929.

      Ota N, Shiojiri N. Comparative study on a novel lobule structure of the zebrafish liver and that of the mammalian liver. Cell Tissue Res. 2022 doi:10.1007/s00441-022-03607-y.

      Raven A, Lu WY, Man TY, Ferreira-Gonzalez S, O'Duibhir E, Dwyer BJ, Thomson JP, Meehan RR, Bogorad R, Koteliansky V, Kotelevtsev Y, Ffrench-Constant C, Boulter L, Forbes SJ. Cholangiocytes act as facultative liver stem cells during impaired hepatocyte regeneration. Nature. 2017 doi:10.1038/nature23015.

      Russell JO, Ko S, Monga SP, Shin D. Notch Inhibition Promotes Differentiation of Liver Progenitor Cells into Hepatocytes via _sox9b_Repression in Zebrafish. Stem Cells Int. 2019 doi:10.1155/2019/8451282.

      Sato K, Marzioni M, Meng F, Francis H, Glaser S, Alpini G. Ductular Reaction in Liver Diseases: Pathological Mechanisms and Translational Significances. Hepatology. 2019 doi: 10.1002/hep.30150.

      So J, Kim A, Lee SH, Shin D. Liver progenitor cell-driven liver regeneration. Exp Mol Med. 2020 doi: 10.1038/s12276-020-0483-0.

      Stoddard M, Huang C, Enyedi B, Niethammer P. Live imaging of leukocyte recruitment in a zebrafish model of chemical liver injury. Sci Rep. 2019 doi: 10.1038/s41598-018-36771-9.

      Wang S, Miller SR, Ober EA, Sadler KC. Making It New Again: Insight Into Liver Development, Regeneration, and Disease From Zebrafish Research. Curr Top Dev Biol. 2017 doi: 10.1016/bs.ctdb.2016.11.012.

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      Referee #2

      Evidence, reproducibility and clarity

      In this study, the authors present a new chemoptogenetic tool, LiverZap, to study regeneration in zebrafish. In combination with LiverZap, the authors use live imaging to study longitudinally LPC-mediated regeneration and the implication of the healthy surrounding tissue.

      Major comments

      The LiverZap is an elegant new tool to induce localized ablation of hepatocytes. It is not as claimed by the authors a real breakthrough: (1) While localized ablation is nice compared to NTR-MTZ model in zebrafish, mice model such as CCl4 chronic injury can also study the interaction between healthy and injured tissue. (2) Although not using MTZ, the system still requires injection or exposure to malachite green derivate dye MG-2I. A few searches suggest that this compound could induce toxicity. Can the authors study and compare the toxicity of malachite green derivate dye MG-2I to the toxicity of MTZ? This is important as this would be indeed a strong argument in favor of the presented tool.<br /> The term ablation is choose because it is anticipated that it induces heaptospecific death. However, the consequences of cell death is not shown. In particular, the inflammatory immune response is not shown nor discussed.<br /> - The difference between mild and severe ablation is hard to grasp. Can the authors explain more clearly the differences between mild and severe: what are the criteria as there is no difference in liver volume between mild and severe ablation? How do you achieve mild or severe ablation? It appears that the severity of the ablation is judged a posteriori and not decided per the experiment.<br /> - The work supports that biliary-driven regeneration also occurs when hepatocyte ablation happens in a small area of interest. Our knowledge is that you need a large defect in hepatocyte or a chronic liver injury ro activate the BDC-driven auxiliary process for regeneration. Could this be a specificity of the fish model?<br /> - Pathways revealed to control liver regeneration or BEC-driven regeneration in fish have not be found to have a similar drastic predominance in rodents. This mitigate perhaps the use of fish for this type of research?

      The authors show that in the case of mild ablation, hepatocytes are responsible for replenishment of the parenchyma, but in the context of severe ablation, LPC-mediated regeneration takes control.<br /> However, when the authors perform localized and controlled ablation, which is small (around 10-20%) and, to my understanding, a mild / local ablation, however the authors show that LPC mediates the regeneration. Can the authors explain the discrepancy between their results?<br /> The last part of the paper about E-Cadherin expression is not convincing. I am not sure about the quality of the IF stainings of E-Cadherin, and it is not helping proving the point of the authors. Can the authors provide better stainings for this figure?<br /> Could the authors provide a bit more information on the live imaging. Exactly how do they achieve imaging for such a long time?

      Minor comments

      It is hard to imagine the full-size liver in Figure 1, bad contrast. Can the authors manually delineate it?<br /> "This finding is very surprising, since current understanding in the field links the generation of new hepatocytes from BECs/LPCs with global hepatocyte death." This statement lacks references.

      Significance

      General assessment:

      Elegant model to ablate hepatocyte in a clean fashion and study regeneration when coupled to imaging technique.<br /> The work supports that biliary-driven regeneration also occurs when hepatocyte ablation happens in a small area of interest. This seems a new concept, the operation of the process needs to be ascertain in other models including humans.<br /> Immune/inflammatory response to the ablation as well as the way it may influence/drive or dictate a regenerative response is not investigated

      Advance:

      The advance pertains to the model because rodents offer ample possibilities to study interaction between 'intact' and 'diseased' cells. Of course the model is attractive as it is rapid, allows for 'real time' in vivo imaging, ..;

      The audience will be a specialised audience (basic research in liver regeneration, in zebrafish technologies, ...)

      Expertise

      I'm a hepatologist, devoted for the last 15 years to the experimental study of the pathophysiology of liver diseases using animal models, cell cultures models and organoids. I 'm not an expert in zebra fish. I have a large interest in regeneration and in particular I produced pioneer work in BEC-driven regeneration that is studied here.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      The manuscript by Ambrosio et al. describes a novel experimental system named "LiverZap", where spatiotemporally-regulated hepatocyte ablation can be achieved in the liver of living zebrafish for the study of the tissue/organ regeneration processes and mechanisms, particularly by in vivo live imaging. Specifically, the authors made use of a binary FAP-TAP system to achieve reactive oxygen species (ROS)-induced hepatocyte cell death upon treatment with the drug MG-2I followed by near-infrared (NIR) illumination in transgenic fish. They found that the system resulted in two categories of hepatocyte ablation, i.e., mild ablation and severe ablation. In the livers where the severe ablation was induced throughout the organ, biliary epithelial cell (BEC)-derived hepatocyte regeneration program was provoked as demonstrated by short-term lineage tracing experiments based on histone inheritance, which is quite consistent with previous studies using different methods of hepatocyte ablation in zebrafish and mice. Taking advantage of the spatial controllability of the LiverZap system, the authors further demonstrated that spatially-restricted severe hepatocyte ablation was sufficient to induce the BEC-dependent regeneration program therein. Interestingly, BECs outside the targeted region also contributed to the local hepatocyte regeneration, as revealed by using a sophisticated photoconvertible BEC labeling system. Finally, a dynamic nature of BEC aggregation and re-distribution upon liver injury was demonstrated to occur in advance of hepatocyte regeneration, reminiscent of the so-called ductular reaction in the mammalian liver. Overall, the authors' claims and the conclusions are well supported by the data presented in the manuscript, except for a few points as listed below.

      Major comments:

      • The authors assessed acridine orange incorporation in BECs upon LiverZap and concluded that LiverZap triggers hepatocyte-specific cell death without a bystander effect in adjacent cells (Figure 1 D-E). What happened to endothelial cells, which could also be affected either directly by ROS production in hepatocytes or indirectly by gross morphological changes in tissue organization?
      • The evaluation criteria for distinguishing mCherry <high> and <low> cells in imaging experiments should be clearly described in the methods section. The authors should also provide some quantitative data regarding the level of correlation between the mCherry<low> hepatocytes and the BEC-derived hepatocytes strictly defined based on the TP1-H2B-EGFP lineage tracing, as the former was used as a surrogate marker for the latter in some experiments.
      • OPTIONAL: In the locally restricted ablation model, do hepatocytes located adjacent to the ROI proliferate and/or contribute to the regeneration of the injured region?
      • OPTIONAL: Figure 4, A-S. It should be of significant interest if the authors could also analyze the BEC dynamics using the locally restricted hepatocyte ablation model, comparing those in the injured region (ROI) and the outside of the ROI.
      • Figure 4, T-V'. The data shown here for the changes in E-cadherin distribution is difficult to understand and interpret. The authors should provide magnified images and better description on how to distinguish the membranous (spotted signals?) and intracellular localization. Quantitative assessment should certainly be a plus, if possible.
      • OPTIONAL: In relation to the above point, it is this reviewer's candid impression that the very last part regarding the possible role of E-cadherin dynamics in regulating the biliary network remodeling is still preliminary compared to the remaining parts, thereby rather depreciating the value of the entire manuscript. Perhaps this part could be published separately, together with more functional evidence regarding the causal relationship between them (e.g., showing the effect of E-cadherin knockdown in hepatocytes on the biliary remodeling and the induction of the BEC-dependent regeneration program)
      • Do zebrafish livers possess lobular structures with the portal-to-central vein axis and the metabolic zonation as typically observed in mammalian livers? As has been described in the manuscript, the "localized" injury patters in the mammalian livers usually occur at the sub-lobular structure levels (i.e., peri-portal region-restricted vs. peri-central region-restricted). Although the "localized" injury model described in this study using the zebrafish livers was indeed localized from the viewpoint of the entire organ (or the lobe), it still seemed much more "global" when considering those situations in the mammalian livers, so that the authors' claim that the former recapitulating the latter might be too exaggerated and somehow misleading. The authors should clarify and discuss this point in the manuscript.

      Minor comments:

      • Figure 4. Panels D and G should correspond to the same one image and the way of labeling be changed (as in Figure 1G). Likewise, in panel J, the bars shown separately as "M" and "S" at 12 dpi should correspond to the same data, so that they should be unified as one bar.
      • Figure S3L. How was the ROI border defined? Perhaps the shape of the ROI should change significantly during regeneration due to dynamic tissue remodeling processes, thereby moving the position of the border as well.
      • The authors should comment in the manuscript as to whether the system can be applicable for induction of more restricted areas (e.g., at a single hepatocyte level; in particular metabolic zones, if existing), as well as for ablation of other hepatic cell types such as BECs and endothelial cells.

      Significance

      The newly developed LiverZap system described in the present study was well designed and has multifaceted advantages compared to other "global ablation systems" that have so far been used in this research field. Indeed, the authors' original finding that the localized hepatocyte ablation provokes activation of BECs outside the injured region and their contribution to hepatocyte renewal, could have never been obtained using previous models. This finding is of considerable novelty and interest in that the localized injury model should better reflect the pathophysiological conditions in various human liver diseases. Thus, the study should make significant contribution to the field in both technological and conceptual ways, providing useful and relevant platforms for the future studies on the mechanisms of liver injury, repair and regeneration.

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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

      This is an interesting manuscript that explores how epithelial cells respond to genetically induced disruption of occluding junction formation. To ask how epithelial integrity is maintained under these conditions, the authors investigated the developing pupal epidermis in Drosophila, where they used genetic mosaic techniques to induce patches of mutant tissue lacking selected components of bicellular or tricellular septate junctions (SJs), respectively. They show that occluding junction defects result in elevated levels of E-Cadherin, F-actin, and activated myosin II at adherens junctions (AJs) in the mutant tissue, suggesting that epithelial cells sense breaches in barrier integrity and respond by reinforcing adhesion and actomyosin contractility. Consistent with this idea, the authors find mechanosensitive proteins (Ajuba, Vinculin) enriched at AJs in the mutant cells, and show that new cell-cell interfaces after cytokinesis are shortened in cells lacking the tricellular SJ (tSJ) component Aka. Moreover, aka mutant cells accumulate beta-Integrin, F-actin and vinculin on their basal side, suggesting that upon disruption of tSJs cells increase matrix adhesion by forming focal adhesions (although the authors did not address whether these structures are bona fide focal adhesions that connect to ECM). The authors go on to ask how disruption of SJs is sensed and translated into enhanced adhesion and contractility. Previous work (Pannen et al. eLife 2020) established that the ESCRT complex is required for retromer-dependent delivery of SJ components to their correct membrane destination, and that loss of ESCRT function leads to disruption of SJs. Building on this and their own earlier work, the authors show that SJ defects are accompanied by enlarged ESCRT III (Shrub:GFP)-positive structures, elevated numbers of HRS-positive vesicles, and accumulation of polyubiquitinated proteins. The latter effect upon SJ disruption was reminiscent of Shrub/ESCRTIII loss of function, leading authors to propose that modulation of ESCRT activity prevents SJ protein degradation in favor of SJ protein recycling. Such a scenario could be expected to result in elevated SJ protein levels at the plasma membrane, but whether this is the case is not addressed in the paper. Instead, the authors switch here to analyzing effects of shrub RNAi on the apical determinant Crumbs, which accumulates at or near AJs in cells lacking bSJ (nrv2) or tSJ (aka) components, consistent with reduced degradation and/or increased recycling of Crumbs protein. Finally, they show that clusters of beta-integrin (Mys), associated with vinculin and F-actin, appear on the basal side of aka-depleted cells, leading the authors to conclude that SJ-defective cells reinforce their adhesion to the ECM, perhaps to prevent extrusion from the epithelium. While the appearance of Mys clusters on the basal side is convincingly demonstrated, I don´t see evidence for apical focal adhesions, as depicted in the cartoon in Fig. 7. If focal adhesion-like structures exist on the apical side, to what kind of ECM molecules should they attach there?

      Overall, the manuscript describes interesting new findings that are well documented and should be of interest to a broad audience of cell and developmental biologists. However, the following questions and technical issues remain to be addressed before the manuscript will be ready for publication.

      Major comments:

      The title refers to a "mechanism sensing paracellular diffusion barrier alteration", and in the discussion (line 325) authors state that "loss of bSJs and tSJs by altering the paracellular diffusion barrier triggers an ESCRT-dependent response...". However, no experiments to assess paracellular barrier function (epithelial permeability) are shown in the paper, and it is not clear that the ESCRT-dependent responses described here are triggered by altered barrier function per se, as stated by the authors, or by changes in other SJ-dependent parameters, such as cell adhesion or intra-membrane mobility of lipids and proteins. Statements about paracellular barrier alteration should be rephrased accordingly.

      Altered epithelial barrier function will likely influence osmoregulation via changes in organismal hormonal status and gene expression, which may contribute to the phenotypes described here. How much time passed between induction of mutant clones and phenotypic analysis? The authors should discuss these aspects, and consider that effects of altered barrier function will depend on the distribution and size of clones with defective SJs.

      In the discussion the authors speculate about a "sensing" mechanism based on (hypothetical) altered membrane lipid composition upon loss of SJs. However, such effects would not explain how altered barrier function per se (epithelial permeability) would be sensed by cells, as stated in the title and throughout the text. Please explain.

      How Shrb/ESCRTIII activity could be "redirected" or "modulated" by disruption of SJs remains unclear. Can the authors briefly outline possible mechanisms for modulation of ESCRT activity?

      The presentation of fluorescence intensity data in a rescaled ("standardized") format is uncommon and non-intuitive, as it obscures the true scale (fold-changes) and variation of the data. Also, if data were plotted as a range from 0 to 10, as stated in Materials & Methods, it is not clear why in all graphs (except for a single datapoint in Fig. 5C'?) values start at 1, not at 0. Highest values appear to cluster at 10 and lowest values at 1, suggesting these represent saturated or clipped signals, respectively. Were these datapoints taken into consideration for calculating mean values? Authors need to explain exactly how the analysis was done. Why was this type of representation chosen, and why should it be more appropriate than showing regular normalized data?

      Authors should explain why they jump between different mutant (aka, nrv2) and RNAi (aka, cora nrv2, nrxIV) conditions and different Gal4 driver lines (pnr-Gal4, sca-Gal4) to disrupt SJ integrity. The basis for choosing these different conditions is not always clear and makes results difficult to compare.

      The TEM images shown in Fig. 1A are difficult to interpret, because plasma membrane is barely visible. The images do not seem to contribute much and can be removed from the paper.

      The position of mutant clones is marked by absence of nuclear RFP (Fig. 1B and elsewhere), but drawings of clone boundaries (Fig. 1B) do not match with the pattern of RFP-positive/ -negative nuclei (Fig. 1B'), presumably because different optical sections are shown in Fig. 1B and and B'. This is confusing and needs to be explained.

      Minor comments:

      Line 102: "We recently reported that defects at tricellular Septate Junctions (tSJs) are always accompanied by bicellular Septate Junctions (bSJs) defects". Authors may want to mention that in embryonic and larval epithelia lacking tricellular SJs, bicellular SJs assemble initially, but appear to degenerate during later development (Hildebrand et al. 2015, Byri et al. 2015).

      Line 192 remove "another".

      Line 194: % enrichment and fold enrichment are used; stick to one way.

      Line 259 and elsewhere: Crb "activation" vs. accumulation or mislocalization. What do the authors mean by Crb "activation"?

      Line 346: "FK2 protein": the FK2 antibody does not detect a particular protein, but the polyubiquitin modification, presumably on many different proteins.

      Line 444: "Also, the observed changes at apical level might be mostly due to direct effects." I don´t see experimental evidence to support that the observed changes are mostly due to direct effects. Rephrase or remove.

      Information on how mutant clones were induced needs to be included in Materials and Methods.

      Results referred to as "not shown" should be shown, or corresponding statements be removed from the paper.

      The text needs to be carefully checked for grammatical and typographical errors.

      Significance

      How epithelial cells cope with disruptions of occluding junctions without losing tissue integrity is an important question with far-reaching implications for understanding epithelial biology and disease. This work makes a significant contribution here by carefully describing the interrelationship between disruption of occluding junctions and possible compensatory mechanisms at the level of adherens junctions.

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      Referee #2

      Evidence, reproducibility and clarity

      Septate junctions provide the barrier function in insect tissues, serving as analogs to the vertebrate tight junctions. Here the authors explore an interesting question-how do epithelial tissues respond to loss of barrier function in vivo. They use a powerful and well-studied system, the Drosophila pupal notum, which allows them to bring powerful genetic tools to bear and use state of the art imaging. Their data are lovely and carefully quantified. Together, they reveal some significant surprises. 1. Disrupting septate junctions leads to elevated accumulation of adherens junction proteins and myosin, and reduced apical area. 2. Disrupting septate junctions led to accumulation of many ESCRT-0-positive vesicles and of enlarged ESCRTIII vesicles. 3. Disrupting septate junctions led to elevated accumulation of Crumbs apically and of integrin-based focal adhesions basally. These observations are well supported by the data and in the results section conclusions are carefully drawn. I had some relatively minor comments outlined below about the results. My only significant suggestion concerns the Abstract and Discussion. The Abstract includes a statement that goes well beyond the data shown, and the Discussion is sometimes hard to follow. With these issues corrected, this will provide important new insights for cell and developmental biologists.

      1. The Abstract states: "We report that the weakening of SJ integrity, caused by the depletion of bi- or tricellular SJ components, reduces ESCRT-III/Vps32/Shrub-dependent degradation and promotes instead Retromer-dependent recycling of SJ components." This is too strong, as the role of the retromer, while plausible, is not directly tested. It's fine to speculate about this in the Discussion but drawing a conclusion like this in the Abstract is unwarranted.
      2. Similarly, the title suggests that "ESCRT-III-dependent adhesive and mechanical changes are triggered by a mechanism sensing paracellular diffusion barrier alteration". They show that knocking down septate junctions alters localization of vesicle trafficking machinery, and that it leads to alterations in apparent recycling of cargo, but do they ever really assess whether these changes are ESCRT-III-dependent? Wouldn't this require knocking down ESCRT-III in cells with defects in septate junctions? There was a lot of data in this paper and perhaps I missed it but was this experiment done? I am not suggesting they do it, but that they temper this conclusion if not.
      3. The authors assessed "poly-ubiquitinylated proteins aggregates appearance, marked using anti-FK2" . They need to define FK2-what does it detect.
      4. Fig 4-is this a clone, and are we far from the boundary? Make this clearer
      5. The authors state: "Despite these apparent similarities, we noticed that, in contrast to Shrub depletion, NrxIV did not accumulate in enlarged intracellular compartments upon Cora depletion" Could the authors reference a Figure here?
      6. The authors state: "Hence, if both Shrub and bSJ/tSJ defects lead to Crumb enhanced signals" It might be better to say "altered" as they then point out the differences.
      7. I found the Discussion challenging to follow. Rather than focusing on the core observations, it addresses many, not very well-connected speculative possibilities, and in my opinion, will be challenging for most readers to follow. I would encourage the authors to revisit it from top-to-bottom.

      Referees cross-commenting

      I think we largely agree that the authors present important data, but that certain points need to be better explained or more clearly documented. While Reviewer 1 is correct that adding context about the basolateral polarity proteins would be helpful, I do not feel as strongly about this as a deficit. The authors did not manipulate Scrib, Dlg or Lgl, and i think their polarity functions may be distinct from those of the more "structural" septate junction proteins analyzed here.

      Significance

      Septate junctions provide the barrier function in insect tissues, serving as analogs to the vertebrate tight junctions. Here the authors explore an interesting question-how do epithelial tissues respond to loss of barrier function in vivo. They use a powerful and well-studied system, the Drosophila pupal notum, which allows them to bring powerful genetic tools to bear and use state of the art imaging. Their data are lovely and carefully quantified. Together, they reveal some significant surprises. 1. Disrupting septate junctions leads to elevated accumulation of adherens junction proteins and myosin, and reduced apical area. 2. Disrupting septate junctions led to accumulation of many ESCRT-0-positive vesicles and of enlarged ESCRTIII vesicles. 3. Disrupting septate junctions led to elevated accumulation of Crumbs apically and of integrin-based focal adhesions basally. These observations are well supported by the data and in the results section conclusions are carefully drawn. I had some relatively minor comments outlined below about the results. My only significant suggestion concerns the Abstract and Discussion. The Abstract includes a statement that goes well beyond the data shown, and the Discussion is sometimes hard to follow. With these issues corrected, this will provide important new insights for cell and developmental biologists.

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      Referee #1

      Evidence, reproducibility and clarity

      This paper investigates the cellular response of Drosophila epithelia cells in the notum to damage to septate junctions. They find that disruption of tri- and bi-cellular septate junctions (SJ) integrity alters the distribution of adherens junction (AJ) components including E-cadherin, Myosin-II and others. Loss of SJ increases levels of AJ proteins. They then show that loss of the tri-cellular junction protein Anakonda alters the adhesive and the mechanical properties of the epithelia. They showed Myo-II activation was increased, however laser ablation/recoil studies did not reveal a change in local membrane tensions. Changes in membrane tension were observed by quantifying cell divisions in bicellular septate junction (bSJ) mutants. Building on previous work, they show that defects in SJs lead to ESCRT complex defects, and that loss of tricellular or bicellular septate junction components increase apical-medial Crumbs localization and triggers assembly of focal adhesion contacts. Together, these results show that alterations in SJ structures result in apparently compensatory increases in Crbs and focal adhesion-based intercellular adhesions that is mediated by the ESCRT complex.

      Major comments:

      • Title, abstract and paper body: e.g. title "ESCRT-III-dependent adhesive and mechanical changes are triggered by a mechanism sensing paracellular diffusion barrier alteration in Drosophila epithelial cells"
      • The paper is completely focused on the septate junctions as a paracellular diffusion barrier. However, many of the septate junction components, including Scribble, Dlg, and Lgl, have well documented (if poorly understood) basolateral polarity functions, and considering that septate junctions contain 15 or more cell-cell adhesion proteins, they are also likely to have a adhesive/structural function in addition to paracellular barrier and polarity functions. There is no attempt in the paper to consider or disentangle these multiple roles. Indeed, the introduction and discussion consider the vertebrate tight junction as the analogue of the insect septate junctions when a better view would be that the septate junction is a combination of the claudin-based barrier function of the vertebrate tight junction and the vertebrate basolateral polarity proteins Scribble, Dlg and Lgl that localize similarly and presumably have a function similar to the Drosophila basolateral polarity/SJ proteins for which they are named. Moreover, there are no experiments in the paper to address whether the relevant parameter being sensed in SJ defects is loss of the paracellular barrier, loss of cell adhesion/contact/structure or disruption of the polarity function of the SJ complex. Notably, there aren't any experiments in the paper that test paracellular barrier function. This criticism does not in any way reduce the importance of the paper or the results, but to avoid presenting an overly simplistic and probably misleading view of the cellular processes in play, a more comprehensive discussion of SJs is in order.
      • line 245: "We propose that it is the Shrub activity that is being modified upon SJ alteration, preventing SJ component degradation in favour of SJ component recycling."<br /> line 288, "Thus, as proposed above for Nrx-IV, these data further suggest a hijacking of Shrub activity toward recycling components upon alteration of SJ integrity."<br /> Model in Fig. 7 Arrows showing increased SJ protein delivery in right bottom panel, but decreased bicellular SJ complex formation in the left bottom panel.<br /> The authors demonstrate that in bicellular SJ mutants, there is increased accumulation of Crb, adherens junction components, focal adhesion components, and in the text and in the model in Fig 7 focus on the upregulation of recycling activity. However, as indicated by the reduced bSJs in the left bottom panel in Fig 7, and in the reduced Nrx levels in 3C' and in the text in lines 351-53, the levels of most septate junction proteins drop in the absence of any of 15+ bicellular septate junction mutants. Previously the authors should that reduction of tricellular septate junction proteins increased levels of septate junction proteins in bicellular junctions which the authors translate to increased delivery of "SJ components" to the membrane in SJ mutants as shown in Fig 7 bottom right panel and stated in lines 245 and 288. But the data in the paper, which is consistent with statements on lines 351-353 saying that bicellular SJ mutations cause a general reduction of SJ protein levels, suggests either a more nuanced role of recycling such that Crbs and other proteins show increased recycling in bicellular SJ mutants, but biclellular SJ proteins show decreased recycling, or an alternative scenario in that the SJ proteins are recycled more in a SJ mutant, like Crb is, but SJ proteins don't form stable complexes which leads to their modification that targets them for destruction despite being recycled more. Regardless of the actual explanation, I think readers will be confused by the statements in the current version of the paper about upregulation of recycling activity but apparent reduction of SJ proteins. The authors should address this issue with appropriate changes to text and the model figure.

      Minor comments:

      • The assumption in the paper is that the changes in protein levels result from changes in recycling of the proteins. However, it would be nice to rule out transcriptional regulation. Has anyone established smFISH in the notum that would allow quantification of Crb or other marker RNA to show that there is not increased accumulation of the Crb RNA in the SJ mutant backgrounds?
      • line 58. SJ are only the functional equivalent of tight junctions for paracellular barrier function. SJ have basolateral polarity function that correspond to basolateral polarity proteins in vertebrates, whereas vertebrate TJs are associated with apical complexes. In addition, the mechanical properties of SJ and TJ are probably wildly different since the SJ is a much more elaborate structure with many more cell-cell adhesion proteins than TJs. I feel the presented over-simplification do not adequately inform the reader about alternative functions and therefore hypotheses about the data in the paper.
      • lines 120-121 , Figure 1A-A'. Please quantify the relative frequency of holes observed in the EM sections. Is it every tricellular junction or 1 in 100? Is WT statistically different than mutant?
      • line 126-127 (data not shown). Does EMBO allow data not shown? Just checking current rules.
      • lines 134-135. "We observed similar results upon loss of Gli and M6". Is this data not shown? I couldn't find it. Please either reference a figure or note as "data not shown" if that is allowed.
      • line 319 "We propose that the disruption of SJ barrier in the ...", also line 326 . I suggest the use of "SJ complex" instead of SJ barrier or paracellular diffusion barrier, otherwise the authors need to provide some evidence or rationale that it is the barrier function of the SJ that is triggering the recycling changes rather than the disruption of the polarity or adhesive/structural functions of the SJs.
      • line 341 "Our work shows that a part of the sensing mechanism involves the ESCRT machinery."<br /> I think that the ESCRT machinery is better described as part of a response mechanism to SJ defects than as a "sensor". I don't think the paper presents any evidence that the ESCRT machinery is part of the sensing mechanism for SJ defects. There is lots of evidence that the ESCRT machinery is modified by SJ defects, but that supports a role as part of the response machinery, not as the sensor that directly detects SJ defects.

      Significance

      The topic and results of the paper will be of interest to a wide range of the cell biology community including those studying epithelial integrity, junctions, polarity and endocytic trafficking. The results break new ground in looking at the dynamic relationships between junctional complexes. This paper is generally well written, with the exception of the major comments below which, and the experiments well done. Overall a very interesting paper that is appropriate for a top tier journal.

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      Referee #3

      Evidence, reproducibility and clarity

      In this study, the authors used metabolic labeling of newly-replicated or nascent chromatin followed by quantitative Mass spectrometry (iPOND-MS) to characterize protein composition of nascent chromatin at time points after DNA replication: immediately after a short pulse of EdU labeling (nascent), and after 1 and 2 hours of Thymidine chase (maturing chromatin). The iPOND method was established before but in the current manuscript the authors combined this with inhibiting RNA Pol II transcription at distinct stages to determine the effects on transcription and RNA Pol II cycle on chromatin protein dynamics at the wake of DNA replication. The inhibitors they used are Triptolide, which blocks transcription initiation and induces a proteasomal degradation of all chromatin bound RNA PolII, and DRB, which blocks transcription elongation causing an enrichment of paused RNA PolII. The authors compared the relative enrichment of ~1200 proteins on nascent and maturing chromatin and the effects on transcription inhibition on these proteins.

      The authors found that RNA PolII does not affect the loading or retention of most histones on nascent chromatin except for the histone variant H2A.Z, which requires RNA PolII loading. However, DRB treatment (no elongation) resulted in stabilization of all histones (which the authors do not seem to catch on). Interesting, unlike the histone, both replication-coupled and -independent histone chaperons seem to be enriched immediately behind the fork and are affected by RNA PolII to different extents. They next look at ATP-dependent remodelers and find that most remodeler families are facilitated by RNA PolII loading, while elongation affects some remodeler families and not others. They see the same trend looking at a wide variety of transcription factors. Interestingly, while RNA PolII loading is required for the establishment of some histone post translational modifications (H3K36me3), some others such as H3K9me3 and H4K20me2 are negatively affected. Finally, the authors find that RNA PolII elongation promotes binding of several DNA repair proteins, and speculate that this is because of DNA damage from replication-transcription conflict.

      My main concern about this manuscript is that the relative enrichment of most factors show variability across the time points, which make the interpretation of the data difficult. This becomes more concerning when we look at protein complexes such as the ATP-dependent remodelers. Subunits of the same complex which are expected to bind together show different patterns of enrichment. This raises the concern as to how data was normalized. Furthermore, how do the replicates compare to each other? The others selected ~1200 proteins which were enriched in all three replicates, but how does their relative enrichment compare in the replicates? The authors need to show some kind of comparison across replicates to confirm that the differential relative enrichments are real and biologically meaningful.

      Also, the TF data is very descriptive. Insightful analysis of similarities/differences between types of TFs would be interesting.

      Minor comment: The formaldehyde cross linking used in iPOND makes it difficult to interpret/distinguish what is actually chromatin bound versus what is enriched due to protein-protein cross linking. The authors should highlight that in the limitations section.

      Referees cross-commenting

      I agree with most of Reviewer 1's comments about the lack of proper controls and normalization, which make the interpretations difficult. Particularly all of the controls mentioned under point 1 should not be difficult to perform, and if included, would strengthen the study and the manuscript.<br /> Reviewer 1 makes an important point about normalization, which I totally agree with. Ideally, a spike-in approach would help obtain a much more quantitative and reliable understanding of differential protein enrichment. However, repeating all iPOND experiments with spike-in might be a big ask. What the authors could do at minimum is show how replicates compare with each other. It looks like they pooled all three replicates for analysis, but comparing relative enrichment of all 1257 proteins across replicates would help. The point about delayed histone occupancy is a critical one and difficult to rationalize. To note, histone chaperons are enriched on nascent, but histones are not. Besides, in the current way that the data is analyzed and presented, there are a lot of fluctuations in protein enrichment across the 1-2 hour timepoints of chromatin maturation, which would be very interesting if real. For e.g., Fig. 1I, Triptolide treatment, most of the cluster I and cluster II proteins show medium-high enrichment on nascent, depleted in 1h, but recover in 2h. If the binding/recruitment of these proteins on newly-replicated chromatin is RNA Pol II dependent, why would they come back after 1h? If this real, this would be very interesting. There are several additional examples of problems with quantification/normalization. As for SWI/SNF subunits, both SMARCA4 and SMARCC1 are core subunits and based on several thorough biochemical studies, cannot be expected to bind separately. However, they show different kinetics in DMSO as well as TPL and DRB.

      Another problem of the assay is that it shows genome-wide average. As Reviewer 1 rightly pointed out, transcription inhibition could disproportionally affect chromatin maturation kinetics in different genomic regions. Perhaps it would be interesting to analyze sets of genomic regions separately, such as highly transcribed and lowly transcribed genes. This might be achieved by adding a purification step using pools of DNA sequence probes before or after the streptavidin enrichment.

      Additional comment: The formaldehyde cross linking used in iPOND makes it difficult to interpret/distinguish what is actually chromatin bound versus what is enriched due to protein-protein cross linking. The authors should highlight that in the limitations section.

      On a positive note, it is a very important and timely study and the manuscript has a lot to consider. Addition of proper controls and normalization/analysis of replicates will make it stronger

      Significance

      Overall, it is a very important and timely study, and the manuscript has a lot to consider. There are several recent papers on the kinetics of chromatin maturation behind the replication fork, and this study adds a very important dimension to this ongoing investigation, and will be of interest to a broad readership in the chromatin and transcription field.

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      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript, the authors characterized the re-establishment of chromatin after DNA replication in fibroblasts using iPOND-MS. By using a short pulse of EdU, followed by different length of thymidine-chase, the authors compare the proteome at nascent DNA (just after the EdU pulse) with the proteome on re-established chromatin (1h and 2h post EdU pulse). Moreover, by using two different transcription inhibitors, they investigate the implication of active transcription elongation and of RNAPII binding itself on the reestablishment of chromatin. They show that different transcription factors bind to newly replicated DNA with different kinetics and are affected differentially by transcription inhibition. They also show that upon transcription inhibition by DRB, certain DNA damage repair proteins are depleted, implicating transcription in the recruitment of these factors at nascent DNA. Chromatin remodelers were shown to be enriched on nascent DNA, but triptolide-transcription inhibition reduced their enrichment, implicating RNAPII in the reestablishment of chromatin structure and of steady-state chromatin accessibility. Lastly, the authors show that histone incorporation and histone modification restoration on nascent DNA is mostly uncoupled from transcription with the exceptions of H3.3K36me2 (transcription inhibition by triptolide or DRB drastically reduces restoration) and H4K16ac (DRB treatment increases its incorporation in nascent DNA).

      Overall, the results and the analysis of the datasets appear robust and well executed. Nevertheless, the work provided by the authors feels mainly descriptive and does not provide further mechanistic insights beyond the current state of the art. Some follow-up experiments to study the functional impact on the different enrichment patterns on nascent DNA or the function of the dependency on RNAPII for the reestablishment of steady-state enrichment on chromatin of some factors would have greatly increased the scientific impact of the manuscript. Nevertheless, the proteome of nascent DNA, its kinetic, and the effect of transcription inhibitors will provide interesting information and a useful resource for research groups in the DNA replication, chromatin, epigenetic, and DNA damage repair fields. Thus, in conclusion, I would recommend this manuscript to be published in its current state in a lower tier journal such as MBoC or PLOS ONE journals. If the authors can provide additional mechanistic insights by addressing at least a few of the specific points listed below, I think it would become a stronger candidate for a journal with higher impact.

      Major comments:

      1. At p.7, the authors state: "Altogether, this analysis further confirms that RNAPII's binding and elongation on newly replicated chromatin are a source of genotoxic stress, and identifies dedicated repair factors handling transcription replication conflicts.". I don't think that depleted DNA repair proteins from nascent chromatin upon DRB treatment is enough to claim that the analysis confirms that transcription on nascent DNA is a source of stress. Another possibility could be that transcription helps handling prior DNA damage on nascent DNA without causing the damage. A useful experiment to clarify this point would be the direct quantification of DNA damage markers on nascent chromatin (e.g yH2AX-EdU colocalization quantification by immunofluorescence). Has the yH2AX variant been detected in the iPOND MS dataset? Another possible follow-up experiment could be to detect direct physical DNA damage on nascent DNA for example by using a TUNEL assay or similar DSB mapping method. Can the DNA damage be prevented by DRB or TRP addition?
      2. Figure 1B-E: Can the authors also show quantifications of EU, RNAPII and EdU at the 1h and 2h timepoints after the chase?
      3. The authors state in p.7 that "The other proteins are DNA repair proteins involved in fork quality control and HR as well as transcription replication conflicts (Berti et al., 2020).". I think this gives rise to the question if the effect of DRB treatments on the enrichment of certain proteins at nascent DNA is due to the inhibition of transcription elongation inhibition on nascent DNA or in front of replication forks, affecting the enrichment of proteins implicated in handling transcription-replication conflicts in front of the fork and not on nascent DNA itself. The authors should address the possibility that some of the proteins enriched in the iPOND-MS datasets could be there because they are enriched in front of the replisome instead of on the nascent DNA.
      4. On this topic, transcription inhibition is performed for two hours prior to the EdU pulse and iPOND-MS procedures. For the DRB treatment, I would expect RNAPII to be paused/stalled prior to the passage of the replication fork that will replicate the analyzed EdU-labelled nascent DNA. This would mean that replication forks during the EdU pulse will encounter paused/stalled RNAPII, generating potential problems. Such interference would most probably lead to chromatin removal of RNAPII from the chromatin. Surprisingly, the authors show enrichment of RNAPII at nascent DNA. How can the author differentiate from accumulation of RNAPII in front of the fork, leading to purification by iPOND, and RNAPII on nascent DNA. Also, if the accumulation of RNAPII is on the nascent DNA, do the authors suggests that RNAPII gets loaded more on nascent DNA while under DRB inhibition or that stalled RNAPII are mainly by-passed by replication forks, leading to their enrichment on nascent DNA?
      5. At p.14, the authors state: "Because they share the same DNA template, transcription is known to challenge replisome progression at high frequency, from RNAPII constituting a roadblock to progressing replisomes, to generate RNA:DNA hybrids (Berti et al., 2020). It is therefore remarkable that behind replisomes, only a handful of DNA repair factors appear to be involved in response to RNAPII binding and elongation.". How does the fact that transcription represents a roadblock in front of the forks makes it remarkable that only a handful of DNA damage repair pathways are involved behind the fork (where they are not a roadblock to any replisome)?
      6. At p.11, the authors states: "As di and tri-methylations require several hours to be re-established following DNA replication (Alabert et al., 2015; Reveron-Gomez et al., 2018), 11 minutes after the passage of the fork, such increase most probably reflects an increase of H4K20me2 and H3K9me3 on recycled parental histones.". Can the authors extend their interpretation of this result? Do the authors think that DRB treatments increase methylation of histones in G1, prior to replication, or specifically in front of the fork (due to conflicts? DNA damage?), and that those methylated histones get recycled on nascent DNA?
      7. Figure 4: The authors perform the histone PTM analysis under 0h (nascent chromatin) versus 2h (re-established chromatin) timepoints. It would have been insightful to also include a 1h timepoint in this experiment. There appear to be some trends/changes but they do not show statistical significance (e.g. H4K5K12ac or H3K14ac). It might be useful to increase the number of biological replicates (including the 1h timepoint) here, which could improve the confidence in the results and/or discover additional transcription-dependent changes of histone PTM restoration.

      Minor comments:

      1. Fig3I: It would be nice to show a TF from the "Restored within 11 min" category as a comparison point.
      2. In page 14, th authors state: "However, we did not detect significant signs of DNA damage in DRB treated cells (Fig. 2A, 2B).". Which signs the authors looked at?
      3. In the iPOND experiment, which size of DNA fragments is achieved?
      4. At p.14, the authors state: "Because they share the same DNA template, transcription is known to challenge replisome progression at high frequency, from RNAPII constituting a roadblock to progressing replisomes, to generate RNA:DNA hybrids (Berti et al., 2020)." The paper has not addressed the role of RNA:DNA hybrids in these processes.
      5. Fig3D: Is there enough datapoints to state a conclusion?
      6. S1A: mistakes in the x axis labels ("no EU" in a EdU quantification graph, "no EdU" in a EU quantification graph).
      7. S1F is not sufficiently described in the legend. It took me some time and additional efforts to understand what the right panel of S1F was showing.
      8. S2E-F: are the axis wrong? Is it supposed to be Nascent when its comparing total extracts?
      9. A lot of graphs have non-precise axis labels that needs reading of the manuscript and/or legends to understand. For example: 1K-L (distribution, %), 2L (% of the max), 3B-C-D log2(Nascent/2h), 3G IP/Input, 4C (Inhibitor treatment/DMSO (%)), S2E-F (TPL/DRB Nascent/ DMSO Nascent), S3A (IP/Input), S4A (No Y axis label).
      10. FigS4: Assignment of colors in bar graphs of C-J to treatments is not shown. Heatmaps in H and K do not show if these are 0 or 2h. The heatmap in H shows H3 modification and the heatmap in K shows modification in H3.3 but the labels of the modification in K (except the first one) are the names of the modifications of H3, not H3.3. In the legend, GAPDH is written GABDH.

      Significance

      In this manuscript, the authors characterized the re-establishment of chromatin after DNA replication in fibroblasts using iPOND-MS. As mentioned above, the work provided by the authors feels mainly descriptive and incremental and therefore does not provide further mechanistic insights beyond the current state of the art. Some follow-up experiments to study the functional impact on the different enrichment patterns on nascent DNA or the function of the dependency on RNAPII for the reestablishment of steady-state enrichment on chromatin of some factors would have greatly increased the scientific impact of the manuscript. Nevertheless, the proteome of nascent DNA, its kinetic, and the effect of transcription inhibitors will provide interesting information and a useful resource for research groups in the DNA replication, chromatin, epigenetic, and DNA damage repair fields. Thus, in conclusion, I would recommend this manuscript to be published in its current state in a lower tier journal. If the authors can provide additional mechanistic insights by addressing at least a few of the specific points, I think it would become a stronger candidate for a journal with higher impact.

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      Referee #1

      Evidence, reproducibility and clarity

      The goal was to characterize the changes in the composition of the proteome associated with replicated DNA in conditions of genome-wide inhibition of transcription initiation or transcription elongation. They use iPOND, a MS based technique that identifies proteins specifically associated with replicated DNA labeled with EdU. They use non-synchronized foetal human lung fibroblasts and examine time points immediately after replication (after the 11 min EdU pulse) and 1 and 2 hrs after the Thymidine "chase" later when chromatin has "matured", to assess how the inhibition of transcription influences chromatin maturation and the binding of chromatin associated proteins to replicated DNA.

      The question is pertinent and is in line with the long-standing interest of the group in chromatin replication dynamics. They conclude that 1. RNAPII loading is necessary for the binding of some TFs, chromatin remodelers and DNA repair factors and 2. RNAPII elongation is needed for H2A.Z incorporation , H3K36me2 restoration and DNA repair factor binding. Transcription is on the other hand not needed for nucleosome assembly, histone acetylation and H3K9me3, H3K27me3 and H4K20me2 incorporation or restoration.

      There are two main issues that make the interpretation of their results very difficult and make me question their conclusions:

      1. They don't provide sufficient evidence that the treatments with TPL and DRB do not interfere with replication. The distributions of EdU intensity per EDU+ cell after treatment in Figures 1D-E and S1A are not sufficient. It is not clear why EdU incorporation is so heterogeneous in the cell population (the range of intensities goes from near 0 to 50000!), which makes me wonder if the DMSO treatment also has an effect on replication. I don't think this heterogeneity can simply be explained by the fact that the the cell population is asynchronous. They need to show a -DMSO control as well. Besides since they are only using a positive EdU signal as their criteria for replicating cells, they cannot rule out that some of the EdU signal is coming from DNA repair after replication and depending on how deleterious DMSO/TPL/DRB are to replication the fraction of cells that undergo DNA repair might be significant. More importantly, they need to show that the various treatments don't interfere with the replication program, especially since replication is coupled with new nucleosome assembly and the transcription of replication dependent histone variants is induced during S-phase. Transcription inhibition could disproportionally affect the replication of some parts of the genome more than others and since there is no evidence to the contrary the differences that they observe between the TPL/DRM treated and DMSO treated proteomes bound to replicated DNA could just be because they were isolated from different genomic loci. I am also not convinced that they are able to stop EdU incorporation after 11min with the addition of only equimolar amounts of Thymidine (20µM EDU and 20µM Thymidine). Equimolar amounts of Thymidine are not sufficient to stop EdU incorporation rapidly. They need to show the kinetics of EdU incorporation in synchronized cells +/- Thymidine.<br /> Without these controls it is impossible to draw any meaningful conclusions from the iPOND data.
      2. The normalization of iPOND and total protein MS data is problematic. It seems that each time point from each treatment was first normalized internally to the median of all protein levels in each dataset and then the relative abundances of each protein were normalized to 100% over all treatments and time points. Internal normalization makes it impossible to directly compare time points and treatments between each other. If the enrichment of a protein goes down from one time point to the next it doesn't mean that there is less of that protein on replicated DNA in absolute terms, it just means that there is less of that protein relative to the median of the whole set of proteins at that time point. Their claim that they are comparing iPOND enrichments to total protein abundance is misleading since the data from total protein extracts was also internally normalized so they are comparing relative enrichments in iPOND data to relative enrichments in total cell extracts, which unsurprisingly do not correlate. It is impossible to make any meaningful conclusions about proteome dynamics using this kind of analysis. They should have used external normalization with a "spiked in" protein to be able to directly compare time points and treatments.<br /> Such as it is right now, their analysis produces some puzzling conclusions that I suspect will turn out to be artefacts of their normalization procedure. It is not clear for example why the appearance of histones on replicated DNA would be delayed as they claim: in yeast nucleosomes (new and old recycled ones) are assembled on replicated DNA within minutes of the passage of the replication fork, I don't see why this would not be the case in human cells since the replication machinery is essentially the same in humans and yeast. It is also puzzling why RNAP2 is enriched in the nascent and 1hr time points but then becomes depleted in the 2hr time point in the DRB treatment since global RNAPII levels don't change in the DRB treatment compared to DMSO (Figure 1C). All the conclusions for PTM restoration/incorporation are essentially meaningless: internal normalization makes it impossible to detect whether PTM levels double at the 2hr time point compared to the Nascent time point in the DMSO treatment, as would be expected for all examined PTMs except for H4K5K12Kac which are marks of new histones. Right now, relative PTM levels are all over the place: only histone acetylations seem to increase, while H3K9me3 and H3K27me3 don't change even though they should also double since heterochromatin should also be restored on both sister chromatids. They will only be able to accurately assess the impact of transcription inhibition on PTM restoration when they are able to reliably measure the rate of increase of PTM levels during chromatin maturation.

      Referees cross-commenting

      On reviewer's 2 comment on significance:<br /> I think a thorough descriptive analysis of a biological process is extremely valuable and unlike my colleague, I think these types of studies need to be published in high impact journals with a broad readership. Biological processes need to be described first as completely as possible before we can propose meaningful models on how they function and identify the molecular mechanisms that execute and regulate them. As my colleague is surely aware, thorough descriptive studies of any poorly characterized biological process take years (i.e. at least one grant cycle) and comprehensive follow up mechanistic studies can take even longer than the initial descriptive study and can only be done during the following grant cycle, if the authors were lucky enough to obtain funding. Funding agencies however are more likely to award grants to perform these follow up mechanistic studies if the authors (especially if they are junior PIs) have published in higher impact journals in their previous grant cycle. The kind of thinking exhibited by reviewer 2 disproportionately disadvantages junior PIs that work on understudied biological processes. It is a disservice to scientific progress to dismiss excellent descriptive studies and "downgrade" them to lower impact journals where they will be unfairly labeled as a "work of lesser importance". This kind of thinking is also a disservice to the lower impact journals that often publish works whose quality is comparable to articles published in high impact journals. I value more any comprehensive description of a biological process over what most of the time passes for mechanistic insight that is deemed worthy of publication in a high impact journal i.e. a hastily analyzed phenotype of, more often than not, one single mutant tacked on at the end of a descriptive study. This one mutant phenotype then forms the basis of a somewhat "slapdash" model that is often proven wrong by subsequent publications and that the authors would have probably dismissed themselves had they been given more time to develop and test their model in a follow up publication.<br /> I do not think the main issue with the present study is its descriptive nature. As I said in my review, the main issues are technical: the lack of external normalization of MS data and insufficient evidence of the impact of transcription inhibitors on replication dynamics. The study should not be published in any journal (high or low impact) before those issues are resolved.

      on reviewer 2's remarque 4. in major comments:<br /> iPOND identifies proteins bound to 100-300bp fragments labeled with EdU (i.e. after replication or DNA repair). It is by definition identifying proteins bound to chromatin behind the fork, so I don't think that the isolation of RNAPolII bound in from of the fork is a major issue

      Significance

      I am not convinced by their conclusions and I cannot recommend that the the study be published at this stage due to normalization issues and insufficient evidence that transcription inhibition does not perturb the replication program (see above). They would need to redo all the iPOND experiments using external "spike in" normalization and monitor replication genome-wide before they can make any meaningful conclusions about the transcription dependent composition of the proteome associated with replicated DNA.

      Expertise keywords: Chromatin, Genomics (assay development and bioinformatics analysis) , Replication, Transcription

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      Referee #3

      Evidence, reproducibility and clarity

      The manuscript under evaluation investigates the mutation rate between generations and during regeneration in the planarian species S. polychroa. Abundant adult pluripotent stem cells, the potential somatic stem cell contributions to the germ line, and the regeneration of entire animals from tiny tissue fragments provide interesting conceptual background to these questions. Specifically, the authors assemble a draft genome of a purportedly triploid S. polychroa strain with an accompanying de novo transcriptome. Via shallow whole genome shot gut sequencing, the authors subsequently attempt to estimate the germline mutation rate and the effect of regeneration on the number and the spectrum of detectable mutations. As stated in the abstract, the study "...provides, for the first time, the draft genome assembly of S. polychroa, the germline mutation rate for a planarian species and the mutational spectrum of the regeneration process of a living organism". As detailed under "major points" below, the draft genome assembly is not great and significant concerns remain regarding both regeneration rate statements.

      Major points

      Genome and transcriptome assembly:

      1. As the authors state, ploidy is known to be highly variable across S. polychroa strains and ploidy is an important variable in the estimation of mutation rates. The authors should therefore provide additional experimental evidence that their strain is indeed triploid (e.g., through karyology or FACS genome size estimation).
      2. Assembly quality assessments: As stated, the authors intend to use a de novo assembled short-read transcriptome as an independent assessment of assembly quality. However, no information on the quality of the transcriptome reference is provided. Salient quality control metrics on the size, completeness, and assembly contiguity of their transcriptome need to be provided. The published and publically available S. polychroa transcriptome on PlanMine [cited by the authors] might provide a further useful reference.
      3. The assembly is highly fragmented ( 8700 contigs) and the authors detect a significant number of scaffolding errors within contigs (Fig. 3d) . Moreover, the large number of multi mappers might indicate that the draft assembly has been insufficiently purged of haplotigs. This is especially a concern in a potentially triploid species and should be assessed via a kmer-based approach such as Merqury. Ideally, an independent Illumina dataset from the mutation screening and the reads used for polishing should be used. The analysis of synteny and structural variation relative to S. mediterranea in Figure three is therefore of questionable relevance and should be dropped or significantly condensed.

      Mutation analysis:

      1. The SNP analysis is based on a pipeline that the authors published previously. Beyond this, the manuscript provides insufficient information in terms of technical details (e.g., the salient IsoMut settings, how duplicate reads were treated, and how the SNP calling approach guards against the ever-present possibility of sequencing artifacts). In addition, the authors should discuss whether the pipeline adequately accounts for the i) triploid genome and ii) the fragmented and potentially insufficiently purged assembly. In absence of such information, the validity of the results remains difficult to ascertain.
      2. The statistical support for the germline mutation rate is weak (Fig. 5a). The sample size of the experiment is rather low for such studies, with only four Filia in each group. Furthermore, in both the control and regenerate group, two of the filia are siblings. This creates a data dependence that is not adequately discussed or taken into account for the analysis. For example, the authors conduct a t-test to determine if the number of detected mutations differs between the groups. A critical assumption of the t-test is that the samples are independent, an assumption that is violated when a relatedness structure is present. Similarly, this dependence could further influence the analysis of the mutational profile. Hence the authors either have to increase the sample size or temper the interpretation of their results, including the claimed estimation of germline mutation rate.
      3. The possibility of somatic mosaicism that the authors discuss extensively in the context of regeneration also complicates the interpretation of the clonal mutations between parents and filia. First, somatic mosaicism has been already demonstrated in a different planarian species and discussed in multiple reviews (e.g., PMID: 31221097, PMID: 35862435). This literature needs to be cited adequately.<br /> Second, the plausible contribution of individual somatic stem cells to the germ line leaves open the possibility that the observed parent/offspring differences in the control group also reflect rare pre-existing allele heterogeneities within the parental population of pluripotent stem cells. Therefore, clonal differences between parents and offspring cannot simply be attributed to germline mutations. Third, low, but measurable rates of sex are known to occur even in predominantly parthenogenetic S. polychroa populations (e.g., PMID: 16721392; PMID: 15293852]. These studies need to be cited and the possibility of parental genome contributions needs to be explicitly examined, as it would violate the requirement for an isogenic background stated on page 4. Overall, this means that the author's claim of providing the first quantification of the germ cell mutation rate in planarians is therefore insufficiently justified.<br /> The possibility of somatic mosaicism also impacts the interpretation of the apparent increase in genetic variation during regeneration. Given the limited depth of the sequencing assays, it remains difficult to refute the null hypothesis that the apparent increase in the mutation load of regenerates represents a subsampling of the standing genetic variation in the parent animals (and without the single-cell populational bottleneck of parthenogenetic reproduction). Also, the claims regarding the mutagenic nature of the regeneration process should therefore need to be dropped or significantly toned down.

      Minor comments:

      Fig.2a: The S. polychroa genome size estimates from genomesize.com Table S1 and Figure 2 a: The entries from the animal genome Size Database need to be removed from the figure, as this is published background information and not an analysis result.

      Page 6: The text description of the transcriptome backmapping results (Fig. 3A) is confusing: "...17.4 % were not mapped by GMAP,... the remaining transcripts were mapped as duplicates, at multiple positions or in two chimeric fragments... The authors need to insert the fraction of single mappers, as otherwise, they imply that they only obtain multi-mappers.

      Page 7: PlanMine needs to be cited as the source of the orthology information between S.med and S. pol.

      Page 10: What is the COSMIC database? Please explain/reference.

      Fig. 4 and 5: The experimental set-up cartoon in Fig. 4a is confusing and should more clearly illustrate which of the experimental groups involved regeneration, how many individuals were sequenced, and the meaning of the A/B terminology in subsequent graphs. Moreover, the authors need to ensure consistent symbol use, e.g., Fr1, 2, 3, 4 instead of the current F1, F2, ... in Fig. 5a.

      The authors discuss the potential contribution of methylation to the observed mutation spectrum and conclude that it might not be present in S. mediterranea and S. polychroa. Indeed, the lack of measurable mC in the planarian genome has already been demonstrated (PMID: 24063805). Please cite and shorten the respective text section.

      Fig. 6e: The authors estimate the number of stem cells in tail segments using H3P staining. However, H3P marks only a short segment of the cell cycle and therefore underestimates the number of resident stem cells. This caveat needs to be discussed.

      Typo in Figure S2: The splice site is marked wirth a black

      Significance

      Planarian flatworms harbor abundant pluripotent adult stem cells. These cells are the only division-competent cells in the animal and of pivotal importance to planarian biology. For example, they enable the regeneration of entire animals from tiny tissue pieces or the re-formation of the germ line in sexually reproducing strains. How planarians maintain their genetic identity in the face of abundant pluripotent adult stem cells and a strict soma/germline divide raises many intriguing questions.

      The manuscript provides a preliminary and highly fragmented draft genome assembly of the planarian species S. polychroa, which adds to the available planarian genome information. Based on the genome assembly, the manuscript attempts to measure generational mutation rates during parthenogenetic reproduction and regeneration. The quality of the SNP detection is somewhat difficult to evaluate in the current manuscript and the possibility of somatic heterogeneity in the parents raises concerns regarding the interpretation of the supposed germline mutation rate. The data provide further evidence for somatic mosaicism, which has already been demonstrated in a different planarian species. The extent by which regeneration is mutagenic per se or uncovers standing genetic variation due to the inherent population sub-sampling also remains unclear. Overall, the manuscript stands out as one of the first intra-organismal population genomics studies in the field. But I think not all its claims are sufficiently supported by data.

      I am a planarian biologist with experience in planarian genomics. I am not an explicit population genetics/genomics expert.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      This paper presents the genome of the planarian Schmidtea polychroa, a sister species to the widely used regenerative model S. mediterranea. The preliminary analysis of the genome is sound and the authors establish that they have produced a good draft assembly. The authors then leverage this assembly to ask novel questions about mutation rates in regenerative planarians, a question that has not been well addressed previously. They use a clear and logical approach to measure mutation rates in relation to both development and regeneration and establish that different clones of stem cells exist in planarians and contribute to regeneration and production of germ cells.

      Overall the work is of a high quality, and logically explained.

      Major comment.

      I could find no statement about raw data availability or metadata availability, or availability of intermediate data analysis files. This makes proper review and consideration of the authors analysis essentially impossible, so all of this must be taken on trust and easy to do but helpful further analyses in the context of the existing data structure can't really be suggested. This is a great shame. Furthermore, in this reviewers' opinion this goes against the principle of open science. A revision must address this issue unless the reviewers' are planning to publish this paper in the 1990s print format only (forgive the sarcasm, but I hope the authors can concede this isn't a good way forward). Without data access the full impact of their very exciting work cannot land. If I have missed reference to access to the data or a GitHub link etc in the paper then I apologise, but I have looked extensively. Sequence Data submission can take time so they should do this in advance and perhaps share the link to the unreleased link too reviewers, and intermediate files and metadata can be shared on GitHub or the like.

      Minor comments.

      I enjoyed reading the paper immensely and I think it touches on many important and interesting theoretical ideas in the field.<br /> With regards to their comments on methylation they should note that previous work on S. mediterranea has rigorously shown that methylation is absent or very low, probably any residual comes from base scavenging from the calf liver food source. Additionally, DMNT 1 and 3 are absent from S. mediterranea, so canonical enzymes for methyl-cytosine addition and maintenance are not available. Citing this would be useful (Jaber-Hijazi et al, 2013, Developmental Biology https://doi.org/10.1016/j.ydbio.2013.09.020). Other work suggests there might be methylation in this group using retriction enzyme based approaches.

      I have some questions that relate to evolution of a parthenogen.<br /> Did the authors ever detect homozygous changes between "parental" generations and offspring or changes from a heterozygous state to a homozygous state?<br /> Given the parthogenic and triploid nature of Polychroa did the authors detect high levels of/accumulation of heterozygous alleles generally in the genome?

      Significance

      This paper will eventually be very significant to the regenerative biology community as it will give us comparative genomic capabilities for the well-established model S. mediterranea. Although not commented on much in the manuscript this is very important. Furthermore, the paper begins the important work of characterising mutation rates in this group of animals that avoid the ageing process entirely, this work will be another important foundation stone in understanding the phenomena that allow for this in this group of animals.

      I have expertise in genome assembly, analysis and annotation, as well in assessing variation in genomes. I also have expertise in the model system used here and its general biology, including regeneration.

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      Referee #1

      Evidence, reproducibility and clarity

      In this paper, using the triploid biotype of planarian Schmidea polychroa, the first half of the paper presents the results of the analysis of genome structure and the second half shows that (de novo) mutations in individuals that undergo regeneration are passed on by the next generation.

      While I think this paper contains interesting biological findings, I am skeptical about its novelty. I was convinced by the results and discussion of the analysis of genome structure, but the results and that of the analysis of (de novo) mutation were very confusing. This may be due to my lack of knowledge in this field. But even so, the author needs to improve this manuscript so that the general reader will better understand it.

      Major comments:

      1. The author mentions that it is important to note that this study was conducted using a parthenogenetic triploid biotype. However, I think that the parthenogenesis undergoing by a triploid biotype of S. polychroa is very unusual. It is not typical apomictic parthenogenesis. Triploid oocytes arise by meiosis from hexaploid oocytes derived from triploid adult somatic stem cells called neoblasts. On the other hand, haploid sperm arise by meiosis from diploid spermatogonia derived from neoblasts. Embryogenesis of triploid eggs then occurs by pseudogamy. Occasional sex is also known to occur even if the offspring's chromosome number remains triploid. I think this background is important information to give the reader. Also, don't the authors need to treat the results in this paper with this complex phenomenon also taken into account?
      2. Fig.4B-C: Analysis by lineage-specific mutations of parental controls.<br /> The authors do not specifically mention or discuss this result. What about the accumulation of mutations within such populations in typical parthenogenesis (daphnia and aphids)? In other words, are the results in Fig. 4B-C due to the special mechanism for parthenogenesis in the triploid S.polychroa as described above?
      3. Throughout this paper, the authors show that regeneration increases de novo mutations in the progeny. The authors conclude that many of the mutations occurred in neoblasts during regeneration. However, I would like you to explain the biological significance of this results in S. polychroa, which naturally does not reproduce by fission and regeneration. There are already reports of mutations accumulating in neoblasts in Dugesia japonica, which reproduce aexually by fission. For these reasons, I do not think this paper presents extremely novel results.
      4. p15, Discussion:<br /> "Tissue regeneration is best seen in the liver of mammals, and the regrowth of relapsed tumours following surgery can also be considered an example of a regenerative process. Mutagenesis accompanying these processes is relevant to subsequent tumorigenesis or the development of resistance, and the planarian system can provide a useful model for the mutagenic effect of tissue regeneration."

      Isn't it an overstatement to associate the regenerative system of planaria with the liver regeneration of mammals?<br /> 5. p10, Results:<br /> "We compared the two de novo spectra to the spectrum of germline heterozygous SNPs, present in all animals, and found that the pattern of germline substitutions resembled more closely the de novo spectrum of the control group (Fig 5D, Fig S3), implying that regeneration has a minor contribution to germline mutations in S. polychroa populations."<br /> p14, Discussion:<br /> "The high similarity of the spectrum of heterozygous SNPs and de novo mutations of control animals suggests that the species primarily reproduces in a non-regenerative manner. The increased mutation rate and the altered mutation spectrum upon regeneration confirmed our hypothesis that regeneration is a mutagenic process."

      I was very confused by these sentences and it took me some time to understand them. Triploid S. polychroa naturally does not reproduce by fission and regeneration, namely a non-regenerative manner. I do not understand why the author insists on this. Please explain the results for the regenerated case in Fig. 5D (0.88) in a way that is also easy to understand. Also, what is the biological significance of asserting here that de novo mutation by regeneration increases in a species that does not increase by regeneration and division in the first place?

      Minor comments:

      1. The author should add a schematic diagram showing the distribution of reproductive organs in Fig.1 to help the reader understand that the ovaries are not included in the regenerative fragment.
      2. P12, line12: Fig 6D-E, it's F, not E, right?
      3. P9, line 8:<br /> "these mutations were missing from the original egg but were present in the egg laid by the parent and thus represent the total mutation load of a generation."

      The author mentions that the de novo mutation found in offspring derived from parents that do not undergo regeneration was already present in the eggs, but I can find no evidence of this. Can you rule out the possibility that these mutations occurred between hatching and adulthood?<br /> 4. p10, Results:<br /> "Interestingly, the majority of mutations were shared in the siblings F4A and F4B. This suggests that the germ cells of these animals were descendants of the same stem cell, which underwent a high number of cell divisions early during the regeneration process prior to oocyte differentiation. The same finding also confirms that the detected clonal filial mutations were present in the respective oocyte and were not generated by embryonic cell divisions."

      The shared de novo mutations detected in the siblings (F4A and F4B) derived from the parent that underwent regeneration in Fig. 5A suggest that the germ cells of these siblings are descended from the same stem cell. The authors say that these mutations occurred in a large number of cell divisions early in the regenerative process prior to oocyte differentiation.

      So why is there no shared de novo mutation in the siblings (Fc4A and Fc4B) derived from the non-regenerating parent in Fig. 5A? As mentioned in Minor comment 3, the author states that the de novo mutations were already present in the parent-laid eggs, but when did these mutations, which are not shared, arise?<br /> 5. p11, Results:<br /> "Interestingly, in the case of FR4A-FR4B sibling pair, shared de novo mutations present in both were subclonal in R4 in a proportion comparable to the other samples (7/15 by WGS, 46.7%), while the three unique mutations could not be detected in R4 by the PCR approach, indicating again that the unique mutations, which amounted to approximately 10% of total clonal filial mutations in these two animals, arose late during germ cell regeneration."

      "during germ cell regeneration." the expression is too vague to know which stage you are referring to. In relation to minor comment 4, why not create a new chart to clearly show when the expected mutations occurred?<br /> 6. p12, Results:<br /> "Altogether 7/30 regenerant mutations were detected in PR animals, and these included those with the highest AF in the regenerants (Fig. 6C). This suggests that parental animals, even before regeneration, contained a diverse set of stem cells, and some of the detected de novo mutations in the filial generation resulted from the expansion of mutation-containing stem cell clones contributing also to germ cells in the regenerant animals."

      If the mutation in the offspring is derived from the parent (PR) prior to the time of tail amputation, wouldn't it be wouldn't it be strange to assume that it is a de novo mutation?<br /> 7. p12, Results:<br /> "The remaining 23/30 R- subclonal mutations may have arisen during regeneration. On average, ~250 dividing neoblasts were detected in cut tails of animals from the same population as the sequenced individuals, as determined by immunofluorescence of phosphorylated H3 histone (Fig 6D-E). However, the high proportions of body cells carrying regenerant-specific mutations suggest that certain stem cells contribute to disproportionately large parts of the regenerated body, including the germline."

      I did not quite understand the relevance of this discussion to the photos shown here of the M period (Fig. 6e).

      Significance

      General assessment: This paper contains important biological information. The finding that mutations in planarian stem cells cause diversity in the next generation of parthenogenesis is very interesting. However, I think that the author needs to carefully explain and change his argument, for example, that the mutations were caused by regeneration, which does not naturally occur in the species used.

      Advance: The finding that accumulation of mutations is occurring in planarian stem cells has already been reported in Dugesia japonica. Please cite the papers and clarify what is the key finding in this paper.

      Audience: Basic Research_Evolutionary Ecology, Developmental Biology (Stem Cells), Reproductive Biology

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

      My field of study is reproductive biology. I am familiar with the transcriptome but unfamiliar with genome analysis.

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      Reply to the reviewers

      We thank the three reviewers for carefully reading our manuscript and for all considerations, ideas, suggestions, and comments. These were all very helpful for us to strengthen the scientific statements of our manuscript. Please, note that all changes are marked in red in the manuscript and supplement. Below you will find, point by point, our responses to all questions and comments.

      Reviewer #1 (Evidence, reproducibility and clarity):

      Overall, this is an exciting work. There are, however, several open questions that the authors could address to facilitate understanding of their work. These points are:

      1.) On page 5, lines 113ff, the authors mention the membrane bulges that they analyse in figure 1. They show these deformations by light (confocal) and electron microscopy. However, the bulges seen by confocal microscopy seem to be bigger that those seen by electron microscopy. The authors could quantify the sizes of the bulges for clarification.

      We quantified the size of the membrane bulges. At the confocal we measured in average 750nm as mean value of identified bulges (n=12) with 650nm as minimal and 890nm as maximal sizes. At the TEM we measure ~243nm as mean value (n=61), with a range between 62nm as minimum and 442 as maximum value. These measurements are shown as Figure 1E.

      Please note that measurements of TEM images do not always capture the three-dimensionality of bulges and may show only parts of them. In addition, ultrastructure is more sensitive and can easily detect small membrane changes that we cannot observe with confocal and airsycan microscopy. In contrast, even with our high-quality objective (63x Zeiss Plan Neofluar, Glycerin, 1.3 NA), standard confocal analysis is limited at ~200nm on the XY axis (airyscan ~110nm) and ~450nm on Z-axis. Therefore, TEM analysis detects smaller bulges than confocal analysis, and consequently, this method detected a large range of bulge lengths between 63nm and 441nm. In contrast, the airyscan method detected a range of bulge length between 0.65 and 0.83 µm. However, confocal and TEM analyses provide evidence of membrane bulges in pio mutant embryos. Please note that we extended our studies and now show membrane bulges in two different pio mutant alleles (17C and 5M) with airsycan microscopy.

      2.) The subject of the manuscript is rather complicated; presentation of data from Figure 1C and D on lines 113ff and 169ff is confusing.

      We apologize and thank the reviewer for careful reading. We revised both paragraphs (lines 108 – 123 and lines 166 - 174) and are confident that the descriptions are now much more understandable. All changes are marked in red.

      3.) The quality of the sub-images of Figure 2E differs. Especially, the phenotype of the wurst, pio transheterozygous embryo is not well visible.

      We apologize for it. We repeated the experiment with wurst;pio transheterozygotes, and generated wurst;pio double mutant embryos to improve the quality. The gas filling assay is shown in Fig. 3. With brightfield microscopy in overview images (10x air objective) and close-ups of the dorsal trunks (25x Glycerin objectives). Both show the gas-filling defects of dorsal trunk tubes. In a subsequent confocal analysis of chitin stainings in late-stage 17 embryos, we found that tracheal tube lumens are collapsed in the transheterozygotes and double mutant embryos.

      4.) Lines 246ff: the protein size are given for the mCherry:chimeric proteins; an estimate of the native Pio portions should be given.

      The endogenous Pio protein has a calculated mass of about 50.82 kDa. We state it now in the according legend of Fig. 6.

      5.) In Figure 6A, the appearance of chitin in the wildtype tube is different compared to the Np mutant situation, more filamentous. Can the authors comment on that?

      The author is correct. The chitin cable formation in Np mutant embryos is normal but lacks the condensation process, and, therefore, fiber structure of the chitin matrix differs from control embryos in late stage 16 and stage 17 embryos (see Drees et al., PLOS Genetics, 2019).

      6.) In the discussion section, I would appreciate if the timing of events was discussed or even shown in a model. The central question is: how are the functions of Pio and Np coordinated in time? As I understand, Np should not cleave Pio before morphogenesis is completed. Is there any example in the literature for how such an interaction could be controlled? The overexpression of Np shows that either the ratio between Np and Pio is important, or the btl promoter expresses Np at the "wrong" time point.

      We thank the reviewer for this interesting comment.

      Of course, we did not measure forces, but it has been published that axial forces appear at the apical cell membrane during stage 16 tube expansion. Our data show that Np cleaves Pio ZP domain and subsequent release increase during stage 16. The cleaved and released Pio enriches in the lumen during stage 16, from where cleaved Pio is internalized during stage 17 with the help of Wurst-mediated endocytosis. This is supported by several in vivo studies, video microscopy, antibody stainings and biochemical data, such as the interaction of Pio and Dumpy as well as the identification of different Pio products with and without Np cleavage. Moreover, we found membrane bulges that increase in size during stage 16 and identified a subsequent tear-off of the chitin matrix in Np mutant embryos. Thus, we propose that Np is required to cleave Pio-Dumpy linkages at the membrane-matrix when tubes elongate and postulated forces appear at the cell membrane during tube elongation in stage 16 embryos.

      We stated this in the discussion as follows:

      “The membrane defects observed in both Pio and Np mutants indicate errors in the coupling of the membrane matrix due to the involvement of Pio (Figs. 1,7). ..., the large membrane bulges in Np mutants affect the membrane and the apical matrix (Fig. 7). Since apical Pio is not cleaved in Np mutants (Fig. 7D), the matrix is not uncoupled from the membrane as in pio mutant embryos but is likely more intensely coupled, which leads to tearing of the matrix axially along the membrane bulges (Figs. 7, 9), when the tube expands in length.”

      How could Np be regulated at the membrane? Np is a zymogen that very likely undergoes ectodomain shedding for activation, similar to what has been described for matriptases. Additionally, human matriptase requires transient interaction of the stem region with its cognate inhibitor HAI-2, which Drosophila lacks (see Drees et al., PLOS Gen, 2019). Thus, the regulation of Np activation is not known.

      Further, we observed that Dumpy is not degraded in Np mutant embryos during stage 17. Nevertheless, in a previous publication, we showed that btl-G4 driven Np expression rescues Np mutant phenotypes in a time-specific manner. We used the btl-G4 driver line for these rescue experiments to express Np in tracheal cells. This restored tracheal Dumpy degradation in Np mutant embryos. Thus, btlG-G4 driven Np overexpression is able to rescue Np mutant tracheal phenotypes in a time-specific manner, although Gal4 is expressed from early tracheal development onwards. Further, btl-Gal4 driven Np expression mimics the endogenous Np, which is expressed from stage 11 onwards in all tracheal cells throughout embryogenesis (see Drees et al., PLOS Gen, 2019).

      Based on these experiments, we conclude that the btl-G4-driven Np overexpression can cleave Pio ZP domain in stage 16 embryos at the correct time.

      However, the ratio of Np expression and Pio is essential in the way that btl-Gal4 driven Gal4 Np overexpression may cause cleavage of a higher number of Pio proteins and the release of critical Pio-Dumpy linkages at the cell membrane and matrix. Thus, increased Pio shedding into the lumen reduces Pio linkages at the membrane, resulting in a pio mutant like tracheal overexpansion in btl-Gal4 driven Gal4 Np overexpression.

      Finally, we were able to prove the reviewer’s question in a new experiment. We used btl-Gal4 driven UAS-Np embryos for Pio antibody staining. This revealed Pio enrichment at the tracheal chitin cable in stage 14 and 15 embryos. In contrast, stage 16 embryos showed numerous Pio puncta appearing across the entire tube lumen, indicating that Np mediates Pio shedding specifically in stage 16 embryos and not before. This Np-controlled Pio releases modifies tube length control.

      Therefore, we stated this in the manuscript as follows:

      Results:

      “Our data assumes that Np overexpression may enhance Pio shedding in stage 16 embryos, affecting the Pio-mediated ZP matrix function. Upon breathless (btl)-Gal4-mediated expression of UAS-Np in tracheal cells, we observed a high amount of Pio puncta across the entire tracheal tube lumen, specifically in stage 16 embryos but not in earlier stages (Fig. S13). Consistently tracheal Np overexpression led to tube overexpansion in stage 16 embryos resembling the pio mutant phenotype (Fig. 8A,B). Thus, Np-mediated Pio shedding controls Pio function.”

      Discussion:

      “The btl-Gal4-driven Np expression mimics the endogenous Np from stage 11 onwards in all tracheal cells throughout embryogenesis (Drees et al., 2019), suggesting that Np is not expressed at a wrong time point. However, the ratio between Np and Pio is essential. We assume that Np overexpression increases Pio shedding, resulting in a pio loss-of-function phenotype. Thus, the tube length overexpansion upon Np overexpression indicates that Pio cleavage is required for tube length control.

      Our observation that the membrane deformations are maintained in Np mutant embryos supports our postulated Np function to redistribute and deregulate membrane-matrix associations in stage 16 embryos when tracheal tube length expands. In contrast, Np overexpression potentially uncouples the Pio-Dpy ZP matrix membrane linkages resulting very likely in unbalanced forces causing sinusoidal tubes.”

      7.) Also for the discussion: We have two situations where Pio amounts/density are enhanced at the apical plasma membrane. The wurst experiments on lines 136ff show that Pio amount and density depends on endocytosis; is the wurst phenotype (Figure 2), at least partially, due to over-presentation of Pio? Likewise, in Figure 2C, there is more Pio in Cht2 overexpressing tracheae (but there is overall more Pio in these tracheae) - is actually endocytosis reduced in chitin-less luminal matrices? First: does the Pio signal at the apical plasma membrane correspond to membrane-Pio or free-Pio? Second, as in the case of wurst: would more Pio on the membrane (density) affect tracheal dimensions in Cht2 over expressing tracheae? Or are the consequences of Pio accumulation in the apical plasma membrane different in Cht2 and wurst backgrounds? Maybe cleavage of Pio and its endocytosis are dependent on its interaction with the chitin matrix. These questions connect to the question immediately above: how are the functions of the different players coordinated in space and time? We need a discussion on this issue.

      We thank the reviewer for this very important idea to discuss the functions of the different players in a coordinated space and time and apologize that we haven’t done before.

      As this is an important point, we tried to figure out all questions raised by the reviewer and discussed it in several new paragraphs in the discussion:

      "Indeed, the anti-Pio antibody, which can detect all different Pio variants, showed a punctuate Pio pattern overlapping with the apical cell membrane marker Uif at the dorsal trunk cells of stage 16 embryos. Additionally, Pio antibody also revealed early tracheal expression from embryonic stage 11 onwards, and due to Pio function in narrow dorsal and ventral branches, strong luminal Pio staining is detectable from early stage 14 until stage 17, when airway protein clearance removes luminal contents.

      We generated mCherry::Pio as a tool for in vivo Pio expression and localization pattern analysis during tube lumen length expansion. The mCherry::Pio resembled the Pio antibody expression pattern from early tracheal development onwards. However, luminal mCherry::Pio enrichment occurs specifically during stage 16, when tubes expand. The stage 16 embryos showed mCherry::Pio puncta accumulating apically in dorsal trunk cells. Moreover, mCherry::Pio puncta partially overlapped with Dpy::YFP and chitin at the taenidial folds, forming at apical cell membranes. Supported by several observations, such as antibody staining, Video monitoring, FRAP experiments, and Western Blot studies (Figs. 4,5), these findings indicate that Pio may play a significant role at the apical cell membrane and matrix in dorsal trunk cells of stage 16 embryos.

      Furthermore, we show that Np-mediates Pio ZP domain cleavage for luminal release of the short Pio variant during ongoing tube length expansion. The luminal cleaved mCherry::Pio is enriched at the end of stage 16 and finally internalized by the subsequent airway clearance process during stage 17 after tube length expansion. Such rapid luminal Pio internalization is consistent with a sharp pulse of endocytosis rapidly internalizing the luminal contents during stage 17 (Tsarouhas et al., 2007). Wurst is required to mediate the internalization of proteins in the airways (Behr et al., 2007; Stümpges and Behr, 2011). In consistence, during stage 17, luminal Pio antibody staining fades in control embryos but not in Wurst deficient embryos.

      Nevertheless, Pio and its endocytosis depend on its interaction with the chitin matrix and the Np-mediated cleavage. In stage 16 wurst and mega mutant embryos, we detect Pio antibody staining at the chitin cable, suggesting that Pio is cleaved and released into the dorsal trunk tube lumen. Also, the Cht2 overexpression did not prevent the luminal release of Pio. However, reduced wurst, mega function, and Cht2 overexpression caused an enrichment of punctuate Pio staining at the apical cell membrane and matrix (Figs. 1,2). Although the three proteins are involved in different subcellular requirements, they all contribute to the determination of tube size by affecting either the apical cell membrane or the formation of a well-structured apical extracellular chitin matrix, indicating that changes at the apical cell membrane and matrix in stage 16 embryos affect the Pio pattern at the membrane. It also shows that local Pio linkages at the cell membrane and matrix are still cleaved by the Np function for luminal Pio release, which explains why those mutant embryos do not show pio mutant-like membrane deformations and Np-mutant-like bulges. This is in line with our observations that tracheal Pio overexpression cannot cause tube size defects as the Np function is sufficient to organize local Pio linkages at the membrane and matrix. Therefore, it is unlikely that tracheal tube length defects in wurst and mega mutants as well as in Cht2 misexpression embryos are caused the apical Pio density enrichment.

      Nevertheless, oversized tube length due to the misregulation of the apical cell membrane and adjacent chitin matrix may cause changes to local Pio set linkages and the need for Np-mediated cleavage. Strikingly, we observe a lack of Pio release in Np mutants. This shows that Pio density at the membrane versus lumen depends predominantly on Np function. The molecular mechanisms that coordinate the Np-mediated Pio cleavage are unknown and will be necessary for understanding how tubes resist forces that impact cell membranes and matrices. On the other hand, Pio is required for the extracellular secretion of its interaction partner Dpy. At the same time, Dpy is needed for Pio localization at the cell membrane and its distribution into the tube lumen. Consistently, in vivo, mCherry::Pio and Dpy::eYFP localization patterns overlap at the apical cell surface and within the tube lumen. These observations support our model that Pio and Dpy interact at the cell surface where Np-mediates Pio cleavage to support luminal Pio release by the large and stretchable matrix protein Dpy (Fig. 9).

      Taenidial organization prevents the collapse of the tracheal tube. Therefore, cortical (apical) actin organizes into parallel-running bundles that proceed to the onset of cuticle secretion and correspond precisely to the cuticle's taenidial folds (Matusek et al., 2006; Öztürk-Çolak et al., 2016). Mutant larvae of the F-actin nucleator formin DAAM show mosaic taenidial fold patterns, indicating a failure of alignment with each other and along the tracheal tubes (Matusek et al., 2006). In contrast, pio mutant dorsal tracheal trunks contained increased ring spacing (Fig. 3A). Fusion cells are narrow doughnut-shaped cells where actin accumulates into a spotted pattern. Formins, such as Diaphanous, are essential in organizing the actin cytoskeleton. However, we do not observe dorsal trunk tube fusion defects as found in the presence of the activated diaphanous.

      On the other hand, ectopic expression of DAAM in fusion cells induces changes in apical actin organization but does not cause any phenotypic effects (Matusek et al., 2006). DAAM is associated with the tyrosine kinase Src42A (Nelson et al., 2012), which orients membrane growth in the axial tube dimension (Förster and Luschnig, 2012). The Src42 overexpression elongates tracheal tubes due to flattened axially elongated dorsal trunk cells and AJ remodeling. Although flattened cells and tube overexpansion are similar in pio mutant embryos, we did not observe a mislocalization of AJ components, as found upon constitutive Src42 activation (Förster and Luschnig, 2012). Instead, we detected an unusual stretched appearance of AJs at the fusion cells of pio mutant dorsal trunks, which to our knowledge, has not been observed before and may play a role in regulating axial taenidial fold spacing and tube elongation.

      Self-organizing physical principles govern the regular spacing pattern of the tracheal taenidial folds (Hannezo et al., 2015). The actomyosin cortex and increased actin activity before and turnover at stage 16 drive the regular pattern formation. However, the cell cortex and actomyosin are in frictional contact with a rigid apical ECM. The Src42A mutant embryos contain shortened tube length but increased taenidial fold period pattern due to decreased friction. In contrast, the chitinase synthase mutant kkv1 has tube dilation defects and no regular but an aberrant pearling pattern caused by zero fiction (Hannezo et al., 2015).

      In contrast, pio mutant embryos do not contain tube dilation defects or shortened tubes but increased tube length (Figs. 1; 8; S1). Furthermore, our cbp and antibody stainings reveal the presence of a luminal chitin cable and a solid aECM structure in pio mutant stage 16 embryos (Figs. 8, S1; S6). In addition, apical actin enrichment in tracheal cells of pio mutant embryos appeared wt-like. Nonetheless, pio mutant embryos show an increased taenidial fold period compared with wt, indicating a decreased friction. Thus, we propose that the lack of Pio reduces friction. Reasons might be subtle defects of actomyosin constriction or chitin matrix, which we have not detected in the pio mutant tracheal cells. Further reasons for lower friction might be the loss of Pio set local linkages between apical cortex and aECM in stage 16 embryos, which are modified by Np, as proposed in our model (Fig. 9).

      Heterozygous and homozygous pio mutant embryos generally do not show tubal collapse. However, the loss of Pio and accompanying lack of Dpy secretion in stage 17 pio mutant embryos led to the loss of a Pio/Dpy matrix, impacting the late embryonic maturation and differentiation of a normal chitin matrix at the apical cell surface. TEM images reveal reduced dense chitin matrix material at taenidial folds and misarranged taenidial fold pattern (Figs. 1; S2), suggesting impaired taenidial function prevents tube lumen from collapsing after tube protein clearance. Wurst knockdown and mutant embryos do not show general tube collapse, but luminal chitin fiber organization is disturbed in stage 17 embryos (Behr et al., 2007). Therefore, transheterozygous wurst;pio mutant embryos may combine both defects and suffer from maturation deficits of the chitin/ZP matrix at the apical cell surface and within the tube lumen, which finally causes a high number of embryos with incomplete gas filling due to tube collapse. These maturation deficits are even more dramatic in the wurst;pio double mutants, which show no gas filling.”

      8.) The sentence on line 242ff should be rephrased: "dynamic" and "elastic" are not opposites.

      We thank the reviewer for careful reading. We revised the sentence as follow:

      “Our FRAP data suggest that Pio is the dynamic part of the tracheal ZP-matrix, while the static Dpy modulates mechanical tension within the matrix”

      9.) A central question to me is the amounts and the density of factors in different genetic backgrounds as mentioned above. Is there any mechanism adjusting the amounts or the density of the players according to the size of the apical plasma membrane or the tracheal lumen? Pio seemingly responds to these changes.

      We would like to know the molecular mechanisms that control the density of players at the apical membrane. This question is important and could be the starting point for novel scientific investigations. Mechanisms of protein trafficking, such as exocytosis, recycling and endocytosis regulate delivery and internalization of proteins at the apical cell membrane. Furthermore, protein junctions at the lateral membrane may recognize and therefore may respond to low and high mechanical stresses between cells that appear during tube length expansion. However, we did not observe any hint for misregulation of Pio expression levels in the different mutants which affect endocytosis, SJs and luminal ECM. But we observed a shift of Pio levels between apical cell membrane/matrix and lumen in wurst, mega mutants and Cht2 overexpression. This shift is analyzed with diverse ZEN tools and quantified (Fig. 2D-F; Fig. S4B). As discussed in the new paragraph, this shift is very likely caused by changes at the apical cell membrane and chitin matrix which impact Pio shedding. Moreover, we observe the lack of Pio release in Np mutants. This shows that Pio density at the membrane versus lumen depends predominantly on Np-mediated cleavage. As discussed above, how Np is activated at the apical cell membrane to cleave Pio is not known.

      10.) The connection of Pio and taenidia is mentioned in the results section (page 7) but not discussed.

      We appreciate the careful reading and comments of the reviewer very much. We included the connection of Pio and taenidial in the discussion section as follows:

      “Taenidial organization prevents the collapse of the tracheal tube. Therefore, cortical (apical) actin organizes into parallel-running bundles that proceed to the onset of cuticle secretion and correspond precisely to the cuticle's taenidial folds (Matusek et al., 2006; Öztürk-Çolak et al., 2016). Mutant larvae of the F-actin nucleator formin DAAM show mosaic taenidial fold patterns, indicating a failure of alignment with each other and along the tracheal tubes (Matusek et al., 2006). In contrast, pio mutant dorsal tracheal trunks contained increased ring spacing (Fig. 3A). Fusion cells are narrow doughnut-shaped cells where actin accumulates into a spotted pattern. Formins, such as Diaphanous, are essential in organizing the actin cytoskeleton. However, we do not observe dorsal trunk tube fusion defects as found in the presence of the activated diaphanous.

      On the other hand, ectopic expression of DAAM in fusion cells induces changes in apical actin organization but does not cause any phenotypic effects (Matusek et al., 2006). DAAM is associated with the tyrosine kinase Src42A (Nelson et al., 2012), which orients membrane growth in the axial tube dimension (Förster and Luschnig, 2012). The Src42 overexpression elongates tracheal tubes due to flattened axially elongated dorsal trunk cells and AJ remodeling. Although flattened cells and tube overexpansion are similar in pio mutant embryos, we did not observe a mislocalization of AJ components, as found upon constitutive Src42 activation (Förster and Luschnig, 2012). Instead, we detected an unusual stretched appearance of AJs at the fusion cells of pio mutant dorsal trunks, which to our knowledge, has not been observed before and may play a role in regulating axial taenidial fold spacing and tube elongation.

      Self-organizing physical principles govern the regular spacing pattern of the tracheal taenidial folds (Hannezo et al., 2015). The actomyosin cortex and increased actin activity before and turnover at stage 16 drive the regular pattern formation. However, the cell cortex and actomyosin are in frictional contact with a rigid apical ECM. The Src42A mutant embryos contain shortened tube length but increased taenidial fold period pattern due to decreased friction. In contrast, the chitinase synthase mutant kkv1 has tube dilation defects and no regular but an aberrant pearling pattern caused by zero fiction (Hannezo et al., 2015).

      In contrast, pio mutant embryos do not contain tube dilation defects or shortened tubes but increased tube length (Figs. 1; 8; S1). Furthermore, our cbp and antibody stainings reveal the presence of a luminal chitin cable and a solid aECM structure in pio mutant stage 16 embryos (Figs. 8, S1; S6). In addition, apical actin enrichment in tracheal cells of pio mutant embryos appeared wt-like. Nonetheless, pio mutant embryos show an increased taenidial fold period compared with wt, indicating a decreased friction. Thus, we propose that the lack of Pio reduces friction. Reasons might be subtle defects of actomyosin constriction or chitin matrix, which we have not detected in the pio mutant tracheal cells. Further reasons for lower friction might also be the loss of Pio set local linkages between apical cortex and aECM in stage 16 embryos, which are modified by Np, as proposed in our model (Fig. 9).

      Heterozygous and homozygous pio mutant embryos generally do not show tubal collapse. However, the loss of Pio and accompanying lack of Dpy secretion in stage 17 pio mutant embryos led to the loss of a Pio/Dpy matrix, impacting the late embryonic maturation and differentiation of a normal chitin matrix at the apical cell surface. TEM images reveal reduced dense chitin matrix material at taenidial folds and misarranged taenidial fold pattern (Figs. 1; S2), suggesting impaired taenidial function prevents tube lumen from collapsing after tube protein clearance. Wurst knockdown and mutant embryos do not show general tube collapse, but luminal chitin fiber organization is disturbed in stage 17 embryos (Behr et al., 2007). Therefore, transheterozygous wurst;pio mutant embryos may combine both defects and suffer from maturation deficits of the chitin/ZP matrix at the apical cell surface and within the tube lumen, which finally causes a high number of embryos with incomplete gas filling due to tube collapse. These maturation deficits are even more dramatic in the wurst;pio double mutants, which show no gas filling.”

      11.) Dp remains cytoplasmic in pio mutant background - is the pio mutant phenotype due to defects by lack of Pio AND Dp function? What is the tracheal phenotype of dp mutants?

      It has been discussed that dumpyolvr and pio mutants show similar phenotypes in early tracheal development (Jazwinska, 2003) and it has been discussed that dumpyolvr mutant embryos compromise tube size in combination with shrub mutants. The additional quantifications of the dumpyolvr mutant showed significantly increased tube length (Dong 2014). We used dumpyolvr mutant [In(2L)dpyolvr], an X-ray induced mutation of the dumpy gene locus (Wilkin 2000). dumpyolvr mutant resemble pio null mutant tracheal phenotypes including detached dorsal and ventral branches and oversized tracheal dorsal trunk with curly appearance in late embryos. We included chitin and Uif staining’s of stage 16 dumpy mutant embryos (Fig. S10).

      This data suggest that Pio mutant phenotype is due to a lack of Pio and Dumpy, which would support our model, of Pio and Dumpy protein interaction in the extracellular space of the tube lumen.

      In wt embryos Pio is predominantly in the luminal chitin cable, in contrast in dumpy mutant embryos most Pio is predominantly not at the luminal chitin cable. Less luminal Pio staining in dumpy mutant embryos but Pio accumulation apically shows that Dumpy is required for luminal Pio release in stage 16 embryos. This supports our model that Pio and Dumpy interaction may link membrane and matrix and that this link reacts on mechanical stress during tube expansion by Np-mediated cleavage of Pio and its accompanied luminal release due to linked Dumpy.

      12.) Lines 374ff: the reduced dorsal trunk in Np mutants is not significant; the respective statement should be formulated carefully. If we believe the statistics (no significance), this would mean that attachment of the apical plasma membrane to the luminal chitin via Pio is needed to restrict axial extension; release of Pio is needed for differentiation (taenidia formation, luminal clearance) beyond morphogenesis.

      We agree with the reviewer that the reduction of the dorsal trunks in Np mutant is statistically not significant. However, the mean value is clearly below that of WT. Therefore, we revised our statement as follow: “In Np mutant embryos, tracheal dorsal trunk length shows the tends to be reduced compared to wt embryos.” Further, the btlG4-driven UAS-Np overexpression of Np suggests strong Pio release from the apical membrane and therefore resembles the pio mutant tube length overexpansion (Fig. 8A,B; Fig S13). Thus, our current observations indicate that Np-mediated Pio release at the cell membrane enables precise tube length elongation.

      We thank the referee for discussing that Pio is needed for taenidial fold formation which would fit to our findings in pio null mutant embryos. Pio mutant embryos show the appearance of taenidial folds in stage 16 embryos (airyscan) and stage 17 embryos (TEM images). However, TEM images also show chitin matrix reduction in pio mutant stage 17 embryos. Further, co-stainings of Pio with Crb and Uif, as well as co-stainings of mCherry::Pio with Dpy-GFP and cbp confirms that the Pio localize at the apical cell membrane where taenidial folds form in late stage 16 embryos. Thus, our observations suggest that Pio and Dumpy are required at the apical membrane and matrix to stabilize taenidial folds and tube lumen during 17. This also includes the Np-mediated Pio release at the apical cell membrane. As requested by the referee we summarized Pio function during late tracheal development in our simplified model (see Fig. 9).

      However, it is of note that Np-mediated Pio release increases at late stage 16 (Fig. 5A, 6D; Fig. S13) but is strongly reduced in stage 17 embryos. In contrast, thin taenidial fold are formed at late stage 16 and becomes thicker and form at fusion points during stage 17 and reach their most mature form when the intraluminal chitin cable is cleared (Öztürk-Colak et al., elife, 2016). Thus, the pattern of Pio release and taenidial fold differentiation do not fully match. Moreover, in preliminary experiments we observe Pio antibody staining in stage 17 embryos at the apical cell membrane of dorsal trunks (data not shown). Furthermore, lumen clearance of Obst-A, Knk, Sepr and Verm are not affected in pio mutant embryos, but unknown luminal ECM contents remained (Fig. 1D). Therefore, we will follow this very interesting idea in future experiments.

      Nonetheless, we state in the results that Pio shedding is essential:

      “Our data assumes that Np overexpression may enhance Pio shedding in stage 16 embryos, affecting the Pio-mediated ZP matrix function. Upon breathless (btl)-Gal4-mediated expression of UAS-Np in tracheal cells, we observed a high amount of Pio puncta across the entire tracheal tube lumen, specifically in stage 16 embryos but not in earlier stages (Fig. S13). Consistently tracheal Np overexpression led to tube overexpansion in stage 16 embryos resembling the pio mutant phenotype (Fig. 8A,B). Thus, Np-mediated Pio shedding controls Pio function.”

      13.) Why don't we see the apical Pio signal in Figure 4B?<br />

      The red arrowhead points to apical mCherry::Pio punctuate staining in the Fig. 5B (before 4B) in the close up of the “bleached area” before bleaching and 56min post bleaching. However, in vivo bleaching experiments do not allow additional antibody stainings to detect precisely the apical cell membrane. Further, the Dpy::eYFP marks the tube lumen and the apical cell surface. The latter showed adjacent mCherry::Pio punctuate staining. However, due to bleaching Dpy signal was not detectable in the area.

      14.) The Strep signals in the merges in Figure 7C are not well visible.

      We are not sure which Strep signal the reviewer is referring to in Fig. 7C, which is now Fig. 8C. The top panel shows the Strep signal (right panel) overlapping with GFP in cells that do not express Np or human matriptase. Thus, the TGFB3 ZP domain is not cleaved, and the intracellular GFP and also the extracellular Strep signals are maintained and overlap.

      In contrast, when Np or human matriptase is added, the TGFB3 ZP domain is cleaved and only the intercellular GFP signal is retained, whereas the extracellular Strep signal is released from the cell surface. This explains why the Strep signal is barely detectable in the middle and lower panels of Fig. 8C.

      Reviewer #1 (Significance):

      This work brings together several factors (Pio, Dp, Np, Wst etc) already known to be needed for tracheal morphogenesis and differentiation in the embryo of D. melanogaster. Having worked myself with some of these factors, however, I recognize that the interaction between these factors is novel and very exciting. The experiments strongly indicate a new mechanism of cell-ECM connection that seems to be conserved to some extent (as they provide preliminary data on an example from humans). By integrating the functions of different factors, the work provides ample opportunity for future projects to elucidate this mechanism in detail. Therefore, I expect that it will have a significant impact not only on the field of developmental cell biology but also, due to the conserved proteins involved (ZP proteins, Matriptase), on the field of cell biology of human diseases.

      Reviewer #2 (Evidence, reproducibility and clarity):

      _The figures are clear, and the questions well addressed. However, I find that some of the claims are not completely backed by the data presented and have some suggestions that will hopefully make some points clearer.

      Major comments

      1.) In the abstract and at the end of the introduction the authors claim that they show that Pio, Dpy and Np support the balancing of mechanical stresses during tracheal tube elongation. However, this is not shown in this manuscript, where tension or mechanical stress were not measured and it is therefore speculative._

      As requested by the reviewer, we deleted “support balancing of” at the final sentence of the Introduction. Please, note that we did not use the term balancing of mechanical stresses at the abstract.

      However, we revised the abstract.

      It has been shown previously that forces and mechanical tension rise when apical membrane expands and elastic extracellular matrix, which is anchored to the membrane balances theses forces (Dong et al., 2014). Furthermore, its has been shown that the gigantic and elastic Dumpy protein modulates mechanical tension (Wilkin et al., 2000). Thus, these previous publications state that mechanical tension rise at the apical cell membrane and matrix when tubes expand during stage 16 and that Dpy is part of that molecular process, which we included in the abstract as essential background information.

      “The apical membrane is anchored to the apical extracellular matrix (aECM) and causes expansion forces that elongate the tracheal tubes. The aECM provides a mechanical tension that balances the resulting expansion forces, with Dumpy being an elastic molecule that modulates the mechanical stress on the matrix during tracheal tube expansion.”

      Nonetheless, our results show that Np-mediated Pio cleavage increases during stage 16 as response to tube length expansion which is accompanied by forces as postulated by others (see above). We further observe that the membrane bulges and chitin matrix tear off, when Pio cleavage does not occur in Np mutant embryos. Our data further show that Pio and Dumpy interact and that Pio release is prevented in Dpy mutant embryos. Altogether this suggests that the Np-mediated Pio cleavage responds to tube expansion and requires Dpy for luminal Pio release.

      We therefore claim in the final sentence of the introduction that “…ZP domain proteins Pio and, Dumpy, as well as the protease Np respond to mechanical stresses when tracheal tubes elongate”. The according changes are marked in red.

      2.) The authors state that all pio CRISPR/Cas9 generated mutants display identical tracheal phenotypes, however these data are not shown. Tracheal phenotypes, in particular DT phenotypes, of all mutants generated should be shown in supplementary materials.

      As requested by the reviewer, we included the data in the supplement. The pio5M and pio11R alleles showed embryonic lethality and a 100% gas filling defect resembling the pio17C allele. Additionally, we extended the tracheal analysis with the pio5M allele and identified tube size defects, irregular pattern of taenidial folds and apical membrane deformation, altogether resembling the pio17C allele. These new data are shown in the supplement Fig. S1.

      We clarify this in the results section as follows:

      “The tracheal phenotypes of pio5m are shown in the supplement (Fig. S1B-F). In all other Figures, we show images of the pio17c allele. “

      3.) At stage 16, pio null mutants display DT overelongation phenotypes (Fig. 1). The authors should quantify this phenotype.

      As requested by the reviewer, we quantified the DT overelongation phenotypes for pio5M (Fig. S1). The quantification of pio17C was shown already in Fig. 6B, now Fig 8B.

      4.) The authors analyse Pio distribution under tubular stress, using mega mutants and Chitinase overexpression. Pio localization changes in these genetic backgrounds and this is shown in Figure 2 only in a qualitative manner. The authors should measure Pio localization at the lumen and at the membrane and provide quantitative data.

      As requested by the referee, we measured Pio localization recognized by the anti-Pio antibody at the lumen and at the membrane to provide quantitative data. These are shown in Fig. 2E.

      All images were taken with a Zeiss Airyscan. For statistical analysis we used the the profile tool of the Zeiss ZEN 2.3 black software. This tool allows the measurement and comparison of fluorescence pixel intensities of individual channels. We determined the fluorescent intensities profile across the tube to identify values at apical membrane and tube lumen at minimum 10 different position of DTs (metameres 5 to 6) of two distinct embryos for each genetic background. The maximum values of membranes versus tube lumen were set into ratio and compared between control, mega mutant and Cht2 overexpression. The control embryos showed a ration below 0.4, the Cht2 overexpression a ratio of 1.2 and mega mutants a ratio of about ~0.9. These quantitative data confirm the statement that Pio localization increases at and near the apical cell membrane with respect to the lumen in mega mutants and in Cht2 overexpression embryos.

      5.) Surprisingly and interestingly, wurst;pio transheterozygotes display very strong tracheal defects. The authors say they observe gas filling defects; however it is not clear from figure 2E if this indeed the case. From the panel in the figure, it looks like these embryos suffer from strong tracheal morphogenetic defects. It would be necessary to have a better analysis of these embryos. What is the penetrance of this phenotype. If this is 100% penetrant, one would expect it to be lethal. Therefore, double mutant balanced stocks are not viable? Having analyzed the phenotypes and confirmed which morphogenetic defects the transheterozygote embryos present, how does this genetic interaction fit with the model presented?

      We are thankful to the reviewer for this interesting point of view suggesting that the wurst;pio embryos display tracheal morphogenetic defects. First, our data show that only 11.6% of the wurst;pio transheterozygous embryos completed gas filling and survived until adulthood. In contrast, 88.4% of transheterozygous wurst;pio mutant embryos did not complete gas filling which is now presented in Fig. 3B. The corresponding quantifications is presented in Fig. 3D. Importantly, the 88.4% wurst;pio transheterozygous embryos which show gas filling defects do not hatch as larvae and die.

      As requested, we performed a better morphogenetic analysis, which is presented in Fig. 3C. Analysis of the gas filling defects with light microscopy were repeated with a better objective (Zeiss Apochromat 25x Gly; 0.8 NA). Indeed, this analysis revealed a strongly compromised tube lumen morphology with irregular tube lumen pattern as if tubes twist and bend. This tube lumen deformation was further confirmed with the confocal analysis of chitin staining (cbp). The tube lumen of stage 17 transheterozygous wurst;pio mutant embryos showed irregular lumen pattern with unusual twists and even partially collapsed tubes.

      Furthermore, as asked by the referee, we generated the wurst,pio double mutation. All wurst,pio double mutant embryos lacked gas filling. In a more in-depth analysis of the tube lumen with a high-performance objective we could not identify any normal tube lumen in stage 17 embryos. Instead the double mutant embryos revealed completely collapsed tracheal tubes. This was confirmed by the chitin staining and confocal analysis. All new data are presented in the supplement.

      As shown in our manuscript and in previous publications, neither pio nor wurst mutant embryos affect cell polarity or gross organization of the actin and tubulin cytoskeleton. However, we found that wurst mutant embryos showed irregular apical membrane expansion at tube lumen (Behr et al., 2007; legend Fig. 4), irregular chitin fiber organization and to some extend collapsed tube lumen. In pio mutant embryos we found deformed apical membrane of DTs, irregular pattern of taenidial folds and to some extend collapsed tube lumen. Thus, the apical membrane is their common target of both proteins in late embryonic development, suggesting that pio functions provide stability and wurst functions the internalization of proteins at the apical membrane.

      We discussed it as follows:

      “Nevertheless, Pio and its endocytosis depend on its interaction with the chitin matrix and the Np-mediated cleavage. In stage 16 wurst and mega mutant embryos, we detect Pio antibody staining at the chitin cable, suggesting that Pio is cleaved and released into the dorsal trunk tube lumen. Also, the Cht2 overexpression did not prevent the luminal release of Pio. However, reduced wurst, mega function, and Cht2 overexpression caused an enrichment of punctuate Pio staining at the apical cell membrane and matrix (Figs. 1,2). Although the three proteins are involved in different subcellular requirements, they all contribute to the determination of tube size by affecting either the apical cell membrane or the formation of a well-structured apical extracellular chitin matrix, indicating that changes at the apical cell membrane and matrix in stage 16 embryos affect the Pio pattern at the membrane. It also shows that local Pio linkages at the cell membrane and matrix are still cleaved by the Np function for luminal Pio release, which explains why those mutant embryos do not show pio mutant-like membrane deformations and Np-mutant-like bulges. This is in line with our observations that tracheal Pio overexpression cannot cause tube size defects as the Np function is sufficient to organize local Pio linkages at the membrane and matrix. Therefore, it is unlikely that tracheal tube length defects in wurst and mega mutants as well as in Cht2 misexpression embryos are caused by the apical Pio density enrichment.”

      “Heterozygous and homozygous pio mutant embryos generally do not show tubal collapse. However, the loss of Pio and accompanying lack of Dpy secretion in stage 17 pio mutant embryos led to the loss of a Pio/Dpy matrix, impacting the late embryonic maturation and differentiation of a normal chitin matrix at the apical cell surface. TEM images reveal reduced dense chitin matrix material at taenidial folds and misarranged taenidial fold pattern (Figs. 1; S2), suggesting impaired taenidial function prevents tube lumen from collapsing after tube protein clearance. Wurst knockdown and mutant embryos do not show general tube collapse, but luminal chitin fiber organization is disturbed in stage 17 embryos (Behr et al., 2007). Therefore, transheterozygous wurst;pio mutant embryos may combine both defects and suffer from maturation deficits of the chitin/ZP matrix at the apical cell surface and within the tube lumen, which finally causes a high number of embryos with incomplete gas filling due to tube collapse. These maturation deficits are even more dramatic in the wurst;pio double mutants, which show no gas filling.”

      6.) mCherry::Pio Dpy::eYFP time lapse analysis and FRAP experiments is very interesting. However, it is not clear to which degree bleaching occurs in the tracheal lumen. The authors claim that recovery is very fast and can be seen from minute 2, however, frame-by-frame analysis of Movie S2 does not show a clear different between luminal Pio from minute 0 to minute 2. Rough comparison with the luminal area surrounding the bleached area, does not show a clear difference in luminal Pio before and after photobleaching. To claim fast recovery of luminal Pio after photobleaching, the authors should quantify luminal Pio, before and after bleaching.

      We agree with the reviewer and deleted “fast”. The Video2 shows intracellular mCherry::Pio recovery within 2min after photobleaching. The Video 2 shows extracellular (luminal) recovery within 6min after photobleaching, when first large mCherry::Pio puncta appear at the apical surface of the bleached area. Nonetheless, mCherry::Pio puncta appear in the lumen indicating recovery, whereas Dpy::eYFP did not.

      We state this in the Results section as follows:

      “In stage 16 embryos mCherry::Pio puncta reappeared in tracheal cells within 2 minutes of bleaching and in the tubular lumen within 6 minutes.”

      In addition, in figure 4D, the normalized mCherry::Pio fluorescence in the graph what does it refer to? Intracellular Pio?

      Figure 4D, now 5D, shows Western Blot signals. We guess that you refer to Fig 4B which is Fig. 5B.

      We are sorry for confusion and named it now Fig. 5B’.

      We stated in the Material section:

      “The bleaching was performed with 405nm full laser power (50mW) at the ROI for 20 seconds. A Z-stack covering the whole depth of the tracheal tubes in the ROI were taken at each imaging step. “Fluorescence intensity in the bleached ROIs was measured after correction for embryonic movements using Fiji.”

      Thus, to clarify this point, we added to the legends:

      “Fluorescence intensities refer to the bleached ROIs as indicated with the frame in corresponding Movie S2 and was measured after correction for embryonic movements.”

      7.) When mCherry::Pio Dpy::eYFP time lapse analysis and FRAP experiments was done in an Np mutant background, the authors describe lack of Pio recovery within the lumen (Movie S3). However, when comparing control and Np mutant background embryos, Pio is not properly released into the lumen of Np mutants (as stated by the authors and seen by comparing movies S1 and S4). Furthermore, on minute 0 of the FRAP experiment in Np embryos, there is no detectable Pio in the DT lumen. Therefore, recovery was not expected in Np mutants and should not be claimed as a conclusion for this experiment.

      We thank the reviewer for careful reading and apologize our wrong description. We changed it accordingly as follows:

      “In contrast to the control, extracellular mCherry::Pio is not released into the tube lumen within 56 min after bleaching in Np mutant embryos (Fig. 6C, Video S3).”

      8.) Brodu et al (Dev Cell 2010) have shown that Pio is important for cytoskeletal modulation during tracheal maturation. Pio is important for non-centrosomal microtubule (MT) arrays anchored at the tracheal cell apical membranes. In addition, MT disruption in tracheal cells leads to lumen formation defects (Brodu et al, Dev Cell 2010). In the absence of Pio, the tracheal cytoskeleton is altered, and this could explain some of the results observed. Ideally, the work should be complemented with a basic cytoskeletal analysis, but if this is not possible, the authors should discuss some of the phenotypes in light of this Pio function.

      Dear reviewer, this is a great idea. Therefore, we analyzed F-actin with Phalloidin and beta tubulin (E7 antibody, DSHB) in the dorsal trunk cells of stage 16 control and pio mutant embryos. However, tracheal cells are tiny and only gross irregularities can be realized. So, confocal Z-stack analysis of the stainings did not show gross differences between control and pio mutant embryos. We observe the expected apical subcortical accumulation for the actin and tubulin cytoskeleton in dorsal trunk cells of pio stage 16 mutant embryos which also has been shown for wt embryos elsewhere. These new data are presented in the supplement Fig. S7.

      Minor comments<br /> The model should not be in supplementary materials and should be moved to the main manuscript.

      We thank the reviewer for this suggestion and moved the model to the main part – now Fig.9. As requested by the reviewer 1, we extended the model, showing the timing events of Pio function.

      Throughout the manuscript embryonic stages are described using different nomenclature (stage X, stX and st X). Either way is correct, but the same nomenclature should be used throughout.

      We apologize for the different nomenclature and use "stage X" in the manuscript and "stX" in the figures for space reasons. Legend 1 clarifies the abbreviation.

      In Fig. S1 B and C the authors should specify which pio allele is being analysed (as in Fig. 7). The same should be done in the text.

      That's a fairly good point. To be clear from the beginning, we now state the following in the first paragraph of the results:

      “The tracheal phenotypes of pio5m are shown in the supplement (Fig. S1B-F). In the all other Figures, we show phenotypes of the pio17c allele.”

      Line 131, it is not correct to say that WGA visualizes cell membranes. WGA marks/stains cell membranes.

      Thanks for finding this mistake, it’s now corrected.

      Line 165 "leads to excessive tube dilation and length expansion due to strongly reduced luminal chitin" is not correct. Chitin reduction leads to excessive tube dilation but not to length expansion, as reported in the papers cited at the end of the sentence.

      Thanks very much for careful reading, we deleted “and length expansion” from the sentence.

      Line 220-221, what do authors refer to as "stage 16 wt-like control embryos"?

      Thanks for finding these mistakes. We corrected as follows:

      “In stage 16 embryos mCherry::Pio puncta….”

      Line 221, "some minutes" should be replaced by a specific number of minutes. According to Movie S2 reappearance of tracheal cell Pio happens from minute 16.

      We agree with the reviewer to state the time when mCerry::Pio puncta reappear. We observe first large puncta within two minutes after bleaching in tracheal cells at the ROI (Video S2, lower cell row at the movie). We further observe the reappearance of first large puncta at the ROI within 6 minutes in the tracheal tube lumen.

      We corrected it as follows: “In stage 16 embryos mCherry::Pio puncta reappeared in tracheal cells within 2 minutes of bleaching and in the tubular lumen within 6 minutes.”

      Line 291 "time laps" should be lapse.

      Thanks for finding the typo, it is corrected now.

      Line 302, "Pio was not shedded into the lumen but remained at the cell" should be "Pio was not shed into the lumen but remained in the cell".

      Thanks for finding the typo, it is corrected now.

      _Referees cross-commenting

      I agree. Taken together, all the comments will improve the quality of the work and of a future manuscript. Also, everything seems quite doable and will not present any problems._

      Reviewer #2 (Significance):

      _The findings shown in this manuscript shed light on the regulation of tubulogenesis by ZP proteins and how their interaction with the ECM can be regulated by proteolysis. It was known that Pio is involved in tracheal development, is secreted into the lumen, regulating tube elongation (Jaźwińska et al., Nat.Cell Biol., 2003) and anchoring MTs to the apical membrane during tubulogenesis (Brodu et al, Dev. Cell 2010). This work provides additional molecular insights into Pio dynamics and regulation during tube maturation.<br /> This work will be of interest to a broad cell and developmental biology community as they provide a mechanistic advance in ZP proteins involved in morphogenesis. It is of specific interest to the specialized field of tubulogenesis and tracheal morphogenesis.

      Field of expertise:<br /> Drosophila, morphogenesis, tracheal tubulogenesis, cytoskeleton_

      Reviewer #3 (Evidence, reproducibility and clarity):

      _Summary<br /> In this manuscript, Drees and colleagues analysed, during the formation and growth of tubular systems, how cells combine forces at the cell membranes while maintaining tubular network integrity. A fundamental question is to understand how cells manage to integrate the axial forces to stabilise the cell membrane and the apical extracellular matrix (aECM).<br /> To address this question, the authors study the formation of the tracheal system in Drosophila embryos, a well-established and detailed model system to investigate formation of tubular networks. In particular, they focused on the formation of the larger tube of the tracheal network, the dorsal trunk. The formation of this tube depends in part of axial extension along the antero-posterior axis.<br /> They concentrated their work on the function of Piopio (Pio), a Zona-Pellucida (ZP)-domain protein. They showed that Pio together with the protease Notopleural (Np) contribute the sense and support mechanical stresses when tracheal tubes elongate, thus ensuring normal membrane -aECM morphology.

      Major Comments

      In a previous work, Drees et al. (PLOS Genetics 2019), showed the matriptase-prostasin proteolytic cascade (MPPC), is conserved and essential for both Drosophila ECM morphogenesis and physiology.<br /> The functionally conserved components of the MPPC mediate cleavage of zona pellucida-domain (ZP-domain) proteins, which play crucial roles in organizing apical structures of the ECM in both vertebrates and invertebrates. They showed that ZP-proteins are molecular targets of the conserved MPPC and that cleavage within the ZP-domains is a conserved mechanism of ECM development and differentiation.<br /> Here, Drees et al. investigate further how the coupling between membrane and matrix takes place to ensure proper tube growth.<br /> Pio distribution and phenotypes<br /> They first focused on the tracheal phenotypes observed in a pio null mutant context. So far, the only pio mutant characterised was a point mutation in the ZP domain. Using CRISPR/Cas9, they generated new alleles of pio which are lack of function alleles. In the context, Drees and colleagues observed over-elongated dorsal trunk tubes, with bulges appearing at stage 16 between the apical domain of tracheal cells and adjacent extra-luminal matrix.<br /> Additionally, pio mutant embryos showed impaired tube lumen clearance of the some of the aECM components, which prevent gas-filling of the airways.<br /> To detect Pio distribution, the authors used either anti-Pio antibody directed toward a short stretch with the Pio ZP domain or generated a CRISPR/Cas9 piomCherry::pio line.

      _

      1.) The Pio antibody shows a strong luminal staining as already published. But the authors reported an apical membrane signal in tracheal cells. I find this apical membrane signal really difficult to observe in panel Fig. 2B. The overlap between the Pio dots and the apical membrane labelled with Uif showed in Fig 2C can be due to the 3D projection. It is only when endocytosis is unpaired (Suppl Fig. 2), that data are more convincing.

      We thank the reviewer for this important point, we are sorry for the unconvincing presentation and for having the chance to improve it.

      We show the 3D image of Pio puncta as voxels overlapping with Uif at the apical cell membrane. The amount of Pio voxels overlapping with the Uif marked apical cell membrane increased in mega mutant and due to tracheal Cht2 overexpression. This result was indicated by a representative region (frame) and white arrows and is shown now in Fig. 2C.

      We further used orthogonal projections across the tracheal tube of the airyscan Z-stacks. Random usage confirmed that puncta of Pio antibody staining overlap with Uif at the tube lumen. We observed overlap in controls, but increasing overlap in mega mutant and Cht2 overexpressing embryos. This result is shown now in Fig. 2E.

      However, to overcome any misinterpretations of projections, we used single images of the original airyscan Z-stacks for co-localization analysis with the Zeiss ZEN software (black, 2.3, sp1). We used two available and independent standard methods to compare fluorescence pixel intensities of different channels namely the ZEN co-localization and the ZEN profile tool. Both are described in the Materials section.

      a.) With the co-localization tool we compared directly fluorescence pixel intensities of Pio and Uif. Highest overlap of the intensities, shown in the ZEN tool as third quadrant, were set to white for better visualization in the images. These new images are included as Fig. 2D and show recurrent overlap of Pio and Uif antibody stainings (punctuate pattern) along the apical cell membrane at the dorsal trunk of stage 16 control embryos. This overlap pattern increased in mega mutant and Cht2 overexpression embryos.

      b.) A second approach for comparing fluorescence intensities is the ZEN “profile” tool. Drawing a line across the tube allowed us to compare peak fluorescence pixel intensities of the different channels at distinct regions, such as the apical cell membrane and the tube lumen including the cbp marked chitin cable. This tool detected overlap of peak fluorescence intensities of UIF and Pio antibody staining’s, confirming that Pio is located together with UIF at the apical membrane of dorsal trunk tracheal cells. These new intensity profiles and the corresponding images are presented in the supplement as Fig. S4B-D. Quantifications of this method comparing the ration of Pio peak intensities between the apical cell membrane and the tube lumen are presented as Fig. 2F (as requested by Reviewer 2).

      2.) When the author used their CRISPR/Cas9 piomCherry::pio line to characterise Pio distribution (Fig.4), Pio is localised at the apical plasma membrane before stage 16. Only at stage 16, Pio is detected within the lumen. This timing of Pio release in the lumen is critical for the model proposed by Drees at al. This is an important point to assess the difference between the use of the antibody (which mostly label the lumen) while piomCherry::pio line is mostly at the membrane.

      We agree with the reviewer that the Pio antibody shows a different pattern within the tube lumen of earlier stages. The Pio antibody shows intense extracellular staining from early stage 12 onwards, presumably due to its early function at dorsal and ventral branches, as shown by Anna Jazwinska (Jazwinska et al., 2003). The intense luminal Pio antibody staining, predominantly at the chitin cable, persist until its disappearance due to airway protein clearance during stage 17. Unfortunately, this strong luminal Pio staining made it impossible to examine the Pio distribution pattern in more detail during stage 16. Nevertheless, Np overexpression experiments indicate that luminal Pio release occurs specifically in stage 16 embryos (Fig. S13), which was tested with the Pio antibody, see results, second last paragraph:

      “Our data assumes that Np overexpression may enhance Pio shedding in stage 16 embryos, affecting the Pio-mediated ZP matrix function. Upon breathless (btl)-Gal4-mediated expression of UAS-Np in tracheal cells, we observed a high amount of Pio puncta across the entire tracheal tube lumen, specifically in stage 16 embryos but not in earlier stages (Fig. S13).”

      We further agree with the reviewer that mCherry::Pio was used to characterize in vivo Pio distribution within the dorsal trunk cells and tube lumen during stage 16. The Fig. 5A shows apical mCherry::Pio distribution pattern in early and late stage 16 embryos. Importantly, the appearance of luminal mCherry::Pio increased during stage 16 and mainly enriched at late stage 16. See Figure 5A, red arrowheads point to apical Pio and red arrows to luminal Pio staining.

      Furthermore, as discussed above and shown by different ZEN tools, such as co-localization and fluorescence intensity profile tools, Pio antibody stainings revealed a punctuate pattern at the apical cell membrane of dorsal trunk cells in stage 16 embryos, which is reflected also by the appearance of apical mCherry::Pio puncta at the membrane surface. Additionally, we observed mCherry::Pio puncta also within the tube lumen (see the new Figures S4B & S8). Thus, subcellular Pio distribution at the apical cell membrane and lumen were observed for both, Pio antibody staining and mCherry::pio pattern.

      Nonetheless, there is different luminal appearance between the Pio antibody staining and mCherry::Pio. Pio antibody detects a short stretch at the ZP domain and thus detects all possible Pio variants, uncleaved and cleaved. Due to early tracheal Pio function, Pio enriches within the tube lumen in an intense core-like structure, which is recognized by the Pio antibody and is comparable with the Dpy::eYFP pattern. Also mCherry::Pio labels all Pio variants, uncleaved and cleaved. The spatial temporal mCherry::Pio expression pattern (Fig. S5) is comparable with the Pio antibody pattern and the staining at the membrane in stage 16 embryos. However, mCherry::Pio did not enrich in the lumen in a core-like structure, nonetheless, shows overlap with luminal Dpy::eYFP.

      Jaswinska showed that Pio antibody staining is intracellular in the trachea of stage 11 pio2R-16 point mutation embryos (Jaswinska et al., 2003; Fig 2d). To understand more about the specificity of the antibody, we performed stainings in the null mutant embryos. In contrast, to the high number of intracellular Pio puncta in pio2R-16 point mutation embryos, Pio stainings were much more reduced in pio5m and pio17c mutants, but a low number of Pio puncta were still detectable in the embryos (Fig. S1G,H). It is of note that also dpy mutants showed strongly reduced Pio antibody staining (Fig. S10E). Thus, discussing underlying causes of enriched (Pio antibody) versus non-enriched (mCherry::Pio) luminal staining are speculative. However, observations by Jaswinska et al. (2003) and our new observations, investigating the Pio antibody stainings in pio null mutants, dpy mutants, eYFP::Dpy embryos and NP overexpression may hint to the possibility of cross-reactivity of the Pio antibody to other ZP domains which may intensify the appearance of luminal Pio antibody staining in control embryos.

      Anyway, we clarify the difference in luminal Pio pattern in the discussion as follows:

      “Indeed, the anti-Pio antibody, which detects all different Pio variants, showed a punctuate Pio pattern overlapping with the apical cell membrane markers Crb and Uif at the dorsal trunk cells of stage 16 embryos (Fig. 2; Fig. S3,S4). Additionally, Pio antibody also revealed early tracheal expression from embryonic stage 11 onwards, and due to Pio function in narrow dorsal and ventral branches, strong luminal Pio antibody staining is detectable from early stage 14 until stage 17, when airway protein clearance removes luminal contents. In the pio5m and pio17c mutants Pio stainings were strongly reduced although some puncta were still detectable in the trachea (Fig. S1G,H). Similarly, Pio antibody staining is intracellular in the trachea of stage 11 pio2R-16 point mutation embryos (Jaźwińska et al., 2003). Interestingly, also dpy mutants showed strongly reduced and intracellular Pio antibody staining (Fig. S10E).

      We generated mCherry::Pio as a tool for in vivo Pio expression and localization pattern analysis during tube lumen length expansion. The mCherry::Pio resembled the Pio antibody expression pattern from early tracheal development onwards. However, luminal mCherry::Pio enrichment occurs specifically during stage 16, when tubes expand. The stage 16 embryos showed mCherry::Pio puncta accumulating apically in dorsal trunk cells. Moreover, mCherry::Pio puncta partially overlapped with Dpy::YFP and chitin at the taenidial folds, forming at apical cell membranes. Supported by several observations, such as antibody staining, Video monitoring, FRAP experiments, and Western Blot studies (Figs. 4,5), these findings indicate that Pio may play a significant role at the apical cell membrane and matrix in dorsal trunk cells of stage 16 embryos.”

      3.) Another important point is to explain the discrepancy between the pio mutant alleles. The allele containing a point mutation in the ZP domain shows no over-elongated tubes (Dong et al 2014, Jazwinska et al. 2003) while the lack of function alleles does.

      The reviewer is correct that the pio2R-16 mutation shows only a disintegration phenotype whereas our pio null mutations show in addition tube length defects. However, Dong et al. showed significantly increased dorsal trunk length in shrub; pio2R-16 double mutant embryos when compared with shrub mutant embryos (Supplemental Fig. S4A). Also, the shrub;dpyolvR double mutant embryos revealed increased tube length expansion when compared with shrub mutant embryos. Moreover, their quantifications show that the also dpyolvR mutant embryos revealed significantly increased tube expansion when compared with wt. Altogether these previous findings suggests that Pio and Dpy are involved in controlling tube length control during stage 16.

      Furthermore, we generated three independent pio null mutation alleles, which lost all the essential Pio protein domains, and caused all embryonic lethality, gas-filling defects, branch disintegration phenotype and tube length defects (quantifications are shown in Figs. 9 and S1). In addition, pio null mutations prevent Dpy::eYFP secretion. Thus, we are confident that the observed tube length defects as well as the air-filling defects are due to the loss of Pio, and in particular since these defects could be rescued by Pio Expression in the pio null mutation background, as shown in Fig. 3B.

      So, what could make the difference?

      The described pio2R-16 mutation allele contains a X-ray induced single point mutation that led to an amino acid replacement (V159D) in the ZP domain. It is not clear how the amino acid exchange affects the protein and the ZP domain. It may hamper pio function and maybe this amino acid replacement is problematic for the early tracheal function but not during stage 16. As stated by Jazwinska et al. 2003 (Fig. 2 legend), Pio antibody staining is intracellular in the mutants and extracellular in the trachea of wt at stage 13.

      They further speculate that the mutant Pio protein may retain in the secretory pathway, but this is not confirmed with co-markers. As luminal Pio function is required to provide a barrier for autocellular AJ formation, this fails in pio2R-16 mutation. In contrast, it is still possible that Pio interacts and supports Dpy secretion in pio2R-16 mutation and additionally it is thinkable that intracellular Pio may reach to some extend the apical cell membrane in pio2R-16 mutation stage 16 and thus can support tube size control. But these assumptions are speculations.

      Nevertheless, to clarify this point we explain the discrepancy between the pio2R-16 mutation and pio null mutations alleles as follows:

      “Using CRISPR/Cas9, we generated three pio lack of function alleles (Fig. S1A), all exhibiting embryonic lethality and identical tracheal mutant phenotypes. The tracheal phenotypes of pio5m are shown in the supplement (Fig. S1B-F). In all other Figures, we show images of the pio17c allele. The pio17c and pio5m null mutant embryos revealed the dorsal and ventral branch disintegration phenotype known from a previously described pio2R-16 mutation allele which contains a X-ray induced single point mutation that led to an amino acid replacement (V159D) in the ZP domain (Jaźwińska et al., 2003). Additionally, the late stage 16 pio17c and pio5m null mutant embryos showed over-elongated tracheal dorsal trunk tubes (see below).”

      4.) A minor point, the author should provide hypothesis to explain why only the clearance of CBP, Obstructor-A and Knickkopf are affected in a pio mutant background and not Serpentine and Vermiform.

      We thank the reviewer for careful reading and the comment on this point. We would be happy to see such a scenario which could give us a hind of Pio interaction partners at the chitinous matrix. However, we stated that luminal material, such as Obst-A and Knk are removed from the lumen (see Fig. S5A). We further describe that in pio mutant embryos, luminal Serp and Verm staining appeared reduced but showed wt-like distribution (see Fig. S6) in stage 16 embryos. We do not show Serp and Verm in stage 17 embryos, but they are removed from the tube lumen (not shown). These data are received from immune-staining’s and confocal analysis.

      Nevertheless, we also state that pio mutant embryos revealed lumen clearance defects in TEM analysis, of undefined material in the tube lumen (see Fig. 1D and Fig. S2B).

      To clarify this point we state in the results as follows:

      “Fourth, ultrastructure TEM images revealed aECM remnants in the airway lumen of pio mutant stage 17 embryos, while control embryos cleared their airways (Fig. S2B). Consistently, the in vivo analysis of airways in stage 17 pio mutant embryos revealed lack of tracheal air-filling (Fig 3B). The pan-tracheal expression of Pio in pio mutant embryos rescued the lack of gas filling (Fig 3B). Thus, TEM images suggest that pio mutant embryos showed impaired tube lumen clearance of aECM, which prevented subsequent airway gas-filling. “

      And

      “Also, the pio mutant embryos showed tracheal lumen clearance defects of chitin fibers in ultrastructure (TEM) analysis (Figs. 1D, S2B). In contrast, confocal analysis revealed that well-known chitin matrix proteins, such as Obstructor-A (Obst-A) and Knickkopf (Knk), are removed from the lumen of pio mutants (Fig. S5A). These results suggest that the Pio function did not affect airway clearance of Obst-A and Knk and therefore did not play a central role in airway clearance like Wurst. Nevertheless, airway clearance defects observed in TEM images in pio null mutant embryos and, in addition, defective tube lumen morphology in wurst;pio transheterozygous mutant embryos explain the occurrence of airway gas filling defects.”

      5.) Pio and Dumpy. Dumpy (Dpy) is another ZP domain protein secreted by the tracheal cells and detected in the lumen. To follow Dpy distribution, Drees and colleagues used a Dpy::eYFP protein trap line, the same used in Dong et al. However, in this latter paper, Dong et al. stated, using a Crb staining, that Dpy is not at the apical cell surface but only in the lumen. However, Drees and colleagues reported (line 227 and Fig. 4C) that Dpy appears both at the apical cell surface and in the lumen of the tracheal system. But they did not show a co-localisation with an apical marker. Furthermore, in their previous work, (Drees et al. 2019) they called the apical staining a "peripheral shell" layer. In addition, in S2R+ cell culture, it is only when Pio and Dpy co-express that Dpy is detected at the cell membrane. The in vivo localisation of Dpy is an important point that needs to be clarified as it is of importance for the final model proposed Supp Fig. 9.<br /> Drees at al. also performed FRAP experiments on Dpy::eYFP protein trap embryos. As excepted as already shown by Dong et al.

      The referee is correct, we state “In stage 16 embryos Dpy::eYFP (Lye et al., 2014) appears at the tracheal apical cell surface and predominantly within the lumen (Fig. 4C).” The corresponding Fig. 4C reveals Dumpy::eYFP staining overlapping with chitin at two subcellular regions: Dpy is enriched as a core-like structure within the lumen overlapping with the chitin cable of the control embryos. Additionally, Dpy::eYFP overlaps with the chitin part that might be part of the apical cell surface. But this observation is hard to see in images in Fig. 4C and we apologize it. We therefore repeated the Dpy::eYFP localization analysis and analyzed in more detail with the ZEN profile tools, which shows peak fluorescence pixel intensities of different channels and provides the possibility to prove, if they overlap in XY axis.

      We asked first, if cbp (chitin) appears at the apical surface of dorsal trunk cells, when Pio becomes cleaved and released. In mid stage 16 embryos cbp staining appeared in the luminal chitin cable and additionally in a distinctive pattern, which fits to the pattern of taenidial folds that start to form. We therefore used the apical cell membrane marker Crumbs to co-stain cbp. Airycsan microscopy fluorescence intensity profile analysis and corresponding close ups images confirmed the overlap of Crb and cbp stainings at this distinctive pattern indicating this shows the chitin matrix at the apical cell surface (Fig. S8A). But there was no overlap of cbp and Crb at the chitin cable structure. Thus, knowing the localization of the apical cell surface chitin matrix, we performed co-stainings of cbp with mCherry::Pio (RFP antibody). This revealed, as expected, overlap of cbp and RFP antibody staining at the apical cell surface chitin matrix (distinct pattern) and with the luminal chitin-cable (Fig. S8B,C). Finally we repeated the stainings and analysis with cbp, mCherry::Pio (RFP antibody) and Dpy::eYFP (GFP antibody). First, these results revealed overlap of Dpy::eYFP and cbp at the apical cell surface and in the tube lumen (Fig. S8D) and second, overlap of punctuate staining of Dpy::eYFP, cbp and mCherry::Pio at the apical cell surface chitin matrix and also at the luminal chitin cable (Fig. S8E).

      Very obvious from images and Z-projection in Fig. 4C is the lack of extracellular Dpy::eYFP staining in pio mutant embryos. Dpy::eYFP enriched intracellularly, and thus, the pio mutant caused Dpy::eYFP mis-expression fits well to our results from S2R+ cell culture. As the reviewer notes, it is only when Pio and Dpy co-express that Dpy is detected at the cell membrane.

      Altogether, Fig. 4C, cell culture experiments and our new stainings support our model, that Pio and Dumpy interact and are co-secreted at the apical cell membrane/surface, where Np mediates Pio cleavage. As requested by reviewer 2, we moved the model to Fig. 9. As requested by reviewer 1, we extended the model for timing events.

      A minor point, the Dpy::eYFP protein trap line used in this study is not listed in the Materials and Methods section of the supplementary data.

      Thanks, we included it into the List of sources (Supplement). This YFP-trap line (called CPTI lines) was published by Claire M. Lye et al., Development, 141, 2014. We cite it in our manuscript.

      6.) The serine protease NP and Pio release. Drees and colleagues have pervious shown, preforming in vitro studies, that protease Notopleural (Np) cleaves the Pio ZP domain (Drees at al. 2019). Here the authors went a step further in demonstrating that it is also true in vivo at stage 17. In addition, they showed that, in Np mutant embryos, mCherry::Pio is mostly detected within tracheal cells and the luminal staining is strongly reduced. In this mutant context, the authors conducted FRAP experiment on the mCherry::Pio signal even very weak in the lumen. They showed hardly no recovery after photobleaching.<br /> In Drosophila S2 cells, Drees and colleagues showed that co-expression of the catalytically inactive NpS990A with mCherry::Pio in showed as a prominent signal the 90kDa mCherry::Pio variant in the cell lysate (Fig. 5B), and live imaging revealed mCherry::Pio localisation at the cell surface (Fig. S6B). However, in this inactive form context, a strong signal is also detected at 60kDA corresponding to a cleaved form of the Pio ZP domain (Fig. 5B), and Pio localisation at the cell surface appears weaker than in controls. They authors did not consider that another protease could be at play.<br /> On the other hand, in their previous work, Drees et al. identified a mutant form of Pio (PioR196A) which is resistant to NP cleavage in vitro. It will be a step forward to establish by CRISPR/cas9, as the authors seems to be successful with this technique, a mutant line carrying this point mutation. It will be important to determine whether the observed phenotype resembles that of a mutant Np phenotype.<br /> In their previous work (PLOS Genetics 2019), in Np mutant embryos, Drees et al. did not report "budge-like" deformations from stage 16 onwards leading to the detachment of the tracheal cell from their adjacent aECM. Either the alleles or the allelic combination is different between the two studies which could explain this difference, or it is a new phenotype that has not been previously described. In the latter case, it becomes important to quantify the proportion of segments showing these bubbles. Is this a rare phenotype to observe?

      We thank the reviewer for the very interesting comments and the careful reading of our manuscripts and the very useful suggestions. We agree, the we cannot exclude the possibility that another protease is involved in the cleavage of Pio. Therefore, we included this important point in the discussion section as follows:

      “Unknown proteases may likely be involved in Pio processing since cleaved mCherry::Pio is also detectable in inactive NpS990A cells.”

      We think the generation of the pioR196A mutant to address Pio localization and tracheal phenotypes is a great idea, which we would like to address in future experiments. Unfortunately, the production of this fly line with such a specific point mutation at this position will take several months, not included the subsequent evaluation and phenotypic analysis of this fly line and mutants. Therefore, we apologize that we cannot pursue this question experimentally. Nevertheless, mentioning the possibility and the requirement of such an experiment is important and we discuss it as follows:

      “Previously we identified a mutation at the Pio ZP domain (R196A) resistant to NP cleavage in cell culture experiments (Drees et al., 2019). Establishing a corresponding mutant fly line would be essential in determining whether the observed phenotype resembles the phenotype of the Np mutant embryos.”

      However, knowing that we are not able to provide a new mutant fly line to evaluate the formation of the dorsal tube when an NP non-cleavable form of Pio is expressed, we sought to use an alternative approach by overexpressing Np in the trachea with btl-Gal4. This shows a clear pairing of Np overexpression and Pio release specifically at stage 16 dorsal trunk and associated tube overexpansion.

      Finally, the reviewer is correct, we did not mention the appearance of bulges in Np mutant tracheal dorsal trunk cells in our previous publication. We used that same Np alleles in 2019 and a closer look at the publication of 2019 likewise shows the appearance of bulges in Np mutant embryos, e.g. Fig. 1B (red-dextran, left part of the tracheal lumen shows bulges) and even the Dpy::YFP matrix tear off at the site of bulges (Fig. 4F’’, above the arrowhead). But we did not know at the time the link with Pio and Dumpy

      However, we agree, it is important to know more about the appearance of the phenotype by means of quantifications. The quantifications of bulges per dorsal trunk (n=16) is shown in Fig. 7B.

      7.) Minor point: I don't understand what the authors are trying to show in supplementary Figure 8. Tracheal cells detach and are found in the lumen?

      We are sorry for the unclear description in the legend. We corrected it as follows in the legend of Fig. S12:

      “This indicates disintegration of apical cell membrane at bulges and subsequent leaking of cellular content into the lumen.”

      8.) Np function conserved matriptase.<br /> In this work, Drees and colleagues showed that Np controls in vivo the cleavage of the Pio ZP domain.<br /> Dumpy and Piopio are not conserved in vertebrates but they both contain a ZP domain which is conserved. The authors tested if other ZP proteins can be cleaved by Np or the human homolog Matriptase. The authors tested in cell culture the ability of the type III Transforming growth factor-β receptor which contains a ZP domain to be cleaved either by Np or Matriptase.<br /> This could be a general mechanism that needs to be extended to other ZP domain proteins and that could be at play to structure the matrix and give it its physical properties.<br /> However, as it is all speculative, I find the discussion section related to these data, for too long and that does not help to understand better the work done in the formation of the tracheal tubes of the drosophila embryo.

      We show that Np mediates cleavage of the Pio ZP domain in vitro and in vivo in Drosophila embryos. We further showed that also the human matriptase was able to cleave the Pio ZP domain. To understand if this is a more general mechanism, we extended our studies with the human TβIII and its ZP domain. These data show that both Drosophila and human matriptases are able to cleave ZP domains of different proteins from different species. These data suggest that Matriptase-mediated ZP domain cleavage is not a Drosophila specific mechanism. We cannot follow the argumentation of the referee to state it all speculative. Nevertheless, we agree that it will need follow up studies to show that the mechanism is more general than two different species and ZP domain proteins. Anyway, as requested by the referee, we deleted the following sentences of the paragraph, since they are speculative in the context of our manuscript and do not directly describe a potential matriptase and ZP domain function:

      “Matriptase degrades receptors and ECM in pulmonary fibrinogenesis in squamous cell carcinoma (Bardou et al., 2016; Martin and List, 2019). TβRIII is a membrane-bound proteoglycan that generates a soluble form upon shedding (López-Casillas et al., 1991), a potent neutralizing agent of TGF-β. Expression of the soluble TβRIII inhibits tumor growth due to the inhibition of angiogenesis (Bandyopadhyay et al., 2002). Idiopathic pulmonary fibrosis (IPF) is associated with a progressive loss of lung function due to fibroblast accumulation and relentless ECM deposition (King et al., 2011; Loomis-King et al., 2013). “

      However, the comparisons of the tubular organ and the phenotypic expressions of the bulging membrane and the aortic aneurysm appear to us as an important element of the article. In both cases, cell membrane loses its integrity and can break in tubular networks. Thus, with our findings on the modification of extracellular ZP proteins, we offer a potential new molecular approach even for clinical investigation.

      9.) Minor points: Pio and cytoskeleton organisation.<br /> Line 78-79, the authors wrongly quoted a work from Brodu et al (2010). Pio does not anchor the microtubule severing enzyme Spastin. Instead, Spastin releases the microtubule-organising centre from its centrosomal location, then Pio contributes to its apical membrane anchoring. It can therefore be assumed that the organisation of the microtubule network is affected in a pio null mutant. In addition, ZP proteins have been shown to link the aECM to the actin cytoskeleton. Therefore, it would be interesting to look at the organisation of the actin and microtubule cytoskeletons in a pio mutant context in which enlarged apical cell surface area are observed.

      We are very thankful for finding this mistake in the introduction. We corrected it as follows:

      “Further, Pio is involved in relocating microtubule organizing center components γ-TuRC (γ-tubulin and Grips; gamma-tubulin ring proteins). This requires Spastin-mediated release from the centrosome and Pio-mediated γ-TuRC anchoring in the apical membrane.”

      Studying cytoskeleton in pio mutant embryos is a helpful idea. Therefore, we analyzed F-actin with Phalloidin and beta tubulin (E7 antibody, DSHB) in the dorsal trunk cells of stage 16 control and pio mutant embryos. However, tracheal cells are tiny and only gross changes can be realized. The confocal Z-stack analysis of the stainings did not show gross differences between control and pio mutant embryos. We observe the expected apical subcortical accumulation for the actin and tubulin cytoskeleton in dorsal trunk cells of pio stage 16 mutant embryos which also has been shown for wt embryos elsewhere. These new data are presented in the supplement Fig. S7.

      _Referees cross-commenting

      I have just read the comments of the other two reviewers, who like me are specialists in the formation of the tracheal system in the drosophila embryo.<br /> I find the comments very fair and balanced. They are in the same spirit as my comments and are very complementary. I hope that all our comments will be constructive for the authors and will improve the quality of their work._

      Reviewer #3 (Significance):

      _Overall, the methodology is sound, the quality of the data is good and the paper is very well written. Authors combine in vivo, in vitro studies as well a cell culture approach. Using CRISPR/Cas9, they generated a large number of new tools allowing in vivo studies.<br /> Drees and colleagues generated new alleles of pio which are lack of function alleles. They described a new phenotype for pio mutant embryos, namely over-elongated tubes. But they authors do not comment on why these new alleles reveal a new phenotype. Furthermore, using their piomCherry::pio line, the authors state that Pio is localised to the plasma membrane. This location is very difficult to assess. Both new results require clarification.<br /> The authors had already demonstrated that Np cleaves the ZP domain of Pio in vitro. Here they demonstrate this in vivo. It appears important to evaluate the formation of the dorsal tube when an NP non-cleavable form of Pio is expressed.<br /> Finally, the model proposing a coupling between the extracellular matrix and the membrane of tracheal cells is very interesting. The demonstration that cleavage of Pio by Np could participate in this coupling is very interesting for those interested in the integration of mechanical stress and cellular deformation. However, such a model has already been discussed in Dong et al (2014). In this article, Dong et al. proposed that a "coupling of the apical membrane and Dpy matrix core is essential for tube length regulation".

      The audience for this article should be specialised and oriented towards basic research. It may be of interest to people working on tubular systems or working on ZP proteins.

      My field of expertise is cell biology and developmental biology in drosophila and formation of tubular networks._

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      Referee #3

      Evidence, reproducibility and clarity

      Summary

      In this manuscript, Drees and colleagues analysed, during the formation and growth of tubular systems, how cells combine forces at the cell membranes while maintaining tubular network integrity. A fundamental question is to understand how cells manage to integrate the axial forces to stabilise the cell membrane and the apical extracellular matrix (aECM).<br /> To address this question, the authors study the formation of the tracheal system in Drosophila embryos, a well-established and detailed model system to investigate formation of tubular networks. In particular, they focused on the formation of the larger tube of the tracheal network, the dorsal trunk. The formation of this tube depends in part of axial extension along the antero-posterior axis.<br /> They concentrated their work on the function of Piopio (Pio), a Zona-Pellucida (ZP)-domain protein. They showed that Pio together with the protease Notopleural (Np) contribute the sense and support mechanical stresses when tracheal tubes elongate, thus ensuring normal membrane -aECM morphology.

      Major Comments

      In a previous work, Drees et al. (PLOS Genetics 2019), showed the matriptase-prostasin proteolytic cascade (MPPC), is conserved and essential for both Drosophila ECM morphogenesis and physiology.<br /> The functionally conserved components of the MPPC mediate cleavage of zona pellucida-domain (ZP-domain) proteins, which play crucial roles in organizing apical structures of the ECM in both vertebrates and invertebrates. They showed that ZP-proteins are molecular targets of the conserved MPPC and that cleavage within the ZP-domains is a conserved mechanism of ECM development and differentiation.<br /> Here, Drees et al. investigate further how the coupling between membrane and matrix takes place to ensure proper tube growth.<br /> Pio distribution and phenotypes<br /> They first focused on the tracheal phenotypes observed in a pio null mutant context. So far, the only pio mutant characterised was a point mutation in the ZP domain. Using CRISPR/Cas9, they generated new alleles of pio which are lack of function alleles. In the context, Drees and colleagues observed over-elongated dorsal trunk tubes, with bulges appearing at stage 16 between the apical domain of tracheal cells and adjacent extra-luminal matrix.<br /> Additionally, pio mutant embryos showed impaired tube lumen clearance of the some of the aECM components, which prevent gas-filling of the airways.<br /> To detect Pio distribution, the authors used either anti-Pio antibody directed toward a short stretch with the Pio ZP domain or generated a CRISPR/Cas9 piomCherry::pio line.<br /> The Pio antibody shows a strong luminal staining as already published. But the authors reported an apical membrane signal in tracheal cells. I find this apical membrane signal really difficult to observe in panel Fig. 2B. The overlap between the Pio dots and the apical membrane labelled with Uif showed in Fig 2C can be due to the 3D projection. It is only when endocytosis is unpaired (Suppl Fig. 2), that data are more convincing.<br /> When the author used their CRISPR/Cas9 piomCherry::pio line to characterise Pio distribution (Fig.4), Pio is localised at the apical plasma membrane before stage 16. Only at stage 16, Pio is detected within the lumen.<br /> This timing of Pio release in the lumen is critical for the model proposed by Drees at al. This is an important point to assess the difference between the use of the antibody (which mostly label the lumen) while piomCherry::pio line is mostly at the membrane.<br /> Another important point is to explain the discrepancy between the pio mutant alleles. The allele containing a point mutation in the ZP domain shows no over-elongated tubes (Dong et al 2014, Jazwinska et al. 2003) while the lack of function alleles does.<br /> A minor point, the author should provide hypothesis to explain why only the clearance of CBP, Obstructor-A and Knickkopf are affected in a pio mutant background and not Serpentine and Vermiform.

      Pio and Dumpy<br /> Dumpy (Dpy) is another ZP domain protein secreted by the tracheal cells and detected in the lumen. To follow Dpy distribution, Drees and colleagues used a Dpy::eYFP protein trap line, the same used in Dong et al. However, in this latter paper, Dong et al. stated, using a Crb staining, that Dpy is not at the apical cell surface but only in the lumen. However, Drees and colleagues reported (line 227 and Fig. 4C) that Dpy appears both at the apical cell surface and in the lumen of the tracheal system. But they did not show a co-localisation with an apical marker. Furthermore, in their previous work, (Drees et al. 2019) they called the apical staining a "peripheral shell" layer. In addition, in S2R+ cell culture, it is only when Pio and Dpy co-express that Dpy is detected at the cell membrane. The in vivo localisation of Dpy is an important point that needs to be clarified as it is of importance for the final model proposed Supp Fig. 9.<br /> Drees at al. also performed FRAP experiments on Dpy::eYFP protein trap embryos. As excepted as already shown by Dong et al.<br /> A minor point, the Dpy::eYFP protein trap line used in this study is not listed in the Materials and Methods section of the supplementary data.

      The serine protease NP and Pio release.<br /> Drees and colleagues have pervious shown, preforming in vitro studies, that protease Notopleural (Np) cleaves the Pio ZP domain (Drees at al. 2019). Here the authors went a step further in demonstrating that it is also true in vivo at stage 17. In addition, they showed that, in Np mutant embryos, mCherry::Pio is mostly detected within tracheal cells and the luminal staining is strongly reduced. In this mutant context, the authors conducted FRAP experiment on the mCherry::Pio signal even very weak in the lumen. They showed hardly no recovery after photobleaching.<br /> In Drosophila S2 cells, Drees and colleagues showed that co-expression of the catalytically inactive NpS990A with mCherry::Pio in showed as a prominent signal the 90kDa mCherry::Pio variant in the cell lysate (Fig. 5B), and live imaging revealed mCherry::Pio localisation at the cell surface (Fig. S6B). However, in this inactive form context, a strong signal is also detected at 60kDA corresponding to a cleaved form of the Pio ZP domain (Fig. 5B), and Pio localisation at the cell surface appears weaker than in controls. They authors did not consider that another protease could be at play.<br /> On the other hand, in their previous work, Drees et al. identified a mutant form of Pio (PioR196A) which is resistant to NP cleavage in vitro. It will be a step forward to establish by CRISPR/cas9, as the authors seems to be successful with this technique, a mutant line carrying this point mutation. It will be important to determine whether the observed phenotype resembles that of a mutant Np phenotype.<br /> In their previous work (PLOS Genetics 2019), in Np mutant embryos, Drees et al. did not report "budge-like" deformations from stage 16 onwards leading to the detachment of the tracheal cell from their adjacent aECM. Either the alleles or the allelic combination is different between the two studies which could explain this difference, or it is a new phenotype that has not been previously described. In the latter case, it becomes important to quantify the proportion of segments showing these bubbles. Is this a rare phenotype to observe?<br /> Minor point: I don't understand what the authors are trying to show in supplementary Figure 8. Tracheal cells detach and are found in the lumen?

      Np function conserved matriptase.<br /> In this work, Drees and colleagues showed that Np controls in vivo the cleavage of the Pio ZP domain.<br /> Dumpy and Piopio are not conserved in vertebrates but they both contain a ZP domain which is conserved. The authors tested if other ZP proteins can be cleaved by Np or the human homolog Matriptase. The authors tested in cell culture the ability of the type III Transforming growth factor-β receptor which contains a ZP domain to be cleaved either by Np or Matriptase.<br /> This could be a general mechanism that needs to be extended to other ZP domain proteins and that could be at play to structure the matrix and give it its physical properties.<br /> However, as it is all speculative, I find the discussion section related to these data, for too long and that does not help to understand better the work done in the formation of the tracheal tubes of the drosophila embryo.

      Minor points: Pio and cytoskeleton organisation.<br /> Line 78-79, the authors wrongly quoted a work from Brodu et al (2010). Pio does not anchor the microtubule severing enzyme Spastin. Instead, Spastin releases the microtubule-organising centre from its centrosomal location, then Pio contributes to its apical membrane anchoring. It can therefore be assumed that the organisation of the microtubule network is affected in a pio null mutant. In addition, ZP proteins have been shown to link the aECM to the actin cytoskeleton. Therefore, it would be interesting to look at the organisation of the actin and microtubule cytoskeletons in a pio mutant context in which enlarged apical cell surface area are observed.

      Referees cross-commenting

      I have just read the comments of the other two reviewers, who like me are specialists in the formation of the tracheal system in the drosophila embryo.<br /> I find the comments very fair and balanced. They are in the same spirit as my comments and are very complementary. I hope that all our comments will be constructive for the authors and will improve the quality of their work.

      Significance

      Overall, the methodology is sound, the quality of the data is good and the paper is very well written. Authors combine in vivo, in vitro studies as well a cell culture approach. Using CRISPR/Cas9, they generated a large number of new tools allowing in vivo studies.<br /> Drees and colleagues generated new alleles of pio which are lack of function alleles. They described a new phenotype for pio mutant embryos, namely over-elongated tubes. But they authors do not comment on why these new alleles reveal a new phenotype. Furthermore, using their piomCherry::pio line, the authors state that Pio is localised to the plasma membrane. This location is very difficult to assess. Both new results require clarification.

      The authors had already demonstrated that Np cleaves the ZP domain of Pio in vitro. Here they demonstrate this in vivo. It appears important to evaluate the formation of the dorsal tube when an NP non-cleavable form of Pio is expressed.

      Finally, the model proposing a coupling between the extracellular matrix and the membrane of tracheal cells is very interesting. The demonstration that cleavage of Pio by Np could participate in this coupling is very interesting for those interested in the integration of mechanical stress and cellular deformation. However, such a model has already been discussed in Dong et al (2014). In this article, Dong et al. proposed that a "coupling of the apical membrane and Dpy matrix core is essential for tube length regulation".

      The audience for this article should be specialised and oriented towards basic research. It may be of interest to people working on tubular systems or working on ZP proteins.

      My field of expertise is cell biology and developmental biology in drosophila and formation of tubular networks.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      This study provides valuable evidence showing a role for ZP proteins Piopio (Pio) and Dumpy (Dpy) during tracheal tube maturation. Following up on previous studies from the same group (Drees, et al, PLOS Genetics, 2019), the authors show that Piopio localization and function is regulated by the protease Notopleural (Np). Failure in this process leads to defects in trachea tubular defects.<br /> Overall, this work reinforces the previously known importance of the protein Pio in tracheal morphogenesis, specifically in tube expansion, and provides new mechanistic insight on how Pio is regulated by proteolysis during this process.<br /> The figures are clear, and the questions well addressed. However, I find that some of the claims are not completely backed by the data presented and have some suggestions that will hopefully make some points clearer.

      Major comments

      In the abstract and at the end of the introduction the authors claim that they show that Pio, Dpy and Np support the balancing of mechanical stresses during tracheal tube elongation. However, this is not shown in this manuscript, where tension or mechanical stress were not measured and it is therefore speculative.

      The authors state that all pio CRISPR/Cas9 generated mutants display identical tracheal phenotypes, however these data are not shown. Tracheal phenotypes, in particular DT phenotypes, of all mutants generated should be shown in supplementary materials.

      At stage 16, pio null mutants display DT overelongation phenotypes (Fig. 1). The authors should quantify this phenotype.

      The authors analyse Pio distribution under tubular stress, using mega mutants and Chitinase overexpression. Pio localization changes in these genetic backgrounds and this is shown in Figure 2 only in a qualitative manner. The authors should measure Pio localization at the lumen and at the membrane and provide quantitative data.

      Surprisingly and interestingly, wurst;pio transheterozygotes display very strong tracheal defects. The authors say they observe gas filling defects; however it is not clear from figure 2E if this indeed the case. From the panel in the figure, it looks like these embryos suffer from strong tracheal morphogenetic defects. It would be necessary to have a better analysis of these embryos. What is the penetrance of this phenotype. If this is 100% penetrant, one would expect it to be lethal. Therefore, double mutant balanced stocks are not viable? Having analyzed the phenotypes and confirmed which morphogenetic defects the transheterozygote embryos present, how does this genetic interaction fit with the model presented?

      mCherry::Pio Dpy::eYFP time lapse analysis and FRAP experiments is very interesting. However, it is not clear to which degree bleaching occurs in the tracheal lumen. The authors claim that recovery is very fast and can be seen from minute 2, however, frame-by-frame analysis of Movie S2 does not show a clear different between luminal Pio from minute 0 to minute 2. Rough comparison with the luminal area surrounding the bleached area, does not show a clear difference in luminal Pio before and after photobleaching. To claim fast recovery of luminal Pio after photobleaching, the authors should quantify luminal Pio, before and after bleaching. In addition, in figure 4D, the normalized mCherry::Pio fluorescence in the graph what does it refer to? Intracellular Pio?

      When mCherry::Pio Dpy::eYFP time lapse analysis and FRAP experiments was done in an Np mutant background, the authors describe lack of Pio recovery within the lumen (Movie S3). However, when comparing control and Np mutant background embryos, Pio is not properly released into the lumen of Np mutants (as stated by the authors and seen by comparing movies S1 and S4). Furthermore, on minute 0 of the FRAP experiment in Np embryos, there is no detectable Pio in the DT lumen. Therefore, recovery was not expected in Np mutants and should not be claimed as a conclusion for this experiment.

      Brodu et al (Dev Cell 2010) have shown that Pio is important for cytoskeletal modulation during tracheal maturation. Pio is important for non-centrosomal microtubule (MT) arrays anchored at the tracheal cell apical membranes. In addition, MT disruption in tracheal cells leads to lumen formation defects (Brodu et al, Dev Cell 2010). In the absence of Pio, the tracheal cytoskeleton is altered, and this could explain some of the results observed. Ideally, the work should be complemented with a basic cytoskeletal analysis, but if this is not possible, the authors should discuss some of the phenotypes in light of this Pio function.

      Minor comments

      The model should not be in supplementary materials and should be moved to the main manuscript.

      Throughout the manuscript embryonic stages are described using different nomenclature (stage X, stX and st X). Either way is correct, but the same nomenclature should be used throughout.

      In Fig. S1 B and C the authors should specify which pio allele is being analysed (as in Fig. 7). The same should be done in the text.

      Line 131, it is not correct to say that WGA visualizes cell membranes. WGA marks/stains cell membranes.

      Line 165 "leads to excessive tube dilation and length expansion due to strongly reduced luminal chitin" is not correct. Chitin reduction leads to excessive tube dilation but not to length expansion, as reported in the papers cited at the end of the sentence.

      Line 220-221, what do authors refer to as "stage 16 wt-like control embryos"?

      Line 221, "some minutes" should be replaced by a specific number of minutes. According to Movie S2 reappearance of tracheal cell Pio happens from minute 16.

      Line 291 "time laps" should be lapse.

      Line 302, "Pio was not shedded into the lumen but remained at the cell" should be "Pio was not shed into the lumen but remained in the cell".

      Referees cross-commenting

      I agree. Taken together, all the comments will improve the quality of the work and of a future manuscript. Also, everything seems quite doable and will not present any problems.

      Significance

      The findings shown in this manuscript shed light on the regulation of tubulogenesis by ZP proteins and how their interaction with the ECM can be regulated by proteolysis. It was known that Pio is involved in tracheal development, is secreted into the lumen, regulating tube elongation (Jaźwińska et al., Nat.Cell Biol., 2003) and anchoring MTs to the apical membrane during tubulogenesis (Brodu et al, Dev. Cell 2010). This work provides additional molecular insights into Pio dynamics and regulation during tube maturation.<br /> This work will be of interest to a broad cell and developmental biology community as they provide a mechanistic advance in ZP proteins involved in morphogenesis. It is of specific interest to the specialized field of tubulogenesis and tracheal morphogenesis.

      Field of expertise:

      Drosophila, morphogenesis, tracheal tubulogenesis, cytoskeleton

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      Referee #1

      Evidence, reproducibility and clarity

      In the manuscript entitled "The proteolysis of ZP proteins is essential to control cell membrane structure and integrity of developing tubes" the authors Leonard Drees, Dietmar Riedel, Reinhard Schuh and Matthias Behr report on their study on the molecular and cellular mechanisms of tracheal tube size determination in the embryo of the fruity fly Drosophila melanogaster. The tracheal tubes of D. melanogaster are a model tissue to understand the molecular and cellular mechanisms of tube formation in animals. In brief, morphogenesis and terminal differentiation of this epithelial tissue runs through several overlapping events starting with the invagination of tracheal precursor cells, their fusion to tubes, the formation of a luminal matrix consisting of chitin and associated proteins for diameter and length determination, endocytosis of luminal material and gas filling at the end. L. Dress and colleagues show that the ZP protein Piopio (Pio), its partner Dumpy (Dp, also a ZP protein) and the Matriptase homolog Notopleural (Np) are needed for tube length determination. Pio with Dp constitutes a molecular anchor between the apical cell membrane and the luminal matrix thereby coordinating growth of the epithelial cells and the luminal matrix. After termination of morphogenesis, Pio is cleaved by the protease Np to initiate tracheal differentiation.

      Overall, this is an exciting work. There are, however, several open questions that the authors could address to facilitate understanding of their work. These points are:

      • On page 5, lines 113ff, the authors mention the membrane bulges that they analyse in figure 1. They show these deformations by light (confocal) and electron microscopy. However, the bulges seen by confocal microscopy seem to be bigger that those seen by electron microscopy. The authors could quantify the sizes of the bulges for clarification.
      • The subject of the manuscript is rather complicated; presentation of data from Figure 1C and D on lines 113ff and 169ff is confusing.
      • The quality of the sub-images of Figure 2E differs. Especially, the phenotype of the wurst, pio transheterozygous embryo is not well visible.
      • Lines 246ff: the protein size are given for the mCherry:chimeric proteins; an estimate of the native Pio portions should be given.
      • In Figure 6A, the appearance of chitin in the wildtype tube is different compared to the Np mutant situation, more filamentous. Can the authors comment on that?
      • In the discussion section, I would appreciate if the timing of events was discussed or even shown in a model. The central question is: how are the functions of Pio and Np coordinated in time? As I understand, Np should not cleave Pio before morphogenesis is completed. Is there any example in the literature for how such an interaction could be controlled? The overexpression of Np shows that either the ratio between Np and Pio is important, or the btl promoter expresses Np at the "wrong" time point.
      • Also for the discussion: We have two situations where Pio amounts/density are enhanced at the apical plasma membrane. The wurst experiments on lines 136ff show that Pio amount and density depends on endocytosis; is the wurst phenotype (Figure 2), at least partially, due to over-presentation of Pio? Likewise, in Figure 2C, there is more Pio in Cht2 overexpressing tracheae (but there is overall more Pio in these tracheae) - is actually endocytosis reduced in chitin-less luminal matrices? First: does the Pio signal at the apical plasma membrane correspond to membrane-Pio or free-Pio? Second, as in the case of wurst: would more Pio on the membrane (density) affect tracheal dimensions in Cht2 over expressing tracheae? Or are the consequences of Pio accumulation in the apical plasma membrane different in Cht2 and wurst backgrounds? Maybe cleavage of Pio and its endocytosis are dependent on its interaction with the chitin matrix. These questions connect to the question immediately above: how are the functions of the different players coordinated in space and time? We need a discussion on this issue.
      • The sentence on line 242ff should be rephrased: "dynamic" and "elastic" are not opposites.
      • A central question to me is the amounts and the density of factors in different genetic backgrounds as mentioned above. Is there any mechanism adjusting the amounts or the density of the players according to the size of the apical plasma membrane or the tracheal lumen? Pio seemingly responds to these changes.
      • The connection of Pio and taenidia is mentioned in the results section (page 7) but not discussed.
      • Dp remains cytoplasmic in pio mutant background - is the pio mutant phenotype due to defects by lack of Pio AND Dp function? What is the tracheal phenotype of dp mutants?
      • Lines 374ff: the reduced dorsal trunk in Np mutants is not significant; the respective statement should be formulated carefully. If we believe the statistics (no significance), this would mean that attachment of the apical plasma membrane to the luminal chitin via Pio is needed to restrict axial extension; release of Pio is needed for differentiation (taenidia formation, luminal clearance) beyond morphogenesis.
      • Why don't we see the apical Pio signal in Figure 4B?
      • The Strep signals in the merges in Figure 7C are not well visible.

      Significance

      This work brings together several factors (Pio, Dp, Np, Wst etc) already known to be needed for tracheal morphogenesis and differentiation in the embryo of D. melanogaster. Having worked myself with some of these factors, however, I recognize that the interaction between these factors is novel and very exciting. The experiments strongly indicate a new mechanism of cell-ECM connection that seems to be conserved to some extent (as they provide preliminary data on an example from humans). By integrating the functions of different factors, the work provides ample opportunity for future projects to elucidate this mechanism in detail. Therefore, I expect that it will have a significant impact not only on the field of developmental cell biology but also, due to the conserved proteins involved (ZP proteins, Matriptase), on the field of cell biology of human diseases.

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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

      The manuscript describes the strategy to efficiently synthesize a natural truncated version of the chemokine CXCL10 that lacks the last 4 amminoacids. In addition, it describes the biological activities of the CXCL10 truncated version (1-73) compared to the full length chemokine (1-77). By performing in vitro and in vivo experiments, authors have found that CXCl10 1-73 is not able to induce signalling and chemotaxis of CXCR3 expressing cells such as T lymphocytes. In addition, this C terminal truncated version does not bind GAGs while retains angiostatic activity, blocking migration and proliferation of endothelial cells.<br /> The paper is written very well, results are presented in a very logical sequence.

      Major comment

      The in vivo experiments shown in supplementary figures 7 and 8 are not significant and I suggest removing them from the manuscript.

      Minor comment

      In figure 9D authors showed the in vivo migration of CXCR3 positive T lymphocytes in the peritoneal cavity. However, the gating strategy showed in supplementary figure 6 is showing all the leukocytes CXCR3 positive. Please clarify.

      Significance

      The manuscript describes the biological activity of a truncated version of CXCL10 a very important chemokine that recruit Th1 lymphocytes and NK cells. The C terminal truncated version of CXCL10 is naturally occurring, but its functions were never described until now.

      The strength of the manuscript is the precise description of the synthesis and of the in vitro biological functions of the truncated CXCL10.

      For this reason, these results are of interest not only for a specialized audience working in the chemokine field, but also for a more broad audience for the development of an inhibitor of CXCR3 or for an angiostatic molecule.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      It has been reported that CXCL10 has several truncated forms (proteoforms) with different C-terminal truncation states, each with different functions. The authors have discovered and reported an efficient peptide synthesis method for CXCL10(a.a. 1-73) proteoform. The authors indicated that they could synthesize CXCL10(a.a. 1-73) proteoform consistent with the known functions of natural CXCL10(a.a. 1-73). The synthesized CXCL10(a.a. 1-73) successfully indicated the reduced effects on lymphocyte migration and similar effects on angiogenesis. These findings open the way for detailed functional analysis of CXCL10 (a.a. 1-73), which has been difficult to study in vivo and has potential for therapeutic use. However, as discussed below, the authors have made several statements that confuse their findings with the discoveries made by previous studies. The Introduction is a typical example of this. Also, there are several major issues noted in the next section.

      Major comments:

      1. Figure 4: Possibly an overinterpretation of results in CXCR3A overexpressing model cells<br /> The authors build their logic for the entire paper by drawing conclusions based on their assumption that CXCR3A overexpression model is equivalent to physiological lymphocytes and endothelial cells. However, CXCR3 has isoforms, including CXCR3A/B/alternative. They have different effects on cell proliferation and migration. Expression levels of CXCR3A/3B may vary among cell types and microenvironments. In addition, the downstream signals pAKT and pERK of CXCR3A/B are regulated by various regulatory factors. Therefore, it is important to perform the experiments shown in Figure 4 with primary lymphocytes and vascular endothelial cells, which are the subject of this paper. Based on the data presented by the authors, experiments with Primary lymphocytes and Endothelial cells would not be difficult.
      2. Figure 5: "In line with the observation of the signaling assays, COOH-terminal processing of CXCL10 also significantly diminishes its chemotactic properties on primary CXCR3+ T lymphocytes."<br /> The authors draw the conclusions described above from the results in Figure 4 and Figure 5. In other words, authors excluded other possibilities without data.<br /> In Figure 5, the Chemotaxis assay was performed in Transwells with 5 miro-meter pores pre-coated with fibronectin. CXCL10 is also known to interact with fibronectin. This suggests that, potentially, the interaction with fibronectin may be important for CXCL10 gradient formation on the transwell. However, interaction data between CXCL10(a.a. 1-73) proteoform and fibronectin is not shown. This information is essential in the interpretation of Figure 5 results.<br /> The authors should consider the possibility that readers unfamiliar with this experimental system may be given a false understanding that Chemotaxis shown here is determined solely via CXCR3A.<br /> Also, please indicate whether the conclusions here are supported in different Pre-coating (e.g. type I collagen, type 4 collagen, human fibronectin). How the activation changes with each Coating here is important information when considering how CXCL10(a.a. 1-73) behaves in the extracellular matrix in vivo. These add to the value of this study and provide important insights for readers to further work with CXCL10(a.a. 1-73).<br /> Furthermore, the Migration chamber here is pre-coated with bovine serum fibronectin. Please provide Lot and purity information for this Serum derived fibronectin. This is considered important both for the reader to reproduce the data and to interpret the results. Since Bovine serum fibronectin is a different species than human CXCL10 (a.a. 1-73), in order to correctly interpret its contribution to the Chemotaxis assay, it is interactions, respectively, should be evaluated.
      3. Figure 6: Over-interpretation of the results<br /> From Figure 6, the authors conclude that CXCL10 (a.a. 1-73) has no change in antiangiogenic action based on data on vascular endothelial cell migration and viability. Cell migration and endothelial cell viability are only one aspect of angiogenesis. It is problematic to conclude from these results that there is no change in "antiangiogenic action".<br /> Also, in Figure 6A, the authors cultured cells in the presence of FGF2 and in the presence of CXCL10 (a.a. 1-73) and CXCL10 (a.a. 1-77) for as long as 49 hours to evaluate Migration. Therefore, the results here include not only pure migration but also its effect on proliferation. However, Figures 6C/6D only show data on the viability of cells, not on the effects on cell proliferation. Therefore, in order to correctly interpret the results, the proliferation of vascular endothelial cells needs to be examined and presented.
      4. Figure 9 and supplemental Figure S6: Gating for T cells (gated as CD3+ NK1.1-) and activated CXCR3+ T cells (gated as CD3+ NK1.1- CXCR3+)<br /> Supplemental Fig. S6 raises a question as to whether the location of the Gating of CD3 and NK1.1 is correct. Please verify if this gating is proper by presenting Isotype control data as the basis.<br /> Gating for CXCR3 also seems to be gated in an unnatural position. Please present Isotype controls data and positive control data and explain the basis for this gating.
      5. Figure 9: Over-interpretation of the results<br /> It would be an oversimplified interpretation of the results here to explain them solely in terms of lymphocyte Migration. The authors should not rule out the possibility that the results obtained here could be due to effects quite different from those shown so far in vitro.<br /> Conclusions should be drawn after examining the following items

      6. Expression of lymphocyte adhesion-related molecules on the surface of vascular endothelial cells

      7. Effects on Tight junction of blood vessels
      8. Effect on vascular permeability

      If the above data are not presented, the authors should clearly describe that the author's conclusion is just one of the possibilities. The readers should be informed of the above possibilities, and the different potential mechanisms involved so that the readers do not misunderstand that the authors' conclusions are definitive conclusions.

      Minor comments:

      Figure 7C: Please provide higher-resolution images

      The quality and resolution of the images are low and very difficult to see. The image is of such low quality that it is barely possible to determine the presence or absence of cells. Here, providing higher-resolution images is important to give the reader a deeper understanding. The desired resolution is a resolution that allows determining what the Filopodia and Lamelipodia morphology of the cell looks like at the Edge of the Scratch, and how it differs or does not differ between CXCL10 (1-73) and 1-77, etc., desirable. Such an image could underpin the other data in this paper. Furthermore, such detailed forms can give the reader insights into more precise molecular mechanisms. In this sense, it is essential to provide high-quality images.

      Line 360-362, page 12 (Results)

      "Various naturally-occurring COOH-terminally truncated CXCL10 proteoforms were detected in human cell-culture supernatant of IFN-γ-stimulated human diploid skin/muscle-derived fibroblasts and primary human keratinocytes and potential processing enzymes were identified (Suppl. fig. 1). "<br /> This statement could be interpreted to mean that what is described in Supplemental figure 1 is identified in this paper. Although it is unlikely that most readers would make such a mistake, unnecessary misleading statements should be avoided.

      Line 508-509, page 16 (Discussion)

      "In the present study, we characterized the effects of a natural COOH-terminal truncation of CXCL10, which involves the shedding of the four endmost COOH-terminal amino-acids, on hallmark chemokine properties of CXCL10."

      The authors state that "we characterized the effects of a natural COOH-terminal truncation of CXCL10," which gives the reader the wrong impression.

      "Natural truncation of CXCL10" means physiological CXCL10, which is truncated form that normally occurs in vivo. These findings have been done in prior papers and were not first characterized in this paper. This should be described as a characterization of the synthesized peptide. This sounds like the authors have taken credit for prior studies.

      Figure 6A&6B: What is the "HRMVEs" on the Y-axis? Nowhere in the paper is there a description of this term.

      Figure 8A&8B: Some error bars are only on one side.

      Significance

      It has been reported that the functions of CXCL10 change dynamically in tissues depending on the C-terminal truncation state. However, this dynamic nature created a mixture of each Proteoforms (CXCL10 with different terminal truncation states), making the analysis of their functions difficult. CXCL10(a.a.1-73) is not commercially available like CXCL10(a.a.1-77) due to its difficult peptide synthesis; pure functional analysis of CXCL10(a.a.1-73) could not be performed in vivo. Therefore, the functions of CXCL10(a.a. 1-73) has been mainly reported as circumstantial evidence or in vitro studies using trace amounts of purified product purified using HPLC.

      In this study, the authors clarify the challenges of peptide synthesis and enable the synthesis of more CXCL10(a.a. 1-73). Thereby paving the way for implementing the function of pure CXCL10(a. 1-73) proteoform not only in vitro but also in vivo. It also potentially opens the door for the application of CXCL10(a.a. 1-73) in therapeutic interventions such as tissue repair.

      However, the paper has the problems mentioned above, and it would be desirable to verify and reinforce the reliability and logical development of the conclusions. Reinforcing additional experimental data such as that and validating the derivation of the conclusions would be a study of significance to basic medical researchers in vascular biology, immunology, and tissue repair, as well as to the clinical research community.

      General assessment:

      Strength:

      The authors have discovered and reported a stable method for synthesizing CXCL10 (a.a. 1-73), which has been difficult to synthesize in the past. This may provide researchers a way to solve the problem that it has been difficult to analyze clear molecular mechanisms due to the mixture of diverse CXCL10 proteoforms. The progress reported here may be expected to facilitate other researchers to investigate more detailed molecular mechanisms and explore unknown functions of CXCL10 (a.a. 1-73).

      It is also expected to solve the problem of in vivo analysis of CXCL10(a.a. 1-73) function, which has been impossible due to yield issues. In the future, this synthetic peptide may open the door to a variety of useful applications, such as therapeutic intervention for severe wound healing.

      Advance:

      The authors wrote the "Introduction" and "Abstract" focusing on the functional "discovery" of CXCL10 (a.a. 1-73). This may prevent the readers from understanding the true value of this study. The most significant finding of this study is the technological advance of increasing the yield of CXCL10 (a.a. 1-73), which has been difficult to synthesize to a level that allows in vivo experiments. Although there are many improvements to be made, I believe this is a significant study for the community described above if this synthesized peptide is widely available in the community.

      Audience:

      The current manuscript is suitable for a Specialized audience. If the issues raised here were solved, it might be suitable for broader audiences, including translational/clinical researchers.

      My field of expertise:

      Molecular biology, biochemistry, vascular biology, hematology and cancer

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript by Dillemans et al reports that synthesis, purification and functional characterisation of the truncated CXCL10 proteoform CXCL10(1-73). This lacks the four endmost COOH-terminal amino acids . The authors report that compared to the full length CXCL10(1- 77), CXCL10(1-73) had (i) diminished affinity for glycosaminoglycans, (ii) exhibited reduced capacity to induce signaling events (e.g. calcium mobilization as well as ERK and Akt phosphorylation) and (ii) reduced chemotactic T lymphocyte responses in vitro and in vivo. However, CXCL10(1-73) retained its anti-angiogenic properties, as assessed by inhibition of spontaneous and FGF-2-induced migration, wound healing and sprouting of human microvascular endothelial cells.

      The work is well performed though the pharmacological analysis is a little superficial and under-developed with incomplete/inconsistent concentration-dependent responses. The manuscript is rather verbose in places.

      Specific points:

      1. Fig 4: how do the authors know that the reduced calcium responses to full length CXCL10 following pr-treatment with the C-terminal truncated CXCL(1-73) is due to desensitisation rather than say partial agonism? They should compare internalisation of CXCR3 and/or loss of surface expression of CXCR3 following treatment with CXCL10 (1-73) versus CXCL13(1-77) to validate this.
      2. The choice of concentration ranges used for CXCL10(1.77) and CXCL10(1-73) across figs 4, 5 and 6 is inconsistent with no explanation given as to why.
      3. Figs 4: The dose response curves are rather limited narrow e.g.1, 3, 10 nM for CXCL10(1-77). The choice of concentrations for CXCL10(1-73) in fig 4 is a little unusual in Fig 4 (9, 45, 270nM). Has the maximum response to CXCL10(1-73) in figs 4-6 been achieved? It would be useful to know the EC50 values for both full length and truncated forms of CXCL10 in figs 4 and 5
      4. Fig 5: in contrast, to Fig 4, this figure has comparable concentration ranges at 5 points across (1-100 nM). What is the rationale for the inconsistent concentration ranges used across different assays?
      5. The bar graphs for pERK, pAkt responses would look better as line graphs and more complete concentration ranges (perhaps use 5 concentrations e.g. over 1-100 nM for CXCL10(1-77).
      6. Fig 6: the inhibitory effects of CXCL10(1-77) and CXCL10( 1-73) seem to occur at a single concentration (120 nM), Can the spontaneous HMVEC migration be further inhibited at higher doses of truncated and full length CXCL10? Both appear to have just reached 50% inhibition at 120 nM.
      7. Fig 7. What is the impact of both proteoforms on FGF-stimulated wound healing?
      8. Why is it necessary to provide Kd values in the main results text when these are already provided in Table 1. This is just one example of verbosity that that is often present in the manuscript
      9. The methods section is also very long.
      10. What phosphorylation sites are detected in the ERK1/2 and Akt ELISA assays? The authors should provide more details on this point. The ELISA assays alone does not really provide convincing analysis of phosphorylation and should be backed up with more robust assays to assess ERK and Akt phosphorylation e.g western blots and/or flow cytometry with phospho-specific Abs.

      Significance

      The study reveals that the COOH-terminal residues of CXCL10 Lys74-Pro77 are important for GAG binding, CXCR3A signaling, T lymphocyte chemotaxis, but dispensable for angiostasis .

      Study is of interest to basic researchers in areas of pharmacology, immunology and structural biology with relevance to drug discovery, inflammation and cancer biology.

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      Reply to the reviewers

      We thank the reviewers for their constructive criticism that helped us to improve the paper. We modified Fig.6I and Fig.7, replaced Fig.8, and added supplementary Figs. 3-5 and supplementary Tables S1-2. The manuscript was extensively re-written. A new paragraph was added in the Discussion section where relative adhesiveness was related to absolute adhesion strength and the cadherin knockdown result to earlier findings.

      Reviewer #1 (Evidence, reproducibility and clarity):

      Summary: This work examines the relationship between cell-cell contacts and pericellular matrix in Xenopus chordamesoderm, which is a tissue actively involved in convergent extension during gastrulation. By lanthanum staining of pericellular materials, the authors found that different types of pericellular matrix are present in cell-cell contacts in the chordamesoderm, which may mediate cell-cell adhesion. Knockdown of C-cadherin, Syndecan-4, fibronectin, and hyaluronic acid leads to the reduced abundance of cell contacts and cell packing density, but this does not seem to affect convergent extension. Based on these observations, the authors propose a model in which cell-cell contacts involve the interdigitation of distinct pericellular matrix units.<br /> Major points:

      1. Knockdown of adhesion molecules separates cells and leads to wide contacts with large interstitial spaces. Data in figure 1 show loosely packed morphant chordamesoderm cells. Intuitively, these should reduce cell-cell adhesion. However, a main conclusion from this manuscript is that reduced abundance of narrower contacts does not decrease adhesiveness. Although depletion of adhesion molecules modifies but not abolishes a contact, non-attached free surfaces increase significantly in morphant cells. It is therefore not easy to understand that how reduced cell contacts have no effect on cell adhesion.

      We added a section to the Discussion to address this issue (p.11ff). We show in the Results section (modified Fig.7) that relative adhesiveness is indeed significantly reduced in the morphants (Syn-4 always being the exception) when compared in the contact width range of normal chordamesoderm. However, contact width is strongly increased in the morphants, and adhesiveness increases linearly with width. We argue that these effects compensate for the initial lowering of adhesiveness. In other words, adhesive contacts become shorter (more gap surface) but wider (see Fig.6I), and become the more adhesive the wider they become. As in the original version of this paper, we then propose a model that explains the empirically observed increase of adhesiveness with width. How the abundance of cell-cell contact is reduced is less clear yet. Pericellular matrix deployment and structure is strongly affected by adhesion factor knockdown, and contact types are altered. Some contact types seem to widen but remain adhesive, others become non-adhesive, and still others may disappear without being replaced (see last paragraph of Discussion). To add detail to these notions and clarify this important issue to satisfaction will require future research.

      Importantly, the adhesiveness was not experimentally tested.

      Due to external circumstances, we were unable to perform additional experiments. However, we used our previously published quantitative data on adhesion in gastrula tissues including the chordamesoderm to interpret our present results for normal and C-cad-depleted chordamesoderm, and to relate relative adhesiveness to absolute adhesion strength, in a new section of the Discussion (p.11ff).

      1. It is surprising that reduced cell contacts, at least narrower cell contacts, do not affect convergent extension. Does this mean that active cell behavior changes in the chordamesoderm, which are required for convergent extension, are independent of cell contact types?

      We actually claimed that all treatments inhibited convergent extension, except for Syn-4 (Barua et al. 2021, and this manuscript, p.3, Fig.1B,C). Syn-4 knockdown had a dramatic effect on cell contacts, cell density and cell shape but none on convergent extension, at least up to the middle gastrula stage. This is surprising and does not fit easily to current views of cell intercalation during convergent extension, but analysing the underlying cell behaviors is beyond the scope of this article.

      1. Although the formation and localization of pericellular materials are differentially affected after knockdown of adhesion molecules, there is no clear evidence showing that different types of pericellular matrix mediate cell-cell adhesion in the chordamesoderm. It is possible that the disrupted distribution of pericellular materials in morphants only represents a secondary consequence of changed cell contacts. This may be supported by the fact that knockdown of adhesion molecules reduces narrow contacts and increases LSM-free gaps.
      2. The relationship between contact width spectra and LSM is also very elusive. Again, changes in contact width or abundance and distribution of LSM may be indirectly caused by loss of adhesion molecules. Therefore, although knockdown of adhesion molecules leads to changes of LSM localization, it cannot be concluded that cell-cell contacts in chordamesoderm are mediated different types of pericellular matrix.

      We find it difficult to interpret for example Fig.5A-F other than assuming an adhesive role for the pericellular matrix, in this case LSM, in normal and morphant tissue. What else would here hold two cells between two gaps together? The contacts are often much too wide for cadherin-cadherin binding. We indeed believe that changes in contact width or abundance are caused by the loss of adhesion molecules, directly or indirectly. Our LSM images show that remarkably, modified contacts (e.g. Fig.3D,F; Fig.5B,C) are still able to keep cells together over some distance, between interstitial gaps, and our quantitative data indicate similarly that e.g. contact widening is consistent with continued adhesion. However, some of the contacts may become non-adhesive, or be lost without being replaced, increasing non-adhesive gap surface. This is discussed now on p.11, middle paragraph.

      1. In contrast to the present observations, works by others using the same morpholinos have shown that Cadherin-dependent cell adhesion, fibronectin-rich extracellular matrix, and Syndecan-4-regulated non-canonical Wnt signaling are required for convergent extension. These discrepancies need to be appropriately addressed.

      As mentioned above, we found that all treatments affected convergent extension, as expected from the work of others and our own, except for Syn-4 depletion. We noticed that in the paper by Munoz et al. on Syn-4 overexpression and knockdown, only late gastrula/early neurula stages were evaluated. Syn-4 knockdown produced moderately strong axis defects, perhaps in part related to impaired neural plate closure. Unfortunately, we did not follow our morphants to these later stages to see whether defects developed then. But our main interest here is cell-cell contacts.

      1. If LSM and LSM-free contacts are similarly adhesive, what will be role of LSM in cell adhesion and how cell adhesion is established in these LSM-free contacts?

      We discuss now more explicitly the notion that gastrula non-epithelial cell adhesion is mediated by a mosaic of pericellular matrix patches of different composition, some containing LSM in different configurations, others not, but each similarly adhesive.

      Minor points:<br /> 1. It may be helpful to clearly define the pericellular matrix in this particular context and its relationship with LSM. It is also necessary to clarify whether the adhesion molecules examined in this work are considered as components of the pericellular matrix.

      We explain the use of these terms at the end of the first paragraph of the Introduction. The most general term is pericellular matrix; part of it is La3+ labeled – LSM; and some of the LSM can be compared to structures which in other systems are termed glycocalyx. We consider the adhesion molecules examined to be part of the pericellular matrix but are aware of other putative functions, like in cell signaling, which may indirectly affect contacts and thus contribute nevertheless to the phenomena studied here.

      1. In figure 1B, it appears that the Cadherin morphant has defects in chordamesoderm elongation and archenteron formation, suggesting impaired convergent extension.

      We find, in agreement with the work of others, that C-cad knockdown impairs convergent extension, and mention this when we describe Fig.1B.

      1. In figure 1C, the Syndecan-4 morphant gastrula clearly shows enhanced anteroposterior elongation of chordamesoderm and archenteron in comparison with the wild-type embryo. This seems to suggest that loss of Syndecan-4 promotes the movements of convergent extension. However, previous studies indicate that both gain and loss of Syndecan-4 impairs convergent extension.

      As mentioned above, late gastrula/early neurula stages were evaluated in the Munoz et al. paper, mid-gastrula stages in our work. One possible explanation would be that mild axis defects develop later, partly in connection with neural tube elongation and closure.

      1. Ideally, in knockdown experiments, control embryos should be injected with corresponding mismatch morpholinos.

      We explain in the Methods section that we only used morpholinos that were extensively characterized in previous publications.

      1. In figure 1E, it is unclear what type of cell contacts the light green arrowheads indicate.

      This is explained now in the figure legend.

      1. Figure 1 legend, "(wt) is from Barua et al. 2021". I am not sure it is appropriate to use previously published data.

      The present data were derived by further evaluations of the same samples and TEM sections as used in Barua et al. 2021. We show the previously published data (acknowledged in the legends) here for easy comparison (instead of citing the previous paper).

      1. There is no light blue arrowhead in figure 2, and in figure 3B and 3I, it seems that the same colored arrows are used to indicate different structures.

      This has been corrected.

      1. Triple-layered contacts are not clearly defined.

      We define this term now repeatedly, as consisting of two LSM layers enclosing a non-labeled layer between them.

      1. Page 2, "based on driven by" should be either "based on" or "driven by".

      Has been corrected.

      1. Page 8, "selectin" should be "selecting".

      Has been corrected.

      Reviewer #1 (Significance):

      Strengths:<br /> Demonstrated the effects of several adhesion molecules on the formation of cell contacts and pericellular matrix in Xenopus chordamesoderm.<br /> Limitations:<br /> The significance of chordamesoderm cell contact changes in convergent extension or gastrulation is not clear;

      Effects on gastrulation of PCM or membrane adhesion molecule depletion have very often been described as mediated by effects on cell signaling. Without excluding such possibilities, we liked to redirect attention here to other putative mechanisms by describing basic effects of treatments on cell-cell contacts including PCM deployment and structure. Future work must relate the specific, often dramatic, contact changes upon depletion of a specific factor to cell behavior during convergent extension and other tissue movements.

      there is no direct evidence showing the functional link between pericellular matrix, cell contacts and cell adhesion;

      Please see our response to main points 3 and 4 above.

      the absence of effects on convergent extension after depletion of several adhesion molecules is not fully consistent with previous reports.

      Please see our response to main points 2 and 5 and minor point 3 above.

      Advance: This work likely provides some fundamental and methodological advances for studying cell-cell adhesion. It shows promise for elucidating mechanisms underlying the regulation of cell contact changes in tissues involved in morphogenetic movements.<br /> Audience:<br /> This work likely interests readership studying embryonic cell adhesion in the field of developmental biology and cell biology. It may be also potentially interesting for people working on glycocalyx pericellular matrix in adult tissues.

      Reviewer #2 (Evidence, reproducibility and clarity):

      Summary: During gastrulation, cells within vertebrate embryos require the ability to both adhere to one another and rearrange with their neighbors to shape the emerging body plan. These authors posit that such flexible adhesive contacts are mediated in part by the pericellular matrix (PCM), including multiple types of glycocalyces containing molecules such as fibronectin, hyaluronic acid, and syndecans, which they previously characterized in multiple embryonic tissues (Barua et al, PNAS, 2021). Here, in a follow-up to their 2021 study, the authors use electron microscopy to characterize the pericellular matrix within the chordamesoderm of Xenopus gastrulae. They identify several types of adhesive contacts within the chordamesoderm and assess how they are altered in the absence of key PCM molecules via morpholino knock-down. They conclude that syndecan-4 and hyaluronic acid comprise and promote assembly of PCM plaques whereas fibronectin and C-cadherin anchor them to cell surfaces. Cell packing density is decreased upon loss of all 4 of these molecules, which the authors attribute to a decrease in the number of cell contacts without affecting the strength of the remaining contacts. They further conclude that adhesiveness increases linearly with contact width, and that this relationship is unaffected by loss of any aforementioned adhesive/ PCM molecules.

      Major comments:<br /> Many conclusions in this manuscript are based on measurements of cell contact angles, which indicate the reduction of tension at cell contacts vs. free cell surfaces and thus relative adhesive strength. While this lab previously applied the same approach to live tissues (David et al, 2014), it is not clear to what extent such measurements accurately reflect adhesive strength in fixed tissues and/or electron micrographs. Especially given the issue of random sectioning planes, which cause distortion of contact angles. Although a correction was applied, the authors note this is not theoretically derived because the heterogeneity of gap sizes made such calculations too difficult. Indeed, it appears that the large gaps between cells within morphant embryos affect contact angle measurements, but if this is corrected for in any way, it is not mentioned.

      Geometrically determined contact angle distortion should affect angle or relative adhesiveness distributions in all conditions or treatments similarly and thus should not or only little affect comparisons of distribution peaks, averages, etc. Beyond this effect of random sectioning planes, we don’t see how large contact width should by itself affect measurements of angles.

      Because this is the sole measure of cell adhesion provided in the study, this reviewer is not convinced of the conclusion that loss of PCM components does not affect adhesive strength.

      In response to this criticism, we re-evaluated our adhesiveness-width data (Fig.7A-E). We noticed that there is indeed a reduction of relative adhesiveness when morphants are compared to normal chordamesoderm within the width range of the latter. But the addition of increased widths in the morphants and the linear increase of adhesiveness with width compensated or overcompensated the initial reduction of adhesiveness.

      Could such measurements not be made from live cells/tissues after manipulating PCM components, as the lab has done previously? Because the lab already has the necessary reagents and expertise for such experiments, the time and resources needed for such measurements shouldn't be prohibitive.

      Due to circumstances, we were unable to perform additional experiments. However, we used our previously published quantitative data on adhesion in gastrula tissues including the chordamesoderm to analyze our present results for normal and C-cad-depleted chordamesoderm, and to relate relative adhesiveness to absolute adhesion strength, in a section added to the Discussion (p.11ff).

      • As mentioned above, these authors previously measured adhesive strength in live Xenopus cells and tissues (David et al, 2014). In that study, they found that C-cadherin MO reduced relative adhesiveness whereas the current study found that relative adhesiveness actually increases in this condition. What explains this discrepancy?

      We explain now in the new Discussion section (p.11ff) and with the help of supplementary Figure S5 how adhesion strength and relative adhesiveness are related overall (tissue surface vs. cell contacts) and at gaps within a tissue (gap free cell surface vs. cell contacts). In the previous study (David et al, 2014), we discussed relative adhesiveness in relation to overall adhesion strength, and both are decreased upon C-cad knockdown. Here we examined these parameters at interstitial gaps, where we find a small increase of relative adhesiveness, due to overcompensation caused by a strong increase of adhesiveness with contact width. Using our David et al, 2014 data we quantitated the effects. We previously found a similar increase of relative adhesiveness at gaps in C-cad morphant ectoderm (Barua et al. 2017) which we could not explain at the time, but explain now by analogy to our chordamesoderm results.

      • No control morpholinos are used, and for the morpholinos that are used, the doses are very large. An equally high dose of control MO should be used to ensure that all observed phenotypes are specific.

      We detail in the Methods section that we used here and in previous publications only previously characterized morpholinos.

      • It appears that all the images analyzed were collected in the sagittal plane, and the analyses don't seem to consider the intrinsic polarity of the chordamesoderm. For example: cells in different positions within the tissue (basal vs. apical), or that WT chordamesoderm cells are mediolaterally polarized and actively intercalating whereas disruption of PCM components like fibronectin disrupts cell intercalation and randomizes cell polarity. It is possible that 1) cell-matrix (in basal cells) and 2) cell-cell (during intercalation) interactions may affect the measurements made in this study. In other words, that cell contacts could differ by position within the embryo and intercalation/polarity status... have such effects been accounted for in the current analysis?

      Here we only analyzed cell contacts deep in the chordamesoderm. Basal contacts were examined to some extent in Barua and Winklbauer, 2022, apical contacts not yet. Our present analysis is based on sagittal sections. The cells in the chordamesoderm are elongated and aligned mediolaterally but not in register, i.e. they are randomly wedged between each other. Thus, all mediolateral positions in cells should be present in our samples. Nevertheless, trends in the occurrence of contacts related to medial-to-lateral positions on cells (e.g. recognizable in spindle-shaped cells as wide vs narrow cell cross-sections) may have escaped our attention, and in particular, the protrusion-bearing medial and lateral ends of cells may develop special contacts. However, our goal in this study was to analyse basic properties of cell-cell contacts in this tissue, as a foundation for further detailed studies.

      • In this study, the authors state that chordamesoderm movements are preserved in syndecan-4 morphants, and in their 2021 article (Barua et al) they state that convergent extension movements are accelerated. But another study describing this MO found that it causes severe convergent extension defects (Munoz et al, NCB, 2006). What explains this discrepancy?

      In their knockdown experiments, Munoz et al. find relatively mild axis defects in late gastrula/early neurula stage embryos while we studied the mid-gastrula. Perhaps defects develop during later stages in Syn-4 morphant embryos.

      Also, the syn-4 morphant showed in Fig. 1 appears more developmentally advanced than the other embryo... if the embryos are not stage matched it could affect the measurements and conclusions drawn from them.

      Stage matching was not possible since C-cad and FN morphants did not involute or engage in convergent extension (i.e. were arrested at the initial gastrula stage), Syn-4 morphants appeared to gastrulate faster than normally. Therefore, embryos were strictly time matched. A limitation remains, that the time course of cell contact development over gastrulation was considered low priority in this initial study and was thus not determined.

      • In figure 7, the authors plot relative adhesion (measured from contact angles) vs. contact width, then fit regression lines to the lower boundaries of these scatter plots. It is not clear why this analysis is focused only on the lower boundaries rather than considering the full spread of the data. Particularly for syn-4 morphants, whose values do not appear to be concentrated along the lower boundary. This analysis is further confused by the introduction of alpha*, which represents relative adhesiveness relative to the regression.

      The lower boundary line is most convenient to extract (Fig.7A’-E’). But we agree that the “interior” of the scatter plot distribution should also be analyzed. Using average adhesiveness gives rise to artifacts since the density of data points decreases strongly with contact width but also with distance from the lower boundary, leading to the preferential disappearance of large adhesiveness values for higher widths. Instead, we constructed a line tracing the highest density in the scatter plot near the lower boundary (Fig.7B’’-E’’), by determining the positions of adhesiveness distribution peaks in consecutive width brackets (new Fig.8, Fig.S3). We abstained from introducing alpha*.

      • Based on these regression lines alone, the authors conclude that all 4 conditions are similar enough to pool the data for further analysis. If these contacts have different properties, which the data in Figures 1-6 suggest they do, it seems inappropriate to pool them together.

      We no longer pooled the data, except in supplementary Fig.S4 where we consider angle distortion. Instead, we show in Fig.8 relative-adhesiveness frequency distributions for different treatments and width brackets. This emphasizes differences between the different adhesion factor depletions and shows that adhesiveness is not simply normal or log-normal distributed, in agreement with different contact types contributing differently though similarly to overall adhesion. It also allows to follow main peaks as they shift position with width, roughly in proportion to the lower surface boundary.

      Based on this pooling, the authors then conclude that relative adhesiveness increases linearly with contact width over the entire width range, regardless of adhesion factor depletion. This again assumes that all contacts (morphant and WT) are functionally equivalent, and that what is observed in morphant embryos in very wide contacts would also hold true in WT contacts. But because WT contacts occupy only a small portion of the width range, we cannot know how they would behave if scaled to be wider, and I am not convinced that very wide morphant contacts are representative of or functionally equivalent to WT. In other words, we cannot know that contact width is the only factor increasing their relative adhesion, given the experimental manipulations that structurally alter these contacts.

      Although differences between contact types are apparent, we think that the contacts function very similarly. We still hold that relative adhesiveness increases with contact width, as seen in each of the separate plots for wt and adhesion factor depletions. But re-evaluating the alpha-width scatter plots now we show that in the narrow width range of normal chordamesoderm, C-cad, FN and Has depletions show similar, significantly decreased relative adhesiveness (Fig.7A-E). With alpha proportional to width, and width strongly increased in morphants, this initial decrease is compensated in total adhesiveness averages. The relative independence of adhesiveness from contact type could hint at non-specific PCM-PCM adhesion (Winklbauer, 2019). We think that although adhesion factor depletion leads to the loss of some contact types or renders others non-adhesive (thus lowering contact abundances), it modifies some contact types (e.g. by widening them) while only moderately lowering their adhesiveness per unit interaction surface.

      Minor comments<br /> - In their descriptions of PCM in different experimental conditions, the authors overstate some conclusions drawn from EM data. For example, that type I glycocalyces are absent in chordamesoderm (although this signal is only reduced),

      We qualified the statement.

      or that because the Has2 morphant phenotype is intermediate between C-cad and fibronectin morphants this indicates an adhesive role for hyaluronic acid.

      Overall, Has2MO increases the abundance of gaps, i.e. HA normally reduces gaps between cells, strongly suggesting an adhesive role of HA. HA is also required for the formation of 10-20 nm gaps, again proposing a direct or at least indirect adhesion-promoting role.

      • The authors state of the data in figure 1 that "All treatments significantly increase the size of non-adhesive gaps", but they don't show a quantification of the gaps size (they show the abundance).

      Has been corrected.

      • The authors state that LSM contacts exist as 10-20 and 20-50 nm subtypes. It is not clear what about the data suggest this division.

      In the LSM width difference spectra, CadMO and SynMO both increase the abundances of ≤ 20 nm contacts and decrease those of 20-50 nm contacts (Fig.4). The different response suggests at least two differently reacting subtypes.

      • In the same paragraph, the authors state that "C-cad and Syn-4... favor LSM width between 20-50 nm." What is meant by "favor"? Given that the number of 20 nm contacts is increased and 50 nm contacts is decreased in both conditions, this statement is unclear.

      The whole paragraph has been reworded.

      • On page 7, the authors say that the size of LSM structures is "consistent with larger plaques being assembled from small units", but if that were the case, wouldn't the plaque sizes be multiples of the size of a single unit? I.e. 100, 200, and 300 nm peaks? Because this is not the case, the data seem more consistent with a continuous range of LSM plaque sizes than with discrete units.

      The size of the units has a peak at 100 nm but a long tail (Fig.6F-H). Moreover, we discuss lateral compression (piling up of PCM material) or active stretching of plaques (to separate units for interdigitation), all factors that would blur plaque length patterns, i.e. we did not expect plaque sizes to be multiples of 100 nm.

      • On page 8, the authors refer repeatedly to LSM volume. Given that these measurements are made from TEM sections, how is volume being measured?

      This is explained now (p.7).

      • The authors present a model in which PCM interdigitates within cell contacts, but this is based on measurements from static tissues alone. Could the measurements of contact width instead be explained by compression of the PCM or some other mechanism? The data as presented don't rule out such possibilities.

      The model is in agreement with the linear increase of relative adhesiveness with contact width, with LSM height at gap surfaces not adding up to adjacent contact width, with visible interdigitation of glycocalyx units (“bushes”) described previously for prechordal mesoderm (Barua et al. 2021), and with the good agreement of calculated unit size with the size of measured LSM units. In addition, it agrees with literature data on endothelial glycocalyx plaques being composed of 100 nm units and of complete interpenetration of glycocalyces during blood cell adhesion.

      Some terms used are not clear, for example: "partial LSM", "triple layer contact", "random removal [of LSM plaques]".

      We point out the meaning of the terms now more clearly. That “partial LSM” is identical with “triple layer contact” (but shorter, for use in figure) is explained in the legend to fig.6.

      • In figure 5, the graphs depict negative "abundance". Recommend "difference in abundance" instead.

      Done. For shortness, Δ Abundance.

      • Statistics: In figure 1I, it is not clear what the asterisk in this graph means or if statistical differences between these groups was determined. And in figure 6, some groups are marked as n.s., but P values for groups that are statistically different are not presented.

      The asterisk in fig.1I was meant to indicate that this column is from Debanjan et al. 2021, but this is indicated by different shading and mentioned in the legend. The non-used n.s. marks were removed.

      Reviewer #2 (Significance):

      This detailed electron microscopy study advances our understanding of pericellular matrix within vertebrate embryos and how loss of its constituent molecules affects cell interactions. It further addresses the relationship between structurally distinct pericellular matrices and their adhesive properties, although this analysis is less convincing. This study adds to a body of literature in which cell-cell and cell-matrix adhesion are known to regulate morphogenetic cell movements, but how such contacts are remodeled as cells rearrange is poorly understood. Previous work has also used measurements from live cells, embryos, and tissues to infer physical forces within embryos such as adhesive strength, cortical tension, and viscosity. This work follows up directly on a previous study from this group that characterized glycocalyces within various tissues within Xenopus gastrulae by electron microscopy. The hypothesis that pericellular matrix enables flexible/fluid adhesion within highly dynamic embryonic tissues is exciting, and is likely to be of interest to developmental biologists - particularly those who apply mechanical concepts to embryos. However, additional evidence, preferably from live tissues and embryos, is needed to support this hypothesis. This assessment is based on over 15 years' experience studying gastrulation morphogenesis in multiple vertebrate species.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      During gastrulation, cells within vertebrate embryos require the ability to both adhere to one another and rearrange with their neighbors to shape the emerging body plan. These authors posit that such flexible adhesive contacts are mediated in part by the pericellular matrix (PCM), including multiple types of glycocalyces containing molecules such as fibronectin, hyaluronic acid, and syndecans, which they previously characterized in multiple embryonic tissues (Barua et al, PNAS, 2021). Here, in a follow-up to their 2021 study, the authors use electron microscopy to characterize the pericellular matrix within the chordamesoderm of Xenopus gastrulae. They identify several types of adhesive contacts within the chordamesoderm and assess how they are altered in the absence of key PCM molecules via morpholino knock-down. They conclude that syndecan-4 and hyaluronic acid comprise and promote assembly of PCM plaques whereas fibronectin and C-cadherin anchor them to cell surfaces. Cell packing density is decreased upon loss of all 4 of these molecules, which the authors attribute to a decrease in the number of cell contacts without affecting the strength of the remaining contacts. They further conclude that adhesiveness increases linearly with contact width, and that this relationship is unaffected by loss of any aforementioned adhesive/ PCM molecules.

      Major comments:

      • Many conclusions in this manuscript are based on measurements of cell contact angles, which indicate the reduction of tension at cell contacts vs. free cell surfaces and thus relative adhesive strength. While this lab previously applied the same approach to live tissues (David et al, 2014), it is not clear to what extent such measurements accurately reflect adhesive strength in fixed tissues and/or electron micrographs. Especially given the issue of random sectioning planes, which cause distortion of contact angles. Although a correction was applied, the authors note this is not theoretically derived because the heterogeneity of gap sizes made such calculations too difficult. Indeed, it appears that the large gaps between cells within morphant embryos affect contact angle measurements, but if this is corrected for in any way, it is not mentioned. Because this is the sole measure of cell adhesion provided in the study, this reviewer is not convinced of the conclusion that loss of PCM components does not affect adhesive strength. Could such measurements not be made from live cells/tissues after manipulating PCM components, as the lab has done previously? Because the lab already has the necessary reagents and expertise for such experiments, the time and resources needed for such measurements shouldn't be prohibitive.
      • As mentioned above, these authors previously measured adhesive strength in live Xenopus cells and tissues (David et al, 2014). In that study, they found that C-cadherin MO reduced relative adhesiveness whereas the current study found that relative adhesiveness actually increases in this condition. What explains this discrepancy?
      • No control morpholinos are used, and for the morpholinos that are used, the doses are very large. An equally high dose of control MO should be used to ensure that all observed phenotypes are specific.
      • It appears that all the images analyzed were collected in the sagittal plane, and the analyses don't seem to consider the intrinsic polarity of the chordamesoderm. For example: cells in different positions within the tissue (basal vs. apical), or that WT chordamesoderm cells are mediolaterally polarized and actively intercalating whereas disruption of PCM components like fibronectin disrupts cell intercalation and randomizes cell polarity. It is possible that 1) cell-matrix (in basal cells) and 2) cell-cell (during intercalation) interactions may affect the measurements made in this study. In other words, that cell contacts could differ by position within the embryo and intercalation/polarity status... have such effects been accounted for in the current analysis?
      • In this study, the authors state that chordamesoderm movements are preserved in syndecan-4 morphants, and in their 2021 article (Barua et al) they state that convergent extension movements are accelerated. But another study describing this MO found that it causes severe convergent extension defects (Munoz et al, NCB, 2006). What explains this discrepancy? Also, the syn-4 morphant showed in Fig. 1 appears more developmentally advanced than the other embryo... if the embryos are not stage matched it could affect the measurements and conclusions drawn from them.
      • In figure 7, the authors plot relative adhesion (measured from contact angles) vs. contact width, then fit regression lines to the lower boundaries of these scatter plots. It is not clear why this analysis is focused only on the lower boundaries rather than considering the full spread of the data. Particularly for syn-4 morphants, whose values do not appear to be concentrated along the lower boundary. This analysis is further confused by the introduction of alpha*, which represents relative adhesiveness relative to the regression.
      • Based on these regression lines alone, the authors conclude that all 4 conditions are similar enough to pool the data for further analysis. If these contacts have different properties, which the data in Figures 1-6 suggest they do, it seems inappropriate to pool them together. Based on this pooling, the authors then conclude that relative adhesiveness increases linearly with contact width over the entire width range, regardless of adhesion factor depletion. This again assumes that all contacts (morphant and WT) are functionally equivalent, and that what is observed in morphant embryos in very wide contacts would also hold true in WT contacts. But because WT contacts occupy only a small portion of the width range, we cannot know how they would behave if scaled to be wider, and I am not convinced that very wide morphant contacts are representative of or functionally equivalent to WT. In other words, we cannot know that contact width is the only factor increasing their relative adhesion, given the experimental manipulations that structurally alter these contacts.

      Minor comments

      • In their descriptions of PCM in different experimental conditions, the authors overstate some conclusions drawn from EM data. For example, that type I glycocalyces are absent in chordamesoderm (although this signal is only reduced), or that because the Has2 morphant phenotype is intermediate between C-cad and fibronectin morphants this indicates an adhesive role for hyaluronic acid.
      • The authors state of the data in figure 1 that "All treatments significantly increase the size of non-adhesive gaps", but they don't show a quantification of the gaps size (they show the abundance).
      • The authors state that LSM contacts exist as 10-20 and 20-50 nm subtypes. It is not clear what about the data suggest this division.
      • In the same paragraph, the authors state that "C-cad and Syn-4... favor LSM width between 20-50 nm." What is meant by "favor"? Given that the number of 20 nm contacts is increased and 50 nm contacts is decreased in both conditions, this statement is unclear.
      • On page 7, the authors say that the size of LSM structures is "consistent with larger plaques being assembled from small units", but if that were the case, wouldn't the plaque sizes be multiples of the size of a single unit? I.e. 100, 200, and 300 nm peaks? Because this is not the case, the data seem more consistent with a continuous range of LSM plaque sizes than with discrete units.
      • On page 8, the authors refer repeatedly to LSM volume. Given that these measurements are made from TEM sections, how is volume being measured?
      • The authors present a model in which PCM interdigitates within cell contacts, but this is based on measurements from static tissues alone. Could the measurements of contact width instead be explained by compression of the PCM or some other mechanism? The data as presented don't rule out such possibilities.
      • Some terms used are not clear, for example: "partial LSM", "triple layer contact", "random removal [of LSM plaques]".
      • In figure 5, the graphs depict negative "abundance". Recommend "difference in abundance" instead.
      • Statistics: In figure 1I, it is not clear what the asterisk in this graph means or if statistical differences between these groups was determined. And in figure 6, some groups are marked as n.s., but P values for groups that are statistically different are not presented.

      Significance

      This detailed electron microscopy study advances our understanding of pericellular matrix within vertebrate embryos and how loss of its constituent molecules affects cell interactions. It further addresses the relationship between structurally distinct pericellular matrices and their adhesive properties, although this analysis is less convincing. This study adds to a body of literature in which cell-cell and cell-matrix adhesion are known to regulate morphogenetic cell movements, but how such contacts are remodeled as cells rearrange is poorly understood. Previous work has also used measurements from live cells, embryos, and tissues to infer physical forces within embryos such as adhesive strength, cortical tension, and viscosity. This work follows up directly on a previous study from this group that characterized glycocalyces within various tissues within Xenopus gastrulae by electron microscopy. The hypothesis that pericellular matrix enables flexible/fluid adhesion within highly dynamic embryonic tissues is exciting, and is likely to be of interest to developmental biologists - particularly those who apply mechanical concepts to embryos. However, additional evidence, preferably from live tissues and embryos, is needed to support this hypothesis. This assessment is based on over 15 years' experience studying gastrulation morphogenesis in multiple vertebrate species.

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

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      This work examines the relationship between cell-cell contacts and pericellular matrix in Xenopus chordamesoderm, which is a tissue actively involved in convergent extension during gastrulation. By lanthanum staining of pericellular materials, the authors found that different types of pericellular matrix are present in cell-cell contacts in the chordamesoderm, which may mediate cell-cell adhesion. Knockdown of C-cadherin, Syndecan-4, fibronectin, and hyaluronic acid leads to the reduced abundance of cell contacts and cell packing density, but this does not seem to affect convergent extension. Based on these observations, the authors propose a model in which cell-cell contacts involve the interdigitation of distinct pericellular matrix units.

      Major points:

      1. Knockdown of adhesion molecules separates cells and leads to wide contacts with large interstitial spaces. Data in figure 1 show loosely packed morphant chordamesoderm cells. Intuitively, these should reduce cell-cell adhesion. However, a main conclusion from this manuscript is that reduced abundance of narrower contacts does not decrease adhesiveness. Although depletion of adhesion molecules modifies but not abolishes a contact, non-attached free surfaces increase significantly in morphant cells. It is therefore not easy to understand that how reduced cell contacts have no effect on cell adhesion. Importantly, the adhesiveness was not experimentally tested.
      2. It is surprising that reduced cell contacts, at least narrower cell contacts, do not affect convergent extension. Does this mean that active cell behavior changes in the chordamesoderm, which are required for convergent extension, are independent of cell contact types?
      3. Although the formation and localization of pericellular materials are differentially affected after knockdown of adhesion molecules, there is no clear evidence showing that different types of pericellular matrix mediate cell-cell adhesion in the chordamesoderm. It is possible that the disrupted distribution of pericellular materials in morphants only represents a secondary consequence of changed cell contacts. This may be supported by the fact that knockdown of adhesion molecules reduces narrow contacts and increases LSM-free gaps.
      4. The relationship between contact width spectra and LSM is also very elusive. Again, changes in contact width or abundance and distribution of LSM may be indirectly caused by loss of adhesion molecules. Therefore, although knockdown of adhesion molecules leads to changes of LSM localization, it cannot be concluded that cell-cell contacts in chordamesoderm are mediated different types of pericellular matrix.
      5. In contrast to the present observations, works by others using the same morpholinos have shown that Cadherin-dependent cell adhesion, fibronectin-rich extracellular matrix, and Syndecan-4-regulated non-canonical Wnt signaling are required for convergent extension. These discrepancies need to be appropriately addressed.
      6. If LSM and LSM-free contacts are similarly adhesive, what will be role of LSM in cell adhesion and how cell adhesion is established in these LSM-free contacts?

      Minor points:

      1. It may be helpful to clearly define the pericellular matrix in this particular context and its relationship with LSM. It is also necessary to clarify whether the adhesion molecules examined in this work are considered as components of the pericellular matrix.
      2. In figure 1B, it appears that the Cadherin morphant has defects in chordamesoderm elongation and archenteron formation, suggesting impaired convergent extension.
      3. In figure 1C, the Syndecan-4 morphant gastrula clearly shows enhanced anteroposterior elongation of chordamesoderm and archenteron in comparison with the wild-type embryo. This seems to suggest that loss of Syndecan-4 promotes the movements of convergent extension. However, previous studies indicate that both gain and loss of Syndecan-4 impairs convergent extension.
      4. Ideally, in knockdown experiments, control embryos should be injected with corresponding mismatch morpholinos.
      5. In figure 1E, it is unclear what type of cell contacts the light green arrowheads indicate.
      6. Figure 1 legend, "(wt) is from Barua et al. 2021". I am not sure it is appropriate to use previously published data.
      7. There is no light blue arrowhead in figure 2, and in figure 3B and 3I, it seems that the same colored arrows are used to indicate different structures.
      8. Triple-layered contacts are not clearly defined.
      9. Page 2, "based on driven by" should be either "based on" or "driven by".
      10. Page 8, "selectin" should be "selecting".

      Significance

      Strengths:

      Demonstrated the effects of several adhesion molecules on the formation of cell contacts and pericellular matrix in Xenopus chordamesoderm.

      Limitations:

      The significance of chordamesoderm cell contact changes in convergent extension or gastrulation is not clear; there is no direct evidence showing the functional link between pericellular matrix, cell contacts and cell adhesion; the absence of effects on convergent extension after depletion of several adhesion molecules is not fully consistent with previous reports.

      Advance:

      This work likely provides some fundamental and methodological advances for studying cell-cell adhesion. It shows promise for elucidating mechanisms underlying the regulation of cell contact changes in tissues involved in morphogenetic movements.

      Audience:

      This work likely interests readership studying embryonic cell adhesion in the field of developmental biology and cell biology. It may be also potentially interesting for people working on glycocalyx pericellular matrix in adult tissues.

  2. Jul 2023
    1. Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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      Reply to the reviewers

      Please find our point-to-point response to the reviewer’s comments below, where we marked all changes implemented in the manuscript in italics.

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

      With the emergence and spread of resistance to Artemisinin (ART), a key component of current frontline malaria combination therapies, there is a growing effort to understand the mechanisms that lead to ART resistance. Previous work has shown that ART resistant parasites harbour mutations in the Kelch13 protein, which in turn leads to reduced endocytosis of host haemoglobin. The digestion of haemoglobin is thought to be critical for the activation of the artemisinin endoperoxide bridge, leading to the production of free radicals and parasite death. However, the mechanisms by which the parasites endocytose host cell haemoglobin remain poorly understood.

      Previous work by the authors identified several proteins in the proximity of K13 using proximity-based labelling (BioID) (Birnbaum et al. 2020). The authors then went on to characterise several of these proteins, showing that when proteins including EPS15, AP2mu, UBP1 and KIC7 are disrupted, this leads to ART resistance and defects in endocytosis leading to the hypothesis that these two processes are inextricably linked.

      In this manuscript, Schmidt et al. set themselves the task of characterising more K13 component candidates identified in their previous work (Birnbaum et al. 2020) that were not previously validated or characterised. They chose 10 candidates and investigated their localisations, and colocalisation with K13, and their involvement in endocytosis and in vitro ART resistance, 2 processes mediated by K13 and some members of the K13 compartments

      The authors show that of their 10 candidates, only 4 can be co-localised with K13. Then, using a combination of targeted gene disruption (TGD) as well as knock sideways (KS), they characterised these 4 proteins found in the K13 compartment. They show that MyoF and KIC12 are involved in endocytosis and are important for parasite growth, however their disruption does not lead to a change in ART sensitivity. The authors also confirm the findings of their previous publication (Birnbaum et al. 2020), using a slightly different TGD

      (note from the authors: we apologise if this has not properly transpired from the manuscript but the difference between the TGDs is substantial and relevant: one has less than 3% of the protein left and hence can be considered to fully inactivate MCA2 and has a growth defect whereas the other contains about two thirds of the protein (1344 amino acids/~66% are left), has no growth defect, although it lacks the MCA2 domain (hence that domain can not be critical for the growth defect)),

      that MCA2 is involved in ART resistance, however they did not check whether its disruption impacts haemoglobin uptake. They also show that KIC11 is not involved in mediating haemoglobin uptake or ART resistance. To finish, the authors used AlphaFold to identify new domains in the proteins of the K13 compartment. This led them to the conclusion that vesicle trafficking domains are enriched in proteins of the K13 compartment involved in endocytosis and in vitro ART resistance.

      The majority of the experiments conducted by the authors are performed to a good standard in biological and technical replicates, with the correct controls. Their findings provide confirmation that their 4 candidate genes seem to be important for parasite growth, and show that some of their candidates are involved in endocytosis. While the KD and KS approaches employed by the authors to study their candidate genes each have their own advantages and can be excellent tools for studying a large sets or genes, this manuscript highlights the many limitations of these approaches. For example, the large tag used for the KS approach can mislocalise proteins or disrupt their function (as is the case for MyoF), resulting in spurious results, or indeed the inability to generate the tagged line (as is the case for MCA2). The KS approach also makes the results of a protein with a dual localisation, like KIC12, extremely difficult to interpret.

      We thank the reviewer for this thorough and insightful review.

      The limitations mentioned above were addressed in the response to the main points and a general detailed response in regards to the systems used for this research are added at the end of this rebuttal. Briefly summarised here: while we agree that there are limitations of the system used, we are convinced that

      • the advantages of using a large tag in most cases outweighs the drawbacks as it permits to track the inactivation of the target, if need be on the individual cell level

      • while not optimal for MyoF, the partial inactivation actually helps in its functional study as detailed in major point 23&28 or reviewer#3 major point 11: it shows a consistent correlation of the phenotype with different causes and degrees of inactivation (this is now better illustrated in Figure 1L1M). Further, regarding the concern of the large tag: the effect of the tag based on localisation was overestimated in the review by what seems to have been a mix up comparing numbers from MyoF with a number from MCA2 (there is a difference, but it is only small) (see reviewer#1 major point #23).

      • KS is the optimal method for most of the assays in this work (e.g. bloated food vacuole assays and RSAs); these assays would be impossible or difficult to use with other inactivation systems currently used in P. falciparum research (see details in the response to the specific points and after the rebuttal)

      In regards to the difficulty to interpret KIC12 data: this is only true for measuring absolute essentiality, everything else we believe we actually have the optimal method. If not KS, which method targets a specific pool of a protein with a dual localisastion? Again, our assays targeting the K13 pool and revealing the specific function would have been difficult or impossible with any other system.

      Ultimately the question is whether any other system would have resulted in a different conclusion on the function of the proteins studied. At present we are confident this would not be the case and other systems probably would not have delivered the specific functional data shown in this work. Clearly, more in depth work will provide more nuanced and detailed insights into the proteins analysed in this work and this likely will also include the use of other systems for specific aspects they are most suitable for. However, this (e.g. different complementations in a diCre cKO) is complex and therefore beyond what fits into this work which had the goal to assess which proteins are true positives for the K13 compartment and to place them into functional groups in regards to endocytosis.

      Moreover, the manuscript is disjointed at times, with the authors choosing to conduct certain experiments for only a subset of genes, but not for others. For example, considering that the aim of this paper was to identify more proteins involved in ART resistance and endocytosis, it is confusing why the authors do not perform the endocytosis assays for all their selected proteins, and why they do not do this for the proteins they identify in their domain search. There is significant room for improvement for this manuscript, and a generally interesting question.

      The reviewer remarks that not every experiment was done for every target. Based on the rebuttal we tried to amend this but also note that there was some sentiment by the reviewers to better stick to the point and not make the manuscript more disjointed. We attempted to balance that as much as possible and hope we were able to honour both aspects (amendments were done as detailed in the point by point response below).

      In regards to endocytosis and choice of targets: We did do endocytosis assays for all proteins that showed a growth phenotype upon inactivation in this work. We therefore assume the reviewer here refers to major point #40 asking for endocytosis assays with KIC4 and KIC5 (which were not studied in this manuscript) as well as MCA2 (point 17). We fully agree with the reviewer that this would fill a gap in the work on K13 compartment proteins but such assays are difficult with TGDs (there are issues with non-comparable samples and compensatory effects) and proteins that are not essential (and hence likely have a smaller impact on endocytosis when truncated). We nevertheless now carried them out, but due to the limitations to do this with these lines would be hesitant to draw definite conclusions (see major point 17 and 40 for details and outcomes).

      But in it's current format, other than confirming that MCA2 is involved in ART resistance (which was already known from the Birnbaum paper), the authors do not further expand our understanding of the link between ART resistance and endocytosis in this manuscript.

      We would like to point out that the importance of the K13 compartment and endocytosis goes beyond ART resistance (see e.g. also newly published papers on the K13 compartment in Toxoplasma, (Wan et al., 2023; Koreny et al., 2023)). Endocytosis is an essential and prominent process in blood stages. However, in contrast to processes such as invasion, our understanding about endocytosis is only rudimentary. Hence, this manuscript provides important insights on an emerging topic that in our opinion deserves more attention:

      • it identifies novel proteins at the K13 compartment and provides 2 new proteins in endocytosis (MyoF and KIC12); getting an as complete as possible list of proteins involved in the process will be critical to study and understand it

      • it leads to the realisation that not all growth-relevant proteins detected at the K13 compartment are needed for endocytosis

      • it provides domains and stage specificity of function for several K13 compartment proteins, overall bolstering the model of endocytosis in ART resistance and providing a framework critical to direct future studies on endocytosis and their detailed mechanistic function at the cytostome

      • the identified vesicle trafficking domains (for instance now also found in UBP1) are expected to strengthen the support for the role of endocytosis of the K13 compartment; this and also the above points are important as (based on the current literature) there still seems to be prominent sentiment in the field that (in part due to the involvement of UBP1 and K13) the cause of ART resistance is due to various unclearly defined stress response pathways

      • with MyoF it also shows the first protein in connection with the K13 compartment that acts downstream of the generation of hemoglobin-filled containers in the parasite and provides the first protein that explains the suspected involvement of actin in endocytosis (so far this was only based on CytD studies)

      Overall we therefore believe this manuscript contains critical information and a framework for future studies on endocytosis and the K13 compartment. We hope the relevance of endocytosis as one of the most prominent and essential processes in the parasites and the connection to various aspects linked with many commercial drugs (in addition to the role of endocytosis in ART resistance), is adequately explained in the introduction. We also would like to mention that the main focus of the work is reflected in the title of the manuscript which does not mention ART susceptibility.

      Major Comments

      1) line 31: please change defined to characterised - defined suggests that novel proteins were identified in this study, which is not the case.

      We apologise, but we do not fully understand this comment. We did identify novel proteins not before known to be at the K13 compartment (MCA2 (admittedly this one was likely but had not previously been verified), MyoF, KIC11 and KIC12). In our view "further defining the composition of the K13 compartment" therefore is an accurate statement. Additionally, the identification of previously not-discovered domains, the stage-specificity and function of these proteins helped to further define the K13 compartment.

      If the reviewer is referring to the fact that the proteins analysed in this study were taken from a previously generated list of hits, we would like to stress that the presence in such a list (obtained from a BioID, but also if from an IP etc) can not be equalled for them to be true positives, they are merely candidates that still need to be experimentally validated. This is what we did in this work to find out which further proteins from the list can be classified as K13 compartment proteins (for hits with lower FDRs this is even more relevant as illustrated by the fact that 6 of the here analysed hits were not at the K13 compartment). In an attempt to address this comment in the manuscript, we changed the wording of this sentence to (line 31): "Here we further defined the composition of the K13 compartment by analysing more hits from a previous BioID, showing that MyoF and MCA2 as well as Kelch13 interaction candidate (KIC) 11 and 12 are found at this site."

      2) line 37: please change 'second' to "another". As explained further below, the authors identified 3 classes of proteins (confer ART resistance + involved in HCCU, involved in HCCU only, or involved in neither).

      We realized that the groups description wasn’t clear in the abstract. Please see response to major comment #41 for a detailed answer to this (endocytosis is an overarching criterion, ART resistance is a subgroup and applies only to those proteins with a function in endocytosis in ring stages). To clarify this (see also major point #8) we added an explanation on the influence of stage-specificity of endocytosis on ART susceptibility to the introduction (line 76): In contrast to K13 which is only needed for endocytosis in ring stages (the stage relevant for in vitro ART resistance), some of these proteins (AP2µ and UBP1) are also needed for endocytosis in later stage parasites (Birnbaum et al., 2020). At least in the case of UBP1, this is associated with a higher fitness cost but lower resistance compared to K13 mutations (Behrens et al., 2021; Behrens et al., 2023). Hence, the stage-specificity of endocytosis functions is relevant for in vitro ART resistance: proteins influencing endocytosis in trophozoites are expected to have a high fitness cost whereas proteins not needed for endocytosis in rings would not be expected to influence resistance.” The abstract was changed in response to this and other comments and hope it is now clearer in regards to the groups.

      3) Line 40: You define KIC11 as essential but according to your data some parasites are still alive and replicating 2 cycles after induction of the knock sideways. Please consider changing "essential" to "important for asexual parasite growth".

      We fully agree with the reviewer, we reworded the sentence as suggested.

      4) Line 40: please change 'second group' to 'this group'

      We reworded this part of the abstract and it know reads: (line 38): “While this strengthened the link of the K13 compartment to endocytosis, many proteins of this group showed unusual domain combinations and large parasite-specific regions, indicating a high level of taxon-specific adaptation of this process.”

      5) line 41: state here that despite it being essential, it is unknown what it is involved in.

      With the newly added data we show that this protein either has a function in invasion or very early ring development although we did not see any evidence for the latter. We therefore changed the sentence to (line 43): “We here identified the first protein of this group that is important for asexual blood stage development and showed that it likely is involved in invasion*..” *

      6) Line 50: the authors should state here that there is actually a reversal in this trend over the last few years.

      Done as suggested.

      7) Line 54: please separate out the references for each of the two statements made in this line (a: that ART resistance is widespread in SEA, and b: that ART resistance is now in Africa) Reference 14 also seems to reference ART resistance in Amazonia - which is not covered by the statement made by the authors (in which case the authors should state ART is now present in Africa and South America). The authors should also reference PMID: 34279219 for their statement that ART resistance is now found in Africa (albeit a different mutation to the one found in SEA).

      Done as suggested.

      8) Line 65: it is also worth mentioning here that there are other mutations in proteins other than K13, such as AP2mu and UBP1 (PMID: 24994911;24270944) that can lead to ART resistance.

      As suggested by the reviewer, we included a sentence about non-K13 mutations linked with reduced ART susceptibility in the introduction (line 74): Beside K13 mutations in other genes, such as Coronin (Demas et al., 2018) UBP1 (Borrmann et al., 2013; Henrici et al., 2020b; Birnbaum et al., 2020; Simwela et al., 2020) or AP2µ (Henriques et al., 2014; Henrici et al., 2020b)* have also been linked with reduced ART susceptibility." *

      We here also added data on fitness cost that is related to this and is also relevant for the issue of proteins with a stage-specific function in endocytosis, making a transition for this statement which might help clarifying the grouping of K13 compartment proteins (see also major point #2).

      9) Line 80, 86: ref 43 is misused. Reference 43 refers to Maurer's clefts trafficking which takes place in the erythrocyte cytosol and is not involved in haemoglobin uptake as far as I know. Please replace ref 43 with one showing the role of actin in haemoglobin uptake.

      We thank the reviewer for pointing this out, Ref 43 was removed from the manuscript.

      10) Line 98: the authors state here that they 'identified' further candidates from the K13 proxiome. This suggests that they identified new proteins in this paper, when in fact the list was already generated in ref 26. All they did was characterise proteins from that list that were not previously characterised. The authors should therefore remove identified from this statement.

      We agree with the reviewer that we did not identify further candidates, we identified new K13 compartment proteins from the list of potential K13 compartment proteins. We therefore changed “identified further candidates” into “identified further K13 compartment proteins” (line 116). Please see also response to major comment #1.

      11) Line 107-108: it is not clear from this sentence why these proteins were left out of the initial analysis in Ref 26. A sentence here explaining this would be valuable for the reader.

      This is a good point. One reason why we did not analyse more in our previous publication was that we had to stop somewhere and adding more would have been very difficult to fit into what was already a packed paper. However, as shown in this work, the list does contain further interesting candidates (e.g. K13 compartment proteins that are involved in endocytosis).

      We altered the relevant part of the introduction to highlight that we previously analysed the top hits, clarifying that the 'remaining' hits analysed in this work were further down in the list. This now reads: (line 113)“We reasoned that due to the high number of proteins that turned out to belong to the K13 compartment when validating the top hits of the K13 BioID (Birnbaum et al., 2020), the remaining hits of these experiments might contain further proteins belonging to the K13 compartment.” We hope this clarifies that we simply moved further down in the candidate list.

      12) Line 117-123: The authors say that PF3D7_0204300, PF3D7_1117900 and PF3D7_1016200 were not studied because they were not in the top 10 hits. However, the current organisation of Supplementary Table 1 shows all 3 proteins among the top 10 hits (MyoF, KIC12, UIS14 and 0907200 being after them). I think the authors should reorganise their table. It is also unclear according to what the proteins in the table are ranked. Could the authors indicate the metric used for the ranking?

      We thank the reviewer for alerting us to this. The issue here is that the 3 non-analysed proteins belong to a 'lower stringency' group comprising hits significant with FDRThe information about ranking is now also included as “Table legend” in the revised manuscript and the Table heading has been changed to: List of putative K13 compartment proteins, proteins selected for further characterization in this manuscript are highlighted.”

      13) Line 129-141: Can the authors be clearer with their explanations of the identification of mutation Y1344Stop? One dataset (ref 61) shows that 52% of African parasites have a mutation in MCA2 in position 1344 leading to a STOP codon. But another dataset (ref 62) shows that the next base is also mutated, reverting the stop codon. That should have been seen in the first dataset as well. Could the authors please clarify.

      This mutation was first spotted in the MalariaGEN database (https://www.malariagen.net) (MalariaGEN et al., 2021), which allows online accessing of the data by using the “variant catalogue” tool, which is in a table format of frequency rather than in a sequence context. Hence, only after further research later on it became evident to us, that this mutation does not occur alone when looking at individual MCA2 sequences from patient samples in (Wichers et al., 2021b). We hope this is accurately reflected in our results section.

      14) Line 147: the authors say that MCA2 is expressed throughout the intraerythrocytic cycle as shown by live cell imaging. In Birnbaum et al 2020 fig 4I, the authors show that MCA2 is mainly expressed between 4 and 16hpi. But in Figure 1B of this manuscript there is a clear multiplication of MCA2 signal between trophozoite and schizont. How do the authors explain this discrepancy? Could expression of the truncated MCA2 be different than the full length? This cannot be assessed as expression and localisation of the full-length HA tag MCA2 is not shown in Schizonts.

      The key difference lies in transcription vs protein expression (usually protein levels peak after mRNA levels peak and - depending on turnover - protein levels can stay high even after mRNA levels have declined). Figure 4 of the Birnbaum et al paper presents transcriptomic data, but with a peak in trophozoites (The axis label in Fig. 4l of that publication is a bit confusing, as hour 0 is at the top, 48 h at the bottom; it is clearer in Fig. S13 of that paper) which would fit very well with the multiplication of the signal between trophozoites and schizonts mentioned by the reviewer. So, overall, the temporal peaks of transcripts and protein of that protein fit well.

      For the signal in rings: Likely the protein has a turnover rate that is sufficiently low for some protein to be taken into the new cycle after re-invasion. Also different transcriptomic datasets e.g. (Otto et al., 2010; Wichers et al., 2019; Subudhi et al., 2020) available on plasmoDB show some mRNA present across the complete asexual development cycle, with each dataset showing maximum peak at a slightly different stage.

      Even when located in foci and hence aiding detection of small amounts of protein (as is the case for MCA2-Y1344-GFP), the MCA2 signal in rings is not strong. For MCA2-TGD, the GFP signal is dispersed and therefore likely below our detection limit, while the same amount of protein concentrated at the K13 compartment is visible as foci in the MCA2-Y1344 cell line. Please note that MCA2-TGD has only 2.8% of the protein left whereas MCA2-Y1344 has 66.5% left and based on our manuscript is almost fully functional, hence fitting the different locations between the two versions.

      Overall we believe this shows that there are actually no significant discrepancies of the expression of the different MCA2 versions.

      15) Line 158: would it not have been more useful for the authors to have episomally expressed MCA2-3xHA in their MCA2Y1344STOP-GFPENDO line to make sure that the truncated protein is indeed going to the correct compartment? The experiments done by the authors suggests that the MCA2Y1344STOP goes to the right location but does not really confirm it.

      We appreciate the reviewers caution here. However, considering that MCA2Y1344STOP-GFPendo co-locates with mCherryK13 and endogenously HA-tagged full length MCA2 does the same to a similar extent, there is in our opinion little doubt that MCA2 is found at the K13 compartment and that this is similar with both constructs. If there are minor differences, these might as well occur if MCA2 is episomally (as suggested in the comment) instead of endogenously expressed. Given the limited insight, we therefore decided against the episomal overexpression (which due to its size of > 6000bp may also be somewhat less straight forward than it may sound).

      16) Line 191: it is stated that MCA2 confers resistance independently of the MCA domain, however in both the MCA2-TGD and MCA2Y1344STOP-GFPENDO parasites, the MCA domain is deleted, and for both parasites, there is resistance (albeit to a lower level in the MCA2Y1344STOP-GFPENDO line). Therefore, how can the authors state that the ART resistance is independent of the MCA domain? This statement should be that resistance is dependent on the loss of the MCA domain.

      We agree that this can’t be categorically excluded. However, a ~5 fold difference in ART sensitivity was observed between the parasites with MCA2 truncated at amino acid 57 compared to those with MCA at amino acid 1344 even though both do not contain the MCA2 domain. Hence, at least this difference is not dependent on the MCA2 domain. The larger construct missing the MCA domain shows only a very moderate reduction in RSA survival, again suggesting the MCA domain is not the main factor. We amended our statement in an attempt to more accurately reflect the data (line 487): This considerable reduction in ART susceptibility in the parasites with the truncation at MCA2 position 57 compared to the parasites still expressing 1344 amino acids of MCA2, despite both versions of the protein lacking the MCA domain, indicates that the influence on ART resistance is not, or only partially due to the MCA domain.” We would be hesitant to state the reviewer's conclusion that “resistance is dependent on the loss of the MCA domain”, as the larger construct missing the MCA2 domain has a milder RSA effect compared to MCA2-TGD, which suggests the reduction in ART susceptibility is independent of the MCA domain. These considerations also agree with the fact that the parasites with the longer MCA2 version (in contrast to the MCA2-TGD) do not have any detectable growth defect which indicates that the protein can fulfil its function without the MCA2 domain.

      17) Line 192: Why did the authors not check if MCA2 is involved in endocytosis? They state later on in the manuscript that they did not do endocytosis assays with TGD lines, however if the authors include the correct controls, this could be easily done. It would also be really interesting to see whether endocytosis gets progressively worse going from WT to MCA2Y1344STOP to MAC2TGD. This experiment (as well as doing endocytosis assays for KIC4 and KIC5 TGD lines) would drastically increase the impact of this study. These experiments would not take more than 3 weeks to perform, and would not require the generation of new lines.

      So far were very hesitant to do bloated FV assays with TGDs (even though TGDs were available for the genes encoding MCA2 and KIC4 and KIC5). The reason for this was:

      1. the fact that these proteins could be disrupted indicated either redundancy or only a partial effect on endocytosis which might lead to only small effects that likely are difficult to pick up in an assay scoring for the rather absolute phenotype of bloated vs non-bloated. Using the refined assay measuring FV size could partly amend this but we note that also FV without hemoglobin have a certain size, reducing the relative effect if there are smaller differences.
      2. a TGD line does not permit tightly controlled inactivation of the target which makes comparing the outcome of bloated food vacuole assays difficult if there are smaller growth and stage differences to the 3D7 control.
      3. in contrast to conditional inactivation parasites, the TGD lines had ample times to adapt to loss of the target protein (compensatory mechanisms are well known for endocytosis, for instance in clathrin mediated endocytosis loss of individual components can be compensated (Chen and Schmid, 2020)). We nevertheless see the reviewer's point that this should at least be attempted and now conducted these assays (see also major point 40). For MCA2 (as requested in this point), the data is shown in Figure S5C-E. This assay showed that in MCA2-TGD, MCA2Y1344STOP-GFPendo (similar to the 3D7 control) >95% of parasites developed bloated food vacuoles. Additionally, we also measured the parasite and food vacuole size of individual cells in an attempt to solve some of the problems with TGDs with such assays. In order to specifically solve problem 2 mentioned above, we analysed the food vacuoles of similarly sized parasites, however, they were non-distinguishable between the three lines. Of note, in agreement with the reduced parasite proliferation rate (Birnbaum et al., 2020) a general effect on parasite and food vacuole size was observed for MCA2-TGD parasites, indicating reduced development speed in these parasites. Hence, it is possible that a potential endocytosis reduction was accompanied by a slowed growth, and the comparison of similarly sized parasites may have obscured the effect. It is therefore not sure if there indeed is no endocytosis phenotype, although we can exclude a strong effect in trophozoites.

      Based on the RSA results at least rings can be expected to have a reduced endocytosis in the MCA2-TGD. Apart from options 1-3 mentioned above, it is therefore possible there is an effect restricted to rings, although in that case the reduced growth in trophozoites would be due to other functions of MCA2. Overall, we can conclude that the MCA2-TGD parasites do not have a strongly reduced endocytosis, but given the fact that the parasites are viable, this is not surprising. Whether the MCA2-TGD has no effect at all on endocytosis we would be very hesitant to postulate based on these results.

      18) The authors should consider re-organising the MCA2 section, first showing that the 3xHA tagged line colocalises with K13, then performing the new truncation.

      We attempted to re-organise as suggested but because we now included additional fluorescence microscopy images of schizont and merozoites (in response to reviewer 2 major comment 3) the main figure would become even larger. To prevent this, we kept the 3xHA data in the supplement.

      19) Line 197: Once again ref 43 is not correct to illustrate that actin/myosin is involved in endocytosis

      We thank the reviewer for pointing this out – we removed Ref 43.

      20) Line 202: the authors state that MyoF localises near the food vacuole from ring stage/trophs onwards. However, how can this statement be made in schizonts based on these images (Fig. 2A), where it doesn't look like MyoF is anywhere near the FV? This statement can only be made for schizonts if co-localised with a FV marker (which is done in Fig. 2B), however, based on the number of MyoF foci, it appears that this was not done for schizonts. Please either remove the statement that MyoF is near the food vacuole from trophs onwards (because it is only seen near the FV up until trophs) or show the data in Fig. 2B of schizonts to substantiate these claims.

      This is a valid point. We originally did not focus on schizonts because most markers end up in some focal area in the forming merozoite but other proteins (such as e.g. K13) also have one or more additional foci at the FV, making interpretation unclear, particularly if the schizont is still organizing to become fully segmented. This is why we generally focused the K13 co-localisations on the trophozoite stage to obtain the clearest information on endocytosis. However, given the fact that this manuscript gives the first localization of MyoF in P. falciparum parasites, we now provide a comprehensive time course (Figure 1C, S1A) including schizonts, which show quite a complex pattern: while the MyoF-GFP localization in trophozoites appeared as multiple foci close to K13 and also the FV, the MyoF-GFP pattern changes in late schizonts (fully segmented) and merozoites, appearing as elongated foci no longer close to K13 or the FV. Of note, this pattern has been previously reported for MyoE in P. berghei (Wall et al., 2019).

      We therefore revised the statement about MyoF localization in schizont to better reflect the observed localization: (line 175): In late schizonts and merozoite the MyoF-GFP signal was not associated with K13, but showed elongated GFP foci (Figure 1C, S2A) reminiscent of the MyoE signal previously reported in P. berghei schizonts (Wall et al., 2019).”

      21) Line 204-206: what does this statement bring to the paper? Is it to show that it is the real localisation of MyoF because 2 tag cell line show the same localisation? I don't think this is needed, especially as later in the manuscript an HA-tag MyoF line is used and show similar localisation.

      We see the reviewers point, but prefer to keep this data included in the supplement, particularly because potential differences in the location of tagged MyoF were a major concern.

      Related to the tag issue: in order to get a better understanding of the effect of C-terminally tagging with different sized tags we now performed a more detailed analysis of the MyoF-3xHA cell line (Figure S2F-G), showing that this cell line shows a growth rate similar to the 3D7 wild type parasites, and has less vesicles than the 2x-FKBP-GFP-2xFKBP cell line, but still slightly, but significantly more than 3D7 parasites. Overall, this indicates that the smaller 3xHA tag has less effect on the parasite, than the larger 2x-FKBP-GFP-2xFKBP tag (see also new Figure 1L, showing a correlation of level of inactivation and the endocytosis phenotype for MyoF).

      22) Line 212: The overlap of K13 with MyoF in Figure 2C 3rd panel (1st trophozoite panel) is not obvious, especially as the MyoF signal seems inexistant. I would advise the authors to replace with a better image. Also, why are there no images of schizonts shown in Figure 2C?

      As suggested we exchanged the trophozoite image of panel Figure 2 C (now Figure 1C) and expanded this panel with images covering the complete asexual development cycle including schizonts in response to this and the previous points. As indicated above (point 20), schizont stages are complex to interpret. While late schizonts likely are not very relevant for endocytosis this is the first description of the location of the protein in this parasite and we therefore now provide a more thorough representation of the MyoF location across asexual stages in Figure1C and S2A.

      23) Line 217: the spatial association of MyoF with K13 is very different when it is tagged with GFP and when it is tagged with 3xHA. The way the authors word it here, it seems that there is agreement with the two datasets, when this is not in fact the case (59% overlap for MyoF-GFP and only 16% overlap with MyoF-3xHA). These data suggest that the GFP and the multiple FKBP tags are doing something to the protein and therefore maybe the ensuing results using this line should not be trusted or be taken with a pinch of salt.

      We agree with the reviewer that the location of this MyoF-GFP in the cell might differ due to the partial inactivation but in contrast to this comment, the data does not indicate any large differences. It seems the reviewer mixed something up (the 59% mentioned might come from the MCA2 figure?). The data with the two lines with differently tagged MyoF co-localised with K13 are actually quite comparable: GFP-tagged vs HA-tagged MyoF overlapping with K13 was 8% vs 16% full overlap, 12% vs 19% partially overlapping foci, 36% vs 63% foci that were touching but not overlapping (compare what now is Figure 1D and Figure S2C). Only in the 'no overlap' there is a much smaller proportion in the HA-tagged line. However, given that these are IFAs which on the one hand are more sensitive to see small protein pools but on the other hand also have pitfalls due to fixing of the cells (e.g. tiny increase in focus size due to fixing could increase the number of touching foci that in live cells might be close but did not touch), some variation can be expected to the live cells. We agree though that the partly reduced functionality of MyoF might be the reason for the consistent tendency of a lower overlap even though the difference is much less than indicated in the comment. We added "with a tendency for higher overlap with K13 which might be due to the partial inactivation of the GFP-tagged MyoF" to the sentence "IFA confirmed the focal localisation of MyoF and its spatial association with mCherry-K13 foci"

      While we expect the fact that the difference between these parasites is only small somewhat reduces the "pinch of salt" with the MyoF line, we do agree that the partial functional inactivation of the GFP-tagged MyoF line may have some impact. However, we do not think that this means the results with the MyoF-GFP line are untrustworthy. On the contrary, it provides insights into its function that in some ways is equivalent to a knock down or TGD. Overall all the MyoF lines show: few vesicles occur in the MyoF-HA-line, more in the MyoF-GFP line and even more after knock sideways of MyoF-GFP. Importantly the severity of this phenotype correlates with the growth rates in these lines. Hence, together with the bloated food vacuole assays, this provides consistent data indicating that MyoF has a role in the transport of HCC to the FV and its level of activity correlates with the number of vesicles and growth. To better highlight this, it is now summarised in Figure 1M.

      24) Line 219: the authors state here that they could not detect MyoF-GFP in rings, when in Figure 2C they show MyoF-GFP in rings, and also show that they could detect MyoF in Sup Fig. 3B with the 3xHA tagged line. Is this a labelling mistake in Figure 2C? If the authors could indeed not see MoyF-GFP in rings, this statement should have been made when Figure 2A was presented, and not so late in the manuscript, which causes confusion.

      We thank the reviewer for pointing this out. We now provide a detailed time course (see also previous points) which shows that there is no detectable MyoF-GFP signal during ring stage development until the stage where the parasites starts the transition to trophozoites (i.e. MyoF-GFP signal could only be observed in parasites already containing hemozoin). In addition to the extended time course in Figure 1C (previously 2C) we included a panel of example ring stage images below to further highlight this. We also changed the labelling of the parasite with MyoF-GFP signal the reviewer mentions in Figure 1C to “late ring stage” (it already contains hemozoin) to clarify this.

      The description of Figure 1A is now changed to: (line 153) *“The tagged MyoF was detectable as foci close to the food vacuole from the stage parasites turned from late rings to young trophozoite stage onwards, while in schizonts multiple MyoF foci were visible (Figure 1A, S2A).” *

      Please see our answer to major comment #45 where we provide an explanation for the difference between MyoF-3xHA and MyoF-GFP signal in ring stage parasites.

      [Figure MyoF]

      25) Line 237: Showing a DNA marker (DAPI, Hoecht) for Figure 2E, and subsequent figures using mislocalisation to the nucleus, would help the reader assess efficiency of the mislocalisation.

      Please see response to major comment #64 for a detailed answer on why we did not include DNA staining in the imaging used to assess mislocalization upon knock-sideways.

      26) Line 254-256: authors should show the results of the bloating assay for parental 3D7 parasites (+ and - rapalog) to see whether the MyoF line - rapalog has increased baseline bloating. This applies to all subsequent FV bloating assays.

      We did do several controls for bloated assays (including +/- rapalog of an irrelevant knock sideways line as well as using a chemical insult for which the control was 3D7 without treatment) in previous work (Birnbaum et al., 2020), which indicated that there is no effect of rapalog to reduce bloating. Although these controls are more stringent, we nevertheless did a 3D7 +/- rapalog control and added this to the manuscript (Figure S2I). As it is not possible to do this side by side with the assays that are already in the manuscript and the +/- rapalog 3D7 cells consistently showed no or very low numbers of cells without bloating (and stringent controls in the past equally did not show an effect), we believe adding this control once suffices.

      27) Line 254-257: The authors say that because fewer parasites show a bloated food vacuole upon inactivation of MyoF it means that less hemoglobin reached the food vacuole. I understand the authors statement, however, shouldn't they look at the size of the food vacuole, instead of the number of parasites with bloated FV, to make such a statement? This has been done for KIC12 so why not doing it for MyoF?

      This was now done and is provided as Figure 1J-K, S2J. The results confirm the assessment scoring bloated vs non-boated food vacuoles.

      28) Line 259-261: these results would be difficult to interpret namely because the authors have dying parasites, which is exacerbated with the protein being knocked sideways. The authors should mention the pitfalls their knock sideways and tagging design here. Line 260-261: RSA is an assay relying on measuring parasite growth 1 cycle after a challenge with ART for 6 hours.

      Fortunately, this concern is unfounded, as the survival (measured by parasitemia after one cycle) of the same sample + and - DHA is assessed, isolating the DHA effect independent of potential growth defects which are cancelled out. Hence, if there were parasites dying in the MyoF line (please note that they might not actually die, but simply grow more slowly), this factor applies for both the + and - ART condition. As we are testing for a decreased susceptibility to ART which would manifest as an increased survival in RSA surfacing above 1%, antagonistic effects of reduced MyoF function and ART treatment would not result in detectable differences as without effect, the RSA survival is always close to zero.

      The same applies for the knock sideways where we assess the survival of +rapalog between +ART and -ART. If the reduced MyoF activity of the knock sideways leads to a decreased survival, this applies to both +ART and -ART. Please also note that rapalog was lifted after the DHA pulse (see e.g. Figure S2K).

      That effects on growth are cancelled out is nicely illustrated for proteins where there is a stronger and more rapid effect on growth upon their conditional inactivation. For instance when KIC7 is knocked aside, there is a considerable increased of RSA survival, even though continued inactivation of KIC7 would have a severe growth defect (Birnbaum et al., 2020). Vice versa, a growth defect alone does not result in reduced RSA susceptibility as evident from knock sideways of an unrelated protein or using a chemical insult (Figure 4H in (Birnbaum et al., 2020) or simply slowing the ring stage by e.g. reducing EXP1 levels (Mesén-Ramírez et al., 2019). Hence, a growth reduction is not expected to alter the RSA outcome. And even if it did, it would only lead to an underestimation of the readout if growth is too severely affected (which would be obvious in the + rapalog without DHA sample, which was not the case).

      In that respect it is valuable to have the rapid kinetics of knock sideways which permit inactivation of a protein before severe growth defects occur (although the only partial responsiveness of MyoF clearly is not the most optimal). In contrast, the absolute loss of a gene (as is the case if diCre is used) prevents (or at least makes it extremely difficult as the timing would need to exactly hit sufficient protein reduction without killing the parasite until the end of the RSA) using this system in these experiments (again see (Mesén-Ramírez et al., 2021) where in a EXP1 diCre based knock out RSA was only possible because we complemented with a lowly, episomally expressed EXP1 copy to have parasites with only a partial phenotype to do this assay).

      29) Line 261-263: the authors sate that MyoF has a function in endocytosis but at a different step compared to K13 compartment proteins. I am not sure what they mean here. Can this be clarified?

      The different steps in endocytosis are explained in the introduction and we now tried to further clarify this (line 98). So far VPS45 (Jonscher et al., 2019), Rbsn5 (Sabitzki et al., 2023), Rab5b (Sabitzki et al., 2023), the phosphoinositide-binding protein PX1 (Mukherjee et al., 2022), the host enzyme peroxiredoxin 6 (Wagner et al., 2022) and K13 and some of its compartment proteins (Eps15, AP2µ, KIC7, UBP1) (Birnbaum et al., 2020) have been reported to act at different steps in the endocytic uptake pathway of hemoglobin. While inactivation of VPS45, Rbsn5, Rab5b, PX1 or actin resulted in an accumulation of hemoglobin filled vesicles (Lazarus et al., 2008; Jonscher et al., 2019; Mukherjee et al., 2022; Sabitzki et al., 2023), indicative of a block during endosomal transport (late steps in endocytosis), no such vesicles were observed upon inactivation of K13 and its compartment proteins (Birnbaum et al., 2020), suggesting a role of these proteins during initiation of endocytosis (early steps in endocytosis).

      VPS45 has not apparent spatial connection to the K13 compartment but the fact that MyoF does - and its inactivation also results in vesicle accumulation - indicates that it is downstream of vesicle initiation, providing the first connection from the initiation phase to the transport phase. More evidence for these different steps of endocytosis has been published in a recent preprint from our lab, where we simultaneously inactivated a protein of both “endocytosis steps” (Sabitzki et al., 2023).

      To clarify this in the results as requested, we changed the statement to: (line 256) Overall, our results indicate a close association of MyoF foci with the K13 compartment and a role of MyoF in endocytosis albeit not in rings and at a step in the endocytosis pathway when hemoglobin-filled vesicles had already formed and hence is subsequent to the function of the other so far known K13 compartment proteins.”

      30) Do the authors mean that it is involved in endocytosis but not in ART resistance? If so, this is a very difficult statement to make since the parasites are dying. Is there any evidence of point mutations in MyoF in the field?

      We split this point to address all issues raised here. Please see response to point 29 which clarifies that this was meant in a different way and our response to point 28 which explains why the dying parasite issue is not expected to affect the RSA (please also note that we do not have evidence of actually dying parasites in the MyoF-2xFKBP-GFP-2xFKBP line, most likely the growth is slowed).

      The mutation issue is interesting. In fact evidence exists that MyoF mutations may be associated with resistance (Cerqueira et al., 2017) (please note that there it is still called MyoC) but in a recent preprint from our lab we did not find any evidence for a significantly changed RSA survival in 12 tested mutations in the corresponding gene (Behrens et al., 2023).

      To clarify this we added the following statement to the discussion (line 709): "Of note, mutations in myoF have previously been found to be associated with reduced ART susceptibility (Cerqueira et al., 2017), but 12 mutations tested in the laboratory strain 3D7 did not result in increased RSA survival (Behrens et al., 2023)*. *

      31) Line 298: the authors state that there is no growth defect in the first cycle when rapalog is added to the KIC11 line, however based on Figure 3D, there is evidently a 25% reduction in growth compared to - rapalog at day 1 post treatment, and a 60% reduction by day 2, which is still within the 1st growth cycle. The authors should either revise their statement or provide an explanation for these findings. The authors should also explain why their Giemsa data in Fig. 3E is not in accordance with their FACS data.

      We think there is a misunderstanding here, as our figure legend was not detailed enough and we apologise if this had been misleading. The growth effect is restricted to invasion or possibly the first hours of ring stage development (see point 4&5, reviewer 2), which in asynchronous cultures more rapidly takes effect as the culture also contains schizonts that immediately generate cells that re-invade but can't due to inactivation of KIC11 (due to the rapid action of the knock sideways, KIC11 is already inactivated). In contrast, in highly synchronous cultures, this effect can only be evident once the parasites reached the schizont stage (starting with rings this takes close to 2 days). We now clarify that Figure 2E (previously Figure 3D) shows growth data obtained with an asynchronous parasite culture, while in Figure 2F the growth assay is performed with tightly synchronized (4h window) parasites as stated in the Figure legend.

      We now explicitly state in each Figure legend and for each growth experiment throughout the manuscript whether we used asynchronous or synchronized parasites for growth assays.

      Related to this, the incorrect y-axis label of what is now Figure 2E mentioned in major comment #58 is now corrected.

      32) Line 301: KIC11 could also be important very early for establishment of the ring stage for example for establishment of the PV. Also, was mislocalisation assessed in rapalog-treated parasites at 72 hours or in cycle 3?

      This is a valid point and this has now been addressed. We performed an invasion/egress assay revealing similar schizont rupture rates, but significantly reduced numbers of newly formed ring stage parasites (Figure 2H, S3G), indicating an effect of KIC11 inactivation either on invasion or possibly the first hours of ring stage development. A very similar point was raised by Reviewer 2, please see reviewer 2; major comment #4. This is now also reflected in line 302, which now reads: ”… indicating an invasion defect or an effect on parasite viability in merozoites or early rings but no effect on other parasite stages (Figure 2F-H, Figure S3F-G).”

      We further included an assessment of mislocalization 80 hours after the induction of knock-sideways by addition of rapalog in Figure S3E which showed mislocalization of KIC11 to the nucleus.

      33) Line 311: the authors should change the sentence from 'not related to endocytosis' to 'not related to endocytosis or ART resistance'.

      Done as suggested.

      34) Line 323-325: Authors say that a nuclear GFP signal can be observed in early schizonts for KIC12. According to the pictures provided in Figure 4A and Figure S5A it is not very obvious. Also faint cytoplasmic GFP signal could only be background as we can see that exposure is higher for schizont pictures

      We changed the sentence (line 339) to: “…nuclear signal and a faint uniform cytoplasmic GFP signal was detected in late trophozoites and early schizonts and these signals were absent in later schizonts and merozoites (Figure 3A, Figure S4A,B).” in order to emphasize that the nuclear signal disappears early during schizont development.

      35) Line 326-328: The authors say that kic12 transcriptional profile indicate mRNA levels peak (no s at peak) in merozoites. Should they show live cell imaging of merozoites then? Because from the Figure 4A schizont pictures where schizonts are almost fully segmented no signal can be observed.

      The observation that mRNA levels of early ring stage expressed proteins tend to increase already in mature schizonts and merozoites is well established (e.g. (Bozdech et al., 2003)). A very good example for this are exported proteins of which most show a transcription peak in schizonts but the proteins are only detected in rings see e.g. (Marti et al., 2004). Hence, our observation for KIC12 is quite typical.

      We originally did not include merozoites, as in the last row of Figure 3B fully developed merozoites within a schizont with already ruptured PVM are shown and no GFP signal can be detected in these parasites. We now provide images of free merozoites in Figure S4A-B showing again no detectable GFP signal.

      We thank the reviewer for pointing out the typo, "peak" has been corrected.

      36) Line 347: The authors state that using the Lyn mislocaliser the nuclear pool of KIC12 is inactivated by mislocalisation to the PPM. This tends to suggest that only the nuclear pool of KIC12 is mislocalised. How is it possible that only the nuclear pool is mislocalised?

      The Lyn mislocaliser is at the PPM which is continuous with the cytostomal neck where the K13 compartment likely is found. The effect of the Lyn mislocalizer on the KIC12 protein pool localizing at the K13 compartment is therefore somewhat unclear. For this reason we already had the following statement in the original submission (line 400): “Foci were still detected in the parasite periphery and it is unclear whether these remained with the K13 compartment or were also in some way affected by the Lyn-mislocaliser.” We would like to stress here that the same does not apply to the nuclear mislocaliser, which is only a trafficking signal delivering KIC12 to the nucleus and hence likely does not affect the nuclear pool of KIC12, only the K13 compartment pool (the main interest of this manuscript).

      We realised that the statement towards the end of this paragraph was unnecessarily ambiguous in regards to the K13 compartment pool of KIC12 which might have caused some confusion about the function of this pool of KIC12 and therefore modified it to (line 374): "Due to the possible influence on the K13 compartment located foci of KIC12 with the Lyn mislocaliser, a clear interpretation in regard to the functional importance of the nuclear pool of KIC12 other than that it confirms the importance of this protein for asexual blood stages is not possible. In contrast, the results with the nuclear mislocaliser indicate that the K13 located pool of KIC12 is important for efficient parasite growth.". It is also important to note that this limitation does not apply to the NLS knock sideways in regard to the K13 compartment and that the endocytosis function of this pool of KIC12 seems solid which with this statement is enforced.

      37) Line 368-369: Effect was also only partial for MyoF. Why didn't you measure the same metrics for MyoF?

      This was now done and is provided as Figure 1J-K, S2J, confirming our previous interpretation, see also point #27 which raises the same point.

      38) Line 379: you don't know if all proteins acting later in endocytosis will have an increased number of vesicles as a phenotype

      This is based on our current definition as stated in the introduction. It assumes a directional vesicular transport of hemoglobin to the food vacuole where inhibition of early stages will prevent transport before HCC-filled autonomous vesicular containers have formed and entered the cell. In contrast later inhibition stops such containers from further transport, leading to their accumulation. Such an accumulation is visible after VPS45-inactivation and other proteins (Jonscher et al., 2019; Mukherjee et al., 2022; Sabitzki et al., 2023) or treatment with cytochalasin D (Lazarus et al., 2008). While it is possible that there may be smaller intermediates formed at the K13 compartment that later on unite or fuse with the compartment evident after VPS45 inactivation and these might be missed due to small size (i.e. inhibition of a step between K13 compartment and an early endosome or equivalent), this would still be upstream of the VPS45 induced containers and hence would be earlier. We therefore believe that based on the framework given in the introduction (see also (Spielmann et al., 2020)) to assume that a phenotype manifesting as reduced food vacuole bloating without formation of detectable vesicles likely signifies inhibition of the process early whereas reduced bloating but with vesicles signifies inhibition later in the process.

      39) Line 413-414: The authors state that no growth defect was observed upon KS of 1365800. Is growth alone enough to say that there is no impact on endocytosis?

      This is an interesting point. The endocytosis proteins we studied so far indicate that efficient impairment of endocytosis manifests as a severe growth defect. Hence, lack of a growth defect can be assumed to be an indicator for absence of an important role for endocytosis (or any other growth relevant process). Clearly there is a gradual response, such as seen in the different MyoF versions resulting in proportional growth and vesicle appearance phenotypes. Hence, a protein with a minor role might have slipped our attention but then it probably is also not a very important protein in endocytosis.

      To further strengthen our assessment of PF3D7_1365800 importance for asexual blood stage development, we now also generated a cell line expressing the PPM Mislocalizer, enabling knock sideways to the PPM. This was done because this protein consistently has a focus at the nucleus that may be within the nucleus. Again this revealed no growth defect upon inactivation (Figure S7D).

      40) Line 432: in this section, the authors state that KIC4 and KIC5 seem to have domains that may suggest these proteins are involved in endocytosis, based on the alpha fold data that is publicly available. Considering the authors have TGD-SLI versions of these lines (Birnbaum et al. 2020) and have already confirmed in this previous publication that they confer resistance to ART; it would make sense to look at endocytosis for these genes. This would be a relatively simple and straightforward experiment, taking no longer than two to three weeks, and would require no additional reagents or line generation. Doing these experiments would add a lot more weight to this final section. The authors later state that KIC4 and 5 are TGD lines, so not the best for endocytosis assays. It is unclear why this would be difficult to do if an adequate control is contained in the experiment (such as parental 3D7). It explains why they did not perform the MCA2 endocytosis assays further up, but in my opinion, an attempt at doing these assays is important and would significantly increase the impact of this paper. Identical as major comment #17.

      As stated in the manuscript and above, we were originally hesitant to do these assays due to the fact that we can't induce inactivation which is less ideal than comparing the identical parasite population split into plus and minus and is further complicated by the likely smaller effect as the TGDs still permitted growth. However, we see the point of the reviewer and now performed these assays using 3D7 as controls and taking extra care to account for stage differences between the TGD lines and 3D7. However, there was no significant difference in the bloated food vacuole assays with these cell lines. Due to the reasons mentioned in major point 17, we are not sure this indeed means these proteins have no role in endocytosis. One possible reason why we were able to obtain these TGDs may have been because the effect on endocytosis is less than in the essential proteins (or is ring stage specific) and in a TGD an endocytosis defect may therefore not be detectable with our assays (see details and further possible explanations in response to point 17).

      In an attempt to address the TGD issue, we generated knock sideways cell lines for KIC4 and KIC5. Unfortunately, the mislocalization of KIC5 to the nucleus was inefficient (see figure below). As this did not result in a growth defect (in contrast to the clear KIC5-TGD growth defect (Birnbaum et al., 2020)), this line is not suitable to study a potential role of this protein in endocytosis. Therefore, we performed the bloated food vacuole assay only with KIC4-2xFKBP-GFP-2xFKBPendo+1xNLSmislocaliser parasites. However, this revealed no effect on HHC uptake, which is in line with the normal growth of KIC4-TGD parasites (Birnbaum et al., 2020) and suggests that this protein could only have a minor or redundant role in endocytosis (it is the line that shows the smallest effect in RSA). As the KIC4 and KIC5 knock sideway lines did not permit any conclusions, we did not include them into the revised manuscript but they can be found here:

      [Figure KIC4 knock sideways & KIC5 knocksideways]

      Figure legend: (A) Live-cell microscopy of knock sideways (+ rapalog) and control (without rapalog) KIC4-2xFKBP-GFP-2xFKBPendo+ 1xNLS mislocaliser parasites 4 and 20 hours after the induction of knock-sideways by addition of rapalog. Scale bar, 5 µm. Relative growth of asynchronous KIC4-2xFKBP-GFP-2xFKBPendo+1xNLSmislocaliser plus rapalog compared with control parasites over five days. Three independent experiments were performed. Growth of knock sideways (+ rapalog) compared to control (without rapalog) KIC4-2xFKBP-GFP-2xFKBPendo+1xNLSmislocaliser (blue) or KIC5-2xFKBP-GFP-2xFKBPendo+1xNLSmislocaliser (red) parasites over five days. Mean relative parasitemia ± SD is shown. (B) Live-cell microscopy of knock sideways (+ rapalog) and control (without rapalog) KIC5-2xFKBP-GFP-2xFKBPendo+1xNLSmislocaliser parasites 4 and 20 hours after the induction of knock-sideways by addition of rapalog. Scale bar, 5 µm. Growth of asynchronous KIC5-2xFKBP-GFP-2xFKBPendo+ 1xNLSmislocaliser plus rapalog compared with control parasites over five days. Four independent experiments were performed. __(C) __Bloated food vacuole assay with KIC4-2xFKBP-GFP-2xFKBPendo+1xNLSmislocaliser parasites 8 hours after inactivation of KIC4 (+rapalog). Cells were categorized as with ‘bloated FV’ or ‘non-bloated FV’ and percentage of cells with bloated FV is displayed; n = 3 independent experiments with each n=19-30 (mean 21.4) parasites analysed per condition. Representative DIC are displayed. Area of the FV, area of the parasite and area of FV divided by area of the corresponding parasites were determined. Mean of each independent experiment indicated by coloured symbols, individual datapoints by grey dots. Data presented according to SuperPlot guidelines (Lord et al., 2020); Error bars represent mean ± SD. P-value determined by paired t-test. Area of FV of individual cells plotted versus the area of the corresponding parasite. Line represents linear regression with error indicated by dashed line.

      41) Line 490-493: the authors state that the K13 compartment proteins fall in two groups, some that are involved in ART resistance AND endocytosis, and some that have different functions. However, in this manuscript the authors have demonstrated 3 flavours that K13 compartment proteins can come in: • Some that confer ART resistance and are involved in HCCU (MCA2) • Some that are involved in HCCU but not ART resistance (MyoF & KIC12) • Some that are involved in neither (KIC11) The authors should therefore revise this statement.

      We agree that this was not well phrased. To account for the fact that not all endocytosis proteins confer increased RSA survival to the parasites when inactivated we changed this statement (line 604): "This analysis suggests that proteins detected at the K13 compartment can be classified into at least two groups of which one comprises proteins involved in endocytosis or in vitro ART resistance whereas the other group might have different functions yet to be discovered.

      Generally, we believe that endocytosis is the overarching criterion and we therefore would like to keep the definitions of the main groups (endocytosis or not). As indicated by the title, the focus of the manuscript is on the K13 compartment for which so far endocytosis is the only experimentally associated function. That this group contains proteins that do not confer reduced ART susceptibility when conditionally inactivated (KIC12 and MyoF) is explained by their stage-specificity, making this a subgroup of the overarching endocytosis group.

      We realise that with the endocytosis data on the KIC4, KIC5 and MCA2 TGD there is now also a subgroup we were unable to demonstrate an endocytosis effect in trophozoites although they show changes in RSA survival. However, as indicated above, we would be hesitant to fully exclude some role of these proteins in endocytosis in rings. Particularly as a comparably small reduction in endocytosis protein activity or abundance is sufficient to increase RSA survival (Behrens et al., 2023). A principal classification of "endocytosis or ART resistance" or "neither endocytosis nor ART resistance" still accounts for this and therefore seems to us to be the most useful, particularly also in light of our domain identification that then can be linked with one or the other group.

      42) Line 508: the authors state that they expanded the repertoire of K13 compartments, when in fact they functionally analysed them - they did not do another BioID to identify more candidates.

      We respectfully disagree with the reviewer in this point, we did expand the repertoire of known K13 compartment proteins. Only independently experimentally validated proteins from proximity biotinylation experiments can be considered part of the K13 compartment (or any other cellular site or complex). Without validation of the location, the identified proteins can only be considered candidates. This is highlighted in this manuscript by the finding that several proteins of the list did not localize at the K13 compartment.

      43) Line 570-572: has anyone ever tested whether CytoD or JAS treatment in rings, is sufficient to mediate ART resistance? Something similar to what was done in PMID 21709259 with protease inhibitors. If not this would be a pretty interesting experiment for the authors to do that could shed more light on the MyoF data. It would take maybe 2 weeks to do and not require the generation of any new lines. This would clarify whether other Myosins other than MyoF are involved in endocytosis, as is suggested by previous publications (PMID: 17944961).

      We now included this experiment. In agreement with a lacking need of MyoF in rings and no effect on RSA survival, there was no increased survival of the parasites in RSA (neither on 3D7 nor on K13 C580Y parasites) after cytD treatment (new part in Figure 1M). We thank the reviewer for pointing out that this experiment might also inform on whether other myosins influence endocytosis in ring stages. We added (line 250): Similarly, also incubation with the actin destabilising agent Cytochalasin D (Casella et al., 1981), had no effect on RSA survival in 3D7 or K13C580Y (Birnbaum et al., 2020) parasites, indicating an actin/myosin independent endocytosis pathway in ring stage parasites (Figure 1M) and speaking against other myosins taking over the MyoF endocytosis function in rings.”

      44) Line 608: inhibitors targeting the metacaspase domain of MCA2 may inadvertently inactivate other essential parts of the protein. They authors should acknowledge this possibility in the text.

      The inhibitors used in the cited studies (Kumari et al., 2018) are validated metacaspase inhibitors, such as Z-FA-FMK (Lopez-Hernandez et al., 2003). Activity against the other parts of PfMCA2 - which apart from the MCA domain shows no homology to other proteins - is therefore unlikely.

      45) Line 624-625: the authors state that MyoF is 'lowly expressed in rings' - indeed this is the case in their MyoF-2xFKBP-GFP-2xFKBP line which the authors established has defects due to the tag, but it appears from their MyoF-3xHA tagged line that it is expressed in rings. The authors should therefore revise their statement, and be careful of making claims based on their defective line and using fluorescence imaging as their only metric. If they do want to make the statement that it is not there in rings, they should also do a western blot, which is much more sensitive since it amplifies the signal compared to an image of one parasite.

      This comment is related to major point #24. We also would like to stress that while the MyoF-GFP line already shows a phenotype, the impression of defectiveness based on its location is due to a mix up (see major point #23).

      We now provide a comprehensive time course of the MyoF-GFP signal (Figure 1C, S2A) showing that there is no detectable MyoF-GFP signal until the transition from ring to trophozoite stage. As this is all under the endogenous promoter, we do not think the partial functional inactivation of the tagging is the reason for the absence of the signal. If anything, we would have expected adding a stably folded structure such as GFP to increase the stability of the protein. The main reason for the discrepancy of MyoF signal in rings between the GFP-tagged line (of note there is also no detectable MyoF-GFP signal in MyoF-2xFKBP-GFP ring stage parasites (Figure S2B)) and the HA-tagged line likely is that IFA is much more sensitive than live GFP detection (similar to the high sensitivity the reviewer mentions in regards to WB). This discrepancy therefore is likely due to the fact that the lowly expressed MyoF only become apparent with the HA-tagged line due to the IFA. We therefore believe that MyoF is 'lowly expressed in rings' is an appropriate description of our results obtained with three different cell lines (MyoF-2xFKBP-GFP-2xFKBP, MyoF-2xFKBP-GFP and MyoF-3xHA). We hope this is sufficiently well reflected in the manuscript where we write ‘a low level of expression of MyoF in ring stage parasites.’ not that it is ‘not there in rings’ (line 174).

      46) Line 635: arguably this is the 3rd variety and not the 2nd (the authors already mentioned 2 types - ones that are involved in HCCU AND ART and those involved in HCCU only). See comment for line 490-493 above.

      See response for major comment #41, we now consistently used "or" instead of "and". See line 490-493 how this was resolved for what previously was line 635.

      47) Line 785: Bloated food vacuole assay/E64 hemoglobin uptake assay method specify that a concentration of 33mM E64protease inhibitor was used. However, in reference 44, cited in the manuscript, a concentration of 33µM E64 was used. Please confirmed if this is just a typo or if 1000x E64 concentration was used which renders the experiment invalid.

      We thank the reviewer for pointing this out, we corrected this typo and will look out for symbol font conversion errors for the resubmission.

      48) Line 788: it is unclear from this section what is considered a bloated food vacuole - is there an area above which the FV is considered bloated? Do the authors do these measurements manually or use an addon in FIJI/ImageJ? What is the cutoff for if a FV is bloated? Please clarify. Additionally, for the representative images + rapalog for Figures 2H and 4H, it would be useful to see where the authors delineate the FV (add a white circle showing what is actually measured).

      The bloated FV assay is well established (Jonscher et al., 2019; Birnbaum et al., 2020; Sabitzki et al., 2023). Although the bloating of the FV is a human judgment call, it is actually quite obvious: bloating appears as an easily spotted bulging of the FV in DIC. As also minor bloating is scored as 'bloated', it is a very conservative assay. Using an-add on to measure this is not straight forward. It is unclear how this bulging effect of the FV in DIC could be spotted by a software and due to the obviousness to human operators, potentially lengthy and complicated efforts to design appropriate machine learning options were not undertaken. The situation faced by the scorer of the assay is evident from Figure S4F-G which contains close to 50 "on rapalog" cells and close to 50 control cells, giving representative cells from all replicas of bloated FV assays with KIC12. Please note that these images shows the most complicated situation as far as bloated assays go, because the phenotype is not 100% (see Figure 3F) compared to e.g. KIC7 inactivation which leads to lack of bloating in almost all cells (see (Birnbaum et al., 2020) Figure 3E) but nevertheless the difference is still obvious. We are aware that in such situations (less than absolute inhibition) this assay scoring of "yes" or "no" is a surrogate for the actual level of inhibition and may be more subjective. This is why in this case we also did the FV size measurements (which are less dependent on human judgment) to further support this and give a better quantifiable measure. Of note, the bloated food vacuole judgments are done "blinded", i.e. the examiner does not know which sample they are looking at.

      In response to this reviewer's point we now also added the FV size refinement of the assay for MyoF inactivation which is one of the cases where inhibition of bloating is not in 100% of the cells (see major comment #27). Please also note here the advantage of the rapidly acting knock sideways technique for these assays which shows the sum of effect 8 h after initiating inactivation and for which we carefully control size of the cells which shows that there is no significant growth reduction over the assay time, excluding secondary effects due to a generally reduced viability. Compared to slower acting systems suggested to have been used instead (see introductory part and significance of this review), the rapid speed of knock sideways reduces the risk of potential pleiotropic or compensatory effects due to the time needed for proteins to be depleted if the gene or mRNA is targeted instead.

      The suggestion to include a ‘white circle’ (raised also as minor comment#27) is useful as an aid to see the food vacuole. However, in contrast to the Figures in (Birnbaum et al., 2020) (where we did add such a circle), we here included the DHE staining images in the figure, labelling the parasite cytosol which readily shows the FV (the FV corresponds to the region where there is no DHE staining). As this shows the position of the FV we would prefer to not obscure the DIC images with additional features to permit the reader to see the difference between bloated or non-bloated food vacuoles and keeping the image as natural as possible.

      49) Line 863-864: this sentence seems to be out of place.

      We thank the reviewer for pointing this out, the details of nucleus staining were moved to the correct part.

      50) Line 875: the authors state that there is a light blue wedge, when the circle consists of grey and black wedges. Please revise this.

      This has been corrected.

      51) Line 1059-1061: it is unclear whether the individual growth curves are different clones or whether they are just the same experiment repeated? If it is the latter, then why are they not combined, as is traditionally done?

      These are the individual replicates of the growth curves shown in Figure 1G of the same cell lines done on a different occasion. We always try to show as much of the primary data as possible and believe that showing individual data points from the different experiments is better than only the combined values which obscure the actual course of each experiment.

      52) Line 919-924: the authors mention a blue and red line, but there is only a black line in figure 3D. Moreover, the experiment of using the LYN mislocaliser was only done for KIC12 according to the manuscript. Additionally, the y axis of the figure states relative growth day 4[%] compared to rapalog, but then on the x axis there are several days. In the text it says there is no growth defect until the second cycle, but from this graph it appears the growth defect is evident as early as 1 day post rapalog treatment. Can the authors please clarify and correct the issues pointed out.

      We thank the reviewer for pointing this out, this was due to a copy & paste error in the figure legend that was now amended. We also fixed the incorrect axis label. For the last part (growth defect) please see detailed answer to Major comment#31 raising the same concern for KIC11 (in synchronous parasites the defect only takes effect once the cells reached the relevant stage whereas in asynchronous cultures there are always cells in the relevant stage that due to the rapid effect of the knock sideways already have a growth phenotype).

      53) Figure 1 panel B & C: the label of the figure where the signal from MCA2Y1344STOP-GFP is shown with the DAPI signal overlayed is deceptive since it suggests that this is the signal of full length MCA2. Please change the label of this panel from MAC2/DAPI to MCA2Y1344STOP/DAPI. The same is true for Panel C for the image labeled MCA2/K13 - please change this to MCA2Y1344STOP/K13.

      Done as requested.

      54) Figure 2B: what stages are these parasites? Please state this in the figure. Based on the MyoF pattern, it looks like rings in the upper panel and trophs in the bottom pannel. Why were schizonts not shown?

      Both are trophozoites (early trophozoite in top panel and late trophozoite in bottom panel). This is now labelled in what now is figure 1B. As stated above, schizont stages are less relevant for the topic of this manuscript and in order to prevent the manuscript from getting more disjointed and keeping it more focussed on the main topic, we decided to not include a schizont in the manuscript. Nevertheless, we included an example image below.

      [Figure MyoF_p40px schizont]

      55) Figure 2D&F: it is not very meaningful when growth assays are shown as a final bar after 4 days of growth. It is much more useful and informative to see a growth curve instead (as is shown in the supplementary), since it shows if the defect is apparent in the first growth cycle or later. With the way the data is currently shown, this is not apparent. I would advise the authors to switch the graph in 2F out of a combined graph of all the biological replicates growth curves for S3D - showing error bars.

      While we in principle fully agree with the reviewer in showing the course of the full experiment (which is available in Figure S2E), the key here is to show the overall difference. Hence, we would like to keep this comparison of the overall effect on growth in what now is Figure 1E and G. It is part of the argument to the doubts this reviewer raises to the function of MyoF (mainly in the overall assessment and the significance statement) to show that the phenotype is actually very consistent (partial inactivation through tagging or further inactivation using knock sideways increases endocytosis phenotypes, correlating with parasite viability).

      Please also note, that the growth curves upon knock sideways shown in Figure 1G, S2E are performed with asynchronous parasite cultures, which doesn’t allow us to draw direct conclusions about growth cycle effects.

      Nevertheless, we now also included the suggested combined data representation in Figure S2E.

      56) Figure 3: why were the calculation of FV area, parasite area and FV/parasite area only done for KIC12 and not done for MyoF? It would be interesting to see if any of these values are different for MyoF - whether the parasites are smaller in area and therefore FV smaller. Please present them Figure 2. Images should be already available and would not require further experiments to be done, only the analysis.

      This now has been done (confirming our results) and is included as Figure 1J-K, S2J. This point was also raised as major comment #37, please also see detailed answer there.

      57) Figure 3B: why is there no spatial association assessment for KIC11 and K13 as was done for the MCA2 and MyoF? The authors should show a pie chart showing the degree of association here as was done for the other proteins.

      This is now included in Figure 2C.

      58) Figure 3D: The y axis of the figure states relative growth day 4[%] compared to rapalog, but then on the x axis the experiment takes place over several days. Is this a typo in the y axis? Additionally, the authors state in line 287-290 that the growth defect upon addition of rapalog is only seen in the second cycle, but from this graph it appears the growth defect is already evident 1 day post rapalog addition. The figure legend also does not make sense for this figure since it mentions a blue and a red line, when there is only a black line present. The legend also mentions the LYN mislocaliser which was used for KIC12 not KIC 11 (see above).

      We apologise for the inadequate legend and colour issues, this was amended. This point was also raised in major comment #31 and #52, please find detailed answer there.

      59) Figure 3E: the colour for Control and Rapalog 4 hpi are very similar and very hard to discern. Please choose an alternative colour or add a pattern to one of the samples. The y axis is also missing a label. Is this supposed to be parasitemia (%)?

      We thank the reviewer for pointing this out, the missing label is now included and the colour has been adapted to make them better distinguishable.

      60) Figure 4A: the ring shown in this figure does not appear to be a ring (it is far too large and appears to have multiple nuclei?). Do the authors have any other representative images to show instead?

      This is in fact a ring, but we realize that we accidentally included an incorrect size bar in the ring image of Figure 4A (now Figure 3A) (size bar for 63x objective instead of the correct one for the 100x objective), we apologise for this oversight. We don’t think this parasite has multiple nuclei, instead the Hoechst signal shows the often elongated nucleus seen in rings that can appear as two foci in Giemsa stained smears which leads to the typical diagnostic feature of P. falciparum rings in diagnostics. In order to exclude any doubts about the nuclear localization of KIC12 in rings, we here attached a panel with more examples of KIC12-2xFKBP-GFP-2xFKBP ring stage parasites.

      [Figure KIC12]

      61) Figure 4B: why is there no spatial association assessment for KIC12 and K13 as was done for the MCA2 and MyoF? The authors should show a pie chart showing the degree of association here as was done for the other proteins. This should be done for the different life cycle stages considering the changing localisation of KIC12.

      This is now provided in Figure S4A. As suggested by the reviewer, we independently quantified the association for ring stage, early trophozoite and late trophozoites stage. As there is no KI12 signal in schizonts, we did not include a quantification for this stage.

      62) Figures 4C&E: it is extremely important to show the DNA stain in both these samples considering that a portion of KIC12 is in the nucleus! Please add the DAPI signal for these figures (as for all other figures!).

      Please see major comment #64 for a detailed answer why we did not include DNA staining in the imaging used to assess mislocalization upon knock-sideways.

      63) Figure 4E: this figure should be presented before 4D (considering the line being presented in 4E is used in an experiment in 4D). The authors should switch the order of these two.

      We see the point the reviewer is raising here, Figure 4D (now Figure 3D) also contains the data with the Lyn mislocaliser while we first talk about the NLS mislocaliser. This permits a better comparison between the two mislocaliser lines. However, first explaining the Lyn-mislocaliser and then going back to the NLS would make it rather complicated for the reader to follow the storyline and therefore we would like to keep the order as it is. We realise that this means the reader has to go back one figure part for seeing the Lyn growth data, but believe this is worth the benefit that the data is there compared to the NLS result.

      64) It is unclear why in many of the fluorescence images the authors do not show the DAPI signal - particularly when colocalising with K13 and when doing the knock sideways experiments. Please add these images to the figures - I would assume they have already been taken, so would simply involved adding the images to the panel.

      We did not include DNA staining (DAPI or Hoechst) for any of the images used to assess the efficacy of mislocalization, as we would prefer to keep the parasites as representative of a viable parasites in culture as possible. Hence they were imaged without DNA stain (these stains are toxic). We would like to point out that a DNA stain is not necessary, as the mislocaliser already marks the nucleus (in the case of the NLS mislocaliser), actually even somewhat more accurately, as it fills the entire nuclear space rather than only the DNA which is marked by DAPI or Hoechst.

      For LYN this admittedly is not the case, there the mislocaliser marks the plasma membrane. However, we think the proper control for efficient mislocalisation is the comparison between the GFP-tagged protein of interest and the mCherry mislocaliser to show mislocalisation, as previously done in our lab (e.g. (Birnbaum et al., 2017; Jonscher et al., 2019; Birnbaum et al., 2020)).

      Due to their toxicity, we also avoided nuclear staining in some other parts of the manuscript when we were of the opinion that a nucleus signal was not necessary.

      65) Throughout the manuscript, there is no western blot confirming the correct size of their modified proteins. This should be provided.

      We did perform Western blot analysis for both MCA2 cell lines. MCA2 is the only gene-product for which we generated a disruption for this work, and together with the severe truncation from previous work, we provided a Western blot-based confirmation of the correct size.

      The MCA2 disruptions are at least partially dispensable for in vitro parasite growth, hence if degradation occurred, this might not have been noticed. In that case we considered it relevant to show that the truncations were of the expected size. The other proteins in the main figures are essential for growth. Hence, if the tagging approach would lead to unexpected changes in protein integrity (which we assume is what was intended by this concern to be assessed with a Western blot), the parasites expressing the tagged MyoF, KIC11 and KIC12 would - due to their importance for asexual blood stage development - not have been obtained. Hence, we can assume the integrity of the tagged protein is very unlikely to have been affected in a functionally relevant way.

      66) None of the figures are appropriate for individuals with colour blindness, limiting their accessibility to the paper. Please change the colour schemes for all fluorescent images using magenta/green or an alternative colour combination appropriate for colourblind individuals.

      We thank the reviewer for this comment. This has now been amended, individual channels of fluorescence microscopy images are now shown in greyscale, while the overlay was changed to green/magenta.

      Minor Comments

      1) line 29: remove 'are'.

      Done.

      2) Line 29: the text says "HCCU is critical for parasite survival but is poorly understood, with the K13 compartment proteins are among the few proteins so far functionally linked to this process." The sentence should be: 'HCCU is critical for parasite survival but is poorly understood, with the K13 compartment proteins among the few proteins so far functionally linked to this process."

      Done.

      3) line 44: remove 'the'

      Done.

      4) Line 48: consider mentioning here that malaria is caused by the parasite Plasmodium - otherwise the first mention of parasite in line 52 is confusing for the non-specialist reader.

      Done.

      5) Line 49: estimated malaria-related death and case numbers are from the 2021 WHO World malaria report. You cite the 2020 WHO World malaria report.

      We now cite the newest WHO report.

      6) Line 53: please insert the word 'have' between now and also.

      Done.

      7) Line 54: please change 'was linked' to is linked

      Done

      8) Line 72: I would specify that free heme is toxic to the parasite. Especially as you mention that hemozoin is nontoxic.

      Sentence would be "where digestion results in the generation of free heme, toxic to the parasite, which is further converted into nontoxic hemozoin"

      Done.

      9) Line 90: authors should either say "in previous works" or "in a previous work"

      The text has been altered to say: “ in a previous work”.

      10) Line 91: "We designated these proteins as K13 interaction candidates (KICs)"

      Done.

      11) Line 95: please change 'rate' to number

      Done.

      12) Line 109: Please include a coma before (ii).

      Done.

      13) Line 112: as shown by Rudlaff et al in the paper you are citing, PPP8 is actually associated with the basal complex. You can say that "(ii) were either linked or had been shown to localise to the inner membrane complex (IMC) or the basal complex (PF3D7...).

      Done.

      14) Line 114: Protein PF3D7_1141300 is called APR1 in the manuscript but ARP1 in Supplementary Table 1. Please correct.

      Done.

      15) Line 131: please define SNP - this is the first use of the acronym.

      Done.

      16) Line 133-134: South-East Asia instead of "South Asia"

      Done.

      17) Line 135: please explain what TGD is - it is referred to over and over again in the manuscript without ever being explained.

      We apologise for this oversight. We now explain what is meant with TGD at the suggested point of the manuscript.

      18) Line 145: change 'Western blot' to western blot - only Southern blot is capitalised since it is named after an individual, while the other techniques are not.

      To the best of our knowledge this issue has not been resolved, some Journals capitalize the “W” (e.g. Science), while others don’t (e.g. Nature). We would prefer to continue to capitalize the “W”, as this is consistent with the original publication from (Burnette, 1981), but if there are strong objections, we would be happy to change this____.

      19) Line 152: add "the" between 'and spatial'

      Done.

      20) Line 158: please define SLI as selected linked integration, since it is the first use of the acronym.

      Done.

      21) Line 178: introduce a coma after protein. Sentence should be "Proliferation assays with the MCAY1344STOP-GFPendo parasites which express a larger portion of this protein, yet still lacking the MCA domain (Figure 1), indicated no growth ...

      Done.

      22) Line 195: the authors could mention that MyoF was previously called MyoC in the Birnbaum 2020 paper. I wanted to check back in the Birnbaum 2020 paper and could not find MyoF

      Good point, this was done.

      23) Line 200: "Expression and localisation of the fusion protein was analysed by fluorescent microscopy". Why expression was not analysed also by western Blot same as for MCA2?

      Please see major comment #64 for a detailed answer.

      24) Line 204: I could not find any mention of MyoF (Pf3D7_1329100) in reference 65. Please remove reference 65 if not correct. Also reference 66 looks at Plasmodium chabaudii transcriptomes so I would specify that "This expression pattern is in agreement with the transcriptional profile of its Plasmodium chabaudii orthologue"

      Reference 65 (Wichers et al., 2019) provides an RNAseq transcriptome dataset for asexual blood stage development of 3D7 (originating from the same source as the 3D7 used in this study). While Ref 66 (Subudhi et al., 2020) indeed contain transcriptomic data from P. chabaudi, the authors also provide a nice 2h window RNAseq transcriptome dataset for asexual blood stage development of Plasmodium falciparum. Both datasets are therefore suitable as reference for the statement about myoF transcription pattern. Both datasets are also easily accessible and show the pattern in a graph in PlasmoDB.

      25) Line 208: Please indicate a reference for P40 being a marker of the food vacuole

      Done.

      26) Line 220-224: The authors should consider changing to " Taken together these results show that MyoF is in foci that are mainly close to K13 and, at times, overlapping, indicating that MyoF is found in a regular close spatial association with the K13 compartment."

      The suggested wording introduces "mainly" for "frequently" and likely was in part motivated by the discrepancy in location between cell lines that we hope we now could clarify to be only minor (see major point #23). We therefore think the original wording appropriately summarises the findings (line 178): “*Taken together these results show that MyoF is in foci that are frequently close or overlapping with K13, indicating that MyoF is found in a regular close spatial association with the K13 compartment and at times overlaps with that compartment.” *

      27) Line 255: In Figure 2H, and subsequent figures showing bloated FV assay, I would delineate the food vacuole with dashed line as in Birnbaum et al. 2020 to help the reader understanding where the food vacuole is.

      In contrast to the Figures in Birnbaum et al. 2020, we here included the DHE staining (parasite cytosol) in images of bloated FV assays which visualizes the FV. We therefore decided to avoid any further marking, to keep the image as unprocessed as possible (see also major point 48).

      28) Line 265-266: Here the title says that KIC11 is a K13 compartment associated protein, but the title of Figure 3 says KIC11 is a K13 compartment protein. I noticed that you make the difference between K13 compartment protein et K13 compartment associated protein for MyoF for example which is not clearly associated with the K13 compartment. Which one is it for KIC11?

      The interpretation of the reviewer is correct, we indeed graded this subconsciously based on level of overlap. Based on the newly added quantification shown in Figure 2C, we describe KIC11 now as K13 compartment protein.

      29) Line 309-310: indicate a reference for your statement "which is in contrast to previously characterised essential K13 compartment proteins".

      Done, we now included Birnbaum et al. 2020 as reference for this.

      30) Line 377: Figure 4I, please correct 1st panel Y axis legend

      Done.

      31) Line 404: replace "dispensability" with dispensable

      Done.

      32) Line 416: can the authors provide any speculation as to why they observed these proteins as hits in the BioID experiments?

      As some of these proteins were less well or less consistently enriched, they could be background of the experiment. Alternatively, some could be proteins that only transiently interact with the K13 compartment.

      33) Line 451: Where the "97% of proteins containing these domains also contain an Adaptin_N domain and function in vesicle adaptor complexes as subunit a" come from. Do you have a reference?

      The statement now includes references and reads (with small changes to original submission): "More than 97% of proteins containing these domains also contain an Adaptin_N (IPR002553) domain (Blum et al., 2021) and in this combination typically function in vesicle adaptor complexes as subunit α (Hirst and Robinson, 1998; Traub et al., 1999) (Figure 5D) but no such domain was detectable in KIC5."

      34) Line 465-467: the same could be said for KIC4 as it also has a VHS domain.

      The critical issue is the combination of domains and their position within the protein. While KIC4 also contains a VHS domain, the VHS domain in KIC4 is N-terminal, not in a central position and it is also not the first structural domain to be identified in KIC4. The similarity to adaptin domains was already described ((Birnbaum et al., 2020) and annotated in PlasmoDB) and these domains are also involved in vesicle formation and trafficking. These aspects of the statement can therefore not be extended to KIC4. With regards to VHS domains being involved in vesicle trafficking, this is already stated in line 538: «KIC4 contained an N-terminal VHS domain (IPR002014), followed by a GAT domain (IPR004152) and an Ig-like clathrin adaptor α/β/γ adaptin appendage domain (IPR008152) (Figure 5A-C, Figure S8). This is an arrangement typical for GGAs (Golgi-localised gamma ear-containing Arf-binding proteins) which are vesicle adaptors first found to function at the trans-Golgi (Dell’Angelica et al., 2000; Hirst et al., 2000)

      35) Line 477-479: Can be rephrased to "However, we found this protein as being likely dispensable for intra-erythrocytic parasite development and no colocalisation with K13 could be demonstrated, suggesting a limited role for PF3D7_1365800 in endocytosis. Or something like that. Makes it clearer.

      We rephrased this sentence and it now reads (line 592): However, we found this protein as being likely dispensable for intra-erythrocytic parasite development and no colocalisation with K13 was observed, suggesting PF3D7_1365800 is not needed for endocytosis“.

      36) Line 535: Have AP-2a or AP-2b been shown to be at the K13 compartment?

      AP2m is at the K13 compartment (Birnbaum et al., 2020). Adaptor complexes are heterotetramers and their subunits do not typically function on their own and this is conserved across evolutionarily distant organisms. In agreement that this is also the case in P. falciparum, Henrici et al. (Henrici et al., 2020a) showed that both, AP-2a and AP-2b, were present in an AP2µ Co-IP, indicating that the AP2 complex consist of the ‘classical’ subunits in P. falciparum. Therefore, the presence of all subunits at the K13 compartment is very likely, although this has only been experimentally confirmed for AP2µ. Of note, for Toxoplasma gondii the presence of AP-2a and AP-2b at the micropore has been experimentally confirmed (Wan et al., 2023; Koreny et al., 2023) and interaction suggested by presence in the same IP as DRPC (Heredero-Bermejo et al., 2019).

      37) Line 569: reference 43 is wrong

      We thanks the reviewer for pointing this out – we removed Ref 43.

      38) Line 746: typo "ot" instead of or.

      Changed.

      39) Line 801: method for Domain Identification using AlphaFold specify that RMSDs of under 5Å over more than 60 amino acids are listed in the results. However, there is a typo in Figure 5B for KIC5 where it says "RMSD 4.0 Å over 8 aa". Please correct.

      Done. In addition, we have now applied a more stringent cut-off of 4Å over more than 60 amino acids to ensure a higher reliability of our hits. This decision was based on results from our preprint (Behrens and Spielmann, 2023). Because of this the phosphatase domain in KIC12 is no longer included in this manuscript and accordingly the following sentence has been deleted. In KIC12 we identified a potential purple acid phosphatase (PAP) domain. However, with the high RMSD of 4.9 Å, the domain might also be a divergent similar fold, such as a C2 domain, which targets proteins to membranes.”

      40) Line 856: In Figure 1E, please use the same Y axis legend as in Figure 2D "relative growth at day 4 [%] compared with 3D7"

      Done.

      41) Figure S1: Some PCR gels check for integration are presented as 5', 3' and ori whereas other gels are presented as ori, 5' and 3'. This is confusing.

      We agree that ideally the order of sample loading should be consistent and we apologise for this. The explanation for this is that these gels were run by different people at different times before we were able to better standardize the loading scheme. However, in the interest of not unnecessarily using resources for something that has a similar meaning, we would prefer not to repeat these PCRs and re-run them only for consistency reasons (as the conclusion is not affected by the different loading schemes).

      42) Figure S1: Why was the expression of only MCA2 was verified by Western blot? What about the other proteins?

      See response to major comment 56.

      43) Line 493: Considering KIC11 was not involved in HCCU or ART resistance it might be worth mentioning in this section that it is of note that there are no domains detected that would be involved in endocytosis.

      We agree that this is the case, however it is also the case for all other proteins that either are not involved in endocytosis and/or lowered susceptibility to ART. We therefore now added a summary statement addressing this in line 602: In contrast, the K13 compartment proteins where no role in ART resistance (based on RSA) or endocytosis was detected, KIC1, KIC2, KIC6, KIC8, KIC9 and KIC11, do not contain such domains (Figure 5E).” We did not add this at the suggested part of the manuscript as at that point the domain search results are not yet introduced and doing this each time for all the individual proteins would disconnect the flow of the manuscript.

      44) Line 503-506: is it wise to generate more drugs that target a pathway that is already highly susceptible to mutations? The authors should add a statement explaining how this might be avoided.

      The only protein for which mutations do not have a large fitness cost is K13 (see also our preprint on fitness cost of ubp1 mutation (Behrens et al., 2023) and even with K13 the level of resistance seems to be limited by amino acid deprivation when endocytosis is reduced (Mesén-Ramírez et al., 2021). We therefore do not think that this pathway is particularly prone for mutations. Further, the number of commercial drugs targeting the "endproduct" of endocytosis (hemoglobin digestion and detoxification of heme) highlight it as the most prominent vulnerability for drug-based intervention if we go by number of commercially available drugs acting on things associated with a single process.

      45) Throughout, scale bars are stated in the figure legends at the end of the legend. This is a slightly confusing format. The authors should consider stating the scale bar for each sub-legend where a fluorescence image is taken.

      Done.

      ** Referees cross-commenting**

      After reading reviewer 2 and 3's comments, I think there are significant overlaps in the key points raised in terms of questions about fusion proteins and their potential partial mis-localisation, better descripton of results and target selection. Overall I think we agree that the work has potential, but in its current form does not represent a major advance. It would be immensely helpful if the manuscript would be carefully edited for a better flow and linear description of results.

      We now rearranged the manuscript for better flow but would like to highlight that the many requests for smaller experimental issues (and "better description of results") worked somewhat in the opposite way of a more linear description. We hope the rearranged version acceptably balances these two issues. The issues raised in regards to target selection and potential partial mis-localisation are addressed in our responses mainly to this reviewer. Please also see comments on systems used at the end of the rebuttal.

      Reviewer #1 (Significance (Required)):

      The authors set out to test whether other proteins that are in the vicinity of K13 are involved in mediating ART resistance and endocytosis. This is an interesting question. However, other than MCA2 which was already known to be involved in mediating ART resistance (and was not tested for its involvement in endocytosis), none of their candidate proteins seem to be involved in mediating both these functions. The authors show that the other proteins tested appear important for parasite growth, with KIC12 and MyoF involved in mediating endocytosis. While these findings are novel, the KS approach used by the authors casts some doubt over the findings, and would mean that these findings would have to be re-tested with a more reliable approach, such as the GlmS system or generating a conditional knockout using the DiCre system. Despite not advancing our understanding of ART resistance, or identifying further players involved in this process, this manuscripts provides two candidates that are involved in mediating endocytosis and a further candidate that appears to be important for parasite growth. Further work on these proteins will be required to understand their exact roles. As stated above, there is currently limited interest for these results (limited to researchers working on endocytosis in apicomplexan parasites and possibly the wider endocytosis field from an evolutionary perspective), however with further work, this could increase the impact and interest of this work substantially.

      The authors do not describe any novel methods/approaches within this work.

      In the significance statement the reviewer indicates that other systems would have been more reliable for the work here. This is addressed in our response above and in a detailed considerations on the properties of conditional inactivation systems at the end of the rebuttal. The systems used in this work were not only chosen because they permit rapid targeting of many different proteins, but because they have merits that are beneficial for our assays. In fact many of the functional assays in this manuscript are difficult or impossible to carry with the suggested conditional inactivation systems (please note that we have extensive experience with the systems considered preferable:

      • DiCre (Birnbaum et al., 2017; Mesén-Ramírez et al., 2019; Mesén-Ramírez et al., 2021; Wichers et al., 2022; Kimmel et al., 2023)

      • glmS (Wichers et al., 2021c; Wichers et al., 2021a; Wichers et al., 2022; Wichers-Misterek et al., 2023)).

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

      In a previous publication the Spielmann lab identified the molecular mechanism of ART resistance in P. falciparum by connecting reduced levels of the protein K13 to decreased endocytosis (uptake of hemoglobin from the RBC cytosol), which results in reduced ART susceptibility. Using quantitative BioID the authors further identified proteins belonging to a K13 compartment, highlighting an unusual endocytosis mechanism.

      In the present manuscript the authors follow up on this work and closely examine ten more proteins of the K13/Eps15-related "proxiome". They successfully link MCA2 to ART resistance in vitro, while the proteins MyoF and KIC12 are involved in endocytosis but do not confer in vitro ART resistance when impaired. They further characterize one candidate (KIC11) that partially colocalizes with K13 in trophozoites but to a lesser degree in schizonts. Growth assays suggest an important function for KIC11 in late stages of the intraerythrocytic developmental cycle. Five analyzed proteins however do not colocalize with the K13 compartment, while a sixth was refractory to endogenous tagging.

      Using AlphaFold predictions of the KIC protein structures the author identify domains in most constituents of the K13 compartment, highlighting vesicle trafficking-related features that were not identified on primary sequence level before.

      The combination of functional data together with structure predictions leads them to propose a refinement of the K13 compartment as being divided into proteins participating in endocytosis and proteins that have an unknown function.

      We thank the reviewer for the assessment of the manuscript and the constructive comments.

      Major comments:

      1) -Table 1 is missing

      We apologise for this mistake; Table 1 is now included.

      2) -Lines 117-123: Given the total list of uncharacterized candidates encompasses 13 proteins, can the author gives the reason why only the top 10 and not all 13 were characterized in this study?

      A similar point has been raised by Reviewer 1 in major comment #12, please see our response there for an explanation why we chose which targets.

      3) -Line 174: 20% of observed MCA2 foci show no overlap with K13 and 21% only partially overlap, can the author confirm that the observed MCA2 foci in schizonts are the ones that co-localize with K13. (Addition of a schizont stage image in Fig 1C would be sufficient).

      We now extended Figure 4C with images of MCA2-Y1344STOP-GFP+mCherryK13 parasites covering the schizont and merozoite stage, showing that the majority of the MCA2 foci in schizonts are also mCherry-K13 positive.

      4) -The localization and observed phenotype of KIC11 is interesting but unfortunately the authors do not explore it further. Does KIC11 localize with markers of e.g. the secretory organelles (micronemes or rhoptries) in schizonts and could therefore be involved in RBC invasion?

      While we intended to focus mainly on the endocytosis aspect of these proteins, we see the reviewer's point and now generated new cell lines enabling assessment of spatial association of KIC11 with markers for rhoptry (ARO), micronemes (AMA1), and inner membrane complex (IMC1c). This revealed that the KIC11-GFP signal in schizonts does not overlap with apical organelle markers and the signal does not resemble a typical apical localization. In addition, we assessed all three organelle markers after inactivating KIC11 by knock sideways which showed that KIC11 inactivation has no apparent effect on the appearance of these markers, suggesting no major alterations in schizont morphology in respect to apical markers. These results are now presented as Figure S3A and in line 304 of the results.

      5) Can the author distinguish if KIC11 is involved in RBC invasion or in establishment of the ring-stage parasite?

      In order to look into this, we performed egress/invasion assays, quantifying schizont and ring stage parasites in tightly synchronized parasites at two different time points (pre-egress: 38-42 hpi & post-egress: 46-50 hpi). This revealed a significant decrease in newly formed ring stage parasite per ruptured schizont in parasites with inactivated KIC11, while the egress efficacy remained unaffected. This indicated an invasion or very early ring stage development defect (new Figure 2H, Figure S3G). To further determine at which point exactly the phenotype occurs (ie during invasion or early after invasion) would require extensive experimentation that goes beyond the scope of this study (e.g. invasion assays using video microscopy with a representative number of parasites or sophisticated flow based quantification assays). We hope by excluding egress and gross changes of apical organelles as well as no indication for similar number of early rings (indicating it is invasion or a very early ring-establishment phenotype) will sufficiently narrow down the phenotype for labs interested in invasion to more definitely answer this question.

      Minor comments:

      1) Table S1: Please add the criterion for the order of proteins (abundance in "proxiome"?) in the table as a separate column. I would also suggest adding a new column that highlights the 10 proteins investigated in this study as I found the color-coding slightly confusing.

      Done as suggested: we now include the “average log2 Ratio normalized Kelch13” values from the four DiQ-BioID experiments performed with K13 in (Birnbaum et al., 2020), as well as the suggested column to highlight the investigated proteins. Please also see reviewer 1 major point # 12 for additional information on the selection criteria and how this was added to the manuscript.

      2) -154-155: There is a discrepancy between the text and Fig1C regarding the % of partial overlapping and non-overlapping foci.

      We thank the reviewer for pointing this out, this was corrected.

      3) -The y-axis label is missing in Fig 3E

      Done.

      4) -Fig 4I left graph, the superscript 2 is missing in μm2

      We thank the reviewer for pointing this out, this is now changed.

      5) -Did the author colocalize KIC11 in schizonts with other proteins found in the K13 compartment group of proteins not involved in endocytosis/ART resistance? This may help to further subgroup these proteins.

      This is an interesting point but would actually be technically challenging to do. For this we would need to generate a KIC11endo parasite line for each of these KICs and then do co-localisation in schizonts. However, the outcome of this likely would not be very clear. The reason for this is as follows. There are foci of KIC11 that do overlap with K13 in schizonts. One can expect that these foci show KIC11 at the K13 compartment and that the other KICs would overlap with KIC11 in these K13 foci in schizonts. Hence, we would also need to see K13 to find the non-K13 compartment KIC11 foci and see if these contained the KIC of interest. This is technically challenging because it would mean we would need a third fluorescent protein which is not that trivial to do. Due to the difficulty to do this and the large amount of work involved and the already considerable amount of data in this manuscript, we believe this will be better suited for a different study.

      6) -As a general comment: to make the beautiful IFAs more accessible to a broader readership, I would encourage the authors to switch the color-coding to green/magenta/blue or an equivalent color system or add grayscale images.

      This was done as suggested, all fluorescence images are now provided as greyscale images and the overlays are shown in magenta/green.

      Reviewer #2 (Significance (Required)):

      Characterizing the molecular components involved in Plasmodium endocytosis will not only reveal interesting biology in these highly adapted parasites, but will more importantly lead to a better understanding and potentially open new avenues for intervention of ART resistance. The here presented manuscript is a carefully executed follow-up on previous work done in Dr. Spielmann's lab focusing on the K13 compartment. The authors use established assays to characterize novel components and reveal three new players in endocytosis with one mediating ART resistance in vitro. The proposition that parts of the K13 compartment have a function other than endocytosis is interesting, but will have to await more data from future studies. Taken together this manuscript adds significantly to our understanding of endocytosis in P. falciparum.

      This work is of interest for cell and molecular biologists working on Apicomplexa, but especially for the Plasmodium community.

      We thank the reviewer for this positive assessment.

      I am a cell and molecular biologist working on Toxoplasma gondii

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

      Summary: The authors characterized 4 proteins from P. falciparum via cellular (co-)localization, endocytosis, parasite growth, and artemisinin resistance assays. These proteins have been identified as candidates for Kelch13 compartment and a possible role in endocytosis in their previously work with quantitative BioID for potential proximity to K13 and Eps15 (Birnbaum et al. 2020). In the current work, additional 6 proteins were not confirmed as being associated to the K13 compartment. This experimental work was complemented by an in-silico analysis of protein domains based on AlphaFold algorithm. For this protein structure evaluation all proteins were chosen, which were experimentally confirmed to be linked to the K13 compartment in the current publication and previous work. With the work 3 novel proteins linked to artemisinin resistance or endocytosis could be functionally described (KIC12, MCA2, and MyoF) and a number of hypotheses were generated.

      We thank the reviewer for the assessment of the manuscript and the constructive comments.

      Major comments:

      The quality of the presented work is solid, the experimental design is adequate, and methods are presented clearly. The publication contains a lot of results both presented in text and in the figures and it is not always straight forward for the reader to follow the descriptions due to many details presented and a lack of context for some of these experiments.

      We thank the reviewer for this overall positive assessment.

      We now reordered the results section in an attempt to increase the flow of the manuscript. We also made changes to improve the context for the results. Given the further (very valid) requests for data on schizonts and invasion, there was an increased danger for a less linear manuscript that we hope to have acceptably managed with the re-arrange.

      Specific suggestions for consideration by the authors to improve the manuscript. Abstract: 1) R 31: Mention how the 4 proteins were identified as candidates, you need to refer to previous work to clarify this

      To clarify this the sentence was changed to (line 31): "Here we further defined the composition of the K13 compartment by analysing more hits from a previous BioID, showing that MyoF and MCA2 as well as Kelch13 interaction candidate (KIC) 11 and 12 are found at this site."

      2) R38: "Second group of proteins" is confusing - different from the 4 mentioned above? Significance to endocytosis unclear. Please unify terminology in the manuscript, see also comment below on proxiome.

      We changed the wording to clarify the group issue in the abstract as follows line 34: "Functional analyses, tests for ART susceptibility as well as comparisons of structural similarities using AlphaFold2 predictions of these and previously identified proteins showed that canonical vesicle trafficking and endocytosis domains were frequent in proteins involved in resistance or endocytosis (or both), comprising one group of K13 compartment proteins, While this strengthened the link of the K13 compartment to endocytosis, many proteins of this group showed unusual domain combinations and large parasite-specific regions, indicating a high level of taxon-specific adaptation of this process. Another group of K13 compartment proteins did not influence endocytosis or ART susceptibility and lacked detectable vesicle trafficking domains. We here identified the first protein of this group that is important for asexual blood stage development and showed that it likely is involved in invasion.”

      3) Abstract can only be understood after reading the full publication

      We attempted to amend this by expanding the abstract, particularly the changes highlighted in the previous two points.

      Results: 4) Table 1 is missing from the submitted materials

      We apologise for this mistake. Table 1 is now included.

      5) Consider to shorten and stratify the result section to focus on the significant data

      We rearranged the results in an attempt to streamline this section and are now starting with MyoF in the revised manuscript. However, as highlighted by the requests from reviewer 1, many details need to be available to support our conclusions. For instance the fact that GFP-tagging partially inactivated MyoF asked for further data to support our conclusion (HA-tagged version, showing that the location of the GFP-tagged version was consistent with the HA-tagged version, showing to what extent the different constructs affected growth and correlated with number of vesicles and bloating, see new figure 1M) or that KIC12 has two locations. Overall, we are therefore hesitant to remove data or description from the result part.

      6) Unclear how the localization and functionalization assays might be impaired by the fusion proteins Significance of ART resistance assay is not clear, in presence of strong growth effects due to inactivation or truncation of genes/proteins

      As indicated also in the example given in the previous point (this reviewer #5), the use of different cell lines (GFP-tagged live cells and small epitope tag in IFA) for targets with an indication for an effect of the tagging confirm that the location we assigned is reasonable. In the case of MyoF, the HA-tagged line, the partial inactivation due to GFP and the further inactivation in the GFP-tagged line by knock sideways show plausible increase of phenotypes (vesicle accumulation and bloated FV assays). Thereby the GFP-tagged line can be seen as a partial inactivation line that further supports our conclusions and overall this paints a consistent picture of the function of this protein in endocytosis (see new Figure 1M better illustrating this). Please note that the difference in location shown by this line compared to the HA-tagged proteins is only small (see also reviewer 1 major point 23ff). See also general discussion on tags at the end of this rebuttal.

      Significance of ART resistance assay: The ‘ART resistance assay’ is done comparing +/- ART (DHA) in identical parasites (originating from the same culture and the same condition). Hence, any growth effects are cancelled out and effects in reducing ART susceptibility would - if at all - be underestimated (see more detailed response to point 28, reviewer 1 and controls in Birnbaum et al., 2020 where we tested an unrelated essential protein, unrelated chemical insult and rapalog on 3D7 and did not detect any effect on RSA survival).

      MCA 7) Stratify results, order by significance of findings, it appears to be described in chronological order, improve readability/flow, eg ART resistance if mentioned in r138, but only reported in r183ff

      We attempted to stratify, but then the reason for generating the partial MCA2 disruption parasite line becomes very arbitrary and would leave the reader wondering why we at all truncated the protein at two thirds of the protein. Hence, we do not see a way around this chronological reporting. However, this part is now not at the start of the experimental results section anymore, possibly making it overall a bit more palatable.

      MyoF 8) R195 to 197 - consider moving to discussion as it is distracting here

      This was shortened and additional information (asked for by reviewer 1, major point 22) to clarify that MyoF was previously called MyoC, was added (line 147): “The presence of MyosinF (MyoF; PF3D7_1329100 previously also MyoC), in the K13 proxiome could indicate an involvement of actin/myosin in endocytosis in malaria parasites. "

      9) Term proxiome is introduced above, but not used in result section - suggest to unify language, eg r195 uses "K13 compartment DiQ-BioIDs" instead, which is not very convenient for the reader

      We carefully reviewed this and made this more consistent.

      10) What is the enrichment factor? Please provide for this and the following proteins, eg in Table 1

      The enrichment factor is log2 enrichment over control and this is now provided in table S1 (see also detailed answer for Reviewer 1 major point 12).

      11) R225 to 243 - overall significance of the growth experiments with mislocaliser is not clear, consider removing from manuscript or explain relevance more clearly

      See also point 28, reviewer 1: This experiment is actually quite important. It shows that if we conditionally inactivate the GFP-tagged MyoF, the growth is further reduced, as stated in line 208. It might have been confusing that the mislocalisation is only partial, but this is equivalent to a partial knock down and hence is useful. This becomes even more relevant with the specific assays following in the next paragraph: while the tagging of MyoF already resulted in vesicles, conditional inactivation with KS generated even more vesicles, showing that the same phenotype was rapidly increased when MyoF was further inactivated by a different means and this also correlated with growth. Hence, this is actually a very consistent phenotype that despite some shortcomings of the tools available to analyse this protein (due to the partial inactivation by the GFP tag) in our eyes looks very convincing. We now added a graph showing the correlation of growth and phenotypes to illustrate this (Figure 1L).

      We also tried to make this clearer by changing line 200 to: Hence, conditional inactivation of MyoF further reduced growth despite the fact that the tag on MyoF already led to a substantial growth defect, indicating an important role for MyoF during asexual blood stage development.” And line 208 to:“ This was even more pronounced upon conditional inactivation of MyoF by KS (Figure 1H), suggesting this is due to a reduced function of MyoF.”

      12) KIC11/KIC12 Enrichment factor?

      The enrichment (’average log2 Ratio normalized Kelch13 from Birnbaum et al. 2020’) is 1.65 for KIC11 and 1.32 for KIC12, which is now also explicitly shown in column D of Table S1.

      ** Referees cross-commenting**

      I would like to applaud reviewer #1 for a great, very thorough review and lots of detailed suggestions. I agree with the conclusions mentioned in the significance evaluation from reviewer #1 and #2: the work presented does not contain novel methods and the scope is rather narrow with the current results. (I am working on clinical studies with novel antimalarial agents)

      Reviewer #3 (Significance (Required)):

      On the one hand side, the authors have wrapped up some of the remaining protein candidates of the K13 compartment and could verify 4 of 10 proteins. The work is of interest for the scientific community working on endocytosis and malaria drug resistance mechanisms. Overall, the conclusions and findings from the previous work, Birnbaum et al. 2020, could be confirmed and extended mainly using the methods previously described. On the other hand, the authors made use of progress in protein structure predictions and identified domains linking the K13 compartment proteins to putative functions. The overlaid protein folds of the newly identified domains in figure 5 look convincing, but I can't comment on the technical details or cut-off used for this in-silico analysis.

      Extended general remarks on the systems used for this work:

      Mainly reviewer 1 suggest (in the general comments and the significance statement) that other systems would have been better suited to use for this work, namely glmS and diCre and also has concerns about the large tag which is seconded by a comment of reviewer 3. In light of this we here provide some extended considerations on the properties for conditional systems and tagging in regards to the goals of this work.

      We would like to point out that we do have experience with the systems considered better-suited by the reviewer (one of the first authors has extensively used glmS (Wichers et al., 2021c; Wichers et al., 2021a; Wichers et al., 2022; Wichers-Misterek et al., 2023) and our lab was one of the first to adopt the diCre system in P. falciparum parasites and we regularly us it (Birnbaum et al., 2017; Mesén-Ramírez et al., 2019; Kimmel et al., 2023)). Clearly, these methods have a lot of strengths but there are a number of issues to be considered for the assays we use in this work (see the next section on conditional inactivation systems). In a nutshell, we believe diCre would give a more reliable readout of the absolute level of "essentiality" (i.e. importance for growth) but is unsuitable or at least difficult to use for the assays that reveal the function of our interest in this work. GlmS basically combines the drawbacks of diCre and knock sideways and hence for most targets is not expected to give a better readout of level of "essentiality" but is similarly difficult to use for our specific assays. The fact that both of these systems are possible to use without adding a tag to the target may be an advantage but without tag one loses some very important features that can be critical to understand the outcome with a given system (see considerations on the tag further below).

      Conditional inactivation systems:

      1. __ speed of inactivation:__ glms acts on mRNA and diCre on the gene level, which makes them slower than techniques acting directly on the protein such as DD or KS. With diCre, mRNA and protein is still left, even if the gene is very rapidly excised. For instance for Kelch13 it takes 3-4 days after excising the gene until protein levels have waned enough that this manifests in a reduced growth (Birnbaum et al., 2017). While in some instances diCre permits same cycle analyses if the protein has a very rapid turn-over (e.g. Rab5a, (Birnbaum et al., 2017)), control in a few hours is still difficult. For vesicle accumulation and bloated food vacuole assays, which are done over comparably short time frames and with specific stages, it is rather challenging to hit the correct time of induction to have all the cells at the correct stage with suitably (and uniformly, ie all cells) sufficiently reduced target protein levels during the assay time. Slow acting systems are also more prone to secondary effects. The more immediate the inactivation, the closer it is to the core of the affected function. With vesicle trafficking processes this is particularly relevant as all vesicle trafficking in a cell is interconnected and there are always recycling pathways that maintain the membrane and protein homeostasis of individual compartments. Particularly for endocytosis there seem to be compensatory capacities at least in other organisms (see e.g. (Chen and Schmid, 2020)). One reason why knock sideways was developed is that it permitted to avoid compensatory changes when vesicle adaptors are inactivated (Robinson et al., 2010).

      The comparably short time frame for malaria parasites to go through different stages during blood stage development also is an issue relevant for inactivation speed. The advantage of speed and the danger of obscured phenotypes is highlighted by our work on VPS45 which showed that in trophozoites this protein is involved in the transport of hemoglobin to the FV whereas in late stages it also has a role in secretory processes. Both of these functions we were able to specifically assess in the same growth cycle using KS to rapidly inactivate the protein (Bisio et al., 2020) but with a slower system would have been more complicated to dissect.

      Speed of effect with glmS: unless the KS does not work well, glmS is slower acting than KS (it does not target the already synthesised protein which can remain in the cell) and also often suffers from only partial inactivation, hence the benefit of using it here is unclear. The option to have an untagged protein is a plus, however it also is a minus, as assessing efficiency (particularly in live cells e.g. for bloated assays etc a fluorescent tag is the only direct option to assess inactivation of target) is critical to ensure the phenotype manifests at the stage of interest.

      lethality/absolute phenotypic effects are detrimental to some assays to study the functions we are interested in for this work: no RSA can be conducted, if the gene is lost and the parasites die. Again, with diCre, one could attempt to hit the point when the parasites have lost sufficient amounts of the target protein when they are placed under ART but then the parasites need to continue growing for ~3 days, which is not possible if the cKO is lethal except for very slowly turning over proteins. However, in that latter case, the parasites likely still had full functionality of the target protein at the beginning of the RSA, when the drug pulse happens and there would be no effect. Knock sideways solves these problems by permitting knock sideways inactivation only under ART (or with a few hours pre-incubation depending on the inactivation speed) to not yet affect growth in a severe manner but inhibiting the process the protein is involved in. It may be possible to use glmS for RSAs, but the slow speed would complicate it (it would not permit control of target protein levels in a matter of a few hours to inactivate the target protein and then re-install it).

      None-absolute inactivation is also a strength for some functional assays. While we really like using diCre, in the case of EXP1 it made it necessary to complement the exp1 cKO parasites with low levels of EXP1 to be able to do functional assays without killing the parasites (Mesén-Ramírez et al., 2019; Mesén-Ramírez et al., 2021). While the lethality issue does not apply to glmS (like knock sideways, it also can be tuned), it is unclear what would be gained over knock sideways. Knockdown levels with glmS vary from gene to gene and cannot be predicted, it is in most cases considerably slower than KS, it requires glucosamine which becomes toxic at higher concentrations and might introduce off target effects and tracking protein levels during the assay would equally need GFP tagging.

      Integration of properties of conditional systems

      Given the above discussed properties, several factors have to be considered to be able to use a system for a given assay. Stage-specific transcription is one example. For diCre a protein not expressed in e.g. rings permits to remove the gene and the protein is never made in that parasite development cycle. We exploited this for instance for two proteins only expressed from the trophozoite stage onwards (Kimmel et al., 2023). However, if lethal (absolute effect problem), this also means one can also only see the phenotype on onset of expression of the target (e.g. if in mitosis, the first nuclear division in case the protein is absolutely essential for the process). This is just one example of such issues. Expression timing, turnover of the protein and homogeneity of stage-specific loss of protein will all influence how clearly the phenotype can be determined. All this will decide the exact time of loss/inactivation of the target protein to levels generating a phenotype and ideally therefore can be monitored during an assay (see considerations on tagging).

      For these reasons vesicle accumulation or bloated food vacuole assays are difficult with slow systems as ideally the target should rapidly be inactivated at the trophozoite stage and the result monitored before the cells have moved to the schizont stage. For this a well responding knock sideways is ideal as the protein can be rapidly taken away (sometimes within seconds) to visualise the immediate, direct effect in the cell.

      As shown for KIC11, there is also no disadvantage of using KS for proteins with other assays or proteins that result in different phenotypes. It permits stage-specific same cycle inactivation without having to worry about the turnover of mRNA and protein (Fig. 2F,G). Thus, besides the advantages of knock sideways for endocytosis related assays and RSAs, we also see no disadvantage of using knock sideways for the functional study of KIC11 which has a role other than endocytosis. KS also permits to specifically target the K13 pool of KIC12, something impossible or very difficult to do with other systems. Hence, we are of the opinion that the system for inactivation was adequate for most of the proteins analysed in this manuscript.

      Large tag: we agree that GFP-tagging can be a disadvantage but in our opinion its benefits often outweigh the drawbacks because it permits easy and immediate (on individual cell level, if need be) monitoring of the presence/location of the target protein (e.g. after KS, but given the discrepancy of the timing between gene excision and protein loss, it might be even more important for techniques such as diCre). No fixing/permeabilisation (prone to artifacts, prevents immediate view of cells) to detect a target with specific antibodies or via a small tag is needed with GFP. Similarly, the use of Western blots to do this is time consuming and impractical if monitoring of left-over protein in the course of an assay such as a bloated food vacuole assay is needed.

      In many cases, adding GFP has no negative effect. In addition, if the bulky folded structure of GFP is tolerated, it usually also tolerates the 2 to 4 12kDa FKBP domains in our standard tag. We also typically add a linker. This approach has worked for a large number of different proteins, including many essential ones for which we would not otherwise have obtained the integration cell lines (Birnbaum et al., 2017; Jonscher et al., 2019; Hoeijmakers et al., 2019; Birnbaum et al., 2020; Kimmel et al., 2023; Sabitzki et al., 2023). Hence, whenever a cell line is obtained with it, this tag in most cases is not a disadvantage. Admittedly an exception in this is MyoF and to some extent maybe MCA2 (we would like to stress that in the case of MCA2 the reason for not being able to obtain the full length tagged cell line is unclear: the protein can be severely truncated to less than 3% of its amino acid sequence and a GFP-tag is tolerated on the version with 2/3s of the protein left, which gives no good reason why the full length was not obtained; a potential reason could be a dominant negative effect). However, we obtained the full length with a small tag detected by IFA for both, MyoF and MCA2 and the location of these agreed well with the GFP tagged versions, indicating that the GFP-tagged versions are useful to show the location of these proteins in live cells.

      There are also tricks to attempt monitoring the effect of e.g. diCre without tagging the target. For instance, if a fluorescent protein is connected to excision without actually being fused to the target (ie excision of the gene leads to its expression of e.g. GFP), which would avoid adding a tag to the target itself. However, the problem with this is that expression of GFP does only show excision, but mRNA producing the target protein and left over target protein may still be there in the cell. All in all, the GFP-tag on the target, while with some drawbacks, is still our preferred method to control to monitor the target protein in the cell (in principle permitting quantification of ablation efficiency on the individual cell level).

      Conclusion on these considerations for this manuscript

      Based on these considerations we do not see the immediate benefit of changing the system for the conclusions drawn from this study and are unsure if they are indeed better suited for this work as suggested. While a more exact readout of "essentiality" might be possible with the diCre system we are of the opinion this is less important than learning the function of a protein which - as outlined above - we believe to be considerably more difficult with diCre and even more so with glmS considering our target functions. The same applies to target specific cellular pools of a protein as done here for KIC12. Clearly MyoF is one example where the employed systems shows limitations, but with the new Figure part showing consistency in phenotype with degree of inactivation (importantly with two different forms of inactivation) and the clarification that the location of the GFP-tagged and HA-tagged versions are actually quite similar in location, we do not think employing an extra system is warranted for the conclusions of this work. Admittedly, the apparent lack of need in ring stags might give an opening to attack MyoF using diCre (by excision before its major expression peak), but depending on lethality this might preclude extended analyses (possibly vesicle assays, for sure not RSAs).

      In the end the question is, if our approach provides the function of target analysed in this work and based on the data in our manuscript and the arguments in the rebuttal, we are reasonably confident that this is the case. It is not very likely the other mentioned techniques would result in a different conclusion on the function of the here studied proteins. In fact, we expect other commonly used techniques to be less suitable for the key assays in this work.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      The authors characterized 4 proteins from P. falciparum via cellular (co-)localization, endocytosis, parasite growth, and artemisinin resistance assays. These proteins have been identified as candidates for Kelch13 compartment and a possible role in endocytosis in their previously work with quantitative BioID for potential proximity to K13 and Eps15 (Birnbaum et al. 2020). In the current work, additional 6 proteins were not confirmed as being associated to the K13 compartment. This experimental work was complemented by an in-silico analysis of protein domains based on AlphaFold algorithm. For this protein structure evaluation all proteins were chosen, which were experimentally confirmed to be linked to the K13 compartment in the current publication and previous work. With the work 3 novel proteins linked to artemisinin resistance or endocytosis could be functionally described (KIC12, MCA2, and MyoF) and a number of hypotheses were generated.

      Major comments:

      The quality of the presented work is solid, the experimental design is adequate, and methods are presented clearly. The publication contains a lot of results both presented in text and in the figures and it is not always straight forward for the reader to follow the descriptions due to many details presented and a lack of context for some of these experiments.

      Specific suggestions for consideration by the authors to improve the manuscript.

      Abstract: - R 31: Mention how the 4 proteins were identified as candidates, you need to refer to previous work to clarify this - R38: "Second group of proteins" is confusing - different from the 4 mentioned above? Significance to endocytosis unclear. Please unify terminology in the manuscript, see also comment below on proxiome - Abstract can only be understood after reading the full publication

      Results: Table 1 is missing from the submitted materials Consider to shorten and stratify the result section to focus on the significant data Unclear how the localization and functionalization assays might be impaired by the fusion proteins Significance of ART resistance assay is not clear, in presence of strong growth effects due to inactivation or truncation of genes/proteins

      MCA Stratify results, order by significance of findings, it appears to be described in chronological order, improve readability/flow, eg ART resistance if mentioned in r138, but only reported in r183ff MyoF R195 to 197 - consider moving to discussion as it is distracting here Term proxiome is introduced above, but not used in result section - suggest to unify language, eg r195 uses "K13 compartment DiQ-BioIDs" instead, which is not very convenient for the reader What is the enrichment factor? Please provide for this and the following proteins, eg in Table 1 R225 to 243 - overall significance of the growth experiments with mislocaliser is not clear, consider removing from manuscript or explain relevance more clearly KIC11/KIC12 Enrichment factor?

      Referees cross-commenting

      I would like to applaud reviewer #1 for a great, very thorough review and lots of detailed suggestions. I agree with the conclusions mentioned in the significance evaluation from reviewer #1 and #2: the work presented does not contain novel methods and the scope is rather narrow with the current results. (I am working on clinical studies with novel antimalarial agents)

      Significance

      On the one hand side, the authors have wrapped up some of the remaining protein candidates of the K13 compartment and could verify 4 of 10 proteins. The work is of interest for the scientific community working on endocytosis and malaria drug resistance mechanisms. Overall, the conclusions and findings from the previous work, Birnbaum et al. 2020, could be confirmed and extended mainly using the methods previously described. On the other hand, the authors made use of progress in protein structure predictions and identified domains linking the K13 compartment proteins to putative functions. The overlaid protein folds of the newly identified domains in figure 5 look convincing, but I can't comment on the technical details or cut-off used for this in-silico analysis.

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      Referee #2

      Evidence, reproducibility and clarity

      In a previous publication the Spielmann lab identified the molecular mechanism of ART resistance in P. falciparum by connecting reduced levels of the protein K13 to decreased endocytosis (uptake of hemoglobin from the RBC cytosol), which results in reduced ART susceptibility. Using quantitative BioID the authors further identified proteins belonging to a K13 compartment, highlighting an unusual endocytosis mechanism.

      In the present manuscript the authors follow up on this work and closely examine ten more proteins of the K13/Eps15-related "proxiome". They successfully link MCA2 to ART resistance in vitro, while the proteins MyoF and KIC12 are involved in endocytosis but do not confer in vitro ART resistance when impaired. They further characterize one candidate (KIC11) that partially colocalizes with K13 in trophozoites but to a lesser degree in schizonts. Growth assays suggest an important function for KIC11 in late stages of the intraerythrocytic developmental cycle. Five analyzed proteins however do not colocalize with the K13 compartment, while a sixth was refractory to endogenous tagging.

      Using AlphaFold predictions of the KIC protein structures the author identify domains in most constituents of the K13 compartment, highlighting vesicle trafficking-related features that were not identified on primary sequence level before. The combination of functional data together with structure predictions leads them to propose a refinement of the K13 compartment as being divided into proteins participating in endocytosis and proteins that have an unknown function.

      Major comments:

      • Table 1 is missing
      • Lines 117-123: Given the total list of uncharacterized candidates encompasses 13 proteins, can the author gives the reason why only the top 10 and not all 13 were characterized in this study?
      • Line 174: 20% of observed MCA2 foci show no overlap with K13 and 21% only partially overlap, can the author confirm that the observed MCA2 foci in schizonts are the ones that co-localize with K13. (Addition of a schizont stage image in Fig 1C would be sufficient).
      • The localization and observed phenotype of KIC11 is interesting but unfortunately the authors do not explore it further. Does KIC11 localize with markers of e.g. the secretory organelles (micronemes or rhoptries) in schizonts and could therefore be involved in RBC invasion? Can the author distinguish if KIC11 is involved in RBC invasion or in establishment of the ring-stage parasite?

      Minor comments:

      • Table S1: Please add the criterion for the order of proteins (abundance in "proxiome"?) in the table as a separate column. I would also suggest adding a new column that highlights the 10 proteins investigated in this study as I found the color-coding slightly confusing.
      • 154-155: There is a discrepancy between the text and Fig1C regarding the % of partial overlapping and non overlapping foci.
      • The y-axis label is missing in Fig 3E
      • Fig 4I left graph, the superscript 2 is missing in μm2
      • Did the author colocalize KIC11 in schizonts with other proteins found in the K13 compartment group of proteins not involved in endocytosis/ART resistance? This may help to further subgroup these proteins.
      • As a general comment: to make the beautiful IFAs more accessible to a broader readership, I would encourage the authors to switch the color-coding to green/magenta/blue or an equivalent color system or add grayscale images.

      Significance

      Characterizing the molecular components involved in Plasmodium endocytosis will not only reveal interesting biology in these highly adapted parasites, but will more importantly lead to a better understanding and potentially open new avenues for intervention of ART resistance. The here presented manuscript is a carefully executed follow-up on previous work done in Dr. Spielmann's lab focusing on the K13 compartment. The authors use established assays to characterize novel components and reveal three new players in endocytosis with one mediating ART resistance in vitro. The proposition that parts of the K13 compartment have a function other than endocytosis is interesting, but will have to await more data from future studies. Taken together this manuscript adds significantly to our understanding of endocytosis in P. falciparum.

      This work is of interest for cell and molecular biologists working on Apicomplexa, but especially for the Plasmodium community.

      I am a cell and molecular biologist working on Toxoplasma gondii

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

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      Referee #1

      Evidence, reproducibility and clarity

      With the emergence and spread of resistance to Artemisinin (ART), a key component of current frontline malaria combination therapies, there is a growing effort to understand the mechanisms that lead to ART resistance. Previous work has shown that ART resistant parasites harbour mutations in the Kelch13 protein, which in turn leads to reduced endocytosis of host haemoglobin. The digestion of haemoglobin is thought to be critical for the activation of the artemisinin endoperoxide bridge, leading to the production of free radicals and parasite death. However, the mechanisms by which the parasites endocytose host cell haemoglobin remain poorly understood.

      Previous work by the authors identified several proteins in the proximity of K13 using proximity-based labelling (BioID) (Birnbaum et al. 2020). The authors then went on to characterise several of these proteins, showing that when proteins including EPS15, AP2mu, UBP1 and KIC7 are disrupted, this leads to ART resistance and defects in endocytosis leading to the hypothesis that these two processes are inextricably linked.

      In this manuscript, Schmidt et al. set themselves the task of characterising more K13 component candidates identified in their previous work (Birnbaum et al. 2020) that were not previously validated or characterised. They chose 10 candidates and investigated their localisations, and colocalisation with K13, and their involvement in endocytosis and in vitro ART resistance, 2 processes mediated by K13 and some members of the K13 compartments

      The authors show that of their 10 candidates, only 4 can be co-localised with K13. Then, using a combination of targeted gene disruption (TGD) as well as knock sideways (KS), they characterised these 4 proteins found in the K13 compartment. They show that MyoF and KIC12 are involved in endocytosis and are important for parasite growth, however their disruption does not lead to a change in ART sensitivity. The authors also confirm the findings of their previous publication (Birnbaum et al. 2020), using a slightly different TGD, that MCA2 is involved in ART resistance, however they did not check whether its disruption impacts haemoglobin uptake. They also show that KIC11 is not involved in mediating haemoglobin uptake or ART resistance. To finish, the authors used AlphaFold to identify new domains in the proteins of the K13 compartment. This led them to the conclusion that vesicle trafficking domains are enriched in proteins of the K13 compartment involved in endocytosis and in vitro ART resistance.

      The majority of the experiments conducted by the authors are performed to a good standard in biological and technical replicates, with the correct controls. Their findings provide confirmation that their 4 candidate genes seem to be important for parasite growth, and show that some of their candidates are involved in endocytosis. While the KD and KS approaches employed by the authors to study their candidate genes each have their own advantages and can be excellent tools for studying a large sets or genes, this manuscript highlights the many limitations of these approaches. For example, the large tag used for the KS approach can mislocalise proteins or disrupt their function (as is the case for MyoF), resulting in spurious results, or indeed the inability to generate the tagged line (as is the case for MCA2). The KS approach also makes the results of a protein with a dual localisation, like KIC12, extremely difficult to interpret.

      Moreover, the manuscript is disjointed at times, with the authors choosing to conduct certain experiments for only a subset of genes, but not for others. For example, considering that the aim of this paper was to identify more proteins involved in ART resistance and endocytosis, it is confusing why the authors do not perform the endocytosis assays for all their selected proteins, and why they do not do this for the proteins they identify in their domain search. There is significant room for improvement for this manuscript, and a generally interesting question. But in it's current format, other than confirming that MCA2 is involved in ART resistance (which was already known from the Birnbaum paper), the authors do not further expand our understanding of the link between ART resistance and endocytosis in this manuscript.

      Major Comments

      line 31: please change defined to characterised - defined suggests that novel proteins were identified in this study, which is not the case.

      line 37: please change 'second' to "another". As explained further below, the authors identified 3 classes of proteins (confer ART resistance + involved in HCCU, involved in HCCU only, or involved in neither).

      Line 40: You define KIC11 as essential but according to your data some parasites are still alive and replicating 2 cycles after induction of the knock sideways. Please consider changing "essential" to "important for asexual parasite growth"

      Line 40: please change 'second group' to 'this group'

      line 41: state here that despite it being essential, it is unknown what it is involved in.

      Line 50: the authors should state here that there is actually a reversal in this trend over the last few years.

      Line 54: please separate out the references for each of the two statements made in this line (a: that ART resistance is widespread in SEA, and b: that ART resistance is now in Africa) Reference 14 also seems to reference ART resistance in Amazonia - which is not covered by the statement made by the authors (in which case the authors should state ART is now present in Africa and South America). The authors should also reference PMID: 34279219 for their statement that ART resistance is now found in Africa (albeit a different mutation to the one found in SEA).

      Line 65: it is also worth mentioning here that there are other mutations in proteins other than K13, such as AP2mu and UBP1 (PMID: 24994911;24270944) that can lead to ART resistance.

      Line 80, 86: ref 43 is misused. Reference 43 refers to Maurer's clefts trafficking which takes place in the erythrocyte cytosol and is not involved in haemoglobin uptake as far as I know. Please replace ref 43 with one showing the role of actin in haemoglobin uptake.

      Line 98: the authors state here that they 'identified' further candidates from the K13 proxiome. This suggests that they identified new proteins in this paper, when in fact the list was already generated in ref 26. All they did was characterise proteins from that list that were not previously characterised. The authors should therefore remove identified from this statement.

      Line 107-108: it is not clear from this sentence why these proteins were left out of the initial analysis in Ref 26. A sentence here explaining this would be valuable for the reader.

      Line 117-123: The authors say that PF3D7_0204300, PF3D7_1117900 and PF3D7_1016200 were not studied because they were not in the top 10 hits. However, the current organisation of Supplementary Table 1 shows all 3 proteins among the top 10 hits (MyoF, KIC12, UIS14 and 0907200 being after them). I think the authors should reorganise their table. It is also unclear according to what the proteins in the table are ranked. Could the authors indicate the metric used for the ranking?

      Line 129-141: Can the authors be clearer with their explanations of the identification of mutation Y1344Stop? One dataset (ref 61) shows that 52% of African parasites have a mutation in MCA2 in position 1344 leading to a STOP codon. But another dataset (ref 62) shows that the next base is also mutated, reverting the stop codon. That should have been seen in the first dataset as well. Could the authors please clarify.

      Line 147: the authors say that MCA2 is expressed throughout the intraerythrocytic cycle as shown by live cell imaging. In Birnbaum et al 2020 fig 4I, the authors show that MCA2 is mainly expressed between 4 and 16hpi. But in Figure 1B of this manuscript there is a clear multiplication of MCA2 signal between trophozoite and schizont. How do the authors explain this discrepancy? Could expression of the truncated MCA2 be different than the full length? This cannot be assessed as expression and localisation of the full-length HA tag MCA2 is not shown in Schizonts. MCA2 expression seems also different for the MCA2TGD-GFP with no expression in rings.

      Line 158: would it not have been more useful for the authors to have episomally expressed MCA2-3xHA in their MCA2Y1344STOP-GFPENDO line to make sure that the truncated protein is indeed going to the correct compartment? The experiments done by the authors suggests that the MCA2Y1344STOP goes to the right location but does not really confirm it.

      Line 191: it is stated that MCA2 confers resistance independently of the MCA domain, however in both the MCA2-TGD and MCA2Y1344STOP-GFPENDO parasites, the MCA domain is deleted, and for both parasites, there is resistance (albeit to a lower level in the MCA2Y1344STOP-GFPENDO line). Therefore, how can the authors state that the ART resistance is independent of the MCA domain? This statement should be that resistance is dependent on the loss of the MCA domain.

      Line 192: Why did the authors not check if MCA2 is involved in endocytosis? They state later on in the manuscript that they did not do endocytosis assays with TGD lines, however if the authors include the correct controls, this could be easily done. It would also be really interesting to see whether endocytosis gets progressively worse going from WT to MCA2Y1344STOP to MAC2TGD. This experiment (as well as doing endocytosis assays for KIC4 and KIC5 TGD lines) would drastically increase the impact of this study. These experiments would not take more than 3 weeks to perform, and would not require the generation of new lines.

      The authors should consider re-organising the MCA2 section, first showing that the 3xHA tagged line colocalises with K13, then performing the new truncation.

      Line 197: Once again ref 43 is not correct to illustrate that actin/myosin is involved in endocytosis

      Line 202: the authors state that MyoF localises near the food vacuole from ring stage/trophs onwards. However, how can this statement be made in schizonts based on these images (Fig. 2A), where it doesn't look like MyoF is anywhere near the FV? This statement can only be made for schizonts if co-localised with a FV marker (which is done in Fig. 2B), however, based on the number of MyoF foci, it appears that this was not done for schizonts. Please either remove the statement that MyoF is near the food vacuole from trophs onwards (because it is only seen near the FV up until trophs) or show the data in Fig. 2B of schizonts to substantiate these claims.

      Line 204-206: what does this statement bring to the paper? Is it to show that it is the real localisation of MyoF because 2 tag cell line show the same localisation? I don't think this is needed, especially as later in the manuscript an HA-tag MyoF line is used and show similar localisation.

      Line 212: The overlap of K13 with MyoF in Fig 2C 3rd panel (1st trophozoite panel) is not obvious, especially as the MyoF signal seems inexistant. I would advise the authors to replace with a better image. Also, why are there no images of schizonts shown in Figure 2C?

      Line 217: the spatial association of MyoF with K13 is very different when it is tagged with GFP and when it is tagged with 3xHA. The way the authors word it here, it seems that there is agreement with the two datasets, when this is not in fact the case (59% overlap for MyoF-GFP and only 16% overlap with MyoF-3xHA). These data suggest that the GFP and the multiple FKBP tags are doing something to the protein and therefore maybe the ensuing results using this line should not be trusted or be taken with a pinch of salt.

      Line 219: the authors state here that they could not detect MyoF-GFP in rings, when in Figure 2C they show MyoF-GFP in rings, and also show that they could detect MyoF in Sup Fig. 3B with the 3xHA tagged line. Is this a labelling mistake in Figure 2C? If the authors could indeed not see MoyF-GFP in rings, this statement should have been made when Figure 2A was presented, and not so late in the manuscript, which causes confusion. Line 237: Showing a DNA marker (DAPI, Hoescht) for Figure 2E, and subsequent figures using mislocalisation to the nucleus, would help the reader assess efficiency of the mislocalisation.

      Line 254-256: authors should show the results of the bloating assay for parental 3D7 parasites (+ and - rapalog) to see whether the MyoF line - rapalog has increased baseline bloating. This applies to all subsequent FV bloating assays.

      Line 254-257: The authors say that because fewer parasites show a bloated food vacuole upon inactivation of MyoF it means that less hemoglobin reached the food vacuole. I understand the authors statement, however, shouldn't they look at the size of the food vacuole, instead of the number of parasites with bloated FV, to make such a statement? This has been done for KIC12 so why not doing it for MyoF?

      Line 259-261: these results would be difficult to interpret namely because the authors have dying parasites, which is exacerbated with the protein being knocked sideways. The authors should mention the pitfalls their knock sideways and tagging design here.<br /> Line 260-261: RSA is an assay relying on measuring parasite growth 1 cycle after a challenge with ART for 6 hours.

      Line 261-263: the authors sate that MyoF has a function in endocytosis but at a different step compared to K13 compartment proteins. I am not sure what they mean here. Can this be clarified? Do the authors mean that it is involved in endocytosis but not in ART resistance? If so, this is a very difficult statement to make since the parasites are dying. Is there any evidence of point mutations in MyoF in the field?

      Line 298: the authors state that there is no growth defect in the first cycle when rapalog is added to the KIC11 line, however based on Figure 3D, there is evidently a 25% reduction in growth compared to - rapalog at day 1 post treatment, and a 60% reduction by day 2, which is still within the 1st growth cycle. The authors should either revise their statement or provide an explanation for these findings. The authors should also explain why their Giemsa data in Fig. 3E is not in accordance with their FACS data.

      Line 301: KIC11 could also be important very early for establishment of the ring stage for example for establishment of the PV. Also, was mislocalisation assessed in rapalog-treated parasites at 72 hours or in cycle 3?

      Line 311: the authors should change the sentence from 'not related to endocytosis' to 'not related to endocytosis or ART resistance'.

      Line 323-325: Authors say that a nuclear GFP signal can be observed in early schizonts for KIC12. According to the pictures provided in Figure 4A and Figure S5A it is not very obvious. Also faint cytoplasmic GFP signal could only be background as we can see that exposure is higher for schizont pictures

      Line 326-328: The authors say that kic12 transcriptional profile indicate mRNA levels peak (no s at peak) in merozoites. Should they show live cell imaging of merozoites then? Because from the Figure 4A schizont pictures where schizonts are almost fully segmented no signal can be observed. Line 347: The authors state that using the Lyn mislocaliser the nuclear pool of KIC12 is inactivated by mislocalisation to the PPM. This tends to suggest that only the nuclear pool of KIC12 is mislocalised. How is it possible that only the nuclear pool is mislocalised? Line 368-369: Effect was also only partial for MyoF. Why didn't you measure the same metrics for MyoF? Line 379: you don't know if all proteins acting later in endocytosis will have an increased number of vesicles as a phenotype

      Line 413-414: The authors state that no growth defect was observed upon KS of 1365800. Is growth alone enough to say that there is no impact on endocytosis?

      Line 432: in this section, the authors state that KIC4 and KIC5 seem to have domains that may suggest these proteins are involved in endocytosis, based on the alpha fold data that is publicly available. Considering the authors have TGD-SLI versions of these lines (Birnbaum et al. 2020) and have already confirmed in this previous publication that they confer resistance to ART; it would make sense to look at endocytosis for these genes. This would be a relatively simple and straightforward experiment, taking no longer than two to three weeks, and would require no additional reagents or line generation. Doing these experiments would add a lot more weight to this final section. The authors later state that KIC4 and 5 are TGD lines, so not the best for endocytosis assays. It is unclear why this would be difficult to do if an adequate control is contained in the experiment (such as parental 3D7). It explains why they did not perform the MCA2 endocytosis assays further up, but in my opinion, an attempt at doing these assays is important and would significantly increase the impact of this paper.

      Line 490-493: the authors state that the K13 compartment proteins fall in two groups, some that are involved in ART resistance AND endocytosis, and some that have different functions. However, in this manuscript the authors have demonstrated 3 flavours that K13 compartment proteins can come in: • Some that confer ART resistance and are involved in HCCU (MCA2) • Some that are involved in HCCU but not ART resistance (MyoF & KIC12) • Some that are involved in neither (KIC11) The authors should therefore revise this statement.

      Line 508: the authors state that they expanded the repertoire of K13 compartments, when in fact they functionally analysed them - they did not do another BioID to identify more candidates.

      Line 570-572: has anyone ever tested whether CytoD or JAS treatment in rings, is sufficient to mediate ART resistance? Something similar to what was done in PMID 21709259 with protease inhibitors. If not this would be a pretty interesting experiment for the authors to do that could shed more light on the MyoF data. It would take maybe 2 weeks to do and not require the generation of any new lines. This would clarify whether other Myosins other than MyoF are involved in endocytosis, as is suggested by previous publications (PMID: 17944961).

      Line 608: inhibitors targeting the metacaspase domain of MCA2 may inadvertently inactivate other essential parts of the protein. They authors should acknowledge this possibility in the text.

      Line 624-625: the authors state that MyoF is 'lowly expressed in rings' - indeed this is the case in their MyoF-2xFKBP-GFP-2xFKBP line which the authors established has defects due to the tag, but it appears from their MyoF-3xHA tagged line that it is expressed in rings. The authors should therefore revise their statement, and be careful of making claims based on their defective line and using fluorescence imaging as their only metric. If they do want to make the statement that it is not there in rings, they should also do a western blot, which is much more sensitive since it amplifies the signal compared to an image of one parasite.

      Line 635: arguably this is the 3rd variety and not the 2nd (the authors already mentioned 2 types - ones that are involved in HCCU AND ART and those involved in HCCU only). See comment for line 490-493 above.

      Line 785: Bloated food vacuole assay/E64 hemoglobin uptake assay method specify that a concentration of 33mM E64protease inhibitor was used. However, in reference 44, cited in the manuscript, a concentration of 33µM E64 was used. Please confirmed if this is just a typo or if 1000x E64 concentration was used which renders the experiment invalid.

      Line 788: it is unclear from this section what is considered a bloated food vacuole - is there an area above which the FV is considered bloated? Do the authors do these measurements manually or use an addon in FIJI/ImageJ? What is the cutoff for if a FV is bloated? Please clarify. Additionally, for the representative images + rapalog for Figures 2H and 4H, it would be useful to see where the authors delineate the FV (add a white circle showing what is actually measured).

      Line 863-864: this sentence seems to be out of place.

      Line 875: the authors state that there is a light blue wedge, when the circle consists of grey and black wedges. Please revise this.

      Line 1059-1061: it is unclear whether the individual growth curves are different clones or whether they are just the same experiment repeated? If it is the latter, then why are they not combined, as is traditionally done?

      Line 919-924: the authors mention a blue and red line, but there is only a black line in figure 3D. Moreover, the experiment of using the LYN mislocaliser was only done for KIC12 according to the manuscript. Additionally, the y axis of the figure states relative growth day 4[%] compared to rapalog, but then on the x axis there are several days. In the text it says there is no growth defect until the second cycle, but from this graph it appears the growth defect is evident as early as 1 day post rapalog treatment. Can the authors please clarify and correct the issues pointed out.

      Figure 1 panel B & C: the label of the figure where the signal from MCA2Y1344STOP-GFP is shown with the DAPI signal overlayed is deceptive since it suggests that this is the signal of full length MCA2. Please change the label of this panel from MAC2/DAPI to MCA2Y1344STOP/DAPI. The same is true for Panel C for the image labeled MCA2/K13 - please change this to MCA2Y1344STOP/K13.

      Figure 2B: what stages are these parasites? Please state this in the figure. Based on the MyoF pattern, it looks like rings in the upper panel and trophs in the bottom pannel. Why were schizonts not shown?

      Figure 2D&F: it is not very meaningful when growth assays are shown as a final bar after 4 days of growth. It is much more useful and informative to see a growth curve instead (as is shown in the supplementary), since it shows if the defect is apparent in the first growth cycle or later. With the way the data is currently shown, this is not apparent. I would advise the authors to switch the graph in 2F out of a combined graph of all the biological replicates growth curves for S3D - showing error bars.

      Figure 3: why were the calculation of FV area, parasite area and FV/parasite area only done for KIC12 and not done for MyoF? It would be interesting to see if any of these values are different for MyoF - whether the parasites are smaller in area and therefore FV smaller. Please present them Figure 2. Images should be already available and would not require further experiments to be done, only the analysis.

      Figure 3B: why is there no spatial association assessment for KIC11 and K13 as was done for the MCA2 and MyoF? The authors should show a pie chart showing the degree of association here as was done for the other proteins.

      Figure 3D: The y axis of the figure states relative growth day 4[%] compared to rapalog, but then on the x axis the experiment takes place over several days. Is this a typo in the y axis? Additionally, the authors state in line 287-290 that the growth defect upon addition of rapalog is only seen in the second cycle, but from this graph it appears the growth defect is already evident 1 day post rapalog addition. The figure legend also does not make sense for this figure since it mentions a blue and a red line, when there is only a black line present. The legend also mentions the LYN mislocaliser which was used for KIC12 not KIC 11 (see above).

      Figure 3E: the colour for Control and Rapalog 4 hpi are very similar and very hard to discern. Please choose an alternative colour or add a pattern to one of the samples. The y axis is also missing a label. Is this supposed to be parasitemia (%)?

      Figure 4A: the ring shown in this figure does not appear to be a ring (it is far too large and appears to have multiple nuclei?). Do the authors have any other representative images to show instead?

      Figure 4B: why is there no spatial association assessment for KIC12 and K13 as was done for the MCA2 and MyoF? The authors should show a pie chart showing the degree of association here as was done for the other proteins. This should be done for the different life cycle stages considering the changing localisation of KIC12.

      Figures 4C&E: it is extremely important to show the DNA stain in both these samples considering that a portion of KIC12 is in the nucleus! Please add the DAPI signal for these figures (as for all other figures!).

      Figure 4E: this figure should be presented before 4D (considering the line being presented in 4E is used in an experiment in 4D). The authors should switch the order of these two.

      It is unclear why in many of the fluorescence images the authors do not show the DAPI signal - particularly when colocalising with K13 and when doing the knock sideways experiments. Please add these images to the figures - I would assume they have already been taken, so would simply involved adding the images to the panel.

      Throughout the manuscript, there is no western blot confirming the correct size of their modified proteins. This should be provided.

      None of the figures are appropriate for individuals with colour blindness, limiting their accessibility to the paper. Please change the colour schemes for all fluorescent images using magenta/green or an alternative colour combination appropriate for colourblind individuals.

      Minor Comments

      line 29: remove 'are'.

      Line 29: the text says "HCCU is critical for parasite survival but is poorly understood, with the K13 compartment proteins are among the few proteins so far functionally linked to this process." The sentence should be: 'HCCU is critical for parasite survival but is poorly understood, with the K13 compartment proteins among the few proteins so far functionally linked to this process."

      line 44: remove 'the'

      Line 48: consider mentioning here that malaria is caused by the parasite Plasmodium - otherwise the first mention of parasite in line 52 is confusing for the non-specialist reader.

      Line 49: estimated malaria-related death and case numbers are from the 2021 WHO World malaria report. You cite the 2020 WHO World malaria report.

      Line 53: please insert the word 'have' between now and also.

      Line 54: please change 'was linked' to is linked

      Line 72: I would specify that free heme is toxic to the parasite. Especially as you mention that hemozoin is nontoxic. Sentence would be "where digestion results in the generation of free heme, toxic to the parasite, which is further converted into nontoxic hemozoin"

      Line 90: authors should either say "in previous works" or "in a previous work"

      Line 91: "We designated these proteins as K13 interaction candidates (KICs)"

      Line 95: please change 'rate' to number

      Line 109: Please include a coma before (ii).

      Line 112: as shown by Rudlaff et al in the paper you are citing, PPP8 is actually associated with the basal complex. You can say that "(ii) were either linked or had been shown to localise to the inner membrane complex (IMC) or the basal complex (PF3D7...).

      Line 114: Protein PF3D7_1141300 is called APR1 in the manuscript but ARP1 in Supplementary Table 1. Please correct.

      Line 131: please define SNP - this is the first use of the acronym.

      Line 133-134: South-East Asia instead of "South Asia"

      Line 135: please explain what TGD is - it is referred to over and over again in the manuscript without ever being explained.

      Line 145: change 'Western blot' to western blot - only Southern blot is capitalised since it is named after an individual, while the other techniques are not.

      Line 152: add "the" between 'and spatial'

      Line 158: please define SLI as selected linked integration, since it is the first use of the acronym.

      Line 178: introduce a coma after protein. Sentence should be "Proliferation assays with the MCAY1344STOP-GFPendo parasites which express a larger portion of this protein, yet still lacking the MCA domain (Figure 1), indicated no growth ...

      Line 195: the authors could mention that MyoF was previously called MyoC in the Birnbaum 2020 paper. I wanted to check back in the Birnbaum 2020 paper and could not find MyoF

      Line 200: "Expression and localisation of the fusion protein was analysed by fluorescent microscopy". Why expression was not analysed also by western Blot same as for MCA2?

      Line 204: I could not find any mention of MyoF (Pf3D7_1329100) in reference 65. Please remove reference 65 if not correct. Also reference 66 looks at Plasmodium chabaudii transcriptomes so I would specify that "This expression pattern is in agreement with the transcriptional profile of its Plasmodium chabaudii orthologue"

      Line 208: Please indicate a reference for P40 being a marker of the food vacuole

      Line 220-224: The authors should consider changing to " Taken together these results show that MyoF is in foci that are mainly close to K13 and, at times, overlapping, indicating that MyoF is found in a regular close spatial association with the K13 compartment."

      Line 255: In Figure 2H, and subsequent figures showing bloated FV assay, I would delineate the food vacuole with dashed line as in Birnbaum et al. 2020 to help the reader understanding where the food vacuole is.

      Line 265-266: Here the title says that KIC11 is a K13 compartment associated protein, but the title of Figure 3 says KIC11 is a K13 compartment protein. I noticed that you make the difference between K13 compartment protein et K13 compartment associated protein for MyoF for example which is not clearly associated with the K13 compartment. Which one is it for KIC11?

      Line 309-310: indicate a reference for your statement "which is in contrast to previously characterised essential K13 compartment proteins".

      Line 377: Figure 4I, please correct 1st panel Y axis legend

      Line 404: replace "dispensability" with dispensable

      Line 416: can the authors provide any speculation as to why they observed these proteins as hits in the BioID experiments?

      Line 451: Where the "97% of proteins containing these domains also contain an Adaptin_N domain and function in vesicle adaptor complexes as subunit " come from. Do you have a reference?

      Line 465-467: the same could be said for KIC4 as it also has a VHS domain.

      Line 477-479: Can be rephrased to "However, we found this protein as being likely dispensable for intra-erythrocytic parasite development and no colocalisation with K13 could be demonstrated, suggesting a limited role for PF3D7_1365800 in endocytosis. Or something like that. Makes it clearer.

      Line 535: Have AP-2 or AP-2 been shown to be at the K13 compartment?

      Line 569: reference 43 is wrong

      Line 746: typo "ot" instead of or.

      Line 801: method for Domain Identification using AlphaFold specify that RMSDs of under 5Å over more than 60 amino acids are listed in the results. However, there is a typo in Figure 5B for KIC5 where it says "RMSD 4.0 Å over 8 aa". Please correct.

      Line 856: In Figure 1E, please use the same Y axis legend as in Figure 2D "relative growth at day 4 [%] compared with 3D7"

      Figure S1: Some PCR gels check for integration are presented as 5', 3' and ori whereas other gels are presented as ori, 5' and 3'. This is confusing. Figure S1: Why was the expression of only MCA2 was verified by Western blot? What about the other proteins?

      Line 493: Considering KIC11 was not involved in HCCU or ART resistance it might be worth mentioning in this section that it is of note that there are no domains detected that would be involved in endocytosis.

      Line 503-506: is it wise to generate more drugs that target a pathway that is already highly susceptible to mutations? The authors should add a statement explaining how this might be avoided.

      Throughout, scale bars are stated in the figure legends at the end of the legend. This is a slightly confusing format. The authors should consider stating the scale bar for each sub-legend where a fluorescence image is taken.

      Referees cross-commenting

      After reading reviewer 2 and 3's comments, I think there are significant overlaps in the key points raised in terms of questions about fusion proteins and their potential partial mis-localisation, better descripton of results and target selection. Overall I think we agree that the work has potential, but in its current form does not represent a major advance. It would be immensely helpful if the manuscript would be carefully edited for a better flow and linear description of results.

      Significance

      The authors set out to test whether other proteins that are in the vicinity of K13 are involved in mediating ART resistance and endocytosis. This is an interesting question. However, other than MCA2 which was already known to be involved in mediating ART resistance (and was not tested for its involvement in endocytosis), none of their candidate proteins seem to be involved in mediating both these functions. The authors show that the other proteins tested appear important for parasite growth, with KIC12 and MyoF involved in mediating endocytosis. While these findings are novel, the KS approach used by the authors casts some doubt over the findings, and would mean that these findings would have to be re-tested with a more reliable approach, such as the GlmS system or generating a conditional knockout using the DiCre system. Despite not advancing our understanding of ART resistance, or identifying further players involved in this process, this manuscripts provides two candidates that are involved in mediating endocytosis and a further candidate that appears to be important for parasite growth. Further work on these proteins will be required to understand their exact roles. As stated above, there is currently limited interest for these results (limited to researchers working on endocytosis in apicomplexan parasites and possibly the wider endocytosis field from an evolutionary perspective), however with further work, this could increase the impact and interest of this work substantially.

      The authors do not describe any novel methods/approaches within this work.

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      Reply to the reviewers

      1. General Statements

      We thank all four reviewers for their helpful and constructive comments. We have gone through each and every comment and proposed how we would address each point raised by the reviewers. We are confident our proposed revisions are feasible within a reasonable and expected time frame. Some of the comments regarding minor typo/aesthetics and extra references have already been addressed in the transferred manuscript. The changes are highlighted in yellow in the transferred manuscript.

      2. Description of the planned revisions

      Reviewer #1

      Major points:

      1. The presented work itself (Figures 1-4) does not need significant adjustments prior to publication, in my view, with only a few points to address. However, the work in Figure 5- doesn't really support the claims the authors make on its own, and would require some additional experiments or at the very least discussion of the caveats to its current form.

      We thank the reviewer for these comments and will follow the reviewer’s suggestion by discussing the caveats regarding the interpretation of Figure 5. We will also add to the discussion to suggest future research approaches beyond the scope of this manuscript that would address the functional importance of localised mRNA translation. We will briefly mention in the discussion methods such as the quantification of the mRNA foci and the disruption of the mRNA localisation signals to disrupt localised translation and the use of techniques such as Sun-Tag (Tanenbaum et al, 2014) and FLARIM (Richer et al, 2021) to visualise local translation directly.

      Tanenbaum et al, 2014 DOI: 10.1016/j.cell.2014.09.039

      Richer et al, 2021 DOI: 10.1101/2021.08.13.456301

      1. Localized glia transcripts, are they "glial/CNS/PNS" significant or are they similar to other known datasets of protrusion transcriptomes? The authors compared their 4801 "total" localized to a local transcriptome dataset from the Chekulaeva lab finding that a significant fraction are localized in both. As the authors note, this is in good agreement with a recent paper from the Talifarro lab showing conservation of localization of mRNAs across different cell types. What the authors haven't done here, is further test this by looking at other non-neuronal projection transcriptomic datasets (for example Mardakheh Developmental Cell 2015, among others). If the predicted glia-localized processes are similar to non-neuronal processes transcriptomes, this would further strengthen this claim and rule out some level of CNS/PNS derived linage driving the similarities between glia and neuronal localized transcripts.

      This is a good point and we thank the review for pointing out this interesting cancer data set. We will do as the reviewer suggests and intersect our data with Mardakheh Dev Cell 2015 to test the further generality of localisation in neurons and glia, in other cell types. Specifically, we plan to intersect both glial (this study) and neuronal (von Kuegelgen & Chekulaeva, 2020) dataset with protrusive breast cancer cells (Mardakeh et al, 2015).

      von Kuegelgen & Chekulaeva, 2020 DOI: 10.1002/wrna.1590

      Mardakeh et al, 2015 DOI: 10.1016/j.devcel.2015.10.005

      1. The presentation/discussion around Figure 3 is a bit weaker than other parts of the manuscript, and it doesn't really contribute to the story in its current form. Notably there is no discussion about the significance of glia in neurological disorders until the very end of the manuscript (page 21), meaning when its first brought up.. it just sits there as a one off side point. The authors might consider strengthening/tightening up the discussion here, if they really want to keep it as a solo main figure rather than integrating it somewhere else/putting it into supplemental. In my view, Figures 2 & 3 should be merged into something a bit more streamlined.

      This is a good point. We plan to strengthen the presentation of Figure 3 and discussion of the significance of glia in neurological disorders by adding a description of the Figure in the Results section and highlighting the significance of glia in nervous system disorders in the Discussion section.

      1. Why aren't there more examples of different mRNAs in Figure 4? Seems a waste to kick them all to supplemental.

      We agree that it could be helpful to show different expression patterns in the main figure. To address this point we will add Pdi (Fig. S4D), which shows mRNA expression in both the glia and the surrounding muscle cell. This pattern is in contrast to Gs2, which is highly specific to glial cells. We will also note that although pdi mRNA is present in both the glia and muscle, Pdi protein is only abundant in the glia, suggesting that translation of pdi mRNA to protein is regulated in a cell-specific manner.

      1. The plasticity experiments, while creative, I think need to be approached far more cautiously in their interpretation. Given that the siRNAs will completely deplete these mRNAs- it really needs to be stressed any/all of the effects seen could just be the result of "defective" or "altered" states in this glial population- which has spill over effects on plasticity in at the NMJ. Without directly visualizing if these mRNAs are locally translated in these processes and assessing if their translation is modulated by their plasticity paradigm, all these experiments can say is that these RNAs are needed in glia to modulate ghost bouton formation in axons. This represents the weakest part of this manuscript, and the part that I feel does not actually backup the claims currently being made. Without any experiments to A. quantify how much of these transcripts are localized vs in the cell body of these glia, B. visualize/quantify the translation of these mRNAs during baseline and during plasticity; the authors cannot use these data to claim that localized mRNAs are required for synaptic plasticity.

      We are grateful to the reviewer for pointing out that we were not precise enough in defining our interpretation of the structural plasticity assay. We did not intend to claim that our results show that local translation of these transcripts is necessary for plasticity, only that these transcripts are localized and are required in the glia for plasticity in the adjacent neuron (in which the transcript levels are not disrupted in the experiment). Definitively proving that these transcripts are required locally and translated in response to synaptic activity would require genetic/chemical perturbations and imaging assays that would require a year or more to complete, so are beyond the scope of this manuscript. To address this point, we will clarify that the results do not show that localized transcripts are required, only that the transcripts are required somewhere specifically in the glial cell (without affecting the neuron level), and we can indeed show in an independent experiment that there are localized transcripts.

      Reviewer #2

      Major points:

      1. The authors analyse the 1700 shortlisted genes for Gene Ontology and associations with austism spectrum disorder, leading to interesting results. However, it is not clear to what extent the enrichments they observe are driven by their presumptive localization or if the associations are driven to a significant extent by the presence of these genes in the selected cell types in the Fly Cell Atlas. One way to address this would be to perform the GO and SFARI analysis on genes that are expressed in the same cells in the Fly Cell Atlas but were not shortlisted from the mammalian cell datasets - the results could then be compared to those obtained with the 1700 localized transcripts.

      This is a fair point raised by the reviewer as genes involved in neurological disease such as Autism Spectrum Disorder may be enriched in CNS/PNS cell types. We will follow the reviewer’s suggestion to perform GO and SFARI gene enrichment analysis in genes that were not shortlisted for presumptive glial localisation.

      1. Although the authors attempt to justify its inclusion, I'm not convinced why it was important to use the whole cell transcriptome of perisynaptic Schwann cells as part of the selection process for localizing transcripts. Including this dataset may reduce the power of the pipeline by including mRNAs that are not localized to protrusions. How many of the shortlisted 1700 genes, and how many of the 11 glial localized mRNAs in Table 5, would be lost if the whole cell transcriptome were excluded. More generally, what is the distribution of the 11 validated localizing transcripts in each dataset in Table 4? This information might be valuable for determining which dataset(s), if any, has the best predictive power in this context.

      We thank the reviewer for raising this point, which we will address with further analysis and adding to the discussion. We propose to address the criticism by running our analysis pipeline without the inclusion of the dataset using Perisynaptic Schwann Cells (PSCs) and then intersect with the PSCs-expressed genes, since their functional similarity with polarised Drosophila glial cells is highly relevant. We also agree with the reviewer that it would be a useful control for us to assess the ‘predictive power’ of each glial dataset by calculating their contribution to the shortlisted 1,700 glial localised transcripts and to the 11 experimentally validated transcripts via in situ hybridisation. To address this point, we plan to add this information in the revised manuscript.

      1. Did the authors check if any of the RNAi constructs are reducing levels of the target mRNA or protein? Doing so would strengthen the confidence in these important results significantly. In any case, the authors should also mention the caveat of potential off-target effects of RNAi.

      We thank the reviewer for their useful comment and agree that the extent to which the RNAi expression reduces the levels of mRNA is not specifically known. We will add a FISH experiment on lac, pdi and gs2 RNAi showing very strong reduction in mRNA levels. We will also add an explanation of the caveats of the use of the RNAi system to the discussion.

      1. Methods: what is the justification for assuming that if the RNAi cross caused embryonic or larval lethality then the 'next most suitable' RNAi line is reporting on a phenotype specific to the gene. If the authors want to claim the effect is associated with different degrees of knockdown they should show this experimentally. An alternative explanation is that the line used for phenotypic analysis in glia is associated with an off-target effect.

      We thank the reviewer for this comment. We agree that off target effects cannot in principle be completely ruled out without considerable additional experimental analysis beyond the scope of this manuscript. To address the criticism we will remove the expression data of the lines that cause lethality and revise the discussion to explain that the level of knockdown in each line is unknown, and would require further experimental exploration.

      Minor points:

      1. It would be helpful to have in the Introduction (rather than the Results, as is currently the case) an operational definition of mRNA localization in the context of the study. And is it known whether or not localization in protrusions is the norm in mammalian glia or the Drosophila larval glia? I ask because it may be that almost all mRNAs diffuse into the protrusion, so this is not a selective process. One interesting approach to test this idea might be to test if the 1700 shortlisted transcripts have a significant underrepresentation of 'housekeeping' functions.

      We thank the reviewer for this excellent suggestion. To address the comment, we will move our explanation of the operational definition of mRNA localization to the Introduction. We will also perform enrichment analysis of housekeeping genes within 1,700 shortlisted transcripts compared to the transcriptome background, as the reviewer suggested.

      Reviewer #3

      Major points:

      1. The authors have pooled data from different studies across different type of glial cells performed from in vitro to in vivo. While pooling datasets may reveal common transcripts enriched in processes, this may not be the best approach considering these are completely different types of glial cells with distinct function in neuronal physiology.

      We thank the reviewer for highlighting the need for us to further justify why we pooled datasets. We will revise the manuscript to better emphasise that the overarching goal of our study was to try to discern a common set of localised transcripts shared between the cells. The problem with analysing and comparing individual data sets is that much of the variation may be due to differences in the methods used and amount of material, rather than differences in the type of cells used. We will revise the discussion to make this point and plan to explain that our approach corresponds well with a previous publication pooling localised mRNA datasets in neurons (von Kugelgen & Chekulaeva 2021).

      von Kuegelgen & Chekulaeva, 2020 DOI: 10.1002/wrna.1590

      1. It is important to note the limitations of the study. For example, DeSeq2 is biased for highly expressed transcripts. How robust was the prediction for low abundance transcripts?

      The presented 1,700 transcripts were shortlisted based on their presence and expression level (TPM) in glial protrusions rather than their relative enrichment. Nevertheless, the reviewer makes a valid criticism of our use of DESeq2, where we compared enriched transcripts in glial and neuronal protrusions in Figure 1D. To address this point we will discuss this caveat in the relevant section.

      The issue raised regarding low abundance transcript prediction raises an important question: does the likelihood of localisation to cell extremities correlate with mRNA abundance? We have already partially addressed this point, since our analysis of the fraction of localised transcripts per expression level quantiles shows only limited correlation. To address this comment, we will add these results in the revised manuscript as a supplementary figure.

      1. The authors identify 1,700 transcripts that they classify as "predicted to be present" in the projections of the Drosophila PNS glia. This was based on the comparison to all the mammalian glial transcripts. Since the authors have access to a transcriptomic study from Perisynaptic Schwann cells (PSCs), the nonmyelinating glia associated with the NMJ isolated from mice; it would be more convincing to then validate the extent of overlap between Drosophila peripheral glial with the mammalian PSCs. This may reveal conserved features of localized transcripts in the PNS, particularly associated with the NMJ function.

      Thank you for the valuable suggestion. A similar point was also raised by [Reviewer #2 - Major point 2] to re-run our pipeline excluding the PSCs dataset and intersect with the PSC transcriptome post-hoc. Please see the above section for our detailed response.

      1. Fig 2: What is the extent of overlap between the translating fractions versus the localized fraction? It will be informative to perform the functional annotation of the translating glial transcripts as identified from Fig 1D.

      This is an interesting question. To address this point, we plan to: (i) compare transcripts that are translated vs. localised in glial protrusions, and (ii) perform functional annotation enrichment analysis on the translated fraction of genes.

      1. "We conclude predicted group of 1,700 are highly likely to be peripherally localized in Drosophila cytoplasmic glial projections". To validate their predictions, the authors test some of these candidates in only one glial cell type. It might be worthy to extend this for other differentially expressed genes localized in another glial type as well.

      The presented in vivo analyses made use of the repo-GAL4 driver, which is active in all glial subtypes, including subperineurial, perineurial and wrapping glia that make distal projection to the larval neuromuscular junction. We agree that subtype-specific analysis would be highly informative, but we believe this is outside the scope of the current work where we aimed to identify conserved localised transcriptomes across all glial subtypes. Nevertheless, to address the comment, we plan to further clarify our use of pan-glial repo-GAL4 driver in the Results and Method section of the revised manuscript.

      1. Figure 5: The authors perform KD of candidate transcripts to test the effect on synapse formation. However, these are KD with RNAi that spans across the entire cell. To make the claim about the importance of "target" RNA localization in glia stronger, ideally, they should disrupt the enrichment specifically in the glial protusions and test the impact on bouton formation. Do these three RNAs have any putative localization elements?

      We agree with the review, that we would ideally test the effect of disruption of mRNA localization (and therefore localised translation). However, we feel these experiments are beyond the scope of this current study, as they will require a long road of defining localisation signals that are small enough to disrupt without affecting other functions. To address this comment we will revise the Discussion section to mention those difficulties explicitly, and clarify the limitations of the approach used in our study for greater transparency.

      Reviewer #4

      Major points:

      1. The authors use FISH to validate the glial expression of their target genes, though these experiments are not quantified, and no controls are shown. The authors should provide a supplemental figure with "no probe" controls, and/or validate the specificity of the probe via glial knockdown of the target gene (see point 2). Furthermore, these data should be quantified (e.g. number of puncta colocalized with NMJ glia membrans).

      Thank you for requesting further information regarding the YFP smFISH probes. We have validated the specificity and sensitivity of the YFP probe in our recent publication (Titlow et al, 2023, Figure 1 and S1). Specifically, we demonstrated the lack of YFP probe signal from wild-type untagged biosamples and showed colocalization of YFP spots with additional probes targeting the endogenous exon of the transcript. Nevertheless, we will address this comment by adding control image panels of smFISH in wild-type (OrR) neuromuscular junction preparations.

      Titlow et al, 2023 DOI: 10.1083/jcb.202205129

      1. For the most part, the authors only use one RNAi line for their functional studies, and they only show data for one line, even if multiple were used. To rule out potential false negatives, the authors should leverage their FISH probes to show the efficacy of their knockdowns in glia. This would serve the dual purpose of validating the new probes (see point 1).

      Thank you for the suggestion. This point was also raised by [Reviewer #2 - Major point 3]. Please see above for our detailed response.

      1. In Figure 5 E, given the severe reduction in size in the stimulated Pdi KD animals, the authors should show images of the unstimulated nerve as well. Do the nerve terminals actually shrink in size in these animals following stimulation, rather than expand? The NMJ looks substantially smaller than a normal L3 NMJ, though their quantification of neurite size in F suggests they're normal until stimulation.

      We share the same interpretation of the data with the reviewer that the neurite area is reduced post-potassium stimulation in pdi knockdown animals. We will follow the reviewer’s suggestion and add an image showing unstimulated neuromuscular junctions.

      Minor points:

      1. The authors claim that there is an enrichment of ASD-related genes in their final list of ~1400 genes that are enriched in glial processes. It is well-appreciated that synaptically-localized mRNAs are generally linked to ASDs. Can the authors comment on whether the transcripts localized to glial processes are even more linked to ASDs and neurological disorders than transcripts known to be localized to neuronal processes?

      This is an interesting point. To address the comment, we will add a comparison of the degree of enrichment of ASD-related genes in neurite vs. glial protrusions in the revised manuscript.

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

      Reviewer #1

      1. The use of blue/green or blue/green/magenta is difficult to resolve in some places. Swapping blue for cyan would greatly aid in visualizing their data.

      This comment is much appreciated. We have swapped blue for cyan in Figures 4 and S4. We have also changed Figure S1 to increase contrast and visibility as per reviewer’s comment.

      1. Make the colouring/formatting of the tables more consistent, its distracting when its constantly changing (also there is no need for a blue background.. just use a basic white table).

      This comment is much appreciated. We have applied a consistent colour palette to the Tables without background colourings and made the formatting uniform.

      Reviewer #2

      1. Introduction: 'Asymmetric mRNA localization is likely to be as important in glia, as it is in neurons,...'. Remove commas

      Thank you for pointing this mistake out. We have made the corresponding edits.

      Reviewer #3

      1. RNA localization in oligodendrocytes has been well studied and characterized. The authors should cite and discuss those papers (PMID: 18442491; PMID: 9281585).

      We thank the reviewer for this useful suggestion. We have added these references to the paper.

      Reviewer #4

      1. In Figure 5D, the authors should include a label to indicate that these images are from an unstimulated condition.

      We thank the reviewer for pointing this out. We have added the label as requested.

      1. The authors are missing a number of key citations for studies that have explored the functional significance of mRNA trafficking in glia, and those that have validated activity-dependent translation:

      - https://pubmed.ncbi.nlm.nih.gov/18490510/

      -https://pubmed.ncbi.nlm.nih.gov/7691830/

      -https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001053

      -https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7450274/

      -https://pubmed.ncbi.nlm.nih.gov/36261025**_/

      _**

      We thank the reviewer for the comment. We have added these references to the text.

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      Referee #4

      Evidence, reproducibility and clarity

      Peripheral localization of mRNAs to cellular protrusions (e.g. axons, astrocyte peri-synaptic processes, myelin sheaths) is important for activity-dependent regulation of nervous system function. In Gala et al., the authors aim to identify conserved, peripherally located mRNAs in through a combination of unbiased transcriptomics and in vivo validation in Drosophila NMJ glia. To that end, they mine several published datasets enriched for peripherally located mRNAs in glia, identify which of these transcripts are conserved in fly, filtered for mRNAs that are enriched in processes over soma, and filtered for mRNAs that were enriched in three glial subtypes that ensheath their model system: Drosophila NMJ. Finally, they go on to validate 11 (of 15) predicted genes as located in NMJ glia, demonstrating that loss of these transcripts, in some cases, impacts synaptic plasticity.<br /> This is an interesting study that complements a growing interest in the community: how do glia locally support the neurons that interact with. I have a number of suggestions to support the conclusions made in this manuscript:

      Major:

      1. The authors use FISH to validate the glial expression of their target genes, though these experiments are not quantified, and no controls are shown. The authors should provide a supplemental figure with "no probe" controls, and/or validate the specificity of the probe via glial knockdown of the target gene (see point 2). Furthermore, these data should be quantified (e.g. number of puncta colocalized with NMJ glia membrans).
      2. For the most part, the authors only use one RNAi line for their functional studies, and they only show data for one line, even if multiple were used. To rule out potential false negatives, the authors should leverage their FISH probes to show the efficacy of their knockdowns in glia. This would serve the dual purpose of validating the new probes (see point 1).
      3. In Figure 5 E, given the severe reduction in size in the stimulated Pdi KD animals, the authors should show images of the unstimulated nerve as well. Do the nerve terminals actually shrink in size in these animals following stimulation, rather than expand? The NMJ looks substantially smaller than a normal L3 NMJ, though their quantification of neurite size in F suggests they're normal until stimulation.

      Minor:

      1. In Figure 5 D, the authors should include a label to indicate that these images are from an unstimulated condition.
      2. The authors claim that there is an enrichment of ASD-related genes in their final list of ~1400 genes that are enriched in glial processes. It is well-appreciated that synaptically-localized mRNAs are generally linked to ASDs. Can the authors comment on whether the transcripts localized to glial processes are even more linked to ASDs and neurological disorders than transcripts known to be localized to neuronal processes?
      3. The authors are missing a number of key citations for studies that have explored the functional significance of mRNA trafficking in glia, and those that have validated activity-dependent translation:
      4. https://pubmed.ncbi.nlm.nih.gov/18490510/
      5. https://pubmed.ncbi.nlm.nih.gov/7691830/
      6. https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001053
      7. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7450274/
      8. https://pubmed.ncbi.nlm.nih.gov/36261025/

      Significance

      Glia are morphologically complex cells that extend tens (microglia/oligodendrocytes) to thousands<br /> (astrocytes) of processes to interact with and support neurons. Given this complex morphology, and the ability of glia to dynamically respond to changes in neuronal activity, there has been a push in recent years to characterize local mechanisms of glia-neuron support. These mechanisms include mRNA trafficking and activity-dependent translation in distal processes. The authors of this study did a nice job computationally identifying a set of putative genes that are enriched in glial processes, which should be of broad interest to the glial community.

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      Referee #3

      Evidence, reproducibility and clarity

      In the manuscript by Gala et al, the authors perform meta-analysis of transcriptomic datasets from different studies focused on synaptically-associated mammalian glial cells. Based on a detailed analysis, the authors predict 1700 localized transcripts and attempted to identify the local transcriptome in glial cells conserved in Drosophila and mammals. This kind of data mining is informative, and can predict the top candidates relatively well, however, the study suffers from lack of depth and a careful assessment comparing across the different datasets. The main concerns have been listed below and suggestions for a more detailed an careful assessment of the data.<br /> 1. The authors have pooled data from different studies across different type of glial cells performed from in vitro to in vivo. While pooling datasets may reveal common transcripts enriched in processes, this may not be the best approach considering these are completely different types of glial cells with distinct function in neuronal physiology.<br /> 2. It is important to note the limitations of the study. For example, DeSeq2 is biased for highly expressed transcripts. How robust was the prediction for low abundance transcripts?<br /> 3. The authors identify 1,700 transcripts that they classify as "predicted to be present" in the projections of the Drosophila PNS glia. This was based on the comparison to all the mammalian glial transcripts. Since the authors have access to a transcriptomic study from Perisynaptic Schwann cells (PSCs), the nonmyelinating glia associated with the NMJ isolated from mice; it would be more convincing to then validate the extent of overlap between Drosophila peripheral glial with the mammalian PSCs. This may reveal conserved features of localized transcripts in the PNS, particularly associated with the NMJ function.<br /> 4. Fig 2: What is the extent of overlap between the translating fractions versus the localized fraction? It will be informative to perform the functional annotation of the translating glial transcripts as identified from Fig 1D.<br /> 5. "We conclude predicted group of 1,700 are highly likely to be peripherally localized in Drosophila cytoplasmic glial projections". To validate their predictions, the authors test some of these candidates in only one glial cell type. It might be worthy to extend this for other differentially expressed genes localized in another glial type as well.<br /> 6. Figure 5: The authors perform KD of candidate transcripts to test the effect on synapse formation. However, these are KD with RNAi that spans across the entire cell. To make the claim about the importance of "target" RNA localization in glia stronger, ideally, they should disrupt the enrichment specifically in the glial protusions and test the impact on bouton formation. Do these three RNAs have any putative localization elements?<br /> 7. RNA localization in oligodendrocytes has been well studied and characterized. The authors should cite and discuss those papers (PMID: 18442491; PMID: 9281585).

      Significance

      In the manuscript by Gala et al, the authors perform meta-analysis of transcriptomic datasets from different studies focused on synaptically-associated mammalian glial cells. Based on a detailed analysis, the authors predict 1700 localized transcripts and attempted to identify the local transcriptome in glial cells conserved in Drosophila and mammals. This kind of data mining is informative, and can predict the top candidates relatively well, however, the study suffers from lack of depth and a careful assessment comparing across the different datasets and the biological significance.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      The manuscript by Gala et al. describes efforts to systematically identify candidate localizing mRNAs within glial cells of the Drosophila larval nervous system. They address this issue with an interesting and novel approach that involves compiling candidate localized transcripts from available mammalian cell datasets and cross-referencing a list of mRNAs from homologous Drosophila genes with a small number of candidates for mRNA localization in Drosophila glia cells that this team identified in a previous study (which they have now validated further). The results provide compelling evidence that their pipeline has predictive power. The authors then go on to perform functional analysis of a small number of validated localizing transcripts and provide evidence that several of these genes are functionally important in glia, including in processes related to synaptic plasticity. These observations raise the possibility that the localization process is functionally important but this was not tested directly (doing so would require a separate long-term study).

      The manuscript is well written and the figures are of high quality. Methodological details are provided, as is quantification where appropriate. However, some points are not yet sufficiently strongly supported by the data yet and the justification for some aspects of the workflow needs clarifying.

      Major points:

      1. The authors analyse the 1700 shortlisted genes for Gene Ontology and associations with austism spectrum disorder, leading to interesting results. However, it is not clear to what extent the enrichments they observe are driven by their presumptive localization or if the associations are driven to a significant extent by the presence of these genes in the selected cell types in the Fly Cell Atlas. One way to address this would be to perform the GO and SFARI analysis on genes that are expressed in the same cells in the Fly Cell Atlas but were not shortlisted from the mammalian cell datasets - the results could then be compared to those obtained with the 1700 localized transcripts.
      2. Although the authors attempt to justify its inclusion, I'm not convinced why it was important to use the whole cell transcriptome of perisynaptic Schwann cells as part of the selection process for localizing transcripts. Including this dataset may reduce the power of the pipeline by including mRNAs that are not localized to protrusions. How many of the shortlisted 1700 genes, and how many of the 11 glial localized mRNAs in Table 5, would be lost if the whole cell transcriptome were excluded. More generally, what is the distribution of the 11 validated localizing transcripts in each dataset in Table 4? This information might be valuable for determining which dataset(s), if any, has the best predictive power in this context.
      3. Did the authors check if any of the RNAi constructs are reducing levels of the target mRNA or protein? Doing so would strengthen the confidence in these important results significantly. In any case, the authors should also mention the caveat of potential off-target effects of RNAi.
      4. Methods: what is the justification for assuming that if the RNAi cross caused embryonic or larval lethality then the 'next most suitable' RNAi line is reporting on a phenotype specific to the gene. If the authors want to claim the effect is associated with different degrees of knockdown they should show this experimentally. An alternative explanation is that the line used for phenotypic analysis in glia is associated with an off-target effect.

      Minor points:

      1. It would be helpful to have in the Introduction (rather than the Results, as is currently the case) an operational definition of mRNA localization in the context of the study. And is it known whether or not localization in protrusions is the norm in mammalian glia or the Drosophila larval glia? I ask because it may be that almost all mRNAs diffuse into the protrusion, so this is not a selective process. One interesting approach to test this idea might be to test if the 1700 shortlisted transcripts have a significant underrepresentation of 'housekeeping' functions.
      2. Introduction: 'Asymmetric mRNA localization is likely to be as important in glia, as it is in neurons,...'. Remove commas

      Significance

      Whilst glial mRNA localization has been reported in other systems, this study is significant as it paves the way for functional and mechanistic dissection of this process in a genetically tractable model system that involves physiologically relevant neuron-glia interactions. The successful results of the data mining approach may also encourage others to address other problems in this manner. The study would be more impactful if additional hits from the 1700 transcripts were validated for glial localization but the findings in the current manuscript are still important. The advance is more methodological than conceptual. The work will appeal to cell biology and systems biology audiences.

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      Referee #1

      Evidence, reproducibility and clarity

      The study of mRNA localization in glia has been largely overlooked in comparison to neurons. However, glia are also polarized cells with long cytoplasmic processes that play important roles in neural function. In this manuscript, Gala and Lee et al attempt to fill in this missing void in the literature. They first use a meta-analysis of existing, cross-species, transcriptomic data to identify a set of 1,700 transcripts that are likely to be localized to the periphery of glia in Drosophila. They then used this list of transcripts to predict which mammalian glial transcripts are also likely to be localized. They're analysis suggests that a large proportion of the mammalian glial transcripts are predicted to be localized to the periphery of glia, and that these transcripts are enriched for functions involved in membrane trafficking, cytoskeleton regulation, local translation, and cell-cell communication. A connection with cross-cellular communication (ie glia-glia, glia-neuron, glia-muscle) at the neuromuscular junction, prompting the authors to assess if some of these transcripts play a role in plasticity of at the NMJ. Using siRNA driver lines, that loss of some of their assessed transcripts prevent new synapse formation- suggestive of a role for mRNA localization and local protein synthesis in driving synaptic plasticity. These findings suggest that mRNA localization is a widespread phenomenon in glia, and that it plays an equally important role as the more widely studied neuronal mRNA localization.

      Key Points

      • 1,700 transcripts are predicted to be localized to the periphery of PNS glia in Drosophila at the NMJ.
      • A large proportion of the mammalian glial transcripts are predicted to be localized to the periphery of glia.
      • Localized transcripts are enriched for functions involved in membrane trafficking, cytoskeleton regulation, local translation, and cell-cell communication.
      • Localized glial transcripts may function in synaptic plasticity.

      Major comments

      Overall, the work presented within manuscript is well reasoned and supports the points the authors generally are trying to make. The work as is, does fall a little on the light side, resting on almost exclusive bioinformatic analysis with some limited validation. The presented work itself (Figures 1-4) does not need significant adjustments prior to publication, in my view, with only a few points to address. However, the work in Figure 5- doesn't really support the claims the authors make on its own, and would require some additional experiments or at the very least discussion of the caveats to its current form.

      1. Localized glia transcripts, are they "glial/CNS/PNS" significant or are they similar to other known datasets of protrusion transcriptomes? The authors compared their 4801 "total" localized to a local transcriptome dataset from the Chekulaeva lab finding that a significant fraction are localized in both. As the authors note, this is in good agreement with a recent paper from the Talifarro lab showing conservation of localization of mRNAs across different cell types. What the authors haven't done here, is further test this by looking at other non-neuronal projection transcriptomic datasets (for example Mardakheh Developmental Cell 2015, among others). If the predicted glia-localized processes are similar to non-neuronal processes transcriptomes, this would further strengthen this claim and rule out some level of CNS/PNS derived linage driving the similarities between glia and neuronal localized transcripts.
      2. The presentation/discussion around Figure 3 is a bit weaker than other parts of the manuscript, and it doesn't really contribute to the story in its current form. Notably there is no discussion about the significance of glia in neurological disorders until the very end of the manuscript (page 21), meaning when its first brought up.. it just sits there as a one off side point. The authors might consider strengthening/tightening up the discussion here, if they really want to keep it as a solo main figure rather than integrating it somewhere else/putting it into supplemental. In my view, Figures 2 & 3 should be merged into something a bit more streamlined.
      3. Why aren't there more examples of different mRNAs in Figure 4? Seems a waste to kick them all to supplemental.
      4. The plasticity experiments, while creative, I think need to be approached far more cautiously in their interpretation. Given that the siRNAs will completely deplete these mRNAs- it really needs to be stressed any/all of the effects seen could just be the result of "defective" or "altered" states in this glial population- which has spill over effects on plasticity in at the NMJ. Without directly visualizing if these mRNAs are locally translated in these processes and assessing if their translation is modulated by their plasticity paradigm, all these experiments can say is that these RNAs are needed in glia to modulate ghost bouton formation in axons. This represents the weakest part of this manuscript, and the part that I feel does not actually backup the claims currently being made. Without any experiments to A. quantify how much of these transcripts are localized vs in the cell body of these glia, B. visualize/quantify the translation of these mRNAs during baseline and during plasticity; the authors cannot use these data to claim that localized mRNAs are required for synaptic plasticity.

      Minor points:

      1. The use of blue/green or blue/green/magenta is difficult to resolve in some places. Swapping blue for cyan would greatly aid in visualizing their data.
      2. Make the colouring/formatting of the tables more consistent, its distracting when its constantly changing (also there is no need for a blue background.. just use a basic white table).

      Significance

      This work would be well received within the field, being at a time where increasing focus on mRNA localization and local protein synthesis are major players in glia along with the more widely studied neurons. Additionally, that mRNAs localized in glia are relevant to neurological disorders also comes at a time where increasingly, especially in neurodegenerative disorders, glia are believed to be drivers of the diseases as well- suggesting that dysregulation of local protein synthesis in glia may play a role. The major weakness of this work is that being largely bioinformatic (from existing datasets), a lot of this is speculative and no conclusive data is shown to demonstrate how/that this glia population is utilizing mRNA localization in protrusions to fuel local protein synthesis for a specific purpose (ie synaptic plasticity).

      This work would be of interested to those broadly interested in glial biology and mRNA localization.

      The reviewers background is in RNA localization and local protein synthesis in neurons.

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      Reply to the reviewers

      Reviewer 1

      In this manuscript Pho et al., characterize the effects of actin confinement versus acto-myosin contractility on nuclear deformation and nuclear envelope (NE) rupture/blebs. They use both genetic and pharmacological perturbations that impact nuclear shape and integrity such as chromatin decompaction and genetic deletion of nucleo-skeleton components (LaminA and LaminB). First, the authors show that modulation of acto-myosin contractility does not affect nuclear height (a proxy for the effects of actin confinement on the nucleus). This finding allowed them to study the effects of acto-myosin contractility on nuclear shape and integrity independently of actin-mediated nuclear confinement. Using fibroblasts expressing a NLS-GFP construct to assess nuclear shape and integrity, the authors show that increased acto-myosin contractility by treatment with Rho activator (CN03) leads to nuclear blebs and NE rupture events (curiously, they show that in LaminA-/- fibroblasts the NE rupture events were not bleb-based and were not affected by CN03). With these results, authors conclude that actin contraction is a major determinant of bleb-based NE rupture, independently of actin confinement. Next, since many studies have associated loss of nucleo-cytoplasmic compartmentalization with increased DNA damage, the authors addressed the occurrence of DNA damage foci in several contexts/perturbations. They find that increased NE rupture frequency by CN03 treatment does not lead to increased DNA damage. Based on the sole finding, authors then claim that DNA damage is mostly associated with abnormal shaped nuclei instead of ruptured nuclei. The data presented is overall convincing and well controlled but a more in-depth characterization of the DNA damage events in VPA-treated cells, LaminB-/- and LaminA-/- cells is needed to support the claim that DNA damage is not associated with NE events in their system. More details below.

      We thank the reviewer for careful consideration of our work. We are happy that the reviewer found, “__The data presented is overall convincing and well controlled…”. __While the author appears positive on the paper as a whole, what follows is the Reviewer’s largest concern with the paper.

      a more in-depth characterization of the DNA damage events…is needed to support the claim that DNA damage is not associated with NE events in their system.” We appreciate the Reviewer’s feedback and agree that this one statement was an overstatement. To address this concern,

      1) We removed this disagreeable/unsupported conclusion,

      2) We replaced it with more measured statement supported by the data (red text highlights changes to the original manuscript),

      3) because we have removed the unsupported conclusion, we prefer not to carry out the suggested experiments because they constitute a completely new study beyond the scope of this manuscript.

      Major points 1,2,3 all focus on the removed unsupported conclusion. They outline numerous new experiments which we feel constitute a new study and separate manuscript and thus beyond the scope of the current manuscript. Instead, we tone down or conclusions clearly labeled by red text.

      __

      __

      __ __

      __Major comments:____

      1-In fig. 4, increased NE rupture frequency by CN03 treatment did not lead to an increase in gH2AX foci, so authors claim that DNA damage is not associated with NE events. However, the increase observed in NE rupture frequency is rather very subtle (from 1.5 to 3 in WT cells; from 2.5 to 4 in VPA treated ells and from 2 to 2.5 in LaminB-/- cells) so I wonder if such a minor increase might be sufficient to cause an increase in DNA damage foci. Moreover, DNA damage events are repaired within minutes and the analysis throughout this manuscript is made on snapshots. Therefore it becomes difficult to draw any conclusion regarding the absence of a link between NE rupture and DNA damage. To assess DNA damage events in a quantitative manner and show that abnormal shape (as opposed to NE rupture) is the main driver of DNA damage, authors should perform live cell imaging of cells co-expressing a DNA damage marker and a NE rupture marker to track DNA damage events following NE rupture.__

      The reviewer raises concerns that we have too strongly stated a conclusion of nuclear rupture affects DNA damage less than shape. Overall, we understand this concern and have revised the text to remove this conclusion and in general tone down our conclusion from this data. We agree with the reviewers point that doubling the frequency either might not be sufficient to increase DNA damage OR a single rupture is sufficient to increase DNA damage. We now state: “ DNA damage measured by γH2AX foci did not significantly increase upon CN03 activation of actin contraction, suggesting that the slight yet significant increase in nuclear blebbing, rupture, and rupture frequency from CN03 did not have a significant impact on DNA damage (Figure 4, C and B).

      We prefer not to. The reviewer’s request for live cell nuclear rupture and DNA damage data is now beyond the scope of the paper because we have revised the manuscript by removing this conclusion. A full analysis of live cell DNA damage analysis in conjunction with nuclear rupture for bleb-based, non-bleb-based normal circularity, and non-bleb-based normal circularity across 4 conditions (WT, VPA, LMNB1-/-, and LMNA-/-) is a paper in and of itself.

      2-Fig. 4C: to show that frequency of NE rupture is less important than abnormal nuclear shape for DNA damage appearance, authors need to quantify gH2AX in normal x blebbed x abnormal nuclei in each one of the conditions (untreated, CN03 and Y27). But again, this analysis must be complemented by live cell imaging experiments to track DNA damage foci appearance (or not?) following NE rupture events.

      We prefer not to Similar to point (1) we have removed this disagreeable conclusion and revised the manuscript. Further extensive studies of DNA damage for nuclear rupture and shape, while interesting, are beyond the scope of the current manuscript.

      We now clearly state this in the Discussion section: “Our data in wild type, VPA, and LMNB1-/- cannot decouple the roles of nuclear shape and ruptures, which are intertwined, causing increased DNA damage (Figure 4). However, we provide novel data that actin contraction is necessary for the behaviors of nuclear blebbing, rupture, and increased DNA damage, independent of changes in actin confinement.

      __ 3- Since VPA treatment increases both the percentage of bleb-based NE rupture and NE rupture frequency in LaminA-/- cells, authors should show that gH2AX foci in this sample remain unaltered to further support their claim that frequency of NE rupture is less important than abnormal nuclear shape.__

      We prefer not to Similar to points (1) and (2) we have revised the manuscript to remove this disagreeable conclusion. Instead, Figure 6 is focused on the interesting phenomena where LMINA-/- nuclei display abnormal nuclear shape and majority non-bleb-based nuclear ruptures. To determine if LMNA-/- nuclei have the capacity to increase nuclear blebbing and bleb-based ruptures, we treated with VPA, which causes both. LMNA-/- with VPA shows that these calls lacking both lamin A and C can form blebs and increase in bleb-based ruptures.

      __ 4-Does methylstat treatment rescue LaminB-/- phenotypes (blebs, ruptures, ...)?__

      We do not see how this relates to a specific change to the manuscript but instead is a direct question of interest.

      The reviewer would like us to clarify how our past work rescued Lamin B1 null (LMNB1-/-) phenotype which we cited in the original manuscript as a supporting point (top of page 16).

      “This new data agrees with our previous data showing that increased heterochromatin levels via histone demethylase inhibition by methylstat treatment (Stephens et al., 2018) and mechanotransduction (Stephens et al., 2019a) rescues nuclear shape in LMNB1-/- nuclei.”

      In our previous publication we showed that methylstat, which increases heterochromatin levels, can suppress nuclear blebbing in LMNB1-/- (Stephens 2018 MBoC). In another manuscript we increased heterochromatin levels via a mechantransduction pathway. This resulted in decreased nuclear blebbing, ruptures, and DNA damage (Stephens 2019 MBoC). However, this manuscript goes on to show that loss of facultative heterochromatin alone via GSK126 can recapitulate the LMNB1-/- which shows loss of facultative heterochromatin (Figure 5).

      __ 5-Auhors mention throughout the manuscript the "actin confinement" component (actin cables localized at the top of the nucleus-not convincingly shown in fig. 2C). Some studies have reported the occurrence of perinuclear actin caps that surround the entire nucleus (DOI: 10.1038/s41467-018-04404-4; DOI: 10.1038/srep40953; DOI: 10.1038/ncb3387). Can the authors investigate the existence of such perinuclear actin ring on the LaminB-/-, LaminA-/- and VPA-treated cells? Could this ring affect NE rupture and shape? Also, if these perinuclear actin rings can be observed, what would they look like in CN03- and Y27-treated cells?__

      The reviewer is requesting that we address possible changes to actin perinuclear cap and surrounding structure. We plan to address this concern by closer analysis of our current data and gather more data if needed.

      __ Minor comments:__

      We will address all the minor comments during revision.__

      1-Please mention/discuss CytoD treatment in main text (Fig 2C).__

      We plan to add text to address this concern.

      __ 2-Can the authors comment on why CN03 treatment on VPA cells does not cause changes in blebs or NE rupture (fig. 3A, B)?__

      We plan to comment on this point.

      __ 3-I'd move fig. 2B to supplement.__

      We prefer to keep this material in the main manuscript Figures as it supports a major support of the title and major conlcusion.

      __ 4-In my opinion schematics on figs. 1A, 2A, 3E are confusing and do not add anything to the manuscript.__

      We prefer to keep this material in the manuscript.

      __ 5-There is something missing on the following sentence, please revise it: "We hypothesized that LMNA-/- nuclei do not show bleb-based behaviors because this perturbation cannot, due to reported disrupted nuclear-actin connections (Broers et al., 2004; Vahabikashi et al., 2022)."__

      We have revised this sentence to read: “We hypothesized that LMNA-/- nuclei do not show bleb-based behaviors because of the reported disruption of nuclear-actin connections (Broers et al., 2004; Vahabikashi et al., 2022).”

      __ 6-Can the authors explain in the Discussion why there is a decrease in gH2AX foci in VPA-treated cells and LaminA-/- cells upon CN03 treatment?__

      We do not have an explanation at this time.

      Significance

      Nuclear deformation is a common event observed in homeostasis and disease and both extra-cellular physical cues and different cellular components play critical roles in nuclear morphology and integrity. It is well known that the actin cytoskeleton exerts a wide range of forces on the nucleus and causes nuclear deformation (via LINC complex) as cells migrate, grow or spread within complex microenvironments. The contribution of mutations of nucleo-skeleton components to nuclear abnormalities and rupture are well described. Additionally, more recently, the contribution of the actin cytoskeleton to nuclear integrity and morphology has also been characterized. However, the role played by actin contraction on nuclear shape and integrity, independently of actin confinement (exerted by the actin cables localized at the top of the nucleus), remain elusive. In this manuscript Pho et al., address this question using cells expressing NLS-GFP to detect nuclear rupture events and potential nuclear deformations. There is no real conceptual advance in this study but there is a novel finding as it shows that acto-myosin contraction affects nuclear integrity and morphology independently of dorsal actin cables (actin confinement). Moreover, the experiments were performed on flat 2D surfaces, a distant scenario from the 3D in vivo landscapes. As a classic cell biology study this manuscript has the potential to be of interest to basic researchers in the field of cell migration, crosstalk of nucleo-skeleton/cytoskeleton and nuclear mechano-sensing.

      We appreciate that Reviewer states

      • “role played by actin contraction on nuclear shape and integrity, independently of actin confinement (exerted by the actin cables localized at the top of the nucleus), remain elusive.”
      • “In this manuscript Pho et al., address this question…” and “[our manuscript] is a novel finding as it shows that acto-myosin contraction affects nuclear integrity and morphology independently of dorsal actin cables (actin confinement).” Thus our manuscript provides a novel finding of interest to the cell biology community

      While individually, chromatin decompaction via VPA, lamin B1 knockout (LMNB1-/-), and lamin A/C knockout (LMNA-/-) have been well-studied, there is no other publication that directly compares them and furthermore decouples the role of actin contraction from confinement.

      __ __

      Reviewer 2

      The authentication of cell lines was not clear. The origin of the cell lines used was not clear. Were they immortalized? Did they examine primary MEFs? The authors did not seem to be aware of convincing data showing that Lamin B1 increases nuclear dispensability, whereas lamin B1 deficiency has the opposite effect.

      Cell lines have been previously published multiple times, but originated from Shimi et al. 2011. We have revised the text to clarify that these are immortalized MEFs used in many previous studies cited in the main manuscript and the materials and methods, and not primary MEFs.

      “ MEFs were immortalized with SV40 large T antigen by retroviral transduction of the gene encoding the SV40 large T antigen as previously described (Shimi et al., 2011, 2015).”

      We also included citations to (Vahabikashi et al., PNAS 2022) which has recently compared many lamin knockouts and knockdowns.

      The reviewer makes an unclear statement about lamin B1 levels and “dispensability” but provides no citations. As cited in the original manuscript lamin B1 null nuclei are one of the most studied models of nuclear blebbing and rupture (Vagas et al., Nucleus 2012; Hatch et al., JCB 2016; Young et al., MBoC 2020). __

      The paper reads like a rough draft. Nomenclature inappropriate.__

      The nomenclature used in this manuscript follows previous publications in the field including for chromatin notation (VPA, Stephens et al., 2017 and all previous manuscripts using this drug), lamin KOs (LMNB1-/- and LMNA-/-, Shimi et al. 2011.), and actin contraction and confinement are field appropriate.

      We will revise the text to clarify nomenclature.

      Significance

      The impact of actin on blebs and nuclear shape is well established. The implications of these findings for distinct roles of the nuclear lamin proteins were not clear. The impact of the interventions on nuclear stiffness was not measured.

      We agree with the reviewer that the impact of actin on nuclear shape is well established. However, the differential roles of actin contraction vs. confinement are not clear, as we state in the intro of the original manuscript. We believe our paper shows for the first time the separate role of actin contraction from actin confinement, where actin contraction is modulated, and we find that actin confinement measured by nuclear height remains the same. Reviewer #1 agrees that our data separating the roles of actin contraction and confinement is a novel finding (See Reviewer #1 Significance above).

      The reviewer states that the distinct roles of lamins are not clear. We use different lamin knockout mutants as phenotypes of nuclear blebbing (LMNB1-/-, along side VPA) and abnormal nuclear shape measured as decreased nuclear circularity with no change in nuclear blebbing (LMNA-/-). These two different phenotypes of nuclear blebbing and abnormal shape also coincide with bleb-based nuclear ruptures and non-bleb-based nuclear ruptures, respectively (Figure 1). We do not examine the full role of lamins in this manuscript, as it is well beyond the scope of this work.

      As cited in the original manuscript, interventions on chromatin (VPA) and lamins (LMNB1-/- and LMNA-/-) have been previously published and thus were not the focus. Nuclear stiffness measurement of perturbation of chromatin compaction via VPA has been published numerous times by our lab (Stephens et al., 2017,2018, 2019 MBoC; Berg et al., 2022 biorxiv) and others (Hobson et al., 2020 MBoC, Shimanoto et al., 2017). Nuclear stiffness in LMNB1-/- and LMNA-/- has been published in (Vahabikashi et al., PNAS 2022). We also have previously shown that depolymerization of actin does not impact nuclear stiffness (Stephens et al., 2017 MBoC), which would suggest that changes in actin contraction would not influence nuclear stiffness. This is supported by the fact that changes in actin contraction to not alter actin confinement pushing down on the nucleus resisting it (force balance) in all conditions except VPA Y27632 (Figure 2).

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      Referee #4

      Evidence, reproducibility and clarity

      This article examines the cellular processes that predispose cells to nuclear blebbing and DNA damage in response to lamin and chromatin perturbations. The authors show key differences in these two types of perturbation and demonstrate a role for actin contractility. The experiments are well controlled and the data analysis generally rigorous. However, prior to acceptance, a number of issues must be fixed to improve the manuscript. I do not know the field sufficiently well to judge the novelty of the data.

      Major issues:

      • page 7, bottom: The authors state that measuring nuclear height gives an indication of confinement and force balance. But, if the nuclear mechanical properties have changed, then the nuclear height could change without any change in contractility. So, the authors would need to also verify that the level of contractility hasn't changed and that the mechanical properties haven't changed to really confirm that the cell height is a good measure of confinement. The level of contractility can be assessed by staining for pMLC. The nuclear mechanical properties may have been measured by others.
      • In general, are the changes in contractility resulting from drug treatments sufficiently large to deform the nucleus? Can the authors show a time course of nuclear height in response to a treatment for WT for example? This would allow to link contractility to nuclear height.
      • Page 9: The authors do not find any change in nuclear shape. Can they measure shape pre/post treatment on the same cells? It could be that the effect is lost in variability unless you do paired measurements?
      • Page 11: the authors find nuclear ruptures unchanged in LMA -/- even when there is no contractility. They then state: "We hypothesized that LMNA-/- nuclei do not show bleb-based behaviors because this perturbation cannot, due to reported disrupted nuclear-actin connections". I do not understand this sentence.
      • To characterise actin contractility better, it would be good to present images of the actin cables in each condition and pre/post treatments. This would allow to visually assess whether the morphology of the F-actin cytoskeleton has changed. This is one of the main topics of the study and as such it should be examined.
      • On all bar charts, the authors should indicate: the number of independent experiments, the number of cells examined.
      • I find the diagrams on Fig 1A, 2A etc do not help to illustrate what the authors think is happening. Can they redraw them in a more informative way?
      • The abstract, introduction, and discussion are overly long and lack focus. These should be rewritten succinctly.

      Minor issues:

      • page 4: inhibitors of Rho-kinase will also modulate actin polymerisation indirectly through the action on Lim-kinase and cofilin.
      • page 5, second paragraph: the authors should state that they are measuring the frequency of ruptures. At first, I thought this might be a mechanical strain.
      • Page 7: In general, it may be useful to discuss the temporal evolution of the c/n and the circularity side by side. The change in circularity over time could be an indicator of mechanical strain, while the c/n would report on any transient loss of integrity of the nuclear membrane.
      • Fig 1B: it would be nice to present the time course of the c/n as well.
      • Fig S1: it might be interesting to characterise the dynamics/amplitude of the c/n for the different conditions. There doesn't appear to be any difference between the nuclear blebbing rupture and the non blebbing rupture. This suggests that the two phenomena (nuclear blebbing and nuclear rupture) are independent: i.e. rupture is not causally linked to blebbing.

      Significance

      This article examines the cellular processes that predispose cells to nuclear blebbing and DNA damage in response to lamin and chromatin perturbations. The authors show key differences in these two types of perturbation and demonstrate a role for actin contractility. The experiments are well controlled and the data analysis generally rigorous. However, prior to acceptance, a number of issues must be fixed to improve the manuscript. I do not know the field sufficiently well to judge the novelty of the data.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary: In this manuscript, the authors set out to compare the contributions of chromatin and both A- and B-type lamins to the maintenance of nuclear shape and NE integrity, while also observing how actin contraction or confinement individually contribute to changes in nuclear shape and integrity. To do this, they used mouse embryonic fibroblasts (MEFs) expressing an NLS-GFP as a readout of nuclear shape and rupture, while perturbing chromatin compaction (using the HDAC inhibitor VPA) or using MEF lines devoid of A-type (LMNA -/-) or B-type (LMNB1 -/-) lamins, while simultaneously decreasing or increasing actin-mediating tensile forces but maintaining actin compressive forces by observing nuclear height. They found that increased actin contraction causes a higher instance of bleb-based nuclear shape changes and ruptures in WT and LMB1-/- cells, as well as cells with chromatin decompaction, but actin contraction did not impact the nuclear morphology or prevalence of bleb-based ruptures with a loss of LMNA. The authors also show that loss of LmnB1 causes chromatin decompaction phenotype, and loss of Lamin A/C creates and increase in bleb-based nuclear ruptures, but only under conditions of chromatin decompaction with VPA.

      Major critiques:

      1. Fig 2B- The authors use γMLC2 as a readout for actin contractility when CN03 and Y27632 is applied. While this method has been used previously as a readout for actin contractility, it would be more convincing if the authors included at least another technique to verify and increase or decrease in actin contractility in some of their conditions (e.g. traction force microscopy).
      2. Fig 2C- the authors suggest that the shape changes and blebbing are not due to actin confinement on the nucleus, because nuclear height does not change. This is an large assumption made from only a small amount of data, considering that there could be increased confinement on the nucleus during actin contractility, yet it is too small to be measured by the side imaging technique described in the paper. The manuscript would benefit greatly from more experiments that could show that the blebs are being made even when there is no confinement.

      Minor critiques:

      1. Fig 2B- the statistical significance asterisks above the MLC2 relative fluorescence graph do not seem to be aligned appropriately with the bars, and it is difficult to know which asterisk belongs to which bar. Same is true for the nuclear height graph. The "vs. WT" box above the nuclear height graph is also confusing in that it is hard to see which statistical

      Significance

      The manuscript creates value in the field, in that the major findings of the relationship between lamins, chromatin, and actin and their respective impacts on nuclear shape and rupture are in agreement with findings from previous studies and therefore bolsters the current model on NE mechanics. The novelty in the paper comes from the reported finding that actin contraction and not confinement is what controls nuclear blebbing and ruptures, although this evidence seems to be limited to a single method (nuclear height measurements) presented in Fig 2 and Fig S3A. In contrast to the author's suggestions, it is possible that during actin contractility, actin cables running over the nucleus could be confining the nucleus to height changes that are too subtle to be measured using a confocal microscope, and there are forces acting on to top of the nucleus that could contribute to the blebbing phenotype observed. In my opinion, the conclusions drawn by the authors in this matter rely too few evidence.

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      Referee #2

      Evidence, reproducibility and clarity

      The authentication of cell lines was not clear. The origin of the cell lines used was not clear. Were they immortalized? Did they examine primary MEFs? The authors did not seem to be aware of convincing data showing that Lamin B1 increases nuclear dispensability, whereas lamin B1 deficiency has the opposite effect.

      The paper reads like a rough draft. Nomenclature inappropriate.

      Significance

      The impact of actin on blebs and nuclear shape is well established. The implications of these findings for distinct roles of the nuclear lamin proteins were not clear. The impact of the interventions on nuclear stiffness was not measured.

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      Referee #1

      Evidence, reproducibility and clarity

      In this manuscript Pho et al., characterize the effects of actin confinement versus acto-myosin contractility on nuclear deformation and nuclear envelope (NE) rupture/blebs. They use both genetic and pharmacological perturbations that impact nuclear shape and integrity such as chromatin decompaction and genetic deletion of nucleo-skeleton components (LaminA and LaminB). First, the authors show that modulation of acto-myosin contractility does not affect nuclear height (a proxy for the effects of actin confinement on the nucleus). This finding allowed them to study the effects of acto-myosin contractility on nuclear shape and integrity independently of actin-mediated nuclear confinement. Using fibroblasts expressing a NLS-GFP construct to assess nuclear shape and integrity, the authors show that increased acto-myosin contractility by treatment with Rho activator (CN03) leads to nuclear blebs and NE rupture events (curiously, they show that in LaminA-/- fibroblasts the NE rupture events were not bleb-based and were not affected by CN03). With these results, authors conclude that actin contraction is a major determinant of bleb-based NE rupture, independently of actin confinement. Next, since many studies have associated loss of nucleo-cytoplasmic compartmentalization with increased DNA damage, the authors addressed the occurrence of DNA damage foci in several contexts/perturbations. They find that increased NE rupture frequency by CN03 treatment does not lead to increased DNA damage. Based on the sole finding, authors then claim that DNA damage is mostly associated with abnormal shaped nuclei instead of ruptured nuclei. The data presented is overall convincing and well controlled but a more in-depth characterization of the DNA damage events in VPA-treated cells, LaminB-/- and LaminA-/- cells is needed to support the claim that DNA damage is not associated with NE events in their system. More details below.

      Major comments:

      1. In fig. 4, increased NE rupture frequency by CN03 treatment did not lead to an increase in gH2AX foci, so authors claim that DNA damage is not associated with NE events. However, the increase observed in NE rupture frequency is rather very subtle (from 1.5 to 3 in WT cells; from 2.5 to 4 in VPA treated ells and from 2 to 2.5 in LaminB-/- cells) so I wonder if such a minor increase might be sufficient to cause an increase in DNA damage foci. Moreover, DNA damage events are repaired within minutes and the analysis throughout this manuscript is made on snapshots. Therefore it becomes difficult to draw any conclusion regarding the absence of a link between NE rupture and DNA damage. To assess DNA damage events in a quantitative manner and show that abnormal shape (as opposed to NE rupture) is the main driver of DNA damage, authors should perform live cell imaging of cells co-expressing a DNA damage marker and a NE rupture marker to track DNA damage events following NE rupture.
      2. Fig. 4C: to show that frequency of NE rupture is less important than abnormal nuclear shape for DNA damage appearance, authors need to quantify gH2AX in normal x blebbed x abnormal nuclei in each one of the conditions (untreated, CN03 and Y27). But again, this analysis must be complemented by live cell imaging experiments to track DNA damage foci appearance (or not?) following NE rupture events.
      3. Since VPA treatment increases both the percentage of bleb-based NE rupture and NE rupture frequency in LaminA-/- cells, authors should show that gH2AX foci in this sample remain unaltered to further support their claim that frequency of NE rupture is less important than abnormal nuclear shape.
      4. Does methylstat treatment rescue LaminB-/- phenotypes (blebs, ruptures, ...)?
      5. Auhors mention throughout the manuscript the "actin confinement" component (actin cables localized at the top of the nucleus-not convincingly shown in fig. 2C). Some studies have reported the occurrence of perinuclear actin caps that surround the entire nucleus (DOI: 10.1038/s41467-018-04404-4; DOI: 10.1038/srep40953; DOI: 10.1038/ncb3387). Can the authors investigate the existence of such perinuclear actin ring on the LaminB-/-, LaminA-/- and VPA-treated cells? Could this ring affect NE rupture and shape? Also, if these perinuclear actin rings can be observed, what would they look like in CN03- and Y27-treated cells?

      Minor comments:

      1. Please mention/discuss CytoD treatment in main text (Fig 2C).
      2. Can the authors comment on why CN03 treatment on VPA cells does not cause changes in blebs or NE rupture (fig. 3A, B)?
      3. I'd move fig. 2B to supplement.
      4. In my opinion schematics on figs. 1A, 2A, 3E are confusing and do not add anything to the manuscript.
      5. There is something missing on the following sentence, please revise it: "We hypothesized that LMNA-/- nuclei do not show bleb-based behaviors because this perturbation cannot, due to reported disrupted nuclear-actin connections (Broers et al., 2004; Vahabikashi et al., 2022)."
      6. Can the authors explain in the Discussion why there is a decrease in gH2AX foci in VPA-treated cells and LaminA-/- cells upon CN03 treatment?

      Significance

      Nuclear deformation is a common event observed in homeostasis and disease and both extra-cellular physical cues and different cellular components play critical roles in nuclear morphology and integrity. It is well known that the actin cytoskeleton exerts a wide range of forces on the nucleus and causes nuclear deformation (via LINC complex) as cells migrate, grow or spread within complex microenvironments. The contribution of mutations of nucleo-skeleton components to nuclear abnormalities and rupture are well described. Additionally, more recently, the contribution of the actin cytoskeleton to nuclear integrity and morphology has also been characterized. However, the role played by actin contraction on nuclear shape and integrity, independently of actin confinement (exerted by the actin cables localized at the top of the nucleus), remain elusive. In this manuscript Pho et al., address this question using cells expressing NLS-GFP to detect nuclear rupture events and potential nuclear deformations. There is no real conceptual advance in this study but there is a novel finding as it shows that acto-myosin contraction affects nuclear integrity and morphology independently of dorsal actin cables (actin confinement). Moreover, the experiments were performed on flat 2D surfaces, a distant scenario from the 3D in vivo landscapes. As a classic cell biology study this manuscript has the potential to be of interest to basic researchers in the field of cell migration, crosstalk of nucleo-skeleton/cytoskeleton and nuclear mechano-sensing.

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      Reply to the reviewers

      We would like to first thank the reviewers for their patience with us given the delays in the generation of our revised manuscript. In addition to a maternity leave taken by Dr. Goyon, we took the reviewers comments very seriously and generated significant amounts of new data to address the insightful comments and suggestions of all three reviewers. The manuscript now has 9 figures, with 7 supplemental figures and 3 tables.

      Our point-by-point rebuttal is below, but the major new additions are:

      • Full analysis of bile acid species in the feces (to complement the liver and serum analysis provided in first submission). We also performed FDG-glucose PET analysis of the mice, which revealed significant alterations in proliferation in the gut in young MAPL KO mice. We did this in response to the concerns raised by reviewers 1 and 3 about the effects of the gut on bile acid regulation, and we discuss our findings below in response to reviewers, and within the revised manuscript.
      • Our initial submission reported an Illumina approach for transcriptomics in livers of male wt and KO mice. We now completed RNAseq analysis on both male and female (littermate) control and MAPL KO mice at 3 months old. This validated what we had seen in the Illumina arrays, but allowed us a deeper look into transcriptional and sex specific changes that we present in response to review and within the revised manuscript. We have also expanded our feed/fast cycle analysis of the dynamic changes in gene expression of bile acid related pathways to further document the disruption in the feedback cycles regulating bile acid synthesis in liver.
      • We have developed assays using primary hepatocytes from +/-MAPL mice for a better analysis of cell autonomous functions and bile acid secretion. This complements the tail-vein rescue experiments we had presented in initial submission.


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

      In this study, the authors have investigated the impact of permanently silencing the expression of the mitochondrial anchored protein ligase (MAPL) in mice on bile acid (BA) metabolism through the alteration of ABCD3 SUMOylation. This ABC pump mediates the uptake of C27-BAs by peroxisomes and hence determines the shortening of the BA sidechain. In addition, other aspects of general metabolism have also been investigated. The study is highly relevant and contains valuable information__.

      AUTHORS: We sincerely thank the reviewer for their supportive comments on the value of our work.

      Major points

      R1: 1) Sidechain shortening is essential for the synthesis of primary C24 BAs. This study suggests that the entrance of C27 BAs in peroxisomes, which depends on ABCD3 activity, is reduced by MAPL-dependent ABCD3 SUMOylation. Thus, knocking out MAPL in mice results in enhanced BA accumulation in serum and liver, presumably by facilitated uptake of C27 by the peroxisomes and stimulation of de novo synthesis of primary BA. Indeed, a decreased C27/C24 BA ratio was found. However, the results suggest that Cyp7a1 is not the main checkpoint for the control of BA synthesis or that Fxr/Fgf15/Cyp7a1 pathway is also affected by MAPL manipulation because Cyp7a1 expression, which could be expected to be downregulated in response to enhanced BA levels, is not affected in MAPL knockout. Moreover, no change in Fgf15 was found (Suppl. Fig. 2C, 2D, 2G). The authors must discuss these surprising findings.

      AUTHORS: We entirely agree with the reviewer that the lack of feedback to downregulate bile acid synthesis through the canonical pathways was very unusual and is one of the novel aspects of our study. While the circulating bile was significantly elevated, this was not sensed by the pathways in the gut or liver to downregulate CYP7A1 through the activation of bile receptors FXR/FGF15. We have rewritten a great deal of the manuscript to be clearer about this, as we also feel the MAPL KO presents us a very unique model of bile acid dysregulation with many unexpected observations. In revision we completed an analysis of ~40 bile acid species within the feces to understand what may be happening in the gut. Interestingly, bile acid levels were decreased in feces, in contrast to the elevations seen in gut and liver (New Fig3). This prompted us to consider a block in bile acid delivery to the gut, or cholestasis. However, such a pathology is generally lethal, yet MAPL KO mice live past 2 years. Histological analysis (presented in New Fig 3) did not reveal any obstructions, necrosis or pathology in the bile canaliculi. Bomb calorimetry of the feces showed equivalent calories in KO mice (New Fig3), suggesting that digestion of food was fully intact, something that would have been altered if bile was absent. Lastly, the secondary bile acids that are generated by the microbiome were elevated in serum and liver, indicative of the successful transit of these bile species through the gut. Therefore, we conclude that the bile does reach the gut, yet appears to be significantly reabsorbed back into circulation without alerting the bile sensing pathways to secrete more FGF15. Ultimately, we have not answered the initial question, since we do not know yet how MAPL is required, directly or indirectly, for bile acid feedback loops. But we realize now that this will take significant effort to resolve mechanistically, something we will continue to work on in the next stage of our project.

      R1: 2) The authors discussed that the alternative acidic pathway is responsible for these changes, but Cyp27a1 was, in fact, moderately downregulated in MAPL knockout mice.

      AUTHORS: We apologize for the confusion. Yes, Cyp27A1 is moderately downregulated in MAPL KO mice, seen now within RNAseq analysis (New Fig 2C) and the western blots (Fig 3C) which we meant to say could reflect a specific feedback loop to inhibit bile acid synthesis from the acidic pathway, rather than through canonical, FXR/FGF15 mediated changes in Cyp7A1. We considered that very little is known about the regulation of the acidic pathway, and perhaps MAPL effects on bile acid metabolism may be more dominant in this loop. However, clearly it remains unresolved how the elevated bile, even in liver, goes undetected by FXR to downregulate Cyp7A1. We have tried to approach our results and discussion in a more systematic way to make these points clearer.

      R1: 3) Serum BAs may reflect a higher BA pool. Nevertheless, this has not been assayed. Enhanced flow of C27-BA precursors into peroxisomes is consistent with increased C24-BA production and reduced intrahepatic concentration of C27-BA in MAPL knockout mice (Suppl. Table 2). However, it is not explained why C27-BA serum concentrations were increased in these animals (Suppl. Table 2 and Suppl. Fig. 2B).

      AUTHORS: We thank the reviewer for pointing that out. We added quantification in the feces of the bile acid species (New Fig2C). Surprisingly we found decreased levels of C27 and C24 bile acids. We are speculating that some of the increased bile acid levels in the serum are due to increased synthesis/flux through the hepatocyte peroxisomes and some due to reabsorption.

      R1: 4) C27-BAs have been described as more toxic species than most C24-BAs. In the liver of MAPL knockout mice, C27-BAs levels were decreased (Suppl. Table 2). Other toxic species such as DCA and CDCA were not markedly changed. Muricholic acids and ursodeoxycholic acid, which were increased, are believed to be non-toxic or even hepatoprotective. Therefore, the relationship between changes in BA homeostasis and liver carcinogenesis should be better justified.

      AUTHORS: We apologize for generalizing too much in assigning elevated bile acid species as potential drivers/contributors of tumorogenesis. We have made note in the text of the reviewers points, including references to the protective nature of some bile species. We cannot yet pinpoint the precise cause of cellular transformation but have tried to balance the discussion around potential changes in 1) proliferative signaling cascades potentially linked to bile signaling, 2) ER stress, which has been linked to tumorogenesis, and 3) the protection against cell death pathways seen in MAPL KO cells.

      R1: 5) SUMOylation may affect transporters which may simulate certain cholestasis with retention in serum of BAs. Expression levels of basolateral Ntcp, Oatps, and canalicular Bsep are required to better understand BA homeostasis. Besides, biliary secretion in MAPL knockout mice would give relevant information on what is actually happening in the biliary function of these animals.

      AUTHORS: We thank the reviewer for an excellent point. To get a better view of the transcriptional changes of the transporters highlighted here, but also of all genes in liver, we completed RNAseq analysis. This showed no change in the mRNA levels of the transporters highlighted, so we performed qRT-PCR analysis from livers during a feed/fast experiment to determine whether the dynamic behavior of expression may be altered upon MAPL loss (New Fig 4). Importantly, we found that the transporters expression was unchanged (except for one of the Oatp which would limit hepatocyte reabsorption). We also added bile secretion from primary hepatocytes reproducing the phenotype. This reinforces our point that MAPL loss affect primarily bile acid flux through the peroxisome and that is enough to have increased bile acid in the serum. Lastly, to test whether bile was successfully transiting into the gut we completed the bile acid analysis of feces, along with bomb calorimetry, PET analysis and histology of the gut, all of which indicate that bile flux to the gut is intact.

      __Reviewer #1 (Significance (Required)):

      The study is relevant and original.__

      AUTHORS: We thank the reviewer for appreciating the strengths of our study.

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

      The present manuscript described novel interacting partners of a mitochondrial/peroxisomal Sumoylation ligase MAPL and describes the phenotype of a newly generated MAPL KO mouse model.

      Major comments:

      R2: The authors describe in the introduction that MAPL has multiple functions, including a role in mitophagy,mitochondrial division, inflammation and cell death. New is a role in regulation of peroxisomal bile salt handling. Also the role in hepatic cell proliferation in vivo has not been demonstrated before. The individual findings are generally convincing. However, the relation between the large number of observations is not clear. The authors postulate that multiple aspects of the MAPL KO mice are related to direct effects on PMP70/ABCD3 sumoylation and/or to effects on bile salts. This connection is highly speculative and mechanistically underexplored. As MAPL function was already implicated in many processes unrelated to bile salts/ABCD3, alternative explanations are likely.

      AUTHORS: We appreciate these critical comments, and agree that MAPL has distinct substrates that play important roles in multiple aspects of mitochondrial (and now peroxisomal) signaling, survival and metabolism. This certainly makes it difficult to assign a given substrate to a broad set of pathologies in a KO mouse model. Our lab has been working on MAPL for over 15 years, and a major question has been the physiological function of MAPL in vivo. Our report that the primary phenotypes appear in liver, that effects global metabolism (lean phenotype, insulin sensitivity), proliferation and cancer provide the first evidence of the importance of MAPL in metabolism. Our BioID approach to identify MAPL partners led us to ABCD3, the peroxisomal bile acid transporter, in addition to the identification of established proteins of the mitochondrial and peroxisomal fission machineries. Furthermore, we provide critical new evidence that MAPLs primary role is not to regulate the degradation of its substrate partners, since they were not stabilized upon inhibition of the proteasome. We confirmed the interaction and SUMOylation of ABCD3 from liver tissue using multiple approaches (BioID, co-IP, SIM beads, glycerol gradients), therefore we do not consider the interaction between the two proteins as speculative. We agree that more will need to be done to develop structure/functional analysis of the SUMOylated bile acid transporter. Important to this study, functional data in vivo demonstrates major increases in bile acid production in liver and (new to the revision) primary hepatocytes, consistent with a role for MAPL mediated SUMOylation to gate ABCD3, the only primary bile acid transporter in peroxisomes. We have attempted to position our findings within the context of MAPL function in other pathways and broadened our discussion in terms of all mechanisms and phenotypes.

      In this revised manuscript we expanded our analysis into the gut, given the important role of the gut in bile acid homeostasis. In searching for an explanation for the disruption in FXR/FGF15 responsiveness, we observed a striking proliferative phenotype in the duodenum. The limited proliferation in duodenum is consistent with previous work showing that bile acids can promote proliferation through a number of mechanisms, from the signaling of TGR5 bile receptors to YAP activation. It may also reflect cell autonomous functions of MAPL in enterocytes responsible for the suppression of proliferation, however the limitation of the proliferative phenotype to the top section of the gut does suggest a link to bile. We have included these data, along with a full bile acid analysis of feces, in the revised manuscript given the essential role of the gut as a driver of the feedback loop for bile acid homeostasis. We hope the reviewer will now be convinced that our work places MAPL as a key metabolic regulator offering a new animal model that highlights some very unusual bile acid phenotypes, and a model of spontaneous hepatocellular carcinoma. These are unexpected phenoptypes for a MAPL KO animal given all previous work into MAPL/MUL1.

      R2: Similarly, the metabolic consequences of bile salt signalling (the authors postulate this may occur via TGR5) versus effects of the ER-stress/FGF21 pathway remain unclear.

      AUTHORS: It is not entirely clear to us which metabolic consequences the reviewer refers to in this concern, so hopefully we will answer the question as intended. Assuming the reviewer refers to the lean, insulin sensitive phenotype of MAPL KO mice, we understand there remains some speculation in the relationship between the bile acids or FGF21 as drivers of the insulin sensitivity. The phenotype mimics FGF21 overexpression, as this hepatokine has been long linked to leanness and insulin sensitivity in mice. The curious finding was in our tail vein rescue experiments, even empty adenovirus induced ER stress, so we could not test if MAPL expression would rescue CHOP expression. Yet we had a full restoration of both FGF21 and circulating bile. This indicates that the ER stress is not the main driver of FGF21 expression (or bile secretion) in this system. Given the direct interactions we observed between MAPL and the bile acid transporter, we hypothesized that the rescue of MAPL would return the gating function of ABCD3, and perhaps that the bile acids themselves were driving FGF21 expression. This is consistent with a 2018 study demonstrating that FGF21 signaling resulted in the downregulation of bile acid synthesis (PMID: 29615519), a potential feedback loop to explain the downregulation of many bile acid enzymes seen in our RNAseq analysis. We have been more careful to state the limitations and outstanding questions in the study. It will take many additional mouse crosses, knock-in models carrying mutations in ABCD3, and many other experiments to resolve this question fully. We sincerely hope the reviewer can agree that the observations are robust and well controlled, and will open new avenues of research in the future.

      R2: The title and discussion is too speculative in my opinion, in particular the claim linking ABCD3 activity to all the metabolic effects observed in the MAPL KO.

      AUTHORS: We have changed the title of the manuscript to better reflect the global consequences of MAPL loss and the novelty of our findings overall.

      - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

      R2: Experimental support for an altered role of ABCD3 activity as CAUSAL for the observed phenotype is essential

      AUTHORS: We argue that our data has established the MAPL-dependent SUMOylation of ABCD3 in vivo using the SIM-bead pull down, our BioID identified this transporter as the top partner of MAPL, validated with immunoprecipitation from liver in floxed and MAPLKO mice, and we observe biochemical alterations in the oligomeric state of ABCD3. Identifying the SUMO sites and generating CRISPR KI mice to confirm effects on bile acid flux would represent another year (at least) of work. We strongly believe that our study provides a number of very important new insights into bile acid metabolism with phenotypes that have not been seen before (as explained by Rev1). We understand that this will be a final documentation of the structure/function relationship between MAPL and ABCD3, but we have established this interaction and substrate/enzyme pairing between a newly identified bile acid transporter (for which very little work has been done anywhere), and an evolutionarily conserved SUMO E3 ligase.

      - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

      R2: As Sumoylation sites can be predicted to some level, an MAPL-insensitive ABCD3 protein could be made and used to link effects of ABCD3 sumoylation to MAPL and consequences of MAPL deficiency. Minimally, data linking the modest effects on ABCD3 activity (for example by PMP70 knockdown in vivo) on the observed phenotype of MAPL KO is required to support the currents aims.

      AUTHORS: Knocking down ABCD3/PMP70 would be possible with tail vein injection. However, the loss of ABCD3 would give the opposite phenotype, where bile acid production would be lost, as already documented in Ferdinandusse et al 2015 (PMID: __25168382). ABCD3 is the only known bile acid transporter in peroxisomes so we do not agree that our phenotypes are so obviously explained by another mechanism, nor why the reviewer considers the effects on bile acid to be “modest”? We suggest, based on established paradigms for the SUMOylation of ion transporters, that the SUMOylation would gate the transporter to inhibit it. We agree this is a next step, but it is beyond the scope of this manuscript. __

      • Are the data and the methods presented in such a way that they can be reproduced? Yes
      • Are the experiments adequately replicated and statistical analysis adequate? Yes.

      R2: However, the initial and main finding of the manuscript, the identification of ABCD3 as MAPL interacting partner is plotted somewhat vague. Seems like data is from a single experiment, while the method section suggests otherwise

      AUTHORS: We have repeated these experiments (BioID, co-IP, SIM bead experiments) numerous times from multiple mouse livers and cell lines. We have ensured the numbers are in the methods and legends. We would also submit that the interaction with ABCD3 is not, in fact, the main finding of the manuscript. The entire characterization of the mouse pathology, ending in the spontaneous development of hepatocellular carcinoma, will be of high interest to researchers in the fields of metabolism, liver biology and cancer, MAPL/MUL1 function, and insulin sensitivity. The BioID and interactomics was (to us) also very important for the things it did NOT find, for example, the substrates others consider to be regulated by MUL1 ubiquitination. Our experiments with MG132 clearly show that the candidate substrates identified in BioID are not targeted for degradation, including Mfn2 and others. MAPL loss did not lead to changes in mitochondrial or peroxisome mass, nor did it significantly alter the gene expression of these proteins (new RNAseq analysis). While some of these aspects represent negative data, it is an important, in vivo demonstration that MAPL is not a key player in mitochondrial quality control. In this sense, the entire study is highly unexpected, with such clear phenotypes in global metabolism, bile acid and liver specific effects, proliferation and cancer.

      Minor comments:

      R2: Abstract states: "BioID revealed the peroxisomal bile acid transporter ABCD3 as a primary MAPL interacting partner, which we show is SUMOylated in a MAPL-dependent manner." The method aspect of this sentence is too unclear as it assumes all readers know what BioID entails.

      AUTHORS: We apologize for the confusion and have clarified that sentence.

      R2: The abstract also states that increased bile salt secretion is occurring. No experimental data supporting increased hepatocellular bile salt secretion is provided, only increased serum levels, which is not the same.

      AUTHORS: We thank the reviewer for pushing us to examine bile acid secretion from primary hepatocytes isolated from MAPL KO or littermate control mice. This took us some time since the MAPL KO hepatocytes didn’t seed as well as control cells, but we adapted our protocols and the cells adhered well. These experiments showed that MAPL KO hepatocytes produce 2-3X more bile acids than wild type hepatocytes over a 48 hours culture period. Therefore we have confirmed that the hepatocytes are producing more bile.

      R2: How was FGF15 measured? The methods section is unclear about this, and the legends indicates this was measured by ELISA. Earlier paper suggests that FGF15 is not easily detectable and controls for the elisa should thus be included (PMID: 26039452).

      AUTHORS: We quantified FGF15 by ELISA using established protocols without any difficulty, and have included all standards and controls in the excel sheets. The paper cited is from 2015, which is nearly 10 years ago so it appears that these tools have improved.

      R2: Figure 1D; last lane with the duplo of the rescue with the mutant MAPL seems missing, only single value is plotted.

      AUTHORS: The quantification presents n=3 biological replicates, the figure is a representative immunoblot of the 3 replicates, where only one mutant MAPL rescue is loaded. We clarified that in the legend.

      Reviewer #2 (Significance (Required)):

      - Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

      R2: The individual findings/observation are very interesting. However, as the causal link and relative contribution of the multitude of processes affected in the MAPL KO remains unclear current impact is limited.

      AUTHORS: We respectfully disagree with the reviewer that the impact is limited by the fact that we are studying an E3 ligase with multiple substrates. Clearly we understand that MAPL has functions at mitochondria regulating DRP1 and other processes, as we have worked on MAPL for over years. The novelty and importance of this study is that it is the first full characterization of a MAPL KO mouse that presents with very unexpected phenotypes that will be used to advance the field in multiple ways. The identification of ABCD3 as a substrate represents the first in peroxisomes to be examined, and very little is known about the regulation of these transporters. Even if there are additional functions of MAPL at play in liver (which I’m sure there are), linking it to bile acid flux, is a novel finding.

      - Place the work in the context of the existing literature (provide references, where appropriate).

      R2: A highly related manuscript recently appeared: mitochondrial ubiquitin ligase MARCH5 is a dual-organelle locating protein that interacts with several peroxisomal proteins. Peroxisomal MARCH5 is required for mTOR inhibition-induced pexophagy by binding and ubiquitinating PMP70 (J Cell Biol. 2022 Jan 3; 221(1): e202103156.) This is not discussed at all. However, it supports that better scientific insight into regulation of peroxisomal processes, including the activity of ABCD3/PMP70 is very relevant to the field.

      AUTHORS: We apologize for omitting the study of MARCH5 in our manuscript. The reviewer is correct that this highlights the unique function of MAPL in the regulation of the transporter through SUMOylation. MAPL loss does not alter the turnover or expression of ABCD3/PMP70 (or peroxisomes for that matter), which is the opposite of MARCH5.

      • State what audience might be interested in and influenced by the reported findings. An audience interesting in peroxisomal function and/or bile salt signalling/toxicity
      • Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. R2: bile salt signalling; transport

      AUTHORS: We understand that the reviewer focused on the first figure (of 9), and perhaps did not appreciate the unique findings we have made in characterizing a very novel bile acid phenotype where the feedback loops are interrupted. The links to cancer were also not mentioned by the reviewer, something we also feel strongly about since it is consistent with the roles of MAPL in cell death pathways. The establishment of a novel animal model of HCC is of value to the community.

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

      R3: In the manuscript „SUMOylation of ABCD3 restricts bile acid synthesis and regulates metabolic homeostasis", the authors showed that MAPL has a critical role in regulation of bile acid synthesis and loss of MAPL leads to changes in metabolism and development of liver cancer. The findings are of certain interest, However, before publication of the manuscript several shortcomings have to be clarified.

      AUTHORS: We thank the reviewer for recognizing that the identification of MAPL as having a critical role in metabolism, bile acid signaling and cancer is of importance to the field.

      Major Comments: R3: Since levels of muricholic acid is drastically increased, the authors should investigate Cyp2c70 expression since this enzyme is responsible for muricholic acid synthesis. Is there are direct regulatory effect of MAPL on Cyp2c70?

      AUTHORS: We thank the reviewer for the suggestion, we measured Cyp2c70 and found no change in MAPL KO livers, as seen in our new RNAseq analysis and qRT-PCR (New Fig4).

      R3: Does MAPL directly regulate the changes on cyp enzymes or is the regulation indirect via acting on the nuclear receptors known to regulate bile acid synthesis such as FXR, CAR, PXR. Please provide data on that.

      AUTHORS: We completed RNAseq analysis from livers of control and MAPLKO mice to generate a more complete picture of precise transcriptional changes in all genes. We also looked specifically at FXR, LXR, PXR and PPARa target genes using qRT-PCR approaches from livers in feed/fasting experiments to examine dynamic changes in expression. Most were unchanged and responded normally, with the exception of some PPARs target genes that were increased, supporting the metabolism necessary to handle high levels of bile acid flux.

      R3: The authors show that Cyp4a14 is increased due to loss of MAPL. Cyp4a14 is also a downstream target of PPARa. The authors should provide data on PPARa signalling in MAPLKO mice, especially on beta oxidation. This may explain why MAPL KO mice are lean.

      AUTHORS: See previous response

      R3: The authors did not investigate bile duct proliferation and activation of cholangiocytes, features which often occur in the context of changes in bile acid homeostasis. Do MAPL. KO mice show increased ductular proiliferation and reactive cholangiocyte phenotype? Please provide data such as expression and staining of CK19, KI67, OPN, VCAM • EGR1 and EGFR are key regulators in HCC and are known to be regulated by bile acids. The authors should investigate whether these key regulators may play a role in development of HCC in MAPL KO mice.

      AUTHORS: We thank the reviewer for these suggestions. We have the Ki67 data included in Figure 8. It shows increased hepatocyte proliferation but not cholangiocytes. Moreover our histology stainings do not support any change in canicular structure. We also measured the EGF receptor activation in our model and found no change (Supplemental Figure 3). We also tried to find other indication of inflammation (as suggested), in our RNAseq dataset, we can find some known inflammatory signals like SPP1/Osteopontin, VCAM1, CCL2, CD68 being increased. However, the pathway analyses did not reveal any increased inflammatory status, which is also supported by absence of immune cell infiltration. It is possible that some immune or inflammation remodeling is happening but not at a large scale and not following the canonical inflammatory liver diseases.

      Reviewer #3 (Significance (Required)):

      R3: The finding that loss of MAPL is involved in regulation of bile acid synthesis is of certain interest for the field of cholestatic liver and bile duct injuries. MAPL KO mice might be an interesting model to study potential therapeutics for these diseases. Furthermore, the fact that MAPL KO mice develop spontaneous HCC is also of particular interest, since such models are quite rare.

      AUTHORS: We thank the reviewer for finding our work ‘of particular interest’

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

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      Referee #3

      Evidence, reproducibility and clarity

      In the manuscript „SUMOylation of ABCD3 restricts bile acid synthesis and regulates metabolic homeostasis", the authors showed that MAPL has a critical role in regulation of bile acid synthesis and loss of MAPL leads to changes in metabolism and development of liver cancer. The findings are of certain interest, However, before publication of the manuscript several short cummings have to be claryfied.

      Major Comments:

      • Since levels of muricholic acid is drastically increased, the authors should investigate Cyp2c70 expression since this enzyme is responsible for muricholic acid synthesis. Is zhere are direct regulatory effect of MAPL on Cyp2c70?
      • Does MAPL directly regulate the changes on cyp enzymes or is the regulation indirect via acting on the nuclear receptors known to regulate bile acid synthesis such as FXR, CAR, PXR. Please provide data on that.
      • The authors show that Cyp4a14 is increased due to loss of MAPL. Cyp4a14 is also a downstream target of PPARa. The authors should provide data on PPARa signalling in MAPL KO mice, especially on beta oxidation. This may explain why MAPL KO mice are lean.
      • The authors did not investigate bile duct proliferation and activation of cholangiocytes, features which often occur in the context of changes in bile acid homeostasis. Do MAPL. KO mice show increased ductular proiliferation and reactive cholangiocyte phenotype? Please provide data such as expression and staining of CK19, KI67, OPN, VCAM
      • EGR1 and EGFR are key regulators in HCC and are known to be regulated by bile acids. The authors should investigate whether these key regulators may play a role in development of HCC in MAPL KO mice.

      Significance

      The finding that loss of MAPL is involved in regulation of bile acid synthsesis is of certain interest for the field of cholestatic liver and bile duct injuries. MAPL KO mice might be an interesting model to study potential therapeutics for these diseases. Furthermore, the fact that MAPL KO mice develop spontaneous HCC is also of particular interest, since such models are quite rare.

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

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

      The present manuscript described novel interacting partners of a mitochondrial/peroxisomal Sumoylation ligase MAPL and describes the phenotype of a newly generated MAPL KO mouse model.

      Major comments:

      • Are the key conclusions convincing?

      The authors describe in the introduction that MAPL has multiple functions, including a role in mitophagy,mitochondrial division, inflammation and cell death. New is a role in regulation of peroxisomal bile salt handling. Also the role in hepatic cell proliferation in vivo has not been demonstrated before. The individual findings are generally convincing. However, the relation between the large number of observations is not clear. The authors postulate that multiple aspects of the MAPL KO mice are related to direct effects on PMP70/ABCD3 sumoylation and/or to effects on bile salts. This connection is highly speculative and mechanistically underexplored. As MAPL function was already implicated in many processes unrelated to bile salts/ABCD3, alternative explanations are likely. Similarly, the metabolic consequences of bile salt signalling (the authors postulate this may occur via TGR5) versus effects of the ER-stress/FGF21 pathway remain unclear. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      Yes, the title and discussion is too speculative in my opinion, in particular the claim linking ABCD3 activity to all the metabolic effects observed in the MAPL KO. - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

      Experimental support for an altered role of ABCD3 activity as CAUSAL for the observed phenotype is essential - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

      As Sumoylation sites can be predicted to some level, an MAPL-insensitive ABCD3 protein could be made and used to link effects of ABCD3 sumoylation to MAPL and consequences of MAPL deficiency. Minimally, data linking the modest effects on ABCD3 activity (for example by PMP70 knockdown in vivo) on the observed phenotype of MAPL KO is required to support the currents aims. - Are the data and the methods presented in such a way that they can be reproduced? Yes - Are the experiments adequately replicated and statistical analysis adequate?

      Yes. However, the initial and main finding of the manuscript, the identification of ABCD3 as MAPL interacting partner is plotted somewhat vague. Seems like data is from a single experiment, while the method section suggests otherwise

      Minor comments:

      • Specific experimental issues that are easily addressable.
      • Are prior studies referenced appropriately?
      • Are the text and figures clear and accurate?

      Abstract states: "BioID revealed the peroxisomal bile acid transporter ABCD3 as a primary MAPL interacting partner, which we show is SUMOylated in a MAPL-dependent manner." The method aspect of this sentence is too unclear as it assumes all readers know what BioID entails. The abstract also states that increased bile salt secretion is occurring. No experimental data supporting increased hepatocellular bile salt secretion is provided, only increased serum levels, which is not the same.

      How was FGF15 measured? The methods section is unclear about this, and the legends indicates this was measured by ELISA. Earlier paper suggests that FGF15 is not easily detectable and controls for the elisa should thus be included (PMID: 26039452). Figure 1D; last lane with the duplo of the rescue with the mutant MAPL seems missing, only single value is plotted. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      no

      Significance

      • Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

      The individual findings/observation are very interesting. However, as the causal link and relative contribution of the multitude of processes affected in the MAPL KO remains unclear current impact is limited. - Place the work in the context of the existing literature (provide references, where appropriate).

      A highly related manuscript recently appeared: mitochondrial ubiquitin ligase MARCH5 is a dual-organelle locating protein that interacts with several peroxisomal proteins. Peroxisomal MARCH5 is required for mTOR inhibition-induced pexophagy by binding and ubiquitinating PMP70 (J Cell Biol. 2022 Jan 3; 221(1): e202103156.) This is not discussed at all. However, it supports that better scientific insight into regulation of peroxisomal processes, including the activity of ABCD3/PMP70 is very relevant to the field. - State what audience might be interested in and influenced by the reported findings.

      An audience interesting in peroxisomal function and/or bile salt signalling/toxicity - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      bile salt signalling; transport

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      Referee #1

      Evidence, reproducibility and clarity

      In this study, the authors have investigated the impact of permanently silencing the expression of the mitochondrial anchored protein ligase (MAPL) in mice on bile acid (BA) metabolism through the alteration of ABCD3 SUMOylation. This ABC pump mediates the uptake of C27-BAs by peroxisomes and hence determines the shortening of the BA sidechain. In addition, other aspects of general metabolism have also been investigated. The study is highly relevant and contains valuable information.

      Major points

      1. Sidechain shortening is essential for the synthesis of primary C24 BAs. This study suggests that the entrance of C27 BAs in peroxisomes, which depends on ABCD3 activity, is reduced by MAPL-dependent ABCD3 SUMOylation. Thus, knocking out MAPL in mice results in enhanced BA accumulation in serum and liver, presumably by facilitated uptake of C27 by the peroxisomes and stimulation of de novo synthesis of primary BA. Indeed, a decreased C27/C24 BA ratio was found. However, the results suggest that Cyp7a1 is not the main checkpoint for the control of BA synthesis or that Fxr/Fgf15/Cyp7a1 pathway is also affected by MAPL manipulation because Cyp7a1 expression, which could be expected to be downregulated in response to enhanced BA levels, is not affected in MAPL knockout. Moreover, no change in Fgf15 was found (Suppl. Fig. 2C, 2D, 2G). The authors must discuss these surprising findings.
      2. The authors discussed that the alternative acidic pathway is responsible for these changes, but Cyp27a1 was, in fact, moderately downregulated in MAPL knockout mice.
      3. Serum BAs may reflect a higher BA pool. Nevertheless, this has not been assayed. Enhanced flow of C27-BA precursors into peroxisomes is consistent with increased C24-BA production and reduced intrahepatic concentration of C27-BA in MAPL knockout mice (Suppl. Table 2). However, it is not explained why C27-BA serum concentrations were increased in these animals (Suppl. Table 2 and Suppl. Fig. 2B).
      4. C27-BAs have been described as more toxic species than most C24-BAs. In the liver of MAPL knockout mice, C27-BAs levels were decreased (Suppl. Table 2). Other toxic species such as DCA and CDCA were not markedly changed. Muricholic acids and ursodeoxycholic acid, which were increased, are believed to be non-toxic or even hepatoprotective. Therefore, the relationship between changes in BA homeostasis and liver carcinogenesis should be better justified.
      5. SUMOylation may affect transporters which may simulate certain cholestasis with retention in serum of BAs. Expression levels of basolateral Ntcp, Oatps, and canalicular Bsep are required to better understand BA homeostasis. Besides, biliary secretion in MAPL knockout mice would give relevant information on what is actually happening in the biliary function of these animals.

      Significance

      The study is relevant and original.

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      Reply to the reviewers

      If you wish to submit a preliminary revision with a revision plan, please use our "Revision Plan" template. It is important to use the appropriate template to clearly inform the editors of your intentions.]

      1. General Statements

      We were naturally pleased to read the enthusiasm coming from both reviewers. Both mentioned that an extension to experimentation in cells would increase the impact of the study, even though both recognize that the biophysical and biochemical experiments constitute a study that is significant and interesting to a broad readership.

      2. Point-by-point description of the revisions

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

      This manuscript by Bryan et al., describes the use of Hydrogen/Deuterium-exchange Mass Spectrometry (HXMS) as a powerful tool to identify key amino acid residues and associated interactions driving liquid-liquid demixing. They have particularly focused on the Chromosomal Passenger Complex (CPC), an important regulator of chromosome segregation, which has recently been shown to undergo liquid-liquid demixing in vitro. Their work presented here allowed them to identify a few key electrostatic interactions as molecular determinants driving the liquid-liquid demixing of the CPC. Their work also shows that crystal packing information of protein molecules, where available, can provide valuable insight into likely factors driving liquid-liquid demixing.

      Major comments:

      [#1] A previous study by Trivedi et al., NCB 2019 identified an unstructured region in Borealin (aa residues 139-160) as the main region driving the phase separation of CPC. Interestingly, this region only shows a moderate reduction in HX upon liquid-liquid demixing. But no experiments or discussions related to this observation are presented in the current version of the manuscript.

      In the Trivedi et al. paper, the authors were careful to state that the region of borealin between 139-160 contributed to phase separation, but there was clearly a remaining propensity to phase separate in vitro in the mutant. Thus, it is fully expected that there should be other regions in the complex that contribute to phase separation. It was satisfying that this region was independently identified in the hydrogen-deuterium exchange experiments and we suggest that a “moderate” reduction is consistent with a protein condensate having liquid properties. Since this region was already characterized we have focused our work in this paper to the new region identified by the hydrogen-deuterium exchange experiments.

      [#2] In the absence of cellular data on if and how these mutations (within the triple-helical bundle region) affect CPC's ability to phase separate in cells, the implication of this work is very limited - One can't say for sure these are interactions driving phase separation of CPC in a cellular environment. In the absence of any cellular data with the mutants described here, much of the discussion on the possible roles of CPC phase separation in cells does not appear relevant to this manuscript. I would suggest that the authors focus mainly on highlighting the power of using HXMS as a tool to characterise the molecular determinants of liquid-liquid demixing at a relatively high resolution.

      We have now added cellular data in the form of one of the key experiments used to explore CPC liquid-liquid demixing utilizing the Cry2 optogenetic system for inducible dimerization. The results of testing WT Borealin versus the mutant we identified is defective in droplet formation are shown in the all new Fig. 6. Some relation of our overall findings, encompassing observations made with purified components and now in cells, to the cellular function of the CPC is pertinent. In light of the reviewer comments, we have also reduced this aspect in the discussion (see the substantial edits on pg. 12).

      Minor comments:

      [#3] The authors should ensure that the introduction cites relevant literature thoroughly. For example, where the potential role of Borealin residues 139-160 in conferring phase separation properties to the CPC is mentioned, the authors failed to cite Abad et al., 2019, which showed the contribution of the same Borealin region in conferring nucleosome binding ability to the CPC.

      We have made this particular change on pg. 4 and also have gone through to ensure we are appropriately citing relevant literature.

      Reviewer #1 (Significance (Required)):

      This is a highly relevant and significant work, particularly considering the rapidly growing list of examples for Phase separation of proteins/protein assemblies and their potential biological roles (in spite of ongoing debates in the field about the cellular relevance of several phase separation claims). The data presented in this manuscript are solid and convincingly establish HXMS as a useful tool to characterise molecular interactions driving liquid-liquid demixing. Considering its applicability to characterise wide-ranging protein assemblies implicated in phase separation, this work will be of interest to a broad readership.

      We thank the reviewer for the strong praise of the significance of our study.

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

      In this manuscript, using the technique of hydrogen/deuterium-exchange mass spectrometry (HXMS), the authors have tried to gain insights into the structure of the chromosomal passenger complex (CPC) within the phase separated chromatin body, known to regulate chromosome segregation in mitosis. The CPC phase separated compartment comprises three regulatory and targeting subunits, INCENP, Survivin, and Borealin, forming a three-helix bundle hetero-trimer. By measuring changes in the polypeptide backbone dynamics of this trimeric INCENP/Survivin/Borealin complex, in the liquid-liquid de-mixed state in comparison to its soluble state, using HXMS measurements, the paper puts forward high-resolution structural details of the phase separated CPC. Using a step-wise mutagenesis approach in conjunction with the information from HXMS measurements and previous crystallographic data, this work also identifies distinct regions/interfaces within this complex harboring crucial salt bridges, which directly contribute toward the liquid-liquid demixing of the CPC. Comments: 1) "The three non-catalytic subunits of the CPC (INCENP1-58, Borealin, and Survivin) form soluble homotrimers that have a propensity to undergo liquid-liquid phase separation.8 " Do the authors mean the hetero-trimeric CPC?

      Yes, we meant heterotrimers. It is now corrected.

      2) For better clarity, the authors can indicate the residue numbers of each of the components INCENP, Survivin, and Borealin in the CPC trimeric helix-bundle crystallographic structure in Fig 1.

      These are included on the revised Figure 1A.

      3) "In the condition we identified, 90% +/- 5% of the ISB protein was found within the rapidly sedimenting droplet population (Fig. 1C)." The authors should include the time-point corresponding to the gel shown in Fig 1C.

      This information is now directly labeled in Fig. 1C.

      4) Prior to the HXMS experiments on the phase-separated ISB protein complex, were the samples subjected to sedimentation to separate the dispersed from the condensed droplet phase? Since several time points after formation of phase-separated ISB complex have been characterized to compare and contrast between the dispersed and the droplet phase, the authors can consider performing a time-dependent sedimentation assay to ascertain the fraction of the ISB complex in the droplet phase.

      The HXMS experiments were not performed on sedimented samples, so this complication in our HX workflow is not necessary. We note that the sedimentation that we include in our study (Figs. 1C, 5E, and S6), involves centrifugation for 10 minutes, and that length of time presents a substantial design challenge to our HX experimentation. We considered it at the outset of our study, but, in the end, our study was facilitated by our finding early on that this separation step was unnecessary. Further, we note that we report statistically significant differences at the earliest HX timepoints in the areas prominently protected from HX upon droplet formation (10 and 100 s; see Fig. 1C for an example). Indeed, we do not observe broadening of our HXMS spectra (examples shown for all timepoints, Fig. 2B,F) that would be expected if there were a large degree of mixed states (i.e. a large population of molecules in the free protein state and a large population of molecules in the droplet state) each having different HXMS rates. One can imagine that this sort of envelope broadening behavior (“EX1-like”) could be observed in other samples where there are multiple substantially populated states of a protein present at a particular timepoint, but this is not what we observe in the experiments we performed in this study.

      5) "At the 100 s timepoint, the most prominent differences between the soluble and droplet state were located within the three-helix bundle of the ISB, with long stretches in two subunits (INCENP and Borealin) and a small region at the N-terminal portion of the impacted a-helix in Survivin (Fig. 1F)" According to Fig 1F, at the 100 s time-point, there is also another small region in Survivin (approximately residues 12-20) that exhibits slower exchange rates in the droplet state. Can the authors comment on whether this region undergoes any conformational change or if it exhibits homotypic interactions retarding the hydrogen/deuterium exchange rates in the droplet phase?

      Our general approach in the Black lab over the past decade-plus of HXMS has been to restrict our conclusions whenever practical to do so to the consensus behavior. This permits multiple partially overlapping peptides to be used to generate confidence in the changes that drive our conclusions. The reviewer carefully recognizes the behavior of a single peptide (in 2 different charge states) that might have actual changes relative to some of the longer peptides that it partially overlaps with, and smaller changes can yield larger percentage changes on small peptides. We have chosen to not include this single peptide in the text describing our main conclusions from the work to be consistent with our longstanding strategy for rigorous interpretation of HXMS data. Our conclusion is that this region of not substantially changed upon droplet formation.

      6) The authors mention that: "By the latest timepoint, 3000 s, there was some diminution in the number of droplets which may indicate the start of a transition of the droplets to a more solid state (i.e., gel-like)." As a result of this time points beyond 3000 s have not been used for comparing Hydrogen/Deuterium exchange rates in the condensed droplet phase with the soluble state. Can the authors comment on what happens to the nature of these specific interactions between the components of the CPC in the 'gel-like state'? A combination of both non-specific weak interactions as well as strong site-specific interactions between macromolecular components has been widely known to contribute towards the formation of several phase-separated compartments. It will be interesting to know the perspective of the authors on what sort of interactions get populated within these compartments to give rise to a more solid gel-like state. At this later time points, do the droplets exhibit reversibility under higher ionic strength conditions? Do the authors have some data to show how the material property of these droplets evolve as a function of time?

      We offered the idea of a transition to a more solid state to the reader because it was a reasonable conclusion, although challenging to prove (something the Stukenberg lab is actively working on, though, see our response to point #9, below). The vast majority of our conclusions in the paper, and essentially all of what we emphasize are the important ones, are based on earlier timepoints where this is not an issue. Thus, we find an extended study of the late-developing features in our droplets something more appropriate for separate studies outside the scope of the current one.

      7) "Examination of the entire time course shows that during intermediate levels of HX (i.e., between 100-1000 s), this region takes about three times as long to undergo the same amount of exchange when the ISB is in the droplet state relative to when it's in the free protein state (Figs. 2B, C and Supplemental Fig. 2). Upon droplet formation, HX protection within Borealin is primarily located in the interacting a-helix and is less pronounced at any given peptide when compared to INCENP peptides (Fig. 2E). Nonetheless, similar to INCENP peptides, it still takes about twice as long to achieve the same level of deuteration for this region of Borealin in the droplet state as compared to the free state." How do the hydrogen/deuterium exchange rates and extent of deuteration in the N-terminal part (residues 98-142) of the Survivin polypeptide chain, constituting the three-helix bundle core, evolve as a function of time? Also, how do the exchange rates for peptides in this region compare with those of the other protein subunits Borealin and INCENP and what inference can be drawn from these differences?

      The peptides from a.a. 98-142 of Survivin exhibit HX protection through the timecourse (and before and after droplet formation) consistent with a folded a-helix (and comparable to the overall HX behavior of the other helices in the 3-helix bundle of the ISB)(Fig. S2). There is subtly slower HX in the droplet state for this region at later timepoints for this portion of Survivin (Fig. S4), and this is explicitly highlighted in the Results section on pg. 6.

      8) The authors mention that mutating either all the glutamate residues or combinations of these residues on the acidic patch on the INCENP subunit, to positively charged residues, causes a decrease in the propensity of phase separation, as formation of salt bridges with Borealin subunit from adjacent hetero-trimeric complexes appears to be the major driving force for phase separation. Can the authors elaborate on how the reduction in the phase separation propensity of these salt-bridge inhibiting mutants might be directly affecting the subsequent localization of the CPC to the inner centromeres? Can the authors supplement their existing in vitro data with further in vivo characterization of CPC recruitment or localization to the centromeres, for each of the constructs exhibiting reduced propensity of phase separation?

      As we state in the introduction, the recruitment to centromeres requires established ‘conventional’ targeting via the specific histone marks to which we refer. We also cite the correlations demonstrated between prior mutations in Borealin (impacting aa 139-160) that both disrupt phase separation in vitro and reduce CPC levels at the centromere. In our revision, we have added what we feel are the most critical cell-based experiments to relate to our HX studies in the new Fig. 6. We are preparing for future studies to study mutants arising from our HX studies, and our plans are to pursue gene replacement approaches that will rigorously test the impact on the mitotic function of the CPC. In the process of these future studies, the impact on localization will be measured, too. As others in the field are investigating the correlations between observations made with purified components and those made in the cell, and where there are nuances at play in how the actual experiments are conducted, we are certain our cell-based studies will extend far beyond the timeframe appropriate for our HX-focused study. Rigorous cell-based studies of mitotic functions are what is needed, however, and we have made our plans with that in mind.

      9) It might be really interesting for the authors to look at the recent preprint from Hedtfeld et al. 2023 Molecular Cell, (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4472737). In this preprint they have recombinantly purified a stoichiometric trimer (referred to as CPC-TARGWT) comprising full length survivin, borealin, and a 1-350 residue fragment of INCENP (instead of 1-58 used in this study) and have tried to assess if any correlation exists between the in-vitro phase behaviour of CPC-TARGWT mutants and their corresponding recruitment to the inner centromere, to form a phase separated compartment. Targeting residues in the BIR domain of Survivin involved in interactions with the N-terminus of the Histone H3, Shugosin 1 or in the recognition of H3T3phos, and substituting them with Alanine or completely deleting C-terminal domain of Borealin (a region implicated in CPC dimerization and centromere recruitment), was found to result in poor centromere localization, although the in vitro phase separation properties of these constructs were found to be indistinguishable, suggesting no evident correlation between the two phenomena. Thus it might be a useful piece of data to correlate the phase separation propensities of the ISB complex variants used in this current study with the extents of their in vivo recruitment to the inner centromere. This maybe beyond the scope of the paper, but it would be good to comment on this.

      For the correlation studies, please refer to our response to point #8, above. From our reading of the June 2023 preprint that the reviewer mentions, the main concern raised by the authors is questioning whether the region first identified in the Trivedi et al paper in Borealin (aa 139-160) has a role in phase separation. As the reviewer noted, Hedtfeld et al report using a complex that includes more of the INCENP protein than used in the Trivedi et al study, complicating the direct comparison between studies. Using the data in figure 5E of the Hedtfeld et al preprint, the authors suggest that the condensate formation of their version of the Borealin mutant D139-160 in vitro complex has similar phase separation properties as the wild type. However, we note that in our inspection of these data we see numerous differences. The mutant forms rounder, and larger condensates than WT and have reduced concentration of protein (less bright intensity). Finally only the WT protein has a “grape bunch” morphology. We note that unpublished data in the Stukenberg lab show these same differences can represent a defect in liquid demixing properties of a version of the purified CPC. While it is intuitive that larger condensates represent more phase separation, the unpublished data mentioned above suggests the opposite is true for the CPC. In particular, the data from the Stukenberg lab suggest the size of a droplet is mostly governed by the amount of droplet fusion in the first minutes after dilution and thus is limited by relatively rapid hardening of the complex. We note that in the course of discussions with the corresponding author of the preprint mentioned by the reviewer we did apprise them of the unpublished observations mentioned, above, in case they saw fit to include in their ongoing studies what would seem to be critical measurements (e.g. measuring circularity, droplet size, droplet intensity, and FRAP) to assess our suspicion that their construct contains a portion of INCENP that can accelerate condensate formation. If true, the Hedtfeld et al data are fully consistent with the Borealin mutant D139-160 having a significant condensate formation potential than the WT protein.

      10[A]) "Our data also provide an important clue about the previously identified region on Borealin that is required for liquid-demixing in vitro and proper CPC assembly in cells 8. Specifically, our data (Fig. 1F, Supplementary Figs. 2, 4A) suggest this region of Borealin adopts secondary structure that undergoes additional HX protection in the liquid-liquid demixed state" This data fits perfectly with previous studies from Trivedi et al. (2019), which states that deletion of the Borealin 139-160 fragment obliterates its phase separation in vitro and also reduces the accumulation of CPC at the centromere. On the contrary, in the recent preprint from Hedtfeld et al. 2023 Molecular Cell, they have shown that the phase separation behaviour of their reconstituted CPC-TARGWT harboring the Borealin 139-160 deletion mutant was found to be indistinguishable from the WT. Can the authors comment on what might be the reason for this difference? Is it possible that this central Borealin region is involved in interactions with the additional fragment of INCENP subunit used in the helical bundle reconstitution, or with other centromere component proteins, whereby the deletion of region is causing inefficient recruitment to the inner centromere? This can be elaborated in the discussion section of the manuscript.

      This is discussed in the response to #9, above. Through this format (the Review Commons procedure for public posting of author responses before submission of the study to a journal), our comments herein will be made public for those with the most interest in comparing our data to what is has been posted on preprint servers. We think that is the most appropriate for now, with more to surely come when the aforementioned results from the Stukenberg lab are posted/published and, hopefully when there is more information about the nature of the droplets reported in the Hedtfeld et al., study.

      10 [B]) It is also well known that in addition to these electrostatic interactions, the core of the ISB helical bundle is formed by an extensive network of hydrophobic interactions. Have the authors ever looked into how perturbing any of these intra-trimeric complex hydrophobic interactions affect their ability to phase separate and perform their subsequent function?

      We think there is some confusion, here. The electrostatics we focus on are between heterotrimers rather than within them. We certainly would predict that disrupting the hydrophobic surface that generates a stable heterotrimer would, in turn, disrupt individual heterotrimers. Our study assumes a stable heterotrimer as a starting point, so we view this type of perturbation as unrelated to our conclusions.

      11) The phase separated CPC compartment is known to enrich several other inner centromere proteins such as the Histone H3, Sgo1, the histone H3T3phos, among others. Have the authors tried to increase the complexity of the reconstituted CPC scaffold by incorporating more components to look into whether that changes any of the interaction interfaces between the ISB trimeric complexes within the condensed phase? Can this CPC compartment be reconstituted using a bottom-up approach?

      We are glad that our studies with a reductionist biochemical reconstitution approach have inspired the questions that require increased complexity. They are now warranted based on the advance we have made in the present study, and hopefully will form the basis for future, separate studies.

      Overall, this paper brings forward a useful technique to probe the conformational landscape of proteins in the condensed droplet phase and compare it with its dispersed phase. This paper serves as an interesting read showing how specific salt-bridge interactions between multiple stoichiometric protein complexes can be the driving force for phase separation.

      Reviewer #2 (Significance (Required)):

      In this manuscript, using the technique of hydrogen/deuterium-exchange mass spectrometry (HXMS), the authors have tried to gain insights into the structure of the chromosomal passenger complex (CPC) within the phase separated chromatin body, known to regulate chromosome segregation in mitosis. The CPC phase separated compartment comprises three regulatory and targeting subunits, INCENP, Survivin, and Borealin, forming a three-helix bundle hetero-trimer. By measuring changes in the polypeptide backbone dynamics of this trimeric INCENP/Survivin/Borealin complex, in the liquid-liquid de-mixed state in comparison to its soluble state, using HXMS measurements, the paper puts forward high-resolution structural details of the phase separated CPC. Using a step-wise mutagenesis approach in conjunction with the information from HXMS measurements and previous crystallographic data, this work also identifies distinct regions/interfaces within this complex harboring crucial salt bridges, which directly contribute toward the liquid-liquid demixing of the CPC.

      Overall, this paper brings forward a useful technique to probe the conformational landscape of proteins in the condensed droplet phase and compare it with its dispersed phase. This paper serves as an interesting read showing how specific salt-bridge interactions between multiple stoichiometric protein complexes can be the driving force for phase separation

      We thank the reviewer for the positive comments on the significance of our study.

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      Referee #2

      Evidence, reproducibility and clarity

      Structural Basis for the Phase Separation of the Chromosome Passenger Complex Nikaela W. Bryan, Ewa Niedzialkowska, Leland Mayne, P. Todd Stukenberg, and Ben E. Black# Reviewer Comments Manuscript Number: RC-2023-02017

      In this manuscript, using the technique of hydrogen/deuterium-exchange mass spectrometry (HXMS), the authors have tried to gain insights into the structure of the chromosomal passenger complex (CPC) within the phase separated chromatin body, known to regulate chromosome segregation in mitosis. The CPC phase separated compartment comprises three regulatory and targeting subunits, INCENP, Survivin, and Borealin, forming a three-helix bundle hetero-trimer. By measuring changes in the polypeptide backbone dynamics of this trimeric INCENP/Survivin/Borealin complex, in the liquid-liquid de-mixed state in comparison to its soluble state, using HXMS measurements, the paper puts forward high-resolution structural details of the phase separated CPC. Using a step-wise mutagenesis approach in conjunction with the information from HXMS measurements and previous crystallographic data, this work also identifies distinct regions/interfaces within this complex harboring crucial salt bridges, which directly contribute toward the liquid-liquid demixing of the CPC.

      Comments: 1. "The three non-catalytic subunits of the CPC (INCENP1-58, Borealin, and Survivin) form soluble homotrimers that have a propensity to undergo liquid-liquid phase separation.8 " Do the authors mean the hetero-trimeric CPC? 2. For better clarity, the authors can indicate the residue numbers of each of the components INCENP, Survivin, and Borealin in the CPC trimeric helix-bundle crystallographic structure in Fig 1. 3. "In the condition we identified, 90% +/- 5% of the ISB protein was found within the rapidly sedimenting droplet population (Fig. 1C)." The authors should include the time-point corresponding to the gel shown in Fig 1C. 4. Prior to the HXMS experiments on the phase-separated ISB protein complex, were the samples subjected to sedimentation to separate the dispersed from the condensed droplet phase? Since several time points after formation of phase-separated ISB complex have been characterized to compare and contrast between the dispersed and the droplet phase, the authors can consider performing a time-dependent sedimentation assay to ascertain the fraction of the ISB complex in the droplet phase. 5. "At the 100 s timepoint, the most prominent differences between the soluble and droplet state were located within the three-helix bundle of the ISB, with long stretches in two subunits (INCENP and Borealin) and a small region at the N-terminal portion of the impacted a-helix in Survivin (Fig. 1F)" According to Fig 1F, at the 100 s time-point, there is also another small region in Survivin (approximately residues 12-20) that exhibits slower exchange rates in the droplet state. Can the authors comment on whether this region undergoes any conformational change or if it exhibits homotypic interactions retarding the hydrogen/deuterium exchange rates in the droplet phase? 6. The authors mention that: "By the latest timepoint, 3000 s, there was some diminution in the number of droplets which may indicate the start of a transition of the droplets to a more solid state (i.e., gel-like)." As a result of this time points beyond 3000 s have not been used for comparing Hydrogen/Deuterium exchange rates in the condensed droplet phase with the soluble state. Can the authors comment on what happens to the nature of these specific interactions between the components of the CPC in the 'gel-like state'? A combination of both non-specific weak interactions as well as strong site-specific interactions between macromolecular components has been widely known to contribute towards the formation of several phase-separated compartments. It will be interesting to know the perspective of the authors on what sort of interactions get populated within these compartments to give rise to a more solid gel-like state. At this later time points, do the droplets exhibit reversibility under higher ionic strength conditions? Do the authors have some data to show how the material property of these droplets evolve as a function of time? 7. "Examination of the entire time course shows that during intermediate levels of HX (i.e., between 100-1000 s), this region takes about three times as long to undergo the same amount of exchange when the ISB is in the droplet state relative to when it's in the free protein state (Figs. 2B, C and Supplemental Fig. 2). Upon droplet formation, HX protection within Borealin is primarily located in the interacting a-helix and is less pronounced at any given peptide when compared to INCENP peptides (Fig. 2E). Nonetheless, similar to INCENP peptides, it still takes about twice as long to achieve the same level of deuteration for this region of Borealin in the droplet state as compared to the free state." How do the hydrogen/deuterium exchange rates and extent of deuteration in the N-terminal part (residues 98-142) of the Survivin polypeptide chain, constituting the three-helix bundle core, evolve as a function of time? Also, how do the exchange rates for peptides in this region compare with those of the other protein subunits Borealin and INCENP and what inference can be drawn from these differences? 8. The authors mention that mutating either all the glutamate residues or combinations of these residues on the acidic patch on the INCENP subunit, to positively charged residues, causes a decrease in the propensity of phase separation, as formation of salt bridges with Borealin subunit from adjacent hetero-trimeric complexes appears to be the major driving force for phase separation. Can the authors elaborate on how the reduction in the phase separation propensity of these salt-bridge inhibiting mutants might be directly affecting the subsequent localization of the CPC to the inner centromeres? Can the authors supplement their existing in vitro data with further in vivo characterization of CPC recruitment or localization to the centromeres, for each of the constructs exhibiting reduced propensity of phase separation? 9. It might be really interesting for the authors to look at the recent preprint from Hedtfeld et al. 2023 Molecular Cell, (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4472737). In this preprint they have recombinantly purified a stoichiometric trimer (referred to as CPC-TARGWT) comprising full length survivin, borealin, and a 1-350 residue fragment of INCENP (instead of 1-58 used in this study) and have tried to assess if any correlation exists between the in-vitro phase behaviour of CPC-TARGWT mutants and their corresponding recruitment to the inner centromere, to form a phase separated compartment. Targeting residues in the BIR domain of Survivin involved in interactions with the N-terminus of the Histone H3, Shugosin 1 or in the recognition of H3T3phos, and substituting them with Alanine or completely deleting C-terminal domain of Borealin (a region implicated in CPC dimerization and centromere recruitment), was found to result in poor centromere localization, although the in vitro phase separation properties of these constructs were found to be indistinguishable, suggesting no evident correlation between the two phenomena. Thus it might be a useful piece of data to correlate the phase separation propensities of the ISB complex variants used in this current study with the extents of their in vivo recruitment to the inner centromere. This maybe beyond the scope of the paper, but it would be good to comment on this. 10. "Our data also provide an important clue about the previously identified region on Borealin that is required for liquid-demixing in vitro and proper CPC assembly in cells 8. Specifically, our data (Fig. 1F, Supplementary Figs. 2, 4A) suggest this region of Borealin adopts secondary structure that undergoes additional HX protection in the liquid-liquid demixed state" This data fits perfectly with previous studies from Trivedi et al. (2019), which states that deletion of the Borealin 139-160 fragment obliterates its phase separation in vitro and also reduces the accumulation of CPC at the centromere. On the contrary, in the recent preprint from Hedtfeld et al. 2023 Molecular Cell, they have shown that the phase separation behaviour of their reconstituted CPC-TARGWT harboring the Borealin 139-160 deletion mutant was found to be indistinguishable from the WT. Can the authors comment on what might be the reason for this difference? Is it possible that this central Borealin region is involved in interactions with the additional fragment of INCENP subunit used in the helical bundle reconstitution, or with other centromere component proteins, whereby the deletion of region is causing inefficient recruitment to the inner centromere? This can be elaborated in the discussion section of the manuscript. 10. It is also well known that in addition to these electrostatic interactions, the core of the ISB helical bundle is formed by an extensive network of hydrophobic interactions. Have the authors ever looked into how perturbing any of these intra-trimeric complex hydrophobic interactions affect their ability to phase separate and perform their subsequent function? 11. The phase separated CPC compartment is known to enrich several other inner centromere proteins such as the Histone H3, Sgo1, the histone H3T3phos, among others. Have the authors tried to increase the complexity of the reconstituted CPC scaffold by incorporating more components to look into whether that changes any of the interaction interfaces between the ISB trimeric complexes within the condensed phase? Can this CPC compartment be reconstituted using a bottom-up approach?

      Overall, this paper brings forward a useful technique to probe the conformational landscape of proteins in the condensed droplet phase and compare it with its dispersed phase. This paper serves as an interesting read showing how specific salt-bridge interactions between multiple stoichiometric protein complexes can be the driving force for phase separation.

      Significance

      In this manuscript, using the technique of hydrogen/deuterium-exchange mass spectrometry (HXMS), the authors have tried to gain insights into the structure of the chromosomal passenger complex (CPC) within the phase separated chromatin body, known to regulate chromosome segregation in mitosis. The CPC phase separated compartment comprises three regulatory and targeting subunits, INCENP, Survivin, and Borealin, forming a three-helix bundle hetero-trimer. By measuring changes in the polypeptide backbone dynamics of this trimeric INCENP/Survivin/Borealin complex, in the liquid-liquid de-mixed state in comparison to its soluble state, using HXMS measurements, the paper puts forward high-resolution structural details of the phase separated CPC. Using a step-wise mutagenesis approach in conjunction with the information from HXMS measurements and previous crystallographic data, this work also identifies distinct regions/interfaces within this complex harboring crucial salt bridges, which directly contribute toward the liquid-liquid demixing of the CPC.

      Overall, this paper brings forward a useful technique to probe the conformational landscape of proteins in the condensed droplet phase and compare it with its dispersed phase. This paper serves as an interesting read showing how specific salt-bridge interactions between multiple stoichiometric protein complexes can be the driving force for phase separation

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      Referee #1

      Evidence, reproducibility and clarity

      This manuscript by Bryan et al., describes the use of Hydrogen/Deuterium-exchange Mass Spectrometry (HXMS) as a powerful tool to identify key amino acid residues and associated interactions driving liquid-liquid demixing. They have particularly focused on the Chromosomal Passenger Complex (CPC), an important regulator of chromosome segregation, which has recently been shown to undergo liquid-liquid demixing in vitro. Their work presented here allowed them to identify a few key electrostatic interactions as molecular determinants driving the liquid-liquid demixing of the CPC. Their work also shows that crystal packing information of protein molecules, where available, can provide valuable insight into likely factors driving liquid-liquid demixing.

      Major comments:

      A previous study by Trivedi et al., NCB 2019 identified an unstructured region in Borealin (aa residues 139-160) as the main region driving the phase separation of CPC. Interestingly, this region only shows a moderate reduction in HX upon liquid-liquid demixing. But no experiments or discussions related to this observation are presented in the current version of the manuscript.

      In the absence of cellular data on if and how these mutations (within the triple-helical bundle region) affect CPC's ability to phase separate in cells, the implication of this work is very limited - One can't say for sure these are interactions driving phase separation of CPC in a cellular environment.

      In the absence of any cellular data with the mutants described here, much of the discussion on the possible roles of CPC phase separation in cells does not appear relevant to this manuscript. I would suggest that the authors focus mainly on highlighting the power of using HXMS as a tool to characterise the molecular determinants of liquid-liquid demixing at a relatively high resolution.

      Minor comments:

      The authors should ensure that the introduction cites relevant literature thoroughly. For example, where the potential role of Borealin residues 139-160 in conferring phase separation properties to the CPC is mentioned, the authors failed to cite Abad et al., 2019, which showed the contribution of the same Borealin region in conferring nucleosome binding ability to the CPC.

      Significance

      This is a highly relevant and significant work, particularly considering the rapidly growing list of examples for Phase separation of proteins/protein assemblies and their potential biological roles (in spite of ongoing debates in the field about the cellular relevance of several phase separation claims). The data presented in this manuscript are solid and convincingly establish HXMS as a useful tool to characterise molecular interactions driving liquid-liquid demixing. Considering its applicability to characterise wide-ranging protein assemblies implicated in phase separation, this work will be of interest to a broad readership.

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      Reply to the reviewers

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

      Centrioles are small cylindrical structures with roles in cell division, motility, and signaling. Typically, centrioles are highly stable structures which can persist for many cell generations. However, in some cells, such as the female germ line of many species, centrioles are programmed for elimination. This process is essential for maintaining centriole number from one generation to the next in sexually reproducing organisms, yet in nearly all species the molecular mechanisms underlying how centrioles are eliminated is unknown. The current study utilizes the nematode C. elegans to explore how centriole architecture changes during the elimination program in the female germ line. Using a suite of light microscopy techniques, the authors provide a stunning visual perspective of how centrioles are disassembled during oogenesis and show that removal of the central tube component SAS-1, a key regulator of centriole stability, is an early event in elimination. I have no major objections to the work and enthusiastically endorse its publication with the following minor revisions.

      Page 9 line 200: In the pcmd-1 mutant, the authors state that centriolar foci devoid of nuclei are present in rachis, but they do not mention in the text that there are also nuclei that lack centriole foci in early pachytene. This is mentioned in the figure legend, but I felt it was important enough to mention in the text.

      As per the reviewer’s suggestion, we will provide this information in the main text as well.

      Page 9 line 211. The authors found that in the absence of dynein heavy or light chain that centrioles remain associated with the nuclear envelope (rather than moving to the periphery). To me this was striking as dynein depletion in the embryo results in the opposite phenotype with centrioles losing attachment to the nuclear envelope and moving to the cell periphery (Gonczy et al. 1999 JCB 147:135). It might be worth pointing this out somewhere in the manuscript and speculating about the reasons for this difference.

      We will expand the Discussion section to better explain the difference of dynein’s involvement in the oocyte versus the embryo.

      Page 11 line 277: The authors state that elimination timing is not affected by the loss of SPD-5. This is a small but important point. It really is the absence of PCMD-1 and not SPD-5, as SPD-5 is still present in the cell. An alternative would be to say "in the absence of PCM" or "in absence of a pericentriolar accumulation of SAS-5".

      Fully agreed, we will modify the text accordingly.

      Figure 4D: Why does loss of PCMD-1 result in a delay in oocyte maturation as judged by RME-2 accumulation? This is not mentioned in the paper. Is this a general response to a loss of PCM or is this specific to a loss of PCMD-1?

      We realize that we were not sufficiently clear in explaining that RME-2 accumulation reflects the maturation state of oocytes. In the revised manuscript, we will clarify this point further and mention that a mild developmental delay (such as in pcmd-1(t3421ts) mutant animals) can impact the number of maturing oocytes present in the proximal gonad, and thereby lead to a slight shift in RME‑2::GFP distribution. See also related minor comment 2 of reviewer 2, and major comment 1 of reviewer 3.

      Figure 7 E and F. The authors measure the tubulin and SAS-4 intensity in wild-type and sas-1(t1521) embryos and conclude that microtubules and SAS-4 signals decay faster in the sas-1 mutant than in the control. To me, this is convinceingly the case with microtubules in panel E but I am not so sure this is the case with SAS-4 as shown in panel F. The differences in SAS-4 levels are much smaller between mutant and control. Could the authors provide statistical analysis to show how significant the differences are?

      We will provide the requested statistical analysis (which indeed shows significance).

      Page 15 line 363. I think this sentence should be reworded to: "Finally, we demonstrate that the central tube protein SAS-1 is the first of the factors analyzed here to leave centrioles..."

      In response to this suggestion and to the related comment of reviewer 2 (see below), we will rephrase this sentence to read “among the centriolar components analyzed to date, SAS-1 is the first to depart”.

      Reviewer #1 (Significance (Required)):

      The work contained in this manuscript represents a fundemental step forward in understanding the process of centriole elimination. The authors have carefully described the stepwise disassembly of the centriole including changes in the architechure during oogenesis. They have identified loss of the centriole stability factor SAS-1, as an early event in the elimination program and have found that in a sas-1 mutant, the centriole disassembles prematurely. They have also shown that loss of SAS-1 is followed by expansion of the centriole and ultimately loss of structural integrity. This work should be of interest to a broad range of scientists including those interested in centrosome dynamics, germ line development, and more generally cell biologists.

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

      Summary In this manuscript Pierron et al. explore the mechanisms of centriole elimination during oogenesis in C. elegans. Centriole elimination is a common feature of oogenesis in many species, but it is relatively poorly understood and understudied. Here, the authors characterise the kinetics with which several key centriole and centrosome proteins are lost during this process in living worms, and they correlate this with an EM and expansion microscopy (U-Ex-STED) analyses of fixed tissues. They conclude that centriole elimination begins with the loss of SAS-1 from the central region of the centrioles, which correlates with the widening of the structure and the loss of the centriole MTs. A remnant structure containing several core centriole proteins remains, however, and this often ultimately detaches from the nuclear envelope and moves towards the plasma membrane in a MT-motor-dependent fashion before it dissipates (although detachment from the nucleus does not seem to be required for the eventual elimination of this residual structure). Intriguingly, centriole loss in this system does not appear to require the down-regulation of PLK activity, which is in contrast to the situation in Drosophila oogenesis.

      The manuscript is generally well written and the data is of a high quality and is logically and clearly presented. Although the ultimate mechanisms regulating centriole elimination remain obscure (i.e. what triggers the loss of SAS-1, and how is this regulated?), the data presented here will be of significant interest to the centriole/centrosome field and I am supportive of publication. I have a few points that the authors should consider prior to publication.

      Major comment:

      In the EM shown in Figure 5F the authors claim that the central tube of the centriole is disrupted, but the other elements (inner tube, MTs and paddlewheel) are not. I don't think this is as clear cut as the authors claim-at least from comparing the images of the one normal centriole (5E) and one centriole that is starting to be eliminated (5F). It seems much harder to distinguish the MTs and the inner tube in the image in 5F. Perhaps this is obvious to the authors as they have compared many more images, but I think they need to find some way of showing this more convincingly (a montage of multiple centrioles)?

      We understand that Figure 5F alone may have left the reviewer wondering whether the central tube is truly the first element to be disrupted during centriole elimination. We plan on strengthening this point by providing additional EM images as a Supplemental Figure.

      This same issue is compounded in Figure 6D where, using a different technique (U-Ex-STED), the authors claim that the centriolar distribution of SAS-1 is gradually disrupted as centriole elimination proceeds. It does look like the amount of SAS-1 has decreased from early prophase to late pachytene, but the central tube it stains doesn't look particularly disrupted and, if anything, the MTs look more disrupted (and also possibly of lower intensity, perhaps explaining why the ratio of SAS-1/tubulin doesn't change very much over these stages, as shown in Figure 6G).

      As the reviewer correctly noticed, there is some variability in central tube removal during oogenesis. In some cases, such as in the centriole on the right of the late pachytene panel in Fig. 6D, SAS-1 signal intensity diminishes uniformly, without apparent holes in the central tube. By contrast, in other cases, such as in the centriole on the left of the late pachytene panel, SAS-1 signal intensity diminution is accompanied by a loss of central tube continuity. We will clarify the writing and qualify our findings on this important point in the revised manuscript.

      These points are important, as throughout the manuscript the authors assume it as a fact that SAS-1 leaves the centriole early (which is clear), and that this leads to the specific loss of the central tube (which, at least on the basis of this data, is not so clear).

      As mentioned above, we will make certain that the results linking SAS-1 departure and central tube loss are explained in a clear and balanced manner in the revised manuscript.

      Minor comments:

      1. The authors state that the kinetics of GFP-SAS-7 or SAS-4 loss were not altered in pcmd-1 mutants (Figure 4A-C; Figure S3E,F). This doesn't look correct to me, as both proteins seem to stay brighter for longer in the mutant embryos (and this is quite easy to see on the quantification graph for SAS-7 in Figure 4C). It looks similar for SAS-4 from the pictures shown in Figure S3E,F, although this data is not quantified (and is there any reason why this data is not quantified?).

      As mentioned in response to reviewers 1 and 3, we will mention in the revised manuscript that a mild developmental delay can impact the number of maturing oocytes present in the proximal gonad, thereby leading to this slight shift in GFP::SAS-7 and GFP::SAS-4 persistence.

      1. The authors state that they demonstrate that SAS-1 is the first component to leave the disassembling centrioles. I would rephrase as they can't know this for sure (i.e. there could be some untested component that leaves earlier).

      In response to this suggestion and to the related comment of reviewer 1 (see above), we will rephrase this sentence to read “among the centriolar components analyzed to date, SAS-1 is the first to depart”.

      In the latter part of the Discussion the authors state that SAS-1 is critical for centriole elimination. I would rephrase, as this seems to suggest it is required for centriole elimination, which is not the case. It might also be worth discussing that the elimination machinery clearly seems to target SAS-1 early on, but we don't yet know what this machinery is or how it is regulated.

      We thank the reviewer for raising this important point, which we will implement in the Discussion accordingly.

      Reviewer #2 (Significance (Required)):

      The manuscript is generally well written and the data is of a high quality and is logically and clearly presented. Although the ultimate mechanisms regulating centriole elimination remain obscure (i.e. what triggers the loss of SAS-1, and how is this regulated?), the data presented here will be of significant interest to the centriole/centrosome field and I am supportive of publication. I have a few points that the authors should consider prior to publication.

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

      Pierron et al. uses C. elegans oocytes to tackle a fundamental, yet heavily under-studied question in developmental biology: how are centrioles are eliminated during gamete formation/maturation? The paper's main conclusion is that SAS-1 (a key protein that make up the central tube in C. elegans centrioles) plays a critical part to regulate the timing of centriole elimination. I congratulate the authors on all the experiments related to SAS-1 part of their story, as they are done meticulously and in unprecedented detail (particularly all the fascinating EM and expansion microscopy data!).

      The paper also concludes that the Polo-like kinase family does not have a central role in this process, in stark contrast to a previous report demonstrating their importance for centriole elimination in Drosophila oogenesis (Pimenta-Marques et al. 2016 Science). Unfortunately, I am less convinced about this part of the paper, and half of my major comments below relate to the experiments/analyses in this regard. I was similarly not very enthusiastic about a part of story that I didn't find very relevant to the main point of the paper: half of the centrioles detach from the nucleus and translocate to plasma membrane prior to their elimination. I find the observations here quite epiphenomenal and lacking a direct/mechanistic relevance to either the PLK or SAS-1 part of the story. In my view, the authors should consider taking this part out.

      Regarding this last suggestion: we think that even if the movement of centrioles remnant is not essential for final removal, an account of this process provides important information about cellular dynamics during oocyte maturation. We note also that the two other reviewers did not raise this point, but leave the final decision to the editor.

      Overall, the piece is well written and organized, however it suffers from several shortcomings that preclude it from publication in its current form. I list my criticisms and suggestions below.

      Major comments:

      1. The authors state firmly at several places in the text that PCM components do not contribute to the timing of centriole elimination (e.g., lines 420-421), particularly given their experiments with Polo kinase paralogs. In my view, the data speaks otherwise. The centriole elimination process appears strikingly premature when SPD5__1__ (another PCM component) is overexpressed with the fluorescent transgene (Figure 1I). The opposite is also true - when another PCM component, PCMD-1, is knockdown by a temperature sensitive allele, the centriole elimination process is severely delayed 2 (Figure 4C). Even more extremely in the epistatic Polo mutant conditions (Fig. S3B), the centrioles do not appear to be eliminated at all__3__ (though the authors prefer to interpret this result differently in line 260-263, which could be flawed per my second comment below). How do the authors explain all these intriguing results? (underlining and numbering added above to clarify our responses point by point hereafter)

      1 > We respectfully disagree, since our quantifications show clearly that the SAS-7 signal disappears with an analogous timing in the line expressing RFP::SPD-5 (Fig. 1J) when compared to the other lines (Fig. 1D, 1F and 1H). The image shown currently for RFP::SPD-5 (Fig. 1I) is somewhat of an outlier compared to the others (Fig. 1C, 1E and 1G), and we will therefore provide a more representative specimen in the revised manuscript to avoid confusion.

      2 > As mentioned also in response to reviewers 1 and 2, we realize that we were not sufficiently clear in explaining that RME-2 accumulation reflects the maturation state of oocytes. In the revised manuscript, we will clarify this point and mention that a mild developmental delay (such as in pcmd-1(t3421ts) mutant animals) can impact the number of maturing oocytes present in the proximal gonad, and thereby lead to a slight shift in RME‑2::GFP distribution (as opposed to representing a delay in centriole elimination in pcmd-1(t3421ts) mutant animals).

      3 > We used plk-1(or683ts); plk-2(ok1936) double mutants to further test whether there might be premature elimination in this strong reduction-of-function condition compared to RNAi-mediated depletion. Although centriolar foci appear to remain for a longer time, these gonads are extremely disorganized, so that our conclusion regarding PLK-1 and PLK-2 are based primarily on the combined data shown in Fig. 3 and Fig. S3, which do not exhibit premature centriole elimination. We will rectify the writing to clarify these points.

      Also, I believe these claims (on the PCM components and their role in centriole elimination) will benefit from more nuanced statements. For instance, although Plk paralogs may not be necessary for the centriole elimination process, some other centrosome components clearly are. Paradoxically, the effects observed here (when disrupting or promoting PCM formation) has the totally opposite effects observed in Pimenta-Marques et al. 2016 Science. The 2016 piece claimed that the loss of PCM renders centrioles more vulnerable to losing their stability (which makes sense). How do the authors interpret their own results (i.e. that a disturbed PCM leads to slower centriole elimination, and vice versa)?

      As suggested by the reviewer, we will consider toning down claims regarding the role of PCM components in centriole elimination. Moreover, we will expand the section in the Discussion comparing our results with the published work of Pimenta-Marques et al. in Drosophila. This being written, as mentioned above, our findings do not suggest that removing the PCM (in pcmd-1(t3421ts) mutant animals) alters centriole elimination timing in C. elegans.

      I invite the authors to more carefully tread these nuances throughout their manuscript, which otherwise may cast major doubt on their claims.

      See point above.

      1. When investigating the role of Polo-like kinases, the authors assume that centriole elimination must follow (or correlate with) the dynamics of RME-2 (as a proxy for oocyte maturation). What guarantees that the centriole elimination process has to follow oocyte maturation? As far as I could tell, there is no direct evidence presented in the paper about this point. Do the authors have direct data (or reference to another work) that this trend must hold true at all times? I can readily see several places in the paper where this correlation doesn't appear to hold (e.g., in Fig. 4D the centriole elimination precedes the oocyte maturation under pcmd-1 condition).

        We will provide further data supporting the view that oocyte maturation and centriole elimination are correlated, whereby premature oocyte maturation mutants, such as let-60(ga89ts) and kin-18(ok395), exhibit precocious elimination.

      To correctly interpret their results on the epistatic Polo mutants, the authors could examine centriole elimination timing with mutants that can pre-maturely trigger or delay oocyte maturation (and do so without affecting the centriole biology itself).

      See above point.

      1. Lines 155-159 on the dimness of the SAS-6 signal make me worried about how successfully the transgenes were generated. Could the authors comment on, or perhaps extend in detail in the Methods section, through what assays the transgenes were validated? For example, did the authors try to rescue a SAS-6-/- with a SAS-6::GFP transgene? I would like to see further support for their validities.

      We will explicitly explain in the Material and Methods section that the SAS-6::GFP transgene indeed rescues the sas-6 null phenotype.

      If the authors can demonstrate the validity of their transgenes more reliably, could they possibly comment on the bunch of seemingly random SAS-6::GFP foci in Fig. 1G?

      We will comment on the presence of small SAS-6::GFP foci in the most mature oocytes, which correspond to potential precursors of centriolar elements later assembled in the embryo.

      1. Starting from line 204, the authors use the percentage of oocytes with detached centrioles (from the nucleus) as a proxy for movement to plasma membrane. This can be very confounding in my view (due to erroneous detachments etc.). As the authors explicitly state that the detachment is a process followed by a directed movement (with a defined velocity) towards the plasma membrane, this calls for a much better measurement in general. The authors should directly measure how far the centrioles are from the closest plasma membrane region in each condition they are examining (and should do this as a function of the "time progression" in different oocytes as they get closer to fertilization).

      As mentioned above, we think that an account of the movement of centriole remnants provides important information about cellular dynamics during oocyte maturation. However, given that this movement is not essential for the elimination of such remnants, it appears that providing additional complex 3D analysis as suggested by the reviewer will not benefit the present manuscript.

      Do the authors observe any propensity in sas1(t1521ts) oocytes as to where the centrioles are being degraded more prominently in the cytoplasm (i.e., when attached to the nucleus vs. when near the plasma membrane)? They could perform analyses à la their assessments in Fig. S2 and see whether they can extract some more information about this. In other words, I am wondering whether SAS-1 regulates the centriole elimination process more prominently at near the nucleus or near the plasma membrane.

      Centriole elimination occurs during pachytene in sas-1(t1521) mutant animals, when nuclei are packed in the gonad and surrounded by little cytoplasm. Therefore, even if foci were to detach from nuclei at this stage, we would not be able to quantify it with certainty. We will discuss these points in the revised manuscript.

      I ask this because the section about "centrioles moving to plasma membrane" appears epiphenomenal and rather random (i.e., the chances of a centriole moving to plasma membrane appears 50-50 under some control conditions - see control RNAi in Fig. 2G for example). Could the authors explore their existing data more closely (like suggested above), to see whether they could find intriguing correlations that tells us a little more about whether the centriole elimination at these two places are achieved differently? Otherwise, I frankly do not think this section contributes significantly to the essence of the story.

      We apologize for the confusion our writing seems to have generated. The chances of moving to the plasma membrane are not 50-50. The actual figure is 78.7% (reported as ~80% in the manuscript, line 187), and stems from the live imaging experiments where every travelling event can be monitored. By contrast, the analysis of fixed specimens is an underestimate as it provides only a snapshot of a dynamic process. We will expand the writing in the revised manuscript to clarify this point.

      Finally, the statements about a deterministic function for the plasma membrane re-localization should be toned down, because unlike what the authors claim in the paper (that ~80% of the centrioles move to plasma membrane), the control data (in Fig. 2B) clearly demonstrates that this number is more like ~60% (hence close to its chances being 50-50).

      Please see response just above.

      The paper carefully quantifies most of the data (for which I sincerely congratulate the authors!), however the experiments in Fig. S3 fall short of this. It would be nice if the authors could do the same here for completion.

      We will provide quantifications for Fig. S3E and S3F. However, due to the high disorganization of plk-1(or683ts); plk-2(ok1936) gonads, the presence of centriolar foci relative to oocyte position cannot be quantified accurately in this case.

      Minor comments:

      1. Sentence in lines 110-113 is too long and perturbs the flow. This should be shortened or be broken into better clauses. Perhaps the following way? "Prior analysis of centriole elimination in C. elegans oogenesis uncovered that this process takes place during diplotene..."

      The text will be modified accordingly.

      What are the orange arrowheads in the figure panels? They are not stated explicitly in the figure legends. My prediction was that they point to regions where centrioles are in another plane (though the overview is depicted from a different slice in the stack). Is this right? Either way, it will be useful to over-guide the reader on these orange arrowheads.

      The meaning of the orange arrowheads is explained in lines 520-521.

      If I am not wrong, the data/graph in Figures S2G and 2E are essentially the same (i.e., the data are duplicated). I couldn't find any statement in the figure legends indicating this. This should be added.

      Apologies about this oversight -the reviewer is correct and we will make a mention of this redundancy in the legend of Fig. S2.

      Some may consider the discussion on C2CD3 a little far-fetched, as this protein localizes to the distal end of centrioles (completely unlike SAS-1). Also, unlike the C. elegans centrioles, mammal centrioles do not contain a discernible central tube, casting doubt on the possibility of speculations made in the Discussion section. I suggest to remove out this paragraph, and instead to explicitly state whether the SAS-1 dependent mechanism could be applicable to other species is unclear.

      We will nuance these thoughts, further stressing their speculative nature, but intend to maintain them in some form as they provide a potential parallel that will be of interest to the human cell biology community.

      Could the authors add in their Discussion section some comment/thought on what the remaining GFP::SAS-7 pool (line 300-302) might possibly be? Curiously, there doesn't seem to be any structure associated with it in their EM tomograms, so it would be helpful to guide the reader further on this interesting finding.

      Although we would love to comment on this further, the remaining GFP::SAS-7 foci lack ultrastructural organization and do not exhibit recognizable electron densities. That this is the case will be stated explicitly in the revised manuscript.

      Reviewer #3 (Significance (Required)):

      General Assessment: This paper's strength is in its rigorous cell biology approaches to tackle a fundamental developmental biology problem. However, some of their conclusions are too firm while not being well-supported by the data, so the paper requires major revision before its publication.

      Advance: Discovery of a new molecular player in the centriole elimination process in worm oocytes, which can pave the way for future discoveries of centriole elimination mechanisms in other species. It is not yet clear whether the results will be broadly applicable, as some of the findings presented are in stark contrast to previous studies published on centriole elimination processes in Drosophila oocytes (e.g., Pimenta-Marques et al. 2016 Science). However, as summarized in the above section, these conclusions require further experimental evidence/support.

      Audience: Centriole elimination mechanisms are not widely studied, so I am not entirely sure whether this piece will be of immediate interest to the broad cell biology community. It will certainly be of general interest to several groups studying centriole elimination mechanisms, as well as developmental biologists trying to understand the oocyte maturation process.

      My expertise: Molecular and cellular mechanisms of cytoplasmic organization in development

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      Referee #3

      Evidence, reproducibility and clarity

      Pierron et al. uses C. elegans oocytes to tackle a fundamental, yet heavily under-studied question in developmental biology: how are centrioles are eliminated during gamete formation/maturation? The paper's main conclusion is that SAS-1 (a key protein that make up the central tube in C. elegans centrioles) plays a critical part to regulate the timing of centriole elimination. I congratulate the authors on all the experiments related to SAS-1 part of their story, as they are done meticulously and in unprecedented detail (particularly all the fascinating EM and expansion microscopy data!).

      The paper also concludes that the Polo-like kinase family does not have a central role in this process, in stark contrast to a previous report demonstrating their importance for centriole elimination in Drosophila oogenesis (Pimenta-Marques et al. 2016 Science). Unfortunately, I am less convinced about this part of the paper, and half of my major comments below relate to the experiments/analyses in this regard. I was similarly not very enthusiastic about a part of story that I didn't find very relevant to the main point of the paper: half of the centrioles detach from the nucleus and translocate to plasma membrane prior to their elimination. I find the observations here quite epiphenomenal and lacking a direct/mechanistic relevance to either the PLK or SAS-1 part of the story. In my view, the authors should consider taking this part out.

      Overall, the piece is well written and organized, however it suffers from several shortcomings that preclude it from publication in its current form. I list my criticisms and suggestions below.

      Major comments:

      1. The authors state firmly at several places in the text that PCM components do not contribute to the timing of centriole elimination (e.g., lines 420-421), particularly given their experiments with Polo kinase paralogs. In my view, the data speaks otherwise. The centriole elimination process appears strikingly premature when SPD5 (another PCM component) is overexpressed with the fluorescent transgene (Figure 1I). The opposite is also true - when another PCM component, PCMD-1, is knockdown by a temperature sensitive allele, the centriole elimination process is severely delayed (Figure 4C). Even more extremely in the epistatic Polo mutant conditions (Fig. S3B), the centrioles do not appear to be eliminated at all (though the authors prefer to interpret this result differently in line 260-263, which could be flawed per my second comment below). How do the authors explain all these intriguing results?

      Also, I believe these claims (on the PCM components and their role in centriole elimination) will benefit from more nuanced statements. For instance, although Plk paralogs may not be necessary for the centriole elimination process, some other centrosome components clearly are. Paradoxically, the effects observed here (when disrupting or promoting PCM formation) has the totally opposite effects observed in Pimenta-Marques et al. 2016 Science. The 2016 piece claimed that the loss of PCM renders centrioles more vulnerable to losing their stability (which makes sense). How do the authors interpret their own results (i.e. that a disturbed PCM leads to slower centriole elimination, and vice versa)?

      I invite the authors to more carefully tread these nuances throughout their manuscript, which otherwise may cast major doubt on their claims. 2. When investigating the role of Polo-like kinases, the authors assume that centriole elimination must follow (or correlate with) the dynamics of RME-2 (as a proxy for oocyte maturation). What guarantees that the centriole elimination process has to follow oocyte maturation? As far as I could tell, there is no direct evidence presented in the paper about this point. Do the authors have direct data (or reference to another work) that this trend must hold true at all times? I can readily see several places in the paper where this correlation doesn't appear to hold (e.g., in Fig. 4D the centriole elimination precedes the oocyte maturation under pcmd-1 condition).

      To correctly interpret their results on the epistatic Polo mutants, the authors could examine centriole elimination timing with mutants that can pre-maturely trigger or delay oocyte maturation (and do so without affecting the centriole biology itself). <br /> 3. Lines 155-159 on the dimness of the SAS-6 signal make me worried about how successfully the transgenes were generated. Could the authors comment on, or perhaps extend in detail in the Methods section, through what assays the transgenes were validated? For example, did the authors try to rescue a SAS-6-/- with a SAS-6::GFP transgene? I would like to see further support for their validities.

      If the authors can demonstrate the validity of their transgenes more reliably, could they possibly comment on the bunch of seemingly random SAS-6::GFP foci in Fig. 1G? 4. Starting from line 204, the authors use the percentage of oocytes with detached centrioles (from the nucleus) as a proxy for movement to plasma membrane. This can be very confounding in my view (due to erroneous detachments etc.). As the authors explicitly state that the detachment is a process followed by a directed movement (with a defined velocity) towards the plasma membrane, this calls for a much better measurement in general. The authors should directly measure how far the centrioles are from the closest plasma membrane region in each condition they are examining (and should do this as a function of the "time progression" in different oocytes as they get closer to fertilization).<br /> 5. Do the authors observe any propensity in sas1(t1521ts) oocytes as to where the centrioles are being degraded more prominently in the cytoplasm (i.e., when attached to the nucleus vs. when near the plasma membrane)? They could perform analyses à la their assessments in Fig. S2 and see whether they can extract some more information about this. In other words, I am wondering whether SAS-1 regulates the centriole elimination process more prominently at near the nucleus or near the plasma membrane.

      I ask this because the section about "centrioles moving to plasma membrane" appears epiphenomenal and rather random (i.e., the chances of a centriole moving to plasma membrane appears 50-50 under some control conditions - see control RNAi in Fig. 2G for example). Could the authors explore their existing data more closely (like suggested above), to see whether they could find intriguing correlations that tells us a little more about whether the centriole elimination at these two places are achieved differently? Otherwise, I frankly do not think this section contributes significantly to the essence of the story.

      Finally, the statements about a deterministic function for the plasma membrane re-localization should be toned down, because unlike what the authors claim in the paper (that ~80% of the centrioles move to plasma membrane), the control data (in Fig. 2B) clearly demonstrates that this number is more like ~60% (hence close to its chances being 50-50). 6. The paper carefully quantifies most of the data (for which I sincerely congratulate the authors!), however the experiments in Fig. S3 fall short of this. It would be nice if the authors could do the same here for completion.

      Minor comments:

      1. Sentence in lines 110-113 is too long and perturbs the flow. This should be shortened or be broken into better clauses. Perhaps the following way? "Prior analysis of centriole elimination in C. elegans oogenesis uncovered that this process takes place during diplotene..."
      2. What are the orange arrowheads in the figure panels? They are not stated explicitly in the figure legends. My prediction was that they point to regions where centrioles are in another plane (though the overview is depicted from a different slice in the stack). Is this right? Either way, it will be useful to over-guide the reader on these orange arrowheads.
      3. If I am not wrong, the data/graph in Figures S2G and 2E are essentially the same (i.e., the data are duplicated). I couldn't find any statement in the figure legends indicating this. This should be added.
      4. Some may consider the discussion on C2CD3 a little far-fetched, as this protein localizes to the distal end of centrioles (completely unlike SAS-1). Also, unlike the C. elegans centrioles, mammal centrioles do not contain a discernible central tube, casting doubt on the possibility of speculations made in the Discussion section. I suggest to remove out this paragraph, and instead to explicitly state whether the SAS-1 dependent mechanism could be applicable to other species is unclear.
      5. Could the authors add in their Discussion section some comment/thought on what the remaining GFP::SAS-7 pool (line 300-302) might possibly be? Curiously, there doesn't seem to be any structure associated with it in their EM tomograms, so it would be helpful to guide the reader further on this interesting finding.

      Significance

      General Assessment: This paper's strength is in its rigorous cell biology approaches to tackle a fundamental developmental biology problem. However, some of their conclusions are too firm while not being well-supported by the data, so the paper requires major revision before its publication.

      Advance: Discovery of a new molecular player in the centriole elimination process in worm oocytes, which can pave the way for future discoveries of centriole elimination mechanisms in other species. It is not yet clear whether the results will be broadly applicable, as some of the findings presented are in stark contrast to previous studies published on centriole elimination processes in Drosophila oocytes (e.g., Pimenta-Marques et al. 2016 Science). However, as summarized in the above section, these conclusions require further experimental evidence/support.

      Audience: Centriole elimination mechanisms are not widely studied, so I am not entirely sure whether this piece will be of immediate interest to the broad cell biology community. It will certainly be of general interest to several groups studying centriole elimination mechanisms, as well as developmental biologists trying to understand the oocyte maturation process.

      My expertise: Molecular and cellular mechanisms of cytoplasmic organization in development

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      In this manuscript Pierron et al. explore the mechanisms of centriole elimination during oogenesis in C. elegans. Centriole elimination is a common feature of oogenesis in many species, but it is relatively poorly understood and understudied. Here, the authors characterise the kinetics with which several key centriole and centrosome proteins are lost during this process in living worms, and they correlate this with an EM and expansion microscopy (U-Ex-STED) analyses of fixed tissues. They conclude that centriole elimination begins with the loss of SAS-1 from the central region of the centrioles, which correlates with the widening of the structure and the loss of the centriole MTs. A remnant structure containing several core centriole proteins remains, however, and this often ultimately detaches from the nuclear envelope and moves towards the plasma membrane in a MT-motor-dependent fashion before it dissipates (although detachment from the nucleus does not seem to be required for the eventual elimination of this residual structure). Intriguingly, centriole loss in this system does not appear to require the down-regulation of PLK activity, which is in contrast to the situation in Drosophila oogenesis.

      The manuscript is generally well written and the data is of a high quality and is logically and clearly presented. Although the ultimate mechanisms regulating centriole elimination remain obscure (i.e. what triggers the loss of SAS-1, and how is this regulated?), the data presented here will be of significant interest to the centriole/centrosome field and I am supportive of publication. I have a few points that the authors should consider prior to publication.

      Major comment:

      In the EM shown in Figure 5F the authors claim that the central tube of the centriole is disrupted, but the other elements (inner tube, MTs and paddlewheel) are not. I don't think this is as clear cut as the authors claim-at least from comparing the images of the one normal centriole (5E) and one centriole that is starting to be eliminated (5F). It seems much harder to distinguish the MTs and the inner tube in the image in 5F. Perhaps this is obvious to the authors as they have compared many more images, but I think they need to find some way of showing this more convincingly (a montage of multiple centrioles)?

      This same issue is compounded in Figure 6D where, using a different technique (U-Ex-STED), the authors claim that the centriolar distribution of SAS-1 is gradually disrupted as centriole elimination proceeds. It does look like the amount of SAS-1 has decreased from early prophase to late pachytene, but the central tube it stains doesn't look particularly disrupted and, if anything, the MTs look more disrupted (and also possibly of lower intensity, perhaps explaining why the ratio of SAS-1/tubulin doesn't change very much over these stages, as shown in Figure 6G).

      These points are important, as throughout the manuscript the authors assume it as a fact that SAS-1 leaves the centriole early (which is clear), and that this leads to the specific loss of the central tube (which, at least on the basis of this data, is not so clear).

      Minor comments:

      1. The authors state that the kinetics of GFP-SAS-7 or SAS-4 loss were not altered in pcmd-1 mutants (Figure 4A-C; Figure S3E,F). This doesn't look correct to me, as both proteins seem to stay brighter for longer in the mutant embryos (and this is quite easy to see on the quantification graph for SAS-7 in Figure 4C). It looks similar for SAS-4 from the pictures shown in Figure S3E,F, although this data is not quantified (and is there any reason why this data is not quantified?).
      2. The authors state that they demonstrate that SAS-1 is the first component to leave the disassembling centrioles. I would rephrase as they can't know this for sure (i.e. there could be some untested component that leaves earlier).
      3. In the latter part of the Discussion the authors state that SAS-1 is critical for centriole elimination. I would rephrase, as this seems to suggest it is required for centriole elimination, which is not the case. It might also be worth discussing that the elimination machinery clearly seems to target SAS-1 early on, but we don't yet know what this machinery is or how it is regulated.

      Significance

      The manuscript is generally well written and the data is of a high quality and is logically and clearly presented. Although the ultimate mechanisms regulating centriole elimination remain obscure (i.e. what triggers the loss of SAS-1, and how is this regulated?), the data presented here will be of significant interest to the centriole/centrosome field and I am supportive of publication. I have a few points that the authors should consider prior to publication.

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      Referee #1

      Evidence, reproducibility and clarity

      Centrioles are small cylindrical structures with roles in cell division, motility, and signaling. Typically, centrioles are highly stable structures which can persist for many cell generations. However, in some cells, such as the female germ line of many species, centrioles are programmed for elimination. This process is essential for maintaining centriole number from one generation to the next in sexually reproducing organisms, yet in nearly all species the molecular mechanisms underlying how centrioles are eliminated is unknown. The current study utilizes the nematode C. elegans to explore how centriole architecture changes during the elimination program in the female germ line. Using a suite of light microscopy techniques, the authors provide a stunning visual perspective of how centrioles are disassembled during oogenesis and show that removal of the central tube component SAS-1, a key regulator of centriole stability, is an early event in elimination. I have no major objections to the work and enthusiastically endorse its publication with the following minor revisions.

      Page 9 line 200: In the pcmd-1 mutant, the authors state that centriolar foci devoid of nuclei are present in rachis, but they do not mention in the text that there are also nuclei that lack centriole foci in early pachytene. This is mentioned in the figure legend, but I felt it was important enough to mention in the text.

      Page 9 line 211. The authors found that in the absence of dynein heavy or light chain that centrioles remain associated with the nuclear envelope (rather than moving to the periphery). To me this was striking as dynein depletion in the embryo results in the opposite phenotype with centrioles losing attachment to the nuclear envelope and moving to the cell periphery (Gonczy et al. 1999 JCB 147:135). It might be worth pointing this out somewhere in the manuscript and speculating about the reasons for this difference.

      Page 11 line 277: The authors state that elimination timing is not affected by the loss of SPD-5. This is a small but important point. It really is the absence of PCMD-1 and not SPD-5, as SPD-5 is still present in the cell. An alternative would be to say "in the absence of PCM" or "in absence of a pericentriolar accumulation of SAS-5".

      Figure 4D: Why does loss of PCMD-1 result in a delay in oocyte maturation as judged by RME-2 accumulation? This is not mentioned in the paper. Is this a general response to a loss of PCM or is this specific to a loss of PCMD-1?

      Figure 7 E and F. The authors measure the tubulin and SAS-4 intensity in wild-type and sas-1(t1521) embryos and conclude that microtubules and SAS-4 signals decay faster in the sas-1 mutant than in the control. To me, this is convinceingly the case with microtubules in panel E but I am not so sure this is the case with SAS-4 as shown in panel F. The differences in SAS-4 levels are much smaller between mutant and control. Could the authors provide statistical analysis to show how significant the differences are?

      Page 15 line 363. I think this sentence should be reworded to: "Finally, we demonstrate that the central tube protein SAS-1 is the first of the factors analyzed here to leave centrioles..."

      Significance

      The work contained in this manuscript represents a fundemental step forward in understanding the process of centriole elimination. The authors have carefully described the stepwise disassembly of the centriole including changes in the architechure during oogenesis. They have identified loss of the centriole stability factor SAS-1, as an early event in the elimination program and have found that in a sas-1 mutant, the centriole disassembles prematurely. They have also shown that loss of SAS-1 is followed by expansion of the centriole and ultimately loss of structural integrity. This work should be of interest to a broad range of scientists including those interested in centrosome dynamics, germ line development, and more generally cell biologists.

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      Reply to the reviewers

      To simplify the reading of our answers, we have numbered the questions of the reviewers. Similarly, to make the identification of the changes easier, we have written the changes in the manuscript in blue, orange and green when they were asked by reviewer 1, 2 or 3 respectively.

      __Reviewer #1 __

      Major comments:

      1. One issue concerns the conclusion that microglial TNFa signaling shapes slow waves during NREM sleep (e.g., title; lines 148, 175-176; 180; 222-223; 288) on the basis of the data shown in Fig. 4b-d. Slow waves normally consist of two components, The amount of changes reported in the study might indeed seem small if the role of microglia was to gate slow-wave-sleep itself. In contrast, the effects we report are in the upper range of the modulations reported for NREM sleep slow-waves: depletion of microglial TNFα yielded a ~17 % decrease (relative to controls) in maximal slope (fig 4d); while sleep deprivation or physiological sleep-wake cycle have been associated with similar changes in the slope of slow-waves:

      2. 6% increase after sleep deprivation and 17% increase after SOM-IN optogenetic stimulation (Funk et al. 2017, ref 17);

      3. 15% above or below average in early and late sleep (Vyazovskiy et al. 2007, ref 79);
      4. Daily fluctuation of the slow-wave slope is smaller that 15% (Hubbard et al. 2020, ref 50). Noteworthy, as noticed by this reviewer, slow saves consist of slow oscillations and delta waves. We also measured the slope and duration of slow oscillations and delta waves and found significant differences in both between control and micTNFα-KO mice. (see response to Reviewer 2 point 8).

      The change in delta peak reported here following microglial TNFα depletion reveals a pre-eminence of slower waves (“d1” waves 0.75-1.75Hz), over the faster waves (“d2” waves 2.5-3.5Hz) the latter being the main form of oscillations potentiated by sleep need (Hubbard et al. 2020; ref 50). Similarly, enhanced SW-slope and short SW-period is associated with high sleep need (Hubbard et al. 2020; ref 50) and we found that microglial TNFα depletion reduces SW slope and enhances SW duration. Together, these results suggest a role of microglial TNFα in modulating delta oscillations in response to sleep need, which gives further support to our recent publication on the role of microglia in the expression of sleep need and sleep homeostasis (Pinto et al 2023; ref 80). Accordingly, we have now replaced in the figure 4 the panel 4b with the value of the peak of delta (whose significance is less clear) with the ratio of power in the two delta bands which has a clearer interpretation in light of the work cited above. Figure 4b and the text were changed accordingly (line 161 of the results and line 277 in the discussion). Similarly, microglia may contribute to changes in SWA related to memory consolidation following intense recruitment of cortical circuits (Huber et al. 2004, ref 83). This is now more clearly stated in the text (line 292).

      We do not claim to have demonstrated that the change in slow oscillations fully explains the loss of memory consolidation, but instead we report the convergent findings that TNFa depletion in microglia produces alterations in sleep slow oscillations of the order of magnitude of sleep-need induced effects, and disrupts memory consolidation known to be sleep-dependent.

      VGAT was used to identify GABAergic synapses in conjunction with GABAA receptors. Of various GABAergic interneurons, somatostatin (SOM)-containing GABAergic interneurons are known to be crucial for generating slow waves during NREM sleep through their axon terminals that target and concentrate in L1 (e.g., Funk et al., 2017, ref. 17). However, not all GABAA receptors in L1 would be associated with the inputs from SOM-containing GABA interneurons. For example, there are parvalbumin-containing GABA interneurons and their activation has been reported to DECREASE slow waves (Funk et al. 2017). This is relevant and should be discussed in relation to the results.

      Answer: As pointed out by the reviewer, we do acknowledge that not all GABAergic synapses in L1 are associated to inputs of SOM+ interneurons. Other than the axonal projections of SOM+ interneurons, axons of the inhibitory neuronal types neurogliaform cells and canopy cells can be found in L1. This does not seem to be the case for PV+ interneurons whose somata is not located in L1 and lack projections to L1 (Schuman et al. 2021 - ref 19). We have now acknowledged and discussed this in the discussion:

      Line 285: “In this study we show that L1 inhibitory synapses are modulated by microglia in a sleep-dependent manner. Our data favor the possibility that the microglia-targeted GABAergic synapses arise from SOM-IN. However, inhibition on L1 also originates local axonal arbors of neurogliaform and canopy cells and the identity of the microglia-regulated presynaptic terminals remains to be established.”

      To follow up on the above, (1) it is unclear why NeuN was used to delineate cell bodies (Fig. 1e). In fact, SOM-containing GABA neurons (see above) have been shown to inhibit pyramidal neurons through presynaptic inhibition of excitatory inputs as well as postsynaptic inhibition of dendrites, but not cell bodies, of pyramidal neurons (see Funk et al. 2017 for references). Some discussion along this line would be useful and potentially important. (2) In addition, it would have been interesting to add an immunolabel for SOM to identify SOM-containing axon terminals associated with VGAT (Figs 1, 2), and this could be done for parvalbumin (see above) terminals as well; however, this analysis is optional and not required.

      Answer:

      • The rationale for analyzing GABAergic synapses in L5 was to assess whether the daily change in synaptic GABAAR observed in L1 had some degree of regional specificity or was rather a widespread event throughout the cortical layers. In this regard, NeuN was used to delineate cell bodies in order to assess changes in synaptic GABAARs at somatic synapses, known to be targeted by parvalbumin inhibitory neurons. In addition to the quantification provided in figure 1e, we also analyzed synaptic GABAARs at non-somatic VGAT clusters in L5 and observed no ZT6/ZT18 difference (data not shown).
      • We do agree with this reviewer that looking selectively at GABAAR located at somatostatin pre-terminals would be extremely interesting. Accordingly, we have now tried to quantify the synaptic GABAR facing SOM positive presynaptic boutons identified in mice expressing tdTomato in SOM interneurons (figure S3). However, GABAAR immunostaining requires using an antigen retrieval method (see below) that, in our hands, bleaches tdTomato fluorescence. Our attempt to retrieve the lost SOM-tdTomato signal by IHC with RFP antibodies was unsuccessful (see below) suggesting that the structure of tdTomato is altered, thereby preventing such quantification.


      Quantification of changes in synaptic GABAARs at synapses targeted by SOM interneurons (SOM-IN) is not technically possible. Top, Confocal images of adult brain cortical layer 1 show signal of GABAARγ2, gephyrin and VGAT obtained with different protocols for tissue processing (fresh-frozen, perfusion with paraformaldehyde and perfusion with paraformaldehyde followed by decloaking chamber-based heat-induced epitope retrieval). Visualization of bona-fide gephyrin+VGAT+ synapses and synaptic GABAAR clusters was only possible by epitope retrieval. Bottom, Confocal images of mice expressing tdTomato in SOM-IN (SOMCre/+R26tdTom/+) show expected signal revealing SOM-tdTomato+ presynaptic boutons after perfusion. When the protocol for epitope retrieval is used, SOM-tdTomato+ signal is lost, which could not be retrieved by IHC with 3 different antibodies (source and catalog number are indicated).

      Minor comments:

      It appears that n's are not consistently reported. Please check.

      Answer: We have now:

      -added n’s in the legend of figure 4.

      -corrected a typo in the legend of figure 5 (line 664: “(f-i) n= 11” instead of “(f, g) n= 11”).

      -in the legend of figure S2, we have added “g. Mean intensity…b-g, n= 53-60 FOVs from 5 mice per group”.

      The Y-axis does not start from zero in some graphs. Although this might be a matter of preference, it can be misleading.

      Answer: The Y-axis that did not start from zero have now been explicitly signaled (figure 4b, d; figure 5b, d, e)

      In the supplementary information PDF, under Immunohistochemistry (IHC): "In direct IHC" in the first line of the paragraph should be "Indirect IHC".

      Answer: this is corrected. The sentence now starts by “Cryostat sections…”. Also, “Table 2 – Antibodies and IHC method used” has been corrected accordingly.



      __Reviewer #2 __

      Major Comments:

      1) (1) There are several instances where the authors state the experiments occurred "across the 24 h light/dark cycle" (Lines 42, 139), "during the sleep/wake cycle" (Lines 87, 242, 248), or "during sleep" (Lines 155, 220, 254). These statements are imprecise and can lead to erroneous interpretations of the data. For molecular studies, data were collected at a light period timepoint (Zeitgeber Time (ZT) 6) and a dark period timepoint (ZT18). While I appreciate the comparisons of the light and dark phases, 2 timepoints are not sufficient to claim that phenomena were tested across the light-dark cycle. More importantly, though, it is not accurate to claim outcomes from data collected during ZT6 occurred "during sleep" (or ZT18 outcomes occurred during wake). Although mice sleep more in the light period vs. the dark period, they are polyphasic sleepers and thus can be awake at ZT6 and asleep at ZT18. Therefore, statements should be edited for accuracy to instead state that phenomena were observed at ZT6/ZT18 or light/dark periods. (2) In addition, any figures (e.g., Figure S1) using x-axis labels of "W" and "S" should be relabeled as "ZT18" and "ZT6," respectively.

      Answer : (1) We agree with this reviewer and we have consistently corrected these imprecise wordings.

      (2) We have relabeled the figure S1 and replaced “W” and “S” by “ZT18” and “ZT6” respectively.

      2) The authors claim that microglial TNFα plays a role in sleep-dependent memory consolidation (Title and Lines 20, 22, 178, 198, 224, 276, 288) based on a series of experiments using tests previously shown to have a sleep-dependent consolidation component. However, the authors did not assess sleep-dependent consolidation in the micTNFα-KO and the tCtl mice, and thus this conclusion cannot be drawn. This is because the experimental paradigms did not include sleep deprivation. Claims that outcomes are sleep-dependent need to be shown as absent/impaired after sleep deprivation especially in mutant (and control) lines that have not been previously tested in this context. As such, claims of sleep-dependent memory consolidation (including in the title) should be removed OR new experiments including sleep deprivation should be included.

      Answer: Previous studies have shown that in the learning tasks that we have used, memory consolidation is lost in sleep-deprived animals (refs 53 to 55). Our current study indicates that memory consolidation is also lost in micTNFα-KO mice. Thus, we believe it would not be informative to perform sleep-deprivation in micTNFα-KO as requested by this reviewer since the outcomes (loss of memory consolidation) cannot be additive. However, we acknowledge that even though microglial TNFα depletion impacts slow waves that are known to play a causal role in consolidating the memory during sleep (refs 51 and 52), it cannot be excluded that TNFα depletion impairs memory consolidation by a non-sleep dependent pathway. Therefore, we have modified the title of the study to prevent misleading interpretations:

      New title: “Microglial TNFα controls synaptic GABAARs, sleep slow waves and memory consolidation

      We have changed wording (lines 225 and 643). We have further added a cautionary note in the discussion:

      Line 299 : “We now show that mice lacking microglial TNFα display impaired memory consolidation when tested in a complex rotator motor learning task or in the floor-texture recognition (FTR) task. In these two tasks, memory consolidation is sleep-dependent. This suggests a possible involvement of microglial TNFα in the sleep processes that promote memory consolidation, while leaving open the possibility of a non-sleep-dependent mechanism.”.

      We are now more cautious in our conclusion and write (line 313) “This work demonstrates that microglia tune slow waves and support memory consolidation probably by acting during sleep

      3) (1) "This shows that P2RX7 and microglial TNFα drive daily fluctuations in CaMKII Thr286-phosphorylation and are required for sleep-dependent GABAAR synaptic upregulation in L1 during the light phase" (Lines 144 - 146). Similar to the above comment, it cannot be definitively concluded the P2X7R or microglial TNFα are required for sleep-dependent GABAAR synaptic upregulation because sleep deprivation studies were not conducted in the P2rx7-KO or micTNFα-KO mice. (2) Furthermore, there is no analysis (or citation) of P2rx7-KO mice sleep-wake expression nor has the micTNFα-KO sleep data been presented at this point to make any determinations on how (possibly perturbed) sleep-wake expression in these mice could affect the stated outcomes.

      Answer:

      (1) As discussed in our previous answer, both sleep deprivation (fig 1d) and inactivation of TNFα or P2RX7 (fig 3) completely prevent the synaptic GABAAR accumulation and CaMKII phosphorylation at ZT6. Performing sleep deprivation on micTNFα-KO and P2RX7 KO is thus not expected to exert an effect since the outcomes (loss of synaptic accumulation and phosphorylation of CaMKII at ZT6) cannot be additive. We actually conducted sleep deprivation studies in micTNFα-KO as suggested by this reviewer (see below). As expected however, sleep deprivation (SD6) has no further effect on GABAAR accumulation and CaMKII phosphorylation on micTNFα-KOs when compared to ZT6.


      Impact of sleep deprivation on synaptic GABAAR and CaMKII phosphorylation in micTNFα-KO mouse brain. In complement to figure 3, SD prevents that increased accumulation of synaptic GABAARγ2 (left) and CaMKII phosphorylation (right) in tCTR, but has no effect in micTNFα-KO (green).

      left: Mean intensity of GABAARγ2 clusters at gephyrin+VGAT+ synapses normalized to ZT18. n= 48 to 65 FOVs from 4-5 mice per group.

      Right: Mean intensity of Thr286-phosphorylated CaMKII signal in L1 normalized to ZT18. n= 37 to 50 FOVs from 4-5 mice per group.

      (2) Please note that analysis of sleep structure of micTNF-KO mice is shown in figure 4, which reveals “that microglial TNFα has limited effects on sleep-wake patterns as shown by the lack of major alterations in the amounts of wake, NREM and REM sleep between micTNFα-KO and tCTL mice along a light/dark cycle (fig. 4a).” (line 158). Similarly, analysis of baseline sleep-wake structure in P2RX7-KO mice revealed no abnormalities (Krueger et al 2010, ref 45). This has now been discussed in the text (lines 143).

      4) There are some details regarding data analysis that are lacking:

      1. How were bouts defined for each arousal state? Answer: We have now defined the bouts in the Materials and Methods (section : “Sleep recording and analysis”) : “Bouts were defined as consecutive 10-s epochs of similar vigilance state and could be as short as one epoch.”

      2. (1) It seems more details are needed for EEG spectra analysis. From what values was the median derived and over what time period? How was each spectral bin normalized and over what time period? (2) What data (i.e., from what time period and duration) are shown in Figure 4b? Same question for Figure 4c-d? Were these time periods the same for controls and mutants given that NREM SWA changes across the light-dark cycle? Answer:

      (1) The spectrum of bouts of 2.56s (512 points at 200Hz) was computed by FFT and the spectrum corresponds to the median of the FFT of all bouts. This information is now in the material and methods section “Spectral analysis”.

      (2) The graphs reported in the text correspond to the 24h time period for both tCTL and micTNFα-KO mice. This has now been clearly indicated in figure 4 legend and in the material and methods section “Slow-wave analysis”.

      1. How was NREM delta power normalized and analyzed and over what time period? Answer: Normalization corresponds to the division for each animal of the spectrum by the total power of the spectrum. Data reported correspond to the 24h time period.

      5) Claims that GABAAR enrichment at synapses is sleep-dependent is based primarily on the data presented in Figure 1d reporting no increase in cortical GABAAR after sleep deprivation. A previous study (not cited) showed sleep deprivation increased GABAAR expression in CaMKIIα+ neurons in barrel cortex (Del Cid-Pellitero et al., Front Syst Neurosci, 2017). It would be helpful if the authors cited and discussed this study.

      Answer: Indeed, Del Cid-Pellitero et al. show that GABAARs located around the soma of layer 5 neurons are increased upon sleep deprivation. Importantly, they used brain tissue collected after perfusion with paraformaldehyde without antigen retrieval. This protocol was shown to result in uniform surface labeling of GABAARs without staining the pool of receptors clustered at synapses (Gasser et al. 2006 Nature Protocols, PMID: 17487173). In this study we performed antigen retrieval that allows visualization of synaptic GABAARs (see figure 1). We and them are thus labelling different pools of GABAARs: synaptic vs. membrane at the soma level (comprising both synaptic and extrasynaptic). On the other hand, as we did not observe a difference in synaptic GABAARs at the soma level in L5 between ZT6 and ZT18, we did not assess sleep-dependency by performing sleep deprivation in this cortical layer. Together, we do not interpret their results as conflicting to our findings, but rather that different sleep- and wake-dependent mechanism exist to regulate the abundance of GABAARs at the subcellular level. We have now cited this work and include a brief discussion:

      Line 58: “Sleep deprivation has previously been shown to increase GABAARs located around excitatory somas60. This suggests that the expression of GABAARs are differentially regulated depending on their subcellular localization”.

      6) Some sentences/conclusions are overstatements:

      1. "...discarding the possibility that lack of synaptic GABAARs enrichment upon PLX3397 treatment results from perturbed sleep during the light phase" (Lines 68 - 69). Only sleep time is reported to make this claim, but bout frequency, bout duration, and EEG spectra could be perturbed with this manipulation. This claim should be edited for accuracy or additional data (e.g., bout and spectral analysis) should be presented. In addition, Line 68 should be edited to state that "...microglia depletion does not alter sleep time during the light phase..." unless additional analyses are provided. Answer: We have now added the bout analysis in microglia depleted mice in extended table 2.

      "TNFα, which is mostly if not exclusively produced by microglia in the brain..." (Lines 93 -94). Although microglia are a major source of TNFα, there is evidence other brain cell types also release TNFα. In addition, the citation provided does not support this exclusivity claim.

      Answer: The reference 29 (Zeisel et al., reference 28 in the previous version) is a single cell transcriptomic study. The data are available online: http://mousebrain.org/adolescent/genesearch.html and show that TNFα mRNA is only detected in microglia. We are not aware of evidence showing that other brain cell types release TNFα. To our knowledge there is no brain RNA seq repository that shows TNFα expression in other brain cell-types e.g :

      • https://celltypes.brain-map.org/rnaseq/mouse_ctx-hpf_smart-seq;
      • http://biogps.org/#goto=genereport&id=21926
      • http://www.brainrnaseq.org/
      • "We thus anticipate that microglial TNFα may control REM by acting at the basal forebrain..." (Line 163). This statement is based on a cited study that reported REMS suppression (and increased NREMS time) after TNFα injection in the subarachnoid space of the basal forebrain. It is unclear to me why this statement is included when ICV and IV TNFα administration also reduce REMS (Shoham et al, Am J Physiol, 1987). Given these data and this statement is not being tested, it does not seem like it needs to be included. It should also be noted a previously reported (but not cited) global TNFα KO mouse (Szentirmai and Kapás, Brain Behav Immun, 2019) also showed increased REMS and REMS bouts, but this seemed to be a dark period phenotype (NREMS and Wake time, bout frequency, and bout duration were unaffected). This is an interesting detail to at least include in the second paragraph of the Discussion. Answer: To comply with this comment, we have now removed the sentence “We thus anticipate…” (line 163, now 160), and we have modified the second paragraph of the discussion so as to include the dark-period specificity described in Szentirmai and Kapás that we now cite.

      7) It is unclear to me why the authors believe ATP in these studies has a neuronal origin (Lines 106, 132, 218) when other cell types also release ATP. Is this because of NMDA treatment? If so, NMDA receptors are also expressed on other cell types like astrocytes (Verkhratsky and Chvátal, Neurochemical Research, 2020). Answer: We do not believe and we did not write that ATP has a neuronal origin. Indeed, we wrote:

      -line 104: “We next identified the signaling pathway between neuron and microglia”.

      -line 130: “ATP released downstream neuronal activity activates microglial P2RX7

      -line 218: “…microglia sense neuronal activity through an ATP/P2RX7 signaling pathway”.

      However, to rule out any misinterpretation, we have now added a sentence that explicitly recall the possible involvement of other cell types:

      Line 132 “Noteworthy, our results do not exclude the possible involvement of other cell types acting between neurons and microglia

      8) Because the authors rationalize investigating memory consolidation based on micTNFα-KO changes in NREM SWA, I am curious if the authors considered parsing NREM SWA into slow oscillations and delta waves as Vaidyanathan et al (eLife, 2021) did. The reason for this is because slow oscillations are shown to be associated with memory consolidation, but delta waves are associated with weakening memories.

      Answer: The parsing between delta waves and slow oscillations (SO) in the Vaidyanathan et al. article is based on a quantile separation of the size of the events (the top 15% of events are called SO while the rest may qualify as delta waves events; this is quite different from our definition which is based on deviations larger than 3 times the estimated standard deviation of slow fluctuations during Wake); it is worth noting that applying the definition from Vaidyanathan et al. to compare groups may introduce a bias in the interpretation if the rate of slow waves is modified between groups of animals. We have performed the parsing of slow events according to Vaidyanathan et al. and found similar changes for Slow Oscillations as using our definition (wider and “slower” SO). The same effect was observed in delta waves, suggesting that microglial TNFα affects both types of slow waves similarly. __ __

      9) For the complex wheel task, micTNFα-KO mice seem to start and end with better performance compared to tCTL on S1 (although it is not clear if this difference was statistically evaluated). Would the conclusions from this experiment change if data were normalized to account for the apparent better starting performance? Answer: Despite a trend for a better performance of micTNFα-KO at S1, no significant difference in the mean performance was found between controls and micTNFα-KO mice at S1. We show below the mean performance at S1 and S2 for controls and micTNFα-KO mice. Moreover, learning within each session (both at S1 and S2) is not altered in micTNFα-KO (figure 5c) revealing that the ability to learn is not affected in microglia TNF-KO mice neither in S1 nor in S2 suggesting that memory impairment across sessions is not the result of saturation of learning capacity.





      Mean performance in the two sessions (S1 and S2) of the complex wheel learning task in control and micTNFα-KO as measured by the mean time on the complex wheel (in seconds) of all trials in each session. ***p Nevertheless, we acknowledge that the apparent better starting performance could lead to misinterpretation of the results, and so as suggested by this reviewer, we normalized the data to the average of the last 3 trials in session 1 and computed using the normalized values the performance improvement (figure 5d) and consolidation (figure 5e). The same results were obtained. We thus interpret that the differences in memory consolidation between S1 and S2 (fig 5d, e) do not stem from changes in baseline performance.




      Results on the complex wheel task following normalization to performance in S1. For each mouse, latency to fall off the complex wheel in each S1 and S2 trials was normalized to the average of the last 3 trials in S1. The normalized values were used to plot the graphs as in figure 5:

      Left, latency to fall in S1 and S2;

      middle, performance improvement;

      right, consolidation of motor learning.

      We have now modified figure 5 accordingly.

      10) Many of the molecular studies emphasized a layer-specific effect in L1 vs. L5. It would be helpful if the authors could link (at least in the Discussion) this cortical-layer specificity with reported microglial TNFα effects on sleep parameters and memory consolidation.

      Answer: According to this comment, we have now proposed a mechanism for the L1 vs L5 specificity in the discussion:

      Line 254: “The layer 1 vs layer 5 specificity may arise from the molecular difference of GABAergic synapses across the somato-dendritic arbour as proposed. Alternatively, but not exclusively, it may result from a differential expression of TNF-R1 along the cortical layers and/or from layer-specific behavior of microglia”.

      We hope that it will help understand our hypothesis of a link between microglial TNFα effect on upper layer synapses with the effect on sleep parameters and memory consolidation that are proposed from line 277 onwards.

      Minor Comments:

      1) For the experiments investigating TNF receptor (TNFR) involvement (fig. S5), it would have been interesting to see the response to human recombinant TNFα which interacts with TNFR1 but not TNFR2 whereas mouse recombinant TNFα interacts with both receptors (Lewis et al, PNAS, 1991).

      Answer: The differential effect of human and mouse TNFα was not known by us. We do agree with this reviewer that the proposed experiment would have been interesting and would further confirm the data shown in Supp fig 5b showing an involvement of TNFR1 but not of TNFR2.

      2) "Synapse plasticity in the sleeping brain likely supports crucial functions of sleep" (Line 33). I believe it is more accurate to instead state sleep supports synapse plasticity. The sentences immediately following also provide examples of sleep/wake mediating plasticity.

      Answer: We have now replaced the sentence in line 29 for “Sleep drives plasticity of excitatory synapses”.

      3) "Moreover, microglia depletion also abolished the reduction of synaptic AMPA receptor subunit GluA2 at ZT6 (fig. S1)..." (Lines 70 -71). The data in this figure shows an increase GluA2, but the data cited showed decreased excitatory transmission. This discrepancy is not discussed.

      Answer: We agree with this reviewer that the we did not discuss the increased GluA2 at synapses upon microglial depletion. However, we believe that this aspect is beyond the scope of this study and although showing the data seemed important, we believed that discussing these data might lose the reader. We have now rephrased this sentence so as not to mislead the reader (line 68):

      Moreover, microglia depletion also reversed the reduction of synaptic AMPA receptor subunit GluA2 at ZT6

      4) If you normalize within treatment (e.g., PLX, SAP, minoc, 4-OHT, apy, PPADS, A74, PSB) rather than to controls (e.g., normalize PLX-iLTP to PLX-bsl instead of CTL-bsl) in Figures 2b-f, 3b-c, do you get the same results? Similarly, if you normalize PLX treatment to CTL ZT18 in Figure 1c-d, do you get the same general outcome?

      Answer: As requested by this reviewer, we have normalized to treatment rather than to control for all the experiments shown in figure 2b-f. The graph below with this normalization shows that the same results are obtained.

      Concerning figures 1c-d and 3b-c, the normalization suggested cannot be performed because CTL and PLX treated mice or mutant mice were not processed for immunohistochemistry simultaneously, and so intensity values are not comparable.

      5) Is there a significance marker missing in Figure 2g for bls vs. BzATP?

      Answer: The significance marker it is not missing. Even though the individual experiments performed consistently show an increase in CaMKII T286 phosphorylation with BzATP treatment, the difference does not reach statistical significance between bsl and BzATP using a nested one-way ANOVA followed by Sidak’s multiple comparison test (p=0.09). There was however a typo in the legend of figure 2g about the statistical test used, which has been corrected (lines 611).

      6) It is unclear if the fig. 5a callout is the correct one at Line 143. If it is correct, then it is confusing (to me) in the way it is currently associated with the text.

      Answer: at line 143 (now 142), the callout is fig S5a (supplementary figure 5a that confirms TNFα depletion) and not 5a.

      7) There is a missing reference in Line 279 after "NOR."

      Answer: we have now added the reference.

      __Reviewer #3 __

      Summary: This paper tries top address how microglia-related TNFa modulate the REM sleep and motor behavior consolidation, using layer I gabaergic synapse enhancement (possible by SOM interneurons).

      The methods and results are solid/convincing and easily to be followed and they seem logically for the conclusion which they state.

      However there are major issues which need to be addressed before formal submission.

      Reviewer #3 (Significance (Required)):

      These works have serious limitations to be addressed before submitting to formal journals.

      (1) The in vivo sleep experiments with micTNFa-KO (fig. 4 and extended table S1, Fig S8 a/b) indicate that layer I GABAergic synapse potentiation modulates the start of down-state of slow-waves, which supposes to affect the NREM sleep. Controversially, NREM sleep is not affected (with SW down state duration increased in KO mice) and only REM sleep is influenced. This is opposite to the literature that GABAergic synapses in the cortex or thalamus determine the power of slow-waves and The fast transition to down state suggests that cortical neurons should be less active for REM sleep compared with NREM sleep.

      Answer This reviewer pointed out that in extended table 1, micTNFα-KO mice spent more time in REM sleep over 24 hours. However, we believe that this increase is marginally significant because there was no parallel decrease in the time spent in Wake or NREM sleep. Accordingly, the mean duration of REM sleep is not affected (extended table 1) and when measuring the amount of REM sleep in 2-hour bins, figure 4a shows no difference between micTNFα-KO and tCTL mice. Additionally, figure 4 shows that there is no change in the electrophysiological features of REM sleep. On the other hand, the electrophysiological parameters of NREM sleep, such as the duration and slope of slow waves, are affected, as pointed out by this reviewer. Therefore, we do not view our findings as controversial. While we have not tested this hypothesis, we do not exclude that the lack of synaptic plasticity observed in micTNFα-KO mice is linked to the prolonged REM sleep.

      (2) REM sleep can consolidate motor learning. However, the data (Fig. 5) do not consistently support this, although the authors cite the literature to support their results with complex motor learning.

      Answer: In line with the comment of this reviewer, Li et al. 2017 (ref 13) study showed that REM sleep deprivation impairs the performance improvement in a motor learning tasks. However, and as noticed by this reviewer, the amount of REM sleep is increased in micTNFα-KO and we are not aware of any study that has shown the consequences of increased REM sleep on motor learning consolidation. The floor-texture recognition task that we have used to show an alteration of consolidation in micTNFα-KO was shown to be NREM sleep-dependent (Miyamoto et al 2016, ref 54).

      (3) NMDA-iLTP needs to be further addressed since NMDA with CNQX in oganotypic slices supposes not be able to activate NMDARs that need co-activation of AMPARs to depolarize to remove the Mg-blockade before NMDAR activation in neurons. To further strengthen this result, ACh should be co-applied with NMDA (if not AMPA) since only REM sleep is enhanced in this study indicating that ACh should be involved to activate neurons during rem sleep, more relevant to REM sleep enhancement.

      Answer: we followed the iLTP protocol in culture described by Petrini et al. (reference 26) and verified an increase in GABAAR accumulation at synapses. Furthermore, in preliminary experiments (not shown), we found that this effect depended on CaMKII, as previously reported by Chiu et al. who used NMDA only on acute slices (reference 27). These findings indicate that the application of NMDA+CNQX on organotypic slices replicates the synaptic effects observed in primary cultures (in which NMDA+CNQX is used) and acute slices (only NMDA). While investigating a novel protocol of synaptic plasticity using Ach and its possible connection to REM sleep is intriguing, we believe it would be more appropriate to explore this in a separate study.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary: This paper tries top address how microglia-related TNFa modulate the REM sleep and motor behavior consolidation, using layer I gabaergic synapse enhancement (possible by SOM interneurons).

      The methods and results are solid/convincing and easily to be followed and they seem logically for the conclusion which they state.

      However there are major issues which need to be addressed before formal submission.

      Significance

      These works have serious limitations to be addressed before submitting to formal journals.

      1. The in vivo sleep experiments with micTNFa-KO (fig. 4 and extended table S1, Fig S8 a/b) indicate that layer I GABAergic synapse potentiation modulates the start of down-state of slow-waves, which supposes to affect the NREM sleep. Controversially, NREM sleep is not affected (with SW down state duration increased in KO mice) and only REM sleep is influenced. This is opposite to the literature that GABAergic synapses in the cortex or thalamus determine the power of slow-waves and The fast transition to down state suggests that cortical neurons should be less active for REM sleep compared with NREM sleep.
      2. REM sleep can consolidate motor learning. However, the data (Fig. 5) do not consistently support this, although the authors cite the literature to support their results with complex motor learning.
      3. NMDA-iLTP needs to be further addressed since NMDA with CNQX in oganotypic slices supposes not be able to activate NMDARs that need co-activation of AMPARs to depolarize to remove the Mg-blockade before NMDAR activation in neurons. To further strengthen this result, ACh should be co-applied with NMDA (if not AMPA) since only REM sleep is enhanced in this study indicating that ACh should be involved to activate neurons during rem sleep, more relevant to REM sleep enhancement.
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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      Pinto et al. sought to determine the role of microglia in sleep and plasticity. Using a combination of in vivo experiments in mice and organotypic slices, they report a molecular circuit whereby microglia respond to ATP via the purinergic receptor P2X7R to release tumor necrosis factor α (TNFα). TNFα then acts in its soluble form at TNF receptor 1 (TNRF1) to increase synaptic enrichment of GABAA receptors (GABAAR) in layer 1 (L1) of cortex (but not L5) during the light phase. These results suggest that microglial TNFα mediates cortical inhibitory synapses in a layer- and time-of-day-specific manner. The authors also showed selectively disrupting microglia TNFα alters slow wave activity (SWA) during non-rapid eye movement sleep (NREMS) and impairs memory consolidation.

      Major Comments:

      The authors performed several well-designed and controlled studies to uncover microglia regulation of GABAAR enrichment at synapses during the light period uncovering a nicely presented molecular circuit that included upstream and downstream mechanisms. They also nicely probed this circuit in vivo with a conditional, microglial-specific depletion of TNFα to determine the role of microglial TNFα in sleep and memory consolidation. Although the experiments were well done, the authors made some conclusions that cannot be determined by the experiments presented in this manuscript. Specifically, there are several claims that some of the phenomena occurred "during sleep" or are "sleep-dependent" when the experiments were not designed to test these claims. I provide more detailed comments below:

      1. There are several instances where the authors state the experiments occurred "across the 24 h light/dark cycle" (Lines 42, 139), "during the sleep/wake cycle" (Lines 87, 242, 248), or "during sleep" (Lines 155, 220, 254). These statements are imprecise and can lead to erroneous interpretations of the data. For molecular studies, data were collected at a light period timepoint (Zeitgeber Time (ZT) 6) and a dark period timepoint (ZT18). While I appreciate the comparisons of the light and dark phases, 2 timepoints are not sufficient to claim that phenomena were tested across the light-dark cycle. More importantly, though, it is not accurate to claim outcomes from data collected during ZT6 occurred "during sleep" (or ZT18 outcomes occurred during wake). Although mice sleep more in the light period vs. the dark period, they are polyphasic sleepers and thus can be awake at ZT6 and asleep at ZT18. Therefore, statements should be edited for accuracy to instead state that phenomena were observed at ZT6/ZT18 or light/dark periods. In addition, any figures (e.g., Figure S1) using x-axis labels of "W" and "S" should be relabeled as "ZT18" and "ZT6," respectively.
      2. The authors claim that microglial TNFα plays a role in sleep-dependent memory consolidation (Title and Lines 20, 22, 178, 198, 224, 276, 288) based on a series of experiments using tests previously shown to have a sleep-dependent consolidation component. However, the authors did not assess sleep-dependent consolidation in the micTNFα-KO and the tCtl mice, and thus this conclusion cannot be drawn. This is because the experimental paradigms did not include sleep deprivation. Claims that outcomes are sleep-dependent need to be shown as absent/impaired after sleep deprivation especially in mutant (and control) lines that have not been previously tested in this context. As such, claims of sleep-dependent memory consolidation (including in the title) should be removed OR new experiments including sleep deprivation should be included.
      3. "This shows that P2RX7 and microglial TNFα drive daily fluctuations in CaMKII Thr286-phosphorylation and are required for sleep-dependent GABAAR synaptic upregulation in L1 during the light phase" (Lines 144 - 146). Similar to the above comment, it cannot be definitively concluded the P2X7R or microglial TNFα are required for sleep-dependent GABAAR synaptic upregulation because sleep deprivation studies were not conducted in the P2rx7-KO or micTNFα-KO mice. Furthermore, there is no analysis (or citation) of P2rx7-KO mice sleep-wake expression nor has the micTNFα-KO sleep data been presented at this point to make any determinations on how (possibly perturbed) sleep-wake expression in these mice could affect the stated outcomes.
      4. There are some details regarding data analysis that are lacking:
        • a. How were bouts defined for each arousal state?
        • b. It seems more details are needed for EEG spectra analysis. From what values was the median derived and over what time period? How was each spectral bin normalized and over what time period? What data (i.e., from what time period and duration) are shown in Figure 4b? Same question for Figure 4c-d? Were these time periods the same for controls and mutants given that NREM SWA changes across the light-dark cycle?
        • c. How was NREM delta power normalized and analyzed and over what time period?
      5. Claims that GABAAR enrichment at synapses is sleep-dependent is based primarily on the data presented in Figure 1d reporting no increase in cortical GABAAR after sleep deprivation. A previous study (not cited) showed sleep deprivation increased GABAAR expression in CaMKIIα+ neurons in barrel cortex (Del Cid-Pellitero et al., Front Syst Neurosci, 2017). It would be helpful if the authors cited and discussed this study.
      6. Some sentences/conclusions are overstatements:
        • a. "...discarding the possibility that lack of synaptic GABAARs enrichment upon PLX3397 treatment results from perturbed sleep during the light phase" (Lines 68 - 69). Only sleep time is reported to make this claim, but bout frequency, bout duration, and EEG spectra could be perturbed with this manipulation. This claim should be edited for accuracy or additional data (e.g., bout and spectral analysis) should be presented. In addition, Line 68 should be edited to state that "...microglia depletion does not alter sleep time during the light phase..." unless additional analyses are provided.
        • b. "TNFα, which is mostly if not exclusively produced by microglia in the brain..." (Lines 93 -94). Although microglia are a major source of TNFα, there is evidence other brain cell types also release TNFα. In addition, the citation provided does not support this exclusivity claim.
        • c. "We thus anticipate that microglial TNFα may control REM by acting at the basal forebrain..." (Line 163). This statement is based on a cited study that reported REMS suppression (and increased NREMS time) after TNFα injection in the subarachnoid space of the basal forebrain. It is unclear to me why this statement is included when ICV and IV TNFα administration also reduce REMS (Shoham et al, Am J Physiol, 1987). Given these data and this statement is not being tested, it does not seem like it needs to be included. It should also be noted a previously reported (but not cited) global TNFα KO mouse (Szentirmai and Kapás, Brain Behav Immun, 2019) also showed increased REMS and REMS bouts, but this seemed to be a dark period phenotype (NREMS and Wake time, bout frequency, and bout duration were unaffected). This is an interesting detail to at least include in the second paragraph of the Discussion.
      7. It is unclear to me why the authors believe ATP in these studies has a neuronal origin (Lines 106, 132, 218) when other cell types also release ATP. Is this because of NMDA treatment? If so, NMDA receptors are also expressed on other cell types like astrocytes (Verkhratsky and Chvátal, Neurochemical Research, 2020).
      8. Because the authors rationalize investigating memory consolidation based on micTNFα-KO changes in NREM SWA, I am curious if the authors considered parsing NREM SWA into slow oscillations and delta waves as Vaidyanathan et al (eLife, 2021) did. The reason for this is because slow oscillations are shown to be associated with memory consolidation, but delta waves are associated with weakening memories.
      9. For the complex wheel task, micTNFα-KO mice seem to start and end with better performance compared to tCTL on S1 (although it is not clear if this difference was statistically evaluated). Would the conclusions from this experiment change if data were normalized to account for the apparent better starting performance?
      10. Many of the molecular studies emphasized a layer-specific effect in L1 vs. L5. It would be helpful if the authors could link (at least in the Discussion) this cortical-layer specificity with reported microglial TNFα effects on sleep parameters and memory consolidation.

      Minor Comments:

      1. For the experiments investigating TNF receptor (TNFR) involvement (fig. S5), it would have been interesting to see the response to human recombinant TNFα which interacts with TNFR1 but not TNFR2 whereas mouse recombinant TNFα interacts with both receptors (Lewis et al, PNAS, 1991).
      2. "Synapse plasticity in the sleeping brain likely supports crucial functions of sleep" (Line 33). I believe it is more accurate to instead state sleep supports synapse plasticity. The sentences immediately following also provide examples of sleep/wake mediating plasticity.
      3. "Moreover, microglia depletion also abolished the reduction of synaptic AMPA receptor subunit GluA2 at ZT6 (fig. S1)..." (Lines 70 -71). The data in this figure shows an increase GluA2, but the data cited showed decreased excitatory transmission. This discrepancy is not discussed.
      4. If you normalize within treatment (e.g., PLX, SAP, minoc, 4-OHT, apy, PPADS, A74, PSB) rather than to controls (e.g., normalize PLX-iLTP to PLX-bsl instead of CTL-bsl) in Figures 2b-f, 3b-c, do you get the same results? Similarly, if you normalize PLX treatment to CTL ZT18 in Figure 1c-d, do you get the same general outcome?
      5. Is there a significance marker missing in Figure 2g for bls vs. BzATP?
      6. It is unclear if the fig. 5a callout is the correct one at Line 143. If it is correct, then it is confusing (to me) in the way it is currently associated with the text.
      7. There is a missing reference in Line 279 after "NOR."

      Significance

      The role of non-neuronal cells in sleep and sleep-related processes like learning and memory is relatively unexplored and this is especially true of microglia. The authors present a nicely done series of rigorous experiments that reveal a microglial-centric molecular circuit of inhibitory synaptic modulation that differs between the light and dark periods and plays a role in NREM slow wave activity and memory consolidation. However, most of the claims of sleep dependency of the reported phenomena have not been directly tested in this manuscript and thus await additional experiments or re-framing of the stated conclusions. Re-framing these conclusions without claims of sleep dependency still provides very interesting and informative data about the mechanistic role of microglia in inhibitory synapse plasticity and memory consolidation that may be of interest to researchers interested in learning and memory, synaptic plasticity, and sleep.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      Using both in vivo and ex vivo approaches in mice, the authors showed that microglia in layer 1 (L1) of the frontal cortex modulate the level of GABAA receptors at L1 GABAergic synapses depending on the light/dark phase and, more specifically, sleep/wake state (high during sleep). This was shown to be mediated through purinergic signaling via microglial P2RX7 receptors followed by microglial release of TNFa then CaMKIIa phosphorylation in neurons. Microglia-selective TNFa-KO mice showed normal sleep/wake cycles except for an increase in REM sleep amount, but cortical EEG slow waves during NREM sleep, a measure of sleep propensity, were slower in the delta range (1-4 Hz), but without any change in delta power. These animals also showed deficits in memory consolidation in two out of three memory tasks used.

      Major comments:

      1. The paper is clearly written and easy to follow, with virtually no typos or editorial errors. Both the introduction and discussion are informative and well-referenced.
      2. The experiments are generally carefully designed including appropriate controls and comparison groups (e.g., L1 vs. L5; GABAAR vs. AMPAR; PLX vs. saporin vs. minocycline). The results are presented appropriately and often in detail including supplementary figures and tables.
      3. One issue concerns the conclusion that microglial TNFa signaling shapes slow waves during NREM sleep (e.g., title; lines 148, 175-176; 180; 222-223; 288) on the basis of the data shown in Fig. 4b-d. Slow waves normally consist of two components, < 1Hz (slow oscillations) and 1-4 Hz (delta waves), and Fig. 4b shows a modest slowing in delta range (from ~2.2 Hz to ~1.7 Hz, from reading the graph for means). Importantly, there was no change in the spectral density in delta range. In the opinion of this reviewer, this is a modest effect and its significance and impact remain to be investigated. Arguably, the effects on memory consolidation could have been a result of microglial TNFa gene deletion elsewhere in the brain of these KO mice. Modification of the claim on slow waves should be considered.
      4. VGAT was used to identify GABAergic synapses in conjunction with GABAA receptors. Of various GABAergic interneurons, somatostatin (SOM)-containing GABAergic interneurons are known to be crucial for generating slow waves during NREM sleep through their axon terminals that target and concentrate in L1 (e.g., Funk et al., 2017, ref. 17). However, not all GABAA receptors in L1 would be associated with the inputs from SOM-containing GABA interneurons. For example, there are parvalbumin-containing GABA interneurons and their activation has been reported to DECREASE slow waves (Funk et al. 2017). This is relevant and should be discussed in relation to the results.
      5. To follow up on the above, it is unclear why NeuN was used to delineate cell bodies (Fig. 1e). In fact, SOM-containing GABA neurons (see above) have been shown to inhibit pyramidal neurons through presynaptic inhibition of excitatory inputs as well as postsynaptic inhibition of dendrites, but not cell bodies, of pyramidal neurons (see Funk et al. 2017 for references). Some discussion along this line would be useful and potentially important. In addition, it would have been interesting to add an immunolabel for SOM to identify SOM-containing axon terminals associated with VGAT (Figs 1, 2), and this could be done for parvalbumin (see above) terminals as well; however, this analysis is optional and not required.

      Minor comments:

      1. It appears that n's are not consistently reported. Please check.
      2. The Y-axis does not start from zero in some graphs. Although this might be a matter of preference, it can be misleading.
      3. In the supplementary information PDF, under Immunohistochemistry (IHC): "In direct IHC" in the first line of the paragraph should be "Indirect IHC".

      Significance

      There is increasing evidence for and interest in the role of microglia in modulating synapses and neuronal circuits underlying various behaviors including sleep. However, the role of cortical microglia in sleep or NREM slow waves (a measure of sleep propensity or sleep need) is unclear. GABAergic synapses in cortical L1 are known to play an important role in NREM slow waves. Thus, the evidence that microglial P2X7-TNFa signaling enriches synaptic GABAA receptors selectively in L1 (not in L5) and during sleep is important and novel.

      On the other hand, in this reviewer's opinion, the effect of microglia-selective TNFa gene deletion on slow waves during NREM sleep seems modest (minor slowing but no change in power) and it is also unclear whether the effects on memory consolidation in two out of three tasks were due to the observed change in slow waves, or other alterations that are likely in these KO mice.

      The reviewer's expertise: sleep neurobiology. Familiar with microglia and much of the techniques, but not ex vivo techniques.

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      Reply to the reviewers

      Reviewer #1

      Further work required on divergence to address the observed level of gene flow between wild and crop populations and the lack of admixed/hybrid genomes.

      • Authors should plot Fst and Dxy along the main scaffolds. It would permit to see whether there is huge peaks of divergence and differentiation between the two populations.

      We will measure Dxy across the genome and plot against Fst in the larger contigs. Check for similarities and differences in these two measures and their relationship with effector locations. Relate results to effectors as well as broader demography as highlighted by the network.

      Virulence can be mediated at the expression level by effector silencing and the reviewer suggests looking for premature stop codons, deletion of an exons, or mutations in the promoter region.

      We will run SNPeff to annotate and classify the severity of variants. We will relate that to potential roles in silencing in effectors relative to non-effector genes.

      Simulations would provide stronger support for conclusions.

      Absolutely. Simulations are important to validate our conclusions and improve our hypotheses as to the levels of selection, rates of gene flow and/or boom and bust. Our simulation would look at:

      • The strength of balancing selection required to preserve diversity in pathogen virulence genes.
      • The strength and direction of selection required to partition that diversity to produce increased Fst we observe at virulence genes.
      • The impact of differential rates of recombination in the wild and crop populations
      • The impact of clonality in the crop population at multiple levels:
      • On divergence between populations and levels of inbreeding (Fis)
      • On boom-and-bust dynamics and the frequency of successive invasions

      To parameterise this model, we would need to estimate the rate of recombination using linkage decay across a near/pseudo chromosomal assembly. Our assembly isn’t contiguous enough to estimate recombination rates for our populations. Given the parameter space we must investigate, combined with unknowns, we feel that the investment required to design and test such a model is significant, including requiring a new (long read, phased) genome assembly. This is our aim but without that data now, we feel that a simulation would not be strong enough to get through the rigor of peer review. We are happy to add a discussion of the importance these next steps to validate our conclusions, in the Discussion section.

      Figure 2 network suggests strong divergence between populations, and this needs further exploration because divergence and the locus level is very low.

      • The reviewer requests a PCA
      • (Reviewer #2 missed the Machine Learning in the supplementary which also speaks to this work).

      Reorder Figure 2 to reduce confusion. Reduce the content in Figure 2 to include only the map, the network, and the admixture plot. Add a new Figure 3 which would include a larger PCA and the supplementary data which uses Machine Learning to attempt to partition individuals into clusters/populations.

      Authors should also look for how many effector genes are non-expressed in cultivated population face to wild population.

      The reviewer’s suggestion of analysis of premature stop codons etc will be done using SNPeff.

      Run Selscan (ZA Szpiech and RD Hernandez (2014) or similar to look at indicators of selection.

      It is feasible to run selection scan software, although this would be heavily caveated because these methods often do not account for clonal expansion in a single population.

      Address Minor Comments

      Reviewer #2

      • Effector candidates were not evaluated/characterized in any form.
      • Authors should compare pathogen features to other related species and try to contrast what stands out, especially the effectors' diversity

      The reviewer referred to a statement in which we suggest that it is difficult to functionally annotate effectors (“According to the authors, it is difficult to functionally annotate these genes in general”). This statement was not intended to suggest that we did not annotate them, only that, because effectors are quickly evolving, fewer of them tend to receive an annotation, as compared to non-effector genes. In fact, we use shared annotations to refer to the presence of shared effector annotations in other rust species.

      Details of cross species functional annotation were included in Supporting Information 01. They included annotation using AHRD, UniProt (Swss-Prot and TrEMBL) blast and InterProScan. AHRD uses a database of unbiased ground truth set of high-quality protein annotations with minimal redundancy to assign GO annotations. UniProt is the world’s leading high-quality protein sequence and functional information. It contains more than 190 million sequences with which to assign functional annotations to proteins. InterProScan was used to assign proteins into families as well as predict domains. All these methods utilise cross species information to assign gene/domain function and ontology (GO), the output from these is included for each gene along with that gene’s population genetic signature (Supporting Information 08).

      In the results section we did highlight effector functional annotations and conservation among the Pucciniales (e.g. Rust Transferred Protein, Alpha-amylase, CSEP-06 and PriA, among others). We will clarify that statement to reflect our efforts in that area and throughout.

      Where exactly the machine learning was used?

      It’s in the supplement. Accounting for this comment as well as Reviewer 1 & 2 about the complexity of Figure 2 we plan to bring those results into the main document. This would allow us to unpack genetic diversity and differentiation as a separate figure from the map and network.

      Address Minor Comments

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      Referee #2

      Evidence, reproducibility and clarity

      Summary: In the present study, using beet (Beta vulgaris) - rust (Uromyces beticola) as a model system, the authors set out to assess how crop pathogens evolve to evade resistance using wild reservoirs. They tested whether 1) the genes necessary to success in wild and crop environments are more genetically differentiated between pathogen populations and 2) the rate of clonal to sexual reproduction is higher in crop pathogen populations. Using freshly obtained 42 pathogen isolates from both crop and wild beets across the east of England, the authors assessed the genetic variation among virulent genes(effectors) between wild and crop pathogens. They found evidence for higher signals of diversity and differentiation in effectors and significant differences in reproductive rates between the wild and crop pathogen populations. They highlight that these findings can be used to identify candidate genes for pathogen survival in crops and develop methods to circumvent crop pathogen resistance. Additionally, they developed a new DNA peel extraction protocol for pathogens and produced a new annotation of Uromyces beticola genome annotation.

      Major comments:

      • The study design and the methodologies are appropriately explained and the statistical analyses are strong enough to draw the conclusions presented in the manuscript. The results are adequately explained and the inferences drawn from them are satisfactory.
      • The putative effectors (virulent genes) were identified based on the assumption that gene products secreted outside of the fungal cell and into the host are host interaction genes, potentially facilitating infection. However, these candidates were not evaluated/characterized in any form. According to the authors, it is difficult to functionally annotate these genes in general. However, I believe at least the predicted functionality can be checked with published adequately annotated genomes of related species. This comparison is lacking in the analysis. Not having confirmed the functionality of at least some of the effectors undermines the finding that the study reflects the actual genetic differentiation in infectious genes.
      • Similarly, comparisons of current findings to a related species/system are missing. Authors should compare pathogen features to other related species and try to contrast what stands out, especially the effectors' diversity.
      • Although the authors claim that machine learning was utilized in the manuscript, where exactly the machine learning was used is not clear. The models used in the analyses are already implemented in the software packages and methods described in the manuscript. I did not see any machine learning method being applied to improve the analysis either. If it is actually used, it would be beneficial to highlight for what and where it was used and how it improved specific analyses.

      Minor comments:

      • Lines 184 - 186: Can the lack of admixture and gene low among these wild isolates also explain this observation? what about the levels of FIS in these isolates? Clonality in these populations may have a significant impact on the genetic diversity in these populations.
      • lines 216-220: Is this also reflected in the excess of heterozygosity non-effectors in these crop populations? The mutations should equally accumulate in both gene categories.
      • lines 219-220: it is not clear which CDS are being referred to here; Are you talking about the correlation between the CDSs of wild and crops or effectors and non-effectors?
      • Figure 1: I suggest separating F & G from the rest
      • Figure 3: D. Unless this is a noe to one window comparison of pi, this plot does not necessarily show a correlation. Please explain how the windows were treated in this comparison.
      • Figure 4: A. I would expect a relatively high correlation between the FST and pi in effectors. Does this include both wild and crop effectors?
      • I spotted a number of typos throughout the manuscript. So I suggest the authors pay attention to punctuation and typos.

      Significance

      This study presents a critical comparative analysis of crop pathogens in their wild populations. It highlights the significance of assessing the crop pathogen genetic diversity against their wild background/relatives to identify how crop pathogens evolve to evade crop resistance. And in turn, it will help us to improve our crop varieties to be better resistant to pathogens in this era of ever-increasing demand for crop production.

      Further, the present study also provides a new methodology with an annotated genome of beet pathogen Uromyces beticola to identify candidate crop resistance genes in other related pathogens. The scientific community will also benefit from the protocol they developed to extract pathogens from host peels.

      Therefore, I believe this work will reach a wide audience in genetics, genomics, agriculture, crop development, and landscape genomics.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      This study presents a pathosystem beet/Uromyces beticola in order to understand how reservoirs can play a role in ermergence of new virulences. To do this, the authors sample cultivated beets and wild beets in England and resequence 42 genomes of U beticola found on these two beet species (24 from sugar beet hosts and 18 from coastal places on wild beet hosts). The authors use population genomics tools to explore population structure and compare diversities of the two populations found. Indeed they found a population of U beticola exclusively living on wild beets, and a population infecting both sugar and wild beets. They compare genes encoding for effectors (important in interactions with hosts) with genes encoding for other proteins. They found that genes encoding for effectors are more diverse in wild compartiment than genes encoding for other types of proteins. In general, the wild compartiment is more diverse than the cultivated one. The authors draw conclusions about the role of reservoir of wild population for emergence of new virulence in the cultivated population. At last, as the authors found excess of heterozygosity in the cultivated population, they conclude about clonal reproduction in this population.

      Major comments:

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

      Although the paper is well written and the population genomics studies were well done, the analyses are still preliminary and hence conclusions are not accurate. Indeed, in Fig2 authors show a tree representing the clear divergence between strains found in sugar beets and the ones found in wild beets (excepted for five isolates found in wild beets but belonging to the cultivated clade). Despite the fact that this apparent divergence is supported by other analyses, the authors do not conclude about the lack of gene flow between theses two populations. Indeed, the gene flow occurred there would have been admixed genomes and no clearcut delineations between the two populations. In other word, they authors have not found hybrids in their sampling. In general, divergence is not really studied in this paper. The comparison between genes encoding for effectors and the ones encoding for other genes is very interesting. However, the authors just forget that sometimes virulence is acquired by effector silencing. Indeed an effector that is no more expressed can not be recognised by host, and then resistance can be overcome. The authors should look for effectors that are no more expressed (with stop codon for example, deletion of an exon, or mutated in the promoter region) in crop population. They could find other good candidates for adaptation. In general, conclusions are badly supported. The authors should use simulations for their model validation. This study strongly deserves it. I will detail this in the following. - Please request additional experiments only if they are essential for the conclusions. Alternatively, ask the authors to qualify their claims as preliminary or speculative, or to remove them altogether. - First validate your assumations with models simulated. If the authors assume that population infecting cultivated beets come from population infecting wild beets, they should be able to confirm this hypothesis by simulations. For instance, authors could use ABC method in order to check the posterior probability of such a model. - Authors should use divergence statistics in order to check whether there is divergence or not on their data. For example, use Dxy in order to check the degree of divergence between wild and cultivated population. As for evidence in Figure 2, there is a strong divergence between the two populations. It could be interesting to check whether there is gene flow or not between these two populations. - Authors should also look for how many effector genes are non expressed in cultivated population face to wild population. - Authors should plot Fst and Dxy along the main scaffolds. It would permit to see whether there is huge peaks of divergence and differentiation between the two populations. - Are the data and the methods presented in such a way that they can be reproduced?

      Yes, the data can be reproduced as well as the analyses. - Are the experiments adequately replicated and statistical analysis adequate?

      Both experiments and statistics are adequate.

      Minor comments:

      • Specific experimental issues that are easily addressable.

      Yes they are - Are prior studies referenced appropriately?

      Yes they are - Are the text and figures clear and accurate?

      Figure 2 is somewhat hard to understand. There is too much data here. - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      I would prefer a PCA plot, just showing strains plotted along the first axis and showing that there is no hybrid.

      Referee Cross-commenting

      Hello everyone I just start the comment session by providing a few points that I think to be important to be treated in a future version of the paper. 1- Characterize the relationships between wild and agricultural populations. As shown on figure 2, the tree presented clearly indicates divergence between wild and agricultural strains. A PCA would be interesting to be plotted, as it may indicate that there is no hybrid between populations. In a general manner, statistics like Dxy or Da as well as Fst should be plotted along the genome. 2- The scenario should be validated using simulations and tested against a null hypothesis. 3- Virulence can be acquired through effector losing function. Thus, variations like occurrence of codon stop, delection in ORF, or mutations altering the promoter region should be checked.

      Significance

      Provide contextual information to readers (editors and researchers) about the novelty of the study, its value for the field and the communities that might be interested.

      The following aspects are important:

      • General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed? This study is interesting in showing the crucial role of wild compartiment as a reservoir of virulence. However, as the data are well produced, their analysis suffer from several flaws. As divergence is not analysed, and only differentiation is shown. In addition, the model proposed is not validated by simulation, not even tested by ABC. In order to be more conclusive, selection should be tested. Fst variance is not a good predictor of selection. I would recommend to use Selscan (ZA Szpiech and RD Hernandez (2014) ) in order to test for selection on genomic data. It would give real clues for selection acting on cultivated population.
      • Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field? This paper is of a broad audience as it treats of large problematic of evolution of plant pathogen.
      • Please define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      I am a population genomicist working on evolution of pathogens.

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      Reply to the reviewers

      We would like to thank reviewers for their insightful comments.

      Overall, there were two major concerns/suggestions:

      • Applicability to humans of the increase of BTC in non-alcoholic steatohepatitis (NASH) and mechanisms of downregulation of BTC by omega-3. We now analyzed __3 __additional human gene expression datasets and show that BTC not only is increased in human NASH (as we have already shown for liver cancer meta-analysis), but is also decreased in livers of patients who received omega-3.

      • One of the reviewers suggested investigating a potential mechanism of how BTC is regulated by omega3 fatty acids. Although a complete answer to this question would require entirely new studies to be done, we still performed additional investigation that was possible within a reasonable timeframe. We found that transcription factor FOXO3 (well-known inhibitor of carcinogenesis) is a highly probable mediator of the DHA inhibitory effect on BTC.

      See all details of items 1 and 2 as well as answers to other (less critical concerns) below after each specific question.

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

      This work by Padiadpu and colleagues investigate the mechanism by which pufa of the n-3 series (mostly DHA) may influence NAFLD progression using systems biology analysis and multiple omics analysis. The work is interesting and may provide a novel view of the topic. However, there are a number of issues the authors may wish to consider in order to improve their manuscript.

      Major issues: Clarity: Since the authors refer to previously published experiments, they must refer to this work in the figure legends and improve the clarity of such legends. Here are a list of issues that must be fixed:

      Fig.1: First panel is not clear. What does the table tell the reader? What are the effects of the different diets on NAFLD?

      All the transcriptomic data are newly generated from the samples of previously published studies. The table shows the number of features changed by DHA and/or EPA in each of the -omics and phenotypic data used in the analysis.

      I understand that the results are published elsewhere, but the authors must provide information regarding the NAFLD/ NASH scores.

      We now added a supplementary table 1a showing the scores.

      Fig.4: Why is there sometimes a DHA diet, sometimes DHA and EPA. Legend is not clear. What does WD + Mean? I guess it is olive oil... But the legend must be improved.

      We added details in the legend for more clarity. Specifically, WD+O means WD + olive oil added as a control for WD+DHA, WD+EPA. As described in the 2nd paragraph of results, when both EPA and DHA had a similar and significant effects in reversing WD effect, it was defined as “EPA&DHA category” of parameters. When only WD+DHA or WD+EPA were significantly changed vs WD+O, those were assigned as “DHA category” or “EPA category”, respectively.

      One issue the authors may consider trying to fix is the specificity of the effect of DHA on BTC.

      Is it really specific? It seems to me that EPA has more or less the same effect. If the effect is DHA-specific, than make this clearer through the text.

      Although BTC expression was reduced by both DHA and EPA comparing to WD, DHA had a statistically significant stronger effect than EPA (Fig. 3D).

      Another issue the authors may wish to investigate is the relationship between W3 consumption and BTC expression in studies performed by other labs (if available on Gene expression omnibus?).

      Thanks for the suggestion. We used publicly available data of human and mouse studies that showed significant increase in liver BTC gene expression in NASH in multiple datasets while a human trial with Omega 3 treatment for one year showed its significant reduction (Figures 3F - human data, S3G-mouse data).

      Finally, a key issue would be to identify the mechanism by which DHA inhibits BTC expression? How does this happen? could such inhibition be induced by other fatty acids of the W3 series? I understand that this is not easy to address but it would significantly strengthen the manuscript.

      Thanks to your question we investigated and found at least one of potential mechanisms contributing to how “DHA inhibits BTC expression”. See details in the answer to next question. As for “other fatty acids” while we agree this is important question, it is outside of the scope of the current study but will be investigated in future studies.

      Moreover, it might be possible to identify the set of genes highly co-regulated with BTC expression and to investigate the possible transcription factors at play in the control of such gene set.

      We really appreciate this question as our efforts in this direction provided one potential mechanism. A direct screen of transcription factor (TF) motifs in genes co-regulated with BTC did not provide any clear results. Therefore, we implemented a combination of network analysis and screen for motifs in BTC gene with the in vivo and in vitro treatment results and found FOXO3 as a candidate TF regulated by DHA upstream of BTC.

      See details of the analysis and results in a new Supplementary Figure S6 and corresponding text located at the end of the results.

      Minor: the authors use the term "beneficial" transcriptome alterations by DHA.

      I do not think it is correct to use "beneficial".

      We agree and removed the word "beneficial”.

      Reviewer #1 (Significance (Required)):

      Strength: This paper uses new approaches to investigate the relationship between W3 consumption and liver gene expression and its relevance to chronic metabolic liver diseases.

      The experiments and data set used to perform systems biology are from an excellent lab (the authors lab) who has published a lot of important and reproducible discoveries in the field of regulation of gene expression by dietary fatty acids.

      The work has high translational relevance in medicine / hepatology / metabolism.

      I am not a qualified reviewer to assess the systems biology that has been done.

      Limitation: The mechanistic link between DHA consumption and BTC expression is not very clear. The specificity of this effect could also be tested (DHA vs other W3 and/or W6).

      Although BTC expression was reduced by both DHA and EPA comparing to WD, DHA had a significantly stronger effect than EPA (Fig. 3D). Other omega fatty acids were not tested but it can be done in future studies.

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

      The authors files a manuscript describing the impact of the suppression of betacellulin as a key mechanism to counteract fibrosis and inflammation in NASH by modulating fatty acids in WD-fed mice.

      Major Comments: (i) No histological analysis was presented and indeed this is of clinical relevance for NASH since diagnosis is still based on biopsy.

      While histological evaluation was presented in the originally published papers (PMID: 28422962, 23303872), it is now provided in Supplementary Table S1a.

      (ii) Human comparative analysis: is done with HCC not with NASH patients.

      This cancer-related dataset is most likely obtained from different etiologies.

      I would suggest comparing these mouse datasets with GSE48452 (human NAFLD-NASH spectra).

      Thanks for this important question. We now analyzed available human data of NASH and show significant increase of BTC expression in two datasets while a human trial with omega-3 treatment for one year showed its significant reduction of BTC expression (Figure 3F) resembling our observations in mice.

      (iii) to compare the inflammation and fibrosis (also lipid metabolism), one can compare these mouse datasets with GSE222576 and cite this preprint (https://doi.org/10.21203/rs.3.rs-2009380/v1)

      Using the suggested dataset (of a chemically induced liver fibrosis), we first observed that Btc gene expression was significantly increased over 10 weeks of the model and now included this result in Fig. S3G.

      We also queried the 66 genes from the network modules described by the authors to check their changes in our NASH model. We observed that 28 genes were differentially expressed in NASH with 14 of them belonging to the module that authors named as “Pathways in Cancer”. Other genes were from the lipid metabolism (4 genes), immunity (2) and inflammation (2 genes). In addition, we observed that several genes we found regulated by omega-3 and changed in this fibrosis model contained other inflammatory genes such as classical macrophage genes (Mmp12, Lgals3, Cd68, Trem2), fibrosis (Col4a1, Col27a1, Itga2b, Itga8) and lipid metabolism (Scd2, Lpl, Soat1). Of note, the preprint has been published and we now cite the corresponding article.

      Minor comments:

      (i) The heatmap in Figure 1B and another heatmap should show all mice not the average to see the variability

      The supplementary figure with all the individual mouse data as another heatmap is added to show the variability and similarity (Figure S1D).

      Reviewer #2 (Significance (Required)): The authors files a manuscript describing the impact of the suppression of betacellulin as a key mechanism to counteract fibrosis and inflammation in NASH by modulating fatty acids.

      This is well designed experiment, and the results are of interest to hepatologists and should be indeed published after consideration of the following points

      Strength is multiOMICs approach.

      Weakness is human applicability.

      We improved human applicability by investigating 3 additional human datasets of NASH (Fig. 3F) and finding consistent changes in BTC expression closely resembling our observations in mouse NASH model, including one trial with omega-3 treatment of patients for one year showing significant reduction in BTC gene expression.

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      Referee #2

      Evidence, reproducibility and clarity

      The authors files a manuscript describing the impact of the suppression of betacellulin as a key mechanism to counteract fibrosis and inflammation in NASH by modulating fatty acids in WD-fed mice.

      Major Comments:

      • (i) No histological analysis was presented and indeed this is of clinical relevance for NASH since diagnosis is still based on biopsy.
      • (ii) Human comparative analysis: is done with HCC not with NASH patients. This cancer-related dataset is most likely obtained from different etiologies. I would suggest comparing these mouse datasets with GSE48452 (human NAFLD-NASH spectra).
      • (iii) to compare the inflammation and fibrosis (also lipid metabolism), one can compare these mouse datasets with GSE222576 and cite this preprint (https://doi.org/10.21203/rs.3.rs-2009380/v1)

      Minor comments:

      • (i) The heatmap in Figure 1B and another heatmap should show all mice not the average to see the variability

      Significance

      The authors files a manuscript describing the impact of the suppression of betacellulin as a key mechanism to counteract fibrosis and inflammation in NASH by modulating fatty acids. This is well designed experiment and the results are of interest to hepatologists and should be indeed published after consideration of the following points

      Strength is multiOMICs approach

      Weakness is human applicability

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      Referee #1

      Evidence, reproducibility and clarity

      This work by Padiadpu and colleagues investigate the mechanism by which pufa of the n-3 series (mostly DHA) may influence NAFLD progression using systems biology analysis and multiple omics analysis.

      The work is interesting and may provide a novel view of the topic.

      However, there are a number of issues the authors may wish to consider in order to improve their manuscript.

      Major issues:

      Clarity:

      Since the authors refer to previously published experiments they must refer to this work in the figure legends and improve the clarity of such legends. Here are a list of issues that must be fixed: Fig.1 : Firts panel is not clear. What does the table tell the reader? What are the effects of the different diets on NAFLD? I understand that the results are published elsewhere, but the authors must provide information regarding the NAFLD/ NASH scores. Fig.4: Why is there sometimes a DHA diet, sometimes DHA and EPA. Legend is not clear. What does WD + Mean? I guess it is olive oil... But the legend must be improved.

      One issue the authors may consider trying to fix is the specificity of the effect of DHA on BTC. Is it really specific? It seems to me that EPA has more or less the same effect. If the effect is DHA-specific, than make this clearer through the text. In this current version of the manusript, the authors alternatively use the term DHA or W3. Related to this issue, it would be nice to know what the composition of the WD is? More specifically, it would be important to know whether it might be W3 deficient.

      Another issue the authors may wish to investigate is the relationship between W3 consumption and BTC expression in studies performed by other labs (if available on Gene expression omnibus?).

      Finally, a key issue would be to identify the mechanism by which DHA inhibits BTC expression? How does this happen? could such inhibition be induced by other fatty acids of the W3 series? I understand that this is not easy to address but it would significantly strengthen the manuscript. Moreover, it might be possible to identify the set of genes highly co-regulated with BTC expression and to investigate the possible transcription factors at play in the control of such gene set.

      Minor: the authors use the term "beneficial" transcriptome alterations by DHA. I do not think it is correct to use "beneficial".

      Significance

      Strenght:

      This paper uses new approaches to investigate the relationship between W3 consumption and liver gene expression and its relevance to chronic metabolic liver diseases. The experiments and data set used to perform systems biology are from and excellent lab (the authors lab) who has published a lot of important and reproducible discoveries in the field of regulation of gene expression by dietary fatty acids.

      Limitation:

      The mechanistic link between DHA consumption and BTC expression is not very clear. The specificity of this effect could also be tested (DHA vs other W3 and/or W6).

      The work has high translational relevance in medicine / hepatology / metabolism.

      I am not a qualified reviewer to assess the systems biology that has been done.

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      Manuscript number: RC-2023-01919

      Corresponding author(s): Fumio, Matsuzaki and Quan, Wu.

      [The “revision plan” should delineate the revisions that authors intend to carry out in response to the points raised by the referees. It also provides the authors with the opportunity to explain their view of the paper and of the referee reports.

      The document is important for the editors of affiliate journals when they make a first decision on the transferred manuscript. It will also be useful to readers of the reprint and help them to obtain a balanced view of the paper.

      If you wish to submit a full revision, please use our "Full Revision" template. It is important to use the appropriate template to clearly inform the editors of your intentions.]

      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 thank two reviewers very much for their comments. Their comments greatly contribute to our revision plan. Reviewer 1 fairly evaluated our data and provided us constructive and supportive comments. We incorporated responses to Reviewer 1’s comments to our revision plan, in which we made some novel analyses and discussions according to Reviewer1’s comments. Reviewer 2 also provided us very helpful comments, which are based on his/her careful reading of our manuscript, especially from the viewpoints of a ferret specialist. These comments help us to improve our manuscript very much, whereas some of the reviewer 2’s requests appear beyond the scope of our paper and against the policy of Review Comments; the standard policy of the review for Review Commons is “do not add new pipeline of experiments” such as adding additional replicates for scRNAseq. We have made revision plans (section 2) according to the order of comments given by reviewer 1 and then next by reviewer 2, considering all the statements of the two reviewers on balance; there are 6 comments from reviewer 1, and 25 comments from reviewer 2. In the section 2, we selected revision plans that we have reflected to the preliminary revision of our manuscript.

      Finally, we would like to note our fundamental interest; we are studying the cortical development of ferrets as a model of brain development to understand what mechanisms are conserved or species-specific during brain size expansion in the mammalian evolution, which, of course, includes humans. It would be great if the ferret model can be a tool used to study tRG cell biology, contributing to understanding the human cortical development.

      For this purpose, it’s been critical to create series of single cell transcriptomes along cortical development. A comparison between humans and ferrets, focused in this paper, is the first attempt, because human data of single cell transcriptomes have been extraordinary enriched. These attempts of comparisons between ferrets and humans will provide valuable information about which mechanism is shared and which is not shared for the cortical development in the gyrencephalic mammals. To represent the usefulness of our approach, we chose finding of tRG in ferrets as a symbolic example, and analyzed its origin and fates.

      Description of the planned revisions

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

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

      Summary: Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

      In this manuscript, the authors conduct a series of single-cell transcriptomic analyses and imaging assays in the developing ferret cortex suggesting that (1) ferrets harbor a radial glia (RG) subtype similar to the truncated radial glia (tRG) described previously in humans that may have the potential to (2) produce ependymal and astrogenic lineages which (3) can also be found in the developing human cortex. These findings appear to be an important step in the validation and development of the ferret model towards a tool that can be used to study tRG cell biology, a feat currently difficult due to the inaccessibility of a genetically tractable source of tRG for molecular and cell biology experiments.

      Major comments:

      - Are the key conclusions convincing?

      I found the key conclusions described above and in the authors' abstract convincing. I found the identification of a distinct, tRG-like cell type from the authors' single-cell transcriptomic analysis of the ferret cortex compelling, particularly because (1) the expression of the previously utilized tRG marker gene CRYAB is specific to the tRG-like cluster and (2) the tRG-like cluster marker genes (including CRYAB) are relatively unique to the tRG-like cluster. I found this strengthened by their morphological analyses showing the tRG-characteristic apical endfoot and short basal process in these CRYAB+ cells in the ferret cortex. I found the combination of imaging and bioinformatic analyses showing the increase in FOXJ1 co-expression in CRYAB+ cells to compellingly suggest that CRYAB+ cells can produce FOXJ1+ ependymal cells, and similarly with the authors' analyses to suggest that tRG-like cells can also contribute to SPARCL1+ astrocyte cells. I found that the cluster score analyses compelling suggest that the tRG-like cells in the ferret dataset correlate with the tRG cells annotated in a separate, human developing cortical dataset. I also appreciated the comparison of astroglial, ependymal, and uncommited ferret tRG sub populations from the pseudo time analysis with the clusters generated from the integrated ferret-human dataset in Fig. 7.

      - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

      - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

      1-1. The weakest claim in the paper is lines 202: "...tRG cells are formed by apical asymmetric division(s) from unique apical IPC". From my understanding, the main evidence that the tRG parent cells shown in Fig. 3 are not tRGs are the data from Fig. 2E-G showing the low amounts of CRYAB+ cells co-expressing KI67, TBR2, or OLIG2 in P5 and P10. Especially given that these timepoints are after those used in Fig. 3, I believe further evidence is needed to confirm the cell type identity of tRG parent cells in Fig. 3. Such experiments (isolating IPCs from ferret cortex and growing in vitro to determine progeny cells) may be outside of the scope of this paper, in which case I believe the text can be strengthened with either (1) presenting the data from the cited Tsunekawa et al, in preparation that would suggest this claim or (2) rephrasing these claims to omit the mention of IPCs.

      We thank the reviewer for the suggestion to revise the definition of tRG parent cells in lines 194-204. This issue is also pointed out by the reviewer 2.

      Revision plan:

      1. We revise the term “IPC” as “mitotic sibling of vRG” and stated that these cells might be tRG (CRYAB+) or non-tRG (CRYAB-) intermediate progenitors. By the term of “intermediate progenitors”, we did not intend to refer to TBR2+ neurogenic IPCs, but rather to an intermediate state of progenitors, in a general sense, with a similar morphology as tRG. To avoid any confusions on this terminology, we revised our manuscript by replacing “IPC” with “a sibling of vRG”.
      2. We delete all statements relevant to Tsunekawa et al. data from the manuscript. We regret that we are not able to include Tsunekawa et al. data because we are planning to submit this data as a separate manuscript, which describes that in ferrets, vRG frequently (30% of apical division) generate non-Tbr2-positive mitotic sibling cells bearing a short basal process during the entire neurogenesis. This study includes a large volume of data with human ones and largely concerns stages that are earlier than that of tRG formation. It is, therefore, not appropriate to combine these data with those described in this manuscript.
      3. As also pointed by reviewer 2, we cannot exclude the possibility that the mitotic sibling cells of vRG with a short basal process (IPC in the previous version of the manuscript) are also CRYAB positive tRG. To clarify the identity and variety of vRG sibling cells at tRG-generating stage, we are examining the sibling pairs of vRG by immunostaining for a mitotic marker Ki67 and CRYAB during P0-P5 after incorporating EGFP by electroporation to label vRG lineages. We will increase the sample size for a quantification and statistical analyses of this newly provided data to incorporate in our fully revised manuscript.

      1-2. I also believe the claim in Line 365-366 is overstated: "We found that ferret (and presumably also human) tRG cells differentiate into ependymal cells and astrogenic cells." While I believe the transcriptomic comparisons suggest the presence of uncommitted tRG in both the ferret and human datasets, I would appreciate further analyses to confirm the prevalence of astroglial and ependymal tRG in the humans and/or functional analyses before claiming that human tRG cells make ependymal and astrogenic cells. I appreciate the authors' note that "GW25 is...the latest stages experimentally available" (line 376-377), but their comparative approaches could be applied to existing datasets of the human cortex (Herring et al., 2022, PMID: 36318921) that span later developmental ages. Identifying the presence of astroglial and ependymal tRGs in this and/or similar datasets would provide more convincing evidence of the tRGs' developmental potential. If this computational analysis is outside the scope of the paper, I believe paring the certainty of these claims (especially lines 379 - 383) and recognizing the need for further functional analyses would negate the need for deeper mechanistic validation.

      We agree with the Reviewer 1 that identifying the presence of astroglial and ependymal tRGs in datasets spanning later developmental stages would provide convincing evidence for the potential of human tRG.

      Revision plan:

      1. We compared our ferret dataset to the human postnatal dataset recommended by the Reviewer 1 (Herring et al., 2022). As a conclusion of our analyses shown below, we found that Herring et al., (2022) dataset was not favorable for a comparative analysis with our ferret dataset regarding the fates of human tRG, because Herring’s human dataset was derived from the prefrontal cortex; This human dataset does not include neither tRG cell population nor ependymal clusters. We have also elaborated our discussion after analyzing Herring et al. dataset in the discussion.
      2. We, therefore, pare down our claim in lines 365-366, by removing “(and presumably human)” to state that “Our pseudotime trajectory analyses and immunohistochemistry analyses strongly suggested that…”.
      3. We also tone down the statements as for the discussion of the relationship between human and ferrets regarding the tRG progeny fates (originally lines from 372 to the end) also elaborated our discussion after analyzing Herring et al. dataset in the same paragraph.

      We will describe the details of our analysis of Herring et al. (2022) below.

      https://www.dropbox.com/scl/fi/a0m72orxfsub66dh3hdbg/reviewer1_2ABC.pdf?rlkey=uzrd8ngclp87p5c8v24mqd1j7&dl=0

      As mentioned above, Herring’s human dataset was derived from the prefrontal cortex, and that it did not include a specific subtype defined as tRG nor other HES1-expressing progenitor clusters such as RG in the original cluster annotation. We, therefore, re-clustered the raw dataset from GW22 (the earliest stage available) up to 10-months after birth by using Seurat pipeline with default parameters (B), and found a CRYAB-expressing population in the original “Astrocyte_GFAP” subtype among astrocyte clusters (A), which distribute in the most of collected stages, from late development through the adulthood. We then examined this dataset to find out whether tRG or its progenies are present.

      After reclustering, CRYAB-expressing cells (with more than 1 raw count) represented 0.15% of the dataset and were grouped as a part of cluster 44, which was mostly derived from postnatal stages (among which 4-months was the most enriched one; C). Several astrocyte markers, such as SPARCL1, HOPX, CLU, and GJA1, as well as CRYAB, were enriched in the cluster 44 as revealed by FindMarkers (Methods). FOXJ1 expression was nearly absent overall in this dataset, indicating the absence of the ependymal cell population, a tRG-descendant cell types in ferrets (C).

      To evaluate the similarity between cluster 44 and tRG or astroglial tRG, we next integrated Herring dataset with our ferret subset (about 15,000 cells) and the human GW25 subset from Bhaduri et al. (2021) of approx. 3,000 cells, both of which contained only progenitor cells. As we have done in Figure 7 of our original manuscript; we have removed cells other than progenitors, astrocytes and oligodendrocytes, such as neurons, microglia, endothelial cells. This resulted in about 20,000 cells in Herring dataset.

      https://www.dropbox.com/scl/fi/nz3iulya5199i95ecr1un/reviewer1_2D.pdf?rlkey=kp7lwxtkn562un1uf9l1axn2p&dl=0

      This integration (D) reveals that Herring’s cluster 44 is closely located to Bhaduri’s human and our ferret tRG clusters on UMAP, but does not overlap with these tRG clusters. This result further suggested that tRG population might be lacking or very rare in this neuron- and glia-dominated dataset, which might be due to the sampling method that targeted the enrichment of neuronal layers (Herring et al., 2022). It is also possible that this fragmented information on astrocyte and ependymal lineages could be due to the regional and/or temporal difference of samples between two human datasets.

      1-3. I believe the most significant advance for this paper is the potential to use ferret tRG cells to model those of the human brain. However to support this claim (see Lines 83-84), I believe a comparison of the ferret tRG cells with existing cortical organoid datasets (Bhaduri et al., 2020, PMID: 31996853) would be helpful. If cortical organoids currently lack the presence of tRG cell types, that would strengthen the importance of the ferret model and the findings of this paper - otherwise, I feel that the use of the ferret model needs to be justified in light of the greater accesibility and genetic tractability of the cortical organoid system.

      We absolutely agree that human organoids are good models to study human brain development.

      Revision plan:

      According to the suggestion of reviewer 1, we analyzed two cortical organoid datasets (Bhaduri et al., 2020; Herring et al., 2022) to examine whether different tRG populations are present in organoids. Our analyses led us to conclude that tRG-like populations seem to be lacking in available organoid datasets; organoids can have CRYAB-expressing astrocyte-like cells in single-cell transcriptome datasets, but the presence of tRG-like cells seem to be unstable and dependent of lines and protocols how organoids are generated. A further assessment on tRGs’ cellular features is required on organoids by immunostaining experiments. In the light of this analysis, we elaborated our discussion by describing observations shown below. Below is our analysis of organoid data.

      Bhaduri dataset contained organoids generated from 4 different lines, which showed a variability in terms of cell distribution on UMAP while overall temporal and differentiation axes were recapitulated (A). While keeping the original cluster annotations except for YH10 line, we performed reclustering. CRYAB was expressed in clusters 26 and 30 enriched in YH10 line, and cluster 29 enriched in 13234 line (B).

      https://www.dropbox.com/scl/fi/8mj6u94t3hkzw6q61o7od/reviewer1_3AB.pdf?rlkey=10xiks25nzn9r90guw9l0onqh&dl=0

      To confirm the identity of these clusters, we integrated organoid dataset with the dataset of primary tissues from the same paper (Bhaduri et al., 2020; C).

      https://www.dropbox.com/scl/fi/qnqv2e87t74uom2pg836d/reviewer1_3CD.pdf?rlkey=mv370b3dlogwvgh6ig8bdathp&dl=0

      As a result of the integration, tRG cells from the primary tissue were not overlapped with organoid-derived CRYAB-expressing cells, although a part of CRYAB-expressing organoid cells were localized in the integrated cluster 16 where primary tRG resided (D). Other cell types that were included in the integrated cluster 16 were “lateRG”, “vRG”, “oRG” from primary tissue dataset, and “glycolyticRG” from organoid dataset. We found that CRYAB-expressing organoid clusters 26 and 30 overlapped with “oRG/astrocyte” clusters of primary tissues.

      Furthermore, we have analyzed another organoid dataset in stages including 5-months, 9-months and 12-months (Herring et al., 2022; E), but found no clusters that specifically expressed CRYAB (F).

      https://www.dropbox.com/scl/fi/b4kiqoqyhhzk4vm5hi1bb/reviewer1_3EF.pdf?rlkey=dd00hju5n4b90wpz2zexi9gxa&dl=0

      1-4. I found the total number of tRG-like cells in the ferret dataset quite small (162), but I understand the difficulty with isolating and sequencing rare cell types from primary tissue sources. I believe most of the transcriptomic analyses were conducted with this low n in consideration, but this caveat is even more reason to pare down the wording for the weaker claims mentioned above.

      We thank the Reviewer for appreciating the difficulties associated with isolating and sequencing rare cell types. We were able to identify a total of 409 tRG cells (in tRG-like cluster) after merging all timepoints of sequencing, (Figure 1C, S3C) as stated in line 162 of the original manuscript. However, to perform pseudotime analyses, we subset our dataset using 6,000 cells in total (excluding neuron and non-progenitor clusters; Methods), which included 162 tRG cells. Pseudotime analysis transcriptomically distinguished tRG into 3 subgroups (Figure 4E). Remaining 247 tRG cells also appear to distribute similarly into these subgroups rather than forming a distinct subregion within tRG cluster (right panel in figure below). Furthermore, we conducted extensive immunohistochemical analyses of tRG-like cells, and we found that both the morphology and gene/protein expression were consistent with the notion that “tRG-like” cluster in our ferret dataset represents tRG defined in humans (Nowakowski et al., 2016).

      Revision plan:

      As for human dataset, we agree that the population of committed tRG was minor. Thus, we pared down our statements regarding the fates of tRG as mentioned in other comments, both in the Results and Discussion.

      https://www.dropbox.com/scl/fi/aqsg5xlbxyoybzwq0xezp/reviewer1_4.pdf?rlkey=oxhmtko08nhvzkmsqxcjf9qua&dl=0

      - Are prior studies referenced appropriately?

      1-5. I found it interesting that tRGs persist and even expand in number in postnatal timepoints (Fig. 2C). I'd be interested to know if this is in line with what is known in human developing cortex. If so, it would strengthen the conclusion that ferret tRGs can model that of humans - and if not, this would either be an important finding regarding tRG function or an important caveat in the use of ferret tRGs to model the cell type in humans.

      We thank the Reviewer for bringing up this issue. This is an important issue because we wanted in this study to use the ferret as a good model for the complex brain development in gyrencephalic animals, in general, to know what characteristics are shared or not, across gyrencephalic species (such as the presence of the OSVZ vs. the temporal scale).

      Revision plan:

      Our study demonstrated the presence of tRG cells up to P10 by immunohistochemistry and scRNA-seq. P5~P10 is the stage where neurogenesis became dominated by gliogenesis in the dorsal cortex in ferrets, although its timing is delayed in the visual cortex. On the other hand, Nowakowski et al. (2016) originally identified and defined CRYAB-expressing tRG, based on morphology and gene expression on human primary tissues during mid-neurogenic stages, while cortical neurogenesis is mostly declined in human postnatal stages. We have failed to find literatures or textbooks describing the presence of CRYAB-expressing tRG, while an ependymal layer was detected in the postnatal human cortices (Honig et al., 1996; preprint Nascimento et al., 2022). At the moment, the lack of information thus makes it difficult to compare the relationship of birth timing with the period of tRG persistence between ferrets and humans. In the revised manuscript, the “Discussion” will include this argument as well as the following difference between humans and ferrets in the RG scaffold.

      Besides birth timing, Nowakowski et al. also reported that radial glia scaffold spanning from the VZ to the pial surface undergoes a transformation during neurogenic stages; tRG becomes the major RG population in the VZ, disconnecting VZ and OSVZ. In contrast, we did not find a discontinuous scaffold stage over the course of ferret neurogenesis. Instead, we still detected CRYAB-negative vRG with an apical attachment and a basal process extending beyond the OSVZ during stages where the peak of tRG expansion is achieved (such as P5 in Figure 2A, S3A). This appears to be a prominent difference between human and ferret corticogenesis.

      - Are the text and figures clear and accurate? Yes

      - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      1-6. For Fig. 2A, I would find it helpful to compare the morphology of GFP+/CRYAB+ cells vs GFP+/CRYAB- cells, with the hypothesis that GFP+/CRYAB- cells will have elongated basal processes. I believe this could be done by finding GFP+/CRYAB- cells in the raw images obtained to generate Fig. 2A (or similar), and showing those cells in an adjacent panel. This side-by-side comparison could provide more support that the CRYAB+ cells from the single-cell analyses are indeed specifically linked to tRG-like morphology.

      Revision plan:

      We prepared the images for GFP+/CRYAB- vRG cells in an adjacent panel in Figure 2A as recommended by the reviewer (below). To better distinguish the morphology of an isolated vRG cell from other labelled cells, we sparsely labeled RG cells with EGFP at P3 by electroporation (Methods), and fixed the samples two days later (right panel). We highlighted the morphology (cell body and basal fiber) of a CRYAB- GFP+ vRG and that of a neighboring CRYAB+ GFP- tRG on the same panel to clarify that vRG did not express CRYAB.

      https://www.dropbox.com/scl/fi/3wrmqdswt69t8pkdy30h7/reviewer1_6.pdf?rlkey=90ixbadan3mxx10m85jnpwphn&dl=0

      Reviewer #1 (Significance (Required)):

      This paper primarily presents a technical advance in the field, showing that tRG cells that can model those found in the developing human cortex are found in the developing ferret cortex.

      - Place the work in the context of the existing literature (provide references, where appropriate).

      - State what audience might be interested in and influenced by the reported findings.

      Several studies in the human and macaque brain have identified the presence of tRGs (deAzevedo et al., 2003; Nowakowski et al., 2016), but understanding the molecular functions and development of these cells - and many human-specific cell types in the brain - is difficult due to the lack of tractable models of human neurodevelopment. Ferrets, given their layered cortices, may be a potential model system for these cell types, but further analyses to determine their transcriptomic similarity to the developing human cortex and their ability to recapitulate human cell types are required in order to evaluate their use as a model system. By generating a useful resource in the ferret single-cell transcriptomic atlas, this study provides evidence that - at least for the tRG subtypes - ferrets may be useful in dissecting the generation and functional importance of tRG cells. With the caveat that a direct comparison with the use of cortical organoids to study tRG is lacking in this paper (see above), I believe this work can provide useful insight into the field's current search for model systems to functionally interrogate human-specific aspects of cortical development.

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

      2-1. In this report, Bilgic and colleagues study the diversity of progenitor cell types in the developing ferret cerebral cortex, a valuable in vivo model to understand cortex expansion and folding, as in primates including human. Using a single-cell transcriptomics approach, they describe a diversity of progenitor cell types and their interrelation by transcriptomic trajectories, which are conserved but biased as development progresses. Most interestingly, they identify in ferret a type of cell only identified in human before, tRG, which they then characterize throughoutly by transcriptomics. They also identify these cells in histological sections, and via time-lapse videomicroscopy they characterize their cell type of origin. They also provide indirect evidence that tRG may be the source of ependymal cells in the ventricle of the mature cerebral cortex, as well as astroglial progenitor cells. Finally, they extend their analyses to identify oRG in ferret based on previous human single cell data, concluding that they have in ferret a quite different transcriptomic profile than in human.

      We would like to thank the reviewer for carefully reading our manuscript and providing us with valuable feedback. However, we would like to clarify that there might have been a misunderstanding regarding our conclusion about the identification of oRG-like cells in ferrets.

      Our study did not conclude that we have identified oRG cells in ferrets with “a quite different transcriptomic profile than in human”. Instead, our findings indicate that unlike oRG cells in human, ferret oRG-like cells did not exhibit specificity for human oRG markers (such as HOPX and CLU) that would enable us to distinguish them from other late RG cells in ferrets. Despite this, oRG score derived from human oRG marker expression showed higher values in predicted ferret oRG-like cells compared to other ferret RG cells, reflecting a similarity of the transcriptome profile between human oRG and ferret oRG-like cells (Figure 7H-I). We will carefully describe our methodology to reach this conclusion in response to reviewer 2’s comment regarding how we determined ferret oRG in a later comment.

      Major issues:

      2-2. The authors must provide evidence that the cortical area they are examining will give rise to Somatosensory cortex. Their sampling area appears more like Cingulate cortex, while somatosensory may be a bit more lateral. The cingulate cortex is a very unique region, with some unique characteristics including lamination and connectivity. It would be important to provide some justification as to why they chose this particular part of the cerebral cortex, and keep this into consideration when discussing the general value of their findings.

      The reviewer 2 seems to misunderstand that we took cortical strips shown in Figure S1A as samples for scRNA seq. If our description in the main text is confusing, that would be our fault.

      In Figure S1A of the original manuscript, we showed the cropped images of the medial part to emphasize the distinguishment of different germinal layers (VZ/iSVZ/oSVZ) and their temporal changes in ferret cortices.

      Revision Plan. To avoid such a misleading, we inserted the dotty lines in the revised Figure S1A to demarcate the tissue parts for scRNAseq, which correspond to almost all lateral cortices, mainly including the somatosensory area 1 and 2 with surrounding areas. We accordingly added the following sentence in the legend, “The approximate boundaries of dorsal cortex area used for scRNA sequencing are highlighted with dotty line segments in the dorsal cortex hemisphere above each strip.”.

      We also show actual sampling for single-cell transcriptomics below. As our sampling was not restricted to the somatosensory cortex, we have revised “somatosensory cortex” as “dorsal cortex” in Lines 131 and 1191 of our manuscript.

      https://www.dropbox.com/scl/fi/9gg508iood73zl02836g6/reviewer2_2.pdf?rlkey=lufevala88ihvc1p6mts463as&dl=0

      2-3. It seems that the single cell datasets were collected from only 1 replica at each developmental stage. Current best practice sets the inclusion of several biological replicates. Whereas this represents multiplying the workload (and costs) and re-doing many of the analyses, it is currently highly valued. On the other hand, the authors already have their analysis pipelines defined, and so the time involved should be much shorter than before.

      We disagree with the reviewer 2’s comment. We would like to clarify that we collected brain tissues in two different ways for the same set of developmental stages; one brain tissue by removing cortical plate (T); another independent brain tissue at the same developmental stage by sorting GFP-labelled lineage from neural progenitors that were electroporated at embryonic stages (AG, Methods). Both manipulations of samples aimed to increase progenitor cell populations in scRNAseq. Therefore, we have two sets of samples of the same temporal series, each prepared in a totally different way. All cell types were present in both methods of collection shown as Supplementary Figure 2E’ (below left) that separates samples by different preparations at each stage (by modifying Supplementary Figure 2E; below right). We believe that the biological replica (n=2) in this manuscript would be sufficiently reliable, judged by its reproducibility.

      https://www.dropbox.com/scl/fi/levyqy9ngvpyio1yl9oif/reviewer2_3.pdf?rlkey=r4aw0hu9cdn68f1pvhp734vxx&dl=0

      Here, we also cite several examples of papers important in the field of single-cell or bulk transcriptomics of brain tissue, where only a single replicate or pair (replica) was taken for experiments on mice, humans and ferrets:

      mice: Ogrodnik et al., 2021 PMID: 33470505, Hochgerner et al., 2018 PMID: 29335606, Joglekar et al., 2021 PMID: 33469025;

      human: Herring et al., 2022 PMID: 36318921, Polioudakis et al., 2019 PMID: 31303374, Mayer et al., 2019 PMID: 30770253, Fietz et al., 2012 PMID: 22753484;

      macaque: Schmitz et al., 2022 PMID: 35322231;

      ferret: Johnson et al., 2018 PMID: 29643508.

      2-4. Single cell QC methods are incomplete as described in Methods. It is key to consider the relative abundance of mitochondrial RNAs when assessing the integrity and validity of cells, and thus a key criterion to select the cells for clustering analysis. The criteria for the selected choice of clustering resolution is also missing.

      The reviewer pointed out an important criterion, the abundance of mitochondria.

      Revision Plan:

      We have now added the mitochondrial QC metrics in the new Figure S2A, and revised the legends as follows: “Violin plots showing the number of genes, mRNAs and the percentage of mitochondrial genes per cell in each sample and time point”. We have computed the percentage of mitochondrial genes for each cell type and found that the majority of cells in each cell type had a value less than 5% while the content value in some cells distributed along the range between 0% and 10%, up to a maximum of 28% (Figure S2A). Despite this, we have decided to include all cells that had less than 30% of mitochondrial genes in our analysis based on the percentage of reads mapped on mitochondrial genome for the following reasons:

      1. The percentage of mitochondrial indicates respiratory activity, rather than apoptosis and the percentage of mitochondrial quite depends on the tissue type and species. For example, in human case, such percentage range from 5%~30% (Mercer et al., 2011 Cell; The human mitochondrial transcriptome).
      2. Unfortunately, unlike human and mouse brains, there is no reference to show the percentage of mitochondrial in ferret brains. Therefore, the suitable way is to keep all of these cells.
      3. These cells showing high percentage of mitochondrial genes are not clustered as an apoptosis cluster in UMAP, instead, these cells are observed in most of clusters (below). Therefore, we believed that these cells are not apoptotic cells and include these cells in further analysis.

      https://www.dropbox.com/scl/fi/4kp3fczxzo6x4fx8hqt8m/reviewer2_4_1.pdf?rlkey=ypojzbuwgelt51qlf56g883s9&dl=0 4. After all, we have obtained similar clustering overall after filtering cells with a higher percentage for mitochondrial genes; we set the threshold to 10%. This filtering resulted in 28,686 cells in our dataset. We then performed our workflow from the normalization step with the same settings that we applied to our original ferret dataset (Methods). Below, we show the results comparing newly generated clusters in this filtered subset on UMAP (left), and the original clusters shown in Figure 1B (right). 26 clusters were obtained in both conditions, and both major cell types and subtypes were conserved after filtering.

      https://www.dropbox.com/scl/fi/0mlk69z7hckpiw03ivfjb/reviewer2_4_2.pdf?rlkey=hfvjrifrytmnywc4vchjvf0ms&dl=0

      Clustering resolution: Our choice of the resolution was based on avoiding over- or under-clustering of ferret cells. After trying several resolution values, including 0.6, 0.8, 1.0 and 1.2, we have decided to use the resolution of 0.8 as the separation of cell types was the most reasonable among other resolutions that we have tried, in a similar way to actual known cell types. For example, the resolution of 0.6 did not distinguish “tRG” cells from “late_RG1” cells, as well as “early_RG” subtypes which were distinctly enriched with different cell cycle markers (Figure S2D). On the other hand, the resolution of 1.2 resulted in an over-clustering of IPC, OPC, DL neurons and microglia.

      2-5. When first describing tRGs (line 171), orthogonal views of the image z-stacks must be shown to demonstrate the full morphology of these cells. The basal process might have been cut during tissue sectioning. The same applies to images in Fig. 2C, 2D, S3A.

      Revision plan:

      We focused on Figure 2A and S3A (2D is a histogram) to show the full morphology of CRYAB+ tRG, because Figure 2A is the initial presentation of tRG in this paper, and Fig. 2A and Fig.S3A images are taken on a 200-micrometer thick section, originally aiming to indicate that CRYAB-positive fiber is short, spanning nearly along the VZ and the SVZ. We made 3D-reconstructions of those images, which are rather better than orthogonal projections, in order to show that CRYAB+ fibers are shorter than those of vRG (terminating at positions around the upper boundary of the SVZ) and that the short basal processes are not due to the cut of long radial fibers during tissue sectioning.

      We will show these 3D-reconstruction below. Please download movie files from the following URLs to look at them clearly.

      Figure 2A

      https://www.dropbox.com/s/qocve596c5xhtlc/%E2%98%85fig2A-Ver02.mp4?dl=0

      Figure S3A

      https://www.dropbox.com/s/v8gqwfi1r8ff5n5/%E2%98%85figS3A-P0%20movie-ver2.mp4?dl=0

      2-6. In Figure 3, the authors perform time-lapse imaging to visualize and characterize the cells and lineage that give rise to tRGs. While very nice and a technical challenge that must be properly acknowledged, they unfortunately only obtained a total of three examples, which is clearly insufficient to reach any meaningful conclusion on this respect. These conclusions, while fascinating, are based only on 3 cell divisions. If this is to be taken as a strong argument for the conclusions of the study, the authors must obtain. If the authors want to make a solid statement out of this experimental approach, they must obtain a sufficient amount of additional data, which will depend on the variability of the results they find.

      We thank the reviewer for appreciating our time-lapse imaging data as very nice and a technical challenge. The number of time-lapse imaging that could follow the cell fates was from “4” samples instead of 3. It is indeed very infrequent and difficult to obtain a complete set of consecutive divisions from vRG, followed by histochemical examinations (fixation, cryo-sectioning and immunostaining of slices). This is because some of EGFP-labeled cells are frequently indistinguishable from each other by overlapping within a clone or with cells in other clones. Therefore, we decided to take a different way to clarify the pathways from vRG and its variety to generate tRG at the tRG-generating stage.

      Revision plan:

      Increasing the number of time-lapse image series will be extremely inefficient because of the reasons described above, perhaps taking a long time such as 3-5 months according to our breeding schedule of ferrets. Therefore, we take an alternative way to clarify the division patterns from vRG to generate tRG, especially focusing on the identity and variety of vRG sibling cells at the tRG-generating stage; we are examining the sibling pair of vRG and/or precursor of tRG to see what kind of cell the vRGs actually generate at their mitosis. For this purpose, we electroporate ferret cortices with the EGFP-expressing plasmid approximately one cell cycle prior to fixation (E38 or P0). We then stain ferret cortices for a mitotic marker Ki67 and tRG marker CRYAB and other markers during the tRG-generating state (P0-P5), assuming the cell cycle length of vRG and IPC as approximately 33h~45h based on our own consecutive EdU labeling experiments and time-lapse imaging.

      2-7. Still regarding the time-lapse results presented in Figure 3, it is unclear why after first division the authors identify the blue cell as IPC, when it has the exact features of tRG: apical process anchored in VZ surface + short basal process. This is applicable to all three examples shown. For example, the authors describe: "the mother IPC of tRG also possessed both an apical endfoot and a short basal fiber (Fig. 3D)". Why is this identified as IPC, when it looks exactly like vRG, NOT as an IPC? The interpretation of IPCs being the mother cells to tRGs must be changed, to those being vRGs. Or else, more convincing data must be provided.

      In fact, their analyses in Fig 4A contradict their interpretation on tRG mother cells, showing that the transcriptomic trajectory leading to tRGs does not inlcude Eomes+ cells, accumulated in the neurogenic state 2. At the end of this section, the authors indicate: "our data suggest that tRG cells are formed by apical asymmetric division(s) from unique apical IPC with a short basal fiber (Tsunekawa et al, in preparation).". Being as important as this point is, if there is solid supporting data the authors must include it in this study.

      We appreciate the reviewer 2’s question about “why is this identified as IPC, when it looks exactly like vRG, NOT as an IPC?”

      Revision plan.

      1. We are confident that this blue-labeled cells in Figure 3A and D are not vRG but mitotic sibling cell (of vRG) with a short basal fiber (that we named IPC in the initial manuscript). We now made the morphological features of these cells clearly visible by constructing 3D-views of the images with different snapshot images (we show below and in the preliminary revision as a supplementary movie). In addition, it divides once as time-lapse imaging revealed, hence this cell is still mitotic, instead of a postmitotic cell. Therefore, we used the term that is generally used for this type of cells, namely, intermediate progenitor cells (IPC), by which we did not intend to refer to TBR2+ neurogenic IPC. We plan to include these revised images into our fully revised manuscript.
      2. We agree the reviewer 2 on the point that this blue-labeled cell may express CRYAB (the next comment of reviewer 2 essentially claims the same point), as we also wrote this possibility in line 204-207 of the original manuscript. It could not be technically possible at the moment to examine CRYAB expression in a cell emerging only in the course of time-lapse imaging. If we could label vRG with a transgenic or knock-in fluorescence marker, which mimics CRYAB gene expression, we could have figured out whether blue cells (the mitotic vRG sibling cells) express the CRYAB gene. Indeed, we tried to knock the EGFP gene in the CRYAB gene many times over a year, but have so far failed. Given that tRG is defined as the cell type expressing CRYAB with a short basal fiber at late-neurogenic stage, irrespective of its mitotic activity, this blue labeled vRG sibling cell in Fig. 3A (and/or Fig. 3D) might express CRYAB, hence can be a “mitotic tRG” (although its possibility seems to be low as shown in Fig. 2E). To avoid any possible misleading, we have changed the term of these cells to a “mitotic vRG sibling cell (or mitotic tRG parental cell) with a short basal process”, and add a comment that “this cell might be mitotic tRG with CRYAB expression”.
      3. As for the TBR2 expression, we do not know these cells that appeared in the course of time-lapse imaging express TBR2 or not. As shown in Fig. 2F, 10% (P10) to 30 % (P5) of CRYAB+ cells express TBR2. On the other hand, “intermediate progenitors” do not necessarily express TBR2 in general. Therefore, we disagree on the reviewer 2’s comment “their analyses in Fig 4A contradict their interpretation on tRG’s parent cells”, but “our analyses in Fig 4A is compatible with our interpretation on tRG’s parent cells in time-lapse imaging”, and that is “a mitotic vRG sibling (or mitotic tRG parental cell) with a short basal fiber divides to produce CRYAB+ tRG at the end of timelapse imaging”. However, to avoid any overstatements or misunderstanding on this issue, we have revised related text as described above.
      4. We are not able to include the data taken by Tsunekawa et al.. This is because we are going to submit a separate paper, which includes a large volume of data with human ones in collaboration with another group and largely concerns stages that are earlier than that of tRG formation. It is, therefore, not practical to combine these data with those described in this manuscript. Therefore, we remove all descriptions related with Tsunekawa et al.

      Below we show snapshot images and 3D-reconstructions for Figure 3A and 3D. Please download movie files from the following URLs to view at them at the highest resolution.

      @Figure 3A:

      1)A time lapse movie (20 min interval) showing images around time 40:00 at which vRG underwent the second division.

      https://www.dropbox.com/s/znx3bboxefhj0jt/%E2%98%85Fig_3A%20movies%20around%2040%20h.mp4?dl=0

      2)Snapshot images for time 40:00

      https://www.dropbox.com/s/6y25mk4jhwqy6v7/%E2%98%85E38-fig3A-sRG-2.png?dl=0

      3) 3D-reconstruction images at the same time point (40:00)

      https://www.dropbox.com/s/so8hesjzy63yxmb/%E2%98%853D-reconstruction%20%2840.00%202nd%20div%29.mp4?dl=0

      4) The entire time-lapse movies of time 0:00-84:00; The mitotic sibling cell of the vRG is indicated by a white arrow.

      https://www.dropbox.com/s/ywua95f8fmohsmc/%E2%98%85Fig3A-arrow-time.mp4?dl=0

      @Figure 3D:

      A revised time-lapse snapshots of Figure 3D.

      https://www.dropbox.com/s/xyet4virt3j9u3t/%E2%98%8520211220%EF%BC%8DP0%EF%BC%8Dtimelaps-xt04corrected.psd?dl=0

      The assignment of the cell has corrected to the right one for the same mitotic cell because cell body position at the first two time points were misassigned in the original manuscript (at the following time points, there is no change).

      Snapshot image at time point of 06:20; https://www.dropbox.com/s/hn3v6ao1qkhnfjh/%E2%98%85Fig3D%20sRG%20at%200620.png?dl=0

      Rotating movie of 3D-reconstruction at time point of 06:40:

      https://www.dropbox.com/s/6taqjr0u21x5tn0/%E2%98%853Drotated%20movie%20of%20time%20point%2006.40.mp4?dl=0

      2-8. Alternative interpretation of time-lapse images (lines 196-197): maybe a tRG can generate one tRG CRYAB+, and one IPC CRYAB-.

      We agree with reviewer 2 that there is an alternative interpretation of cell identity appearing in time-lapse imaging of Fig. 3. In line 196-197, we wrote that “These mother IPC underwent an asymmetric division to generate a non-CRYAB expressing cell and a CRYAB__+ tRG”. As pointed by reviewer 2 here and in the previous comment, we cannot exclude the possibility that this vRG sibling cell may be a mitotic tRG (see our response to the previous reviewer 2 comment). If so, what we observed in Fig. 3A and D could be interpreted as a mitotic tRG, and generate one CRYAB+ tRG and one CRYAB- climbing cell. However, as we haven’t confirmed or stated whether this parent cell was a mitotic tRG, we also did not examine the identity of this sister cell of CRYAB+ tRG. It can be an IPC or nascent neuron or even an astroglial progenitor cell. From our data, we cannot say anything about the identity of the CRYAB-negative sister cell other than that this cell is CRYAB-negative, migrating upward. That is why we did not mention about the identity of this CRYAB-negative sister cell of tRG other than that the sister cell of tRG is CRYAB-negative.

      Revision plan. We changed the term of IPC to “a mitotic vRG sibling cell” and describe the possibility that “This mitotic vRG sibling cell (or mitotic tRG parental cell) can be a mitotic tRG if this cell express CRYAB, and its apical division generates one tRG and one CRYAB-negative climbing cell with an unknown identity, replacing the description of line 196-197.

      2-9. Arrows in Fig 5E are shifted between the top and bottom panels. There is no obvious evidence of mitosis visible. This should be unequivocally labeled with anti-PH3 antibodies.

      We thank reviewer 2 for pointing our careless mistake.

      Revision plan. We have corrected the shifted position of arrows in Figure 5E. We have removed “mitosis” in the title of Figure 5E since the initial manuscript did not include descriptions on mitosis in the text.

      2-10. Line 277: “Transcriptomic trajectories were homologous across the two species”. What does this refer to? What are these trajectories? Pseudotime? Is this statistically tested?

      The meaning of the term “Transcriptomic trajectories” was not clear.

      Revision plan. We revised our description in this part as “Temporal patterns and variety of neural progenitors during the cortical development were similar to each other between humans and ferrets at the single cell transcriptome level”.

      2-11. When comparing tRG cells between ferret and human, the authors indicate a remarkable similarity between the two species as represented by CRYAB, EGR1, and CYR61 expression. As shown in Fig 6E, EGR1 and CYR61 are not expressed selectively in human tRG as they clearly are in ferret tRG. Hence, this argument is not valid.

      In lines 291-292, we mention that “tRG cells also showed a remarkable similarity between the two species (Fig. 6C, 6D), as represented by CRYAB, EGR1, and CYR61 expression (Fig. 6E)”. Here, what we wanted to claim is that the same combination of gene expression (CRYAB, EGR1, and CYR61) is characteristically at relatively high levels in both ferrets and human tRG. As the reviewer 2 claimed, CRYAB and CYR61 genes are highly selective for ferret tRG among mid-late RG types, while the expression of EGR1 and CYR1 are just relatively enriched in tRG than in other cell types in human RG (except for highly selective CRYAB). Irrespective of the difference in their relative enrichment in tRG between humans and ferrets, one can still state that the combination of these marker expression at higher levels is shared in these two species”. We were not able to find which part in the manuscript was the reviewer referring to for the claimed argument (“EGR1 and CYR61 are expressed selectively in human tRG”).

      Revision plan. To clarify our statement, we changed this sentence into “tRG cells also showed a remarkable similarity between the two species (Fig. 6C, 6D), as represented by a high level of expression for the combination of CRYAB, EGR1, and CYR61 (Fig. 6E)”

      2-12. In the last part, the authors try to identify oRG-like cells in ferret by comparison with their transcriptomes identified in human. For this, they decide to call ferret oRG-like cells those that are near human oRGs in the integrated UMAP, as identified in a previous human study. What was the criterion for this? How much near is "near"? The fact that the selected cells have higher oRG scores is expected and obvious, as these cells were selected precisely based on their proximity in the UMAP. Even more importantly, the identification of oRGs in the human study is not unambiguous. Therefore, the correlate in ferret cells is also non-conclusive as to the identity of such cells.

      We apologize for a confusion caused by insufficient explanations for our methodology. We want to clarify that we did not find " ferret oRG-like cells as those near human oRGs in the integrated UMAP." Rather, we try to identify oRG-like cells in ferrets based on the hypothesis that, when comparing ferret and human datasets, oRG-like cells in ferrets would exhibit a higher degree of similarity to human oRG cells than to other cell types. This hypothesis was supported by our observations of other clusters such as tRG, later RG, and IPC (Figure 6 C and D).

      To identify oRG-like cells in ferrets, we utilized the mutual nearest neighbor (MNN) method to determine the similarity between cells from different species (Stuart et al., 2019 PMID: 31178118). For example, when attempting to identify the human cell that was most similar to a given ferret cell (F), we calculated the distance between cell F and all the cells in the human dataset in the high dimensional expression space. This allowed us to identify a human cell (H) that exhibited the smallest distance to cell F. Subsequently, we computed the distance between cell H and all the cells in the ferret dataset. If cell F had the smallest distance to cell H in the human dataset, we considered cells H and F as a pair of mutual nearest neighbors.

      Using this method, we can find all pair of mutual nearest neighbors in two datasets. We then find these pairs that one is human oRG and define the other is oRG-like in ferret. However, upon further investigation of the characteristics of these cells, we would not find any specific markers (such as HOPX and CLU in human oRG) that would enable us to distinguish them from other later RG cells in ferrets.

      Accordingly, only when our strategy to find mutual nearest neighbors is suitable, the selected cells can get higher oRG score, otherwise, the selected set of ferret cells will not show a high oRG score. Therefore, we disagree with the notion that “The fact that the selected cells have higher oRG scores is expected and obvious”.

      We hope this explanation provides a clearer understanding of our methodology and the rationale behind our approach to identifying potential oRG cells in ferrets.

      2-13. Discussion is surprisingly short, given the emphasis that the authors place on the importance of their findings. I would suggest extending it for a better coverage of those findings that have the greatest relevance and interest to a wider readership.

      Thank reviewer 2 for his/her precious advice.

      Revision plan.

      We added several issues discussed in the responses to the reviewers to Discussion. Please look at our responses to comment 2-14 and 2-15 as well as the preliminary manuscript.

      2-14. In Discussion, the authors state that "ferret (and presumably also human) tRG cells differentiate into ependymal cells and astrogenic cells." Again, this conclusion is purely based on transcriptomic trajectories, which must not be confused with cell lineage. This sentence must be rephrased and toned down accordingly.

      We appreciate Reviewer’s comment regarding the difference between transcriptomic trajectories and cell lineage. We agree that transcriptomic trajectories do not necessarily reflect cell lineage. However, relationships along transcriptomic trajectories provides useful information about the differentiation potential of cells. Furthermore, in this study, we examined the temporal and spatial relationships between CRYAB+ tRG and FoxJ1+ ependymal cells that were predicted as tRG descendant cells by transcriptomic trajectories. We could confirm an increasing overlap of FoxJ1+cells with tRG cells along the course of post-natal development in Figure 5. We thus accessed the relationship of the two cell types by not only in silico but also in vivo analyses.

      Revision plan. We disagree with the reviewer 2 as for ferrets, because we accessed the relationship of tRG and their progeny cells by not only in silico but also in vivo analyses.

      On the other hand, as for progenies of human tRG, they were predicted certainly depending on the molecular relationship by comparison with ferrets without histochemical evidence, as pointed by reviewer 2, and the populations of these committed tRG are small. Therefore, we removed “(and presumably also human)” and we tone down about the progeny relationship of tRG as a prediction. We also acknowledge that further studies are needed to confirm the lineage relationships among cell types, as we discussed in the Discussion part.

      2-15. In Discussion: “our cross-species analysis highlights the notable role of tRG as progenitors contributing to the formation of the ependyma and white matter”. As mentioned above, this is only based on transcriptomic trajectories, it is not demonstrated in this study. In vivo analyses of cell fate are needed to support this conclusion, and a more extensive videomicroscopy analysis is needed to confirm the cell lineage progression suggested by transcriptomes.

      The statement “the notable role of tRG as progenitors contributing to the formation of the ependyma and white matter” is certainly a speculation based on our results, but not experimentally indicated yet by such as gene knockout, as the reviewer pointed out. Although we repeatedly tried to knock out the CRYAB gene in ferrets for a year, we have so far failed.

      Revision plan. Taking the comments from reviewer 1 and 2 into account, we largely revised “Discussion” with a more moderate expression, by incorporating comparative analyses with other human datasets, and we also emphasize the importance of in vivo studies as the next step. We just paste the last paragraph of the preliminary revised Discussion. Please see the “Discussion” in the preliminary revision of our manuscript.

      “In ferrets, genetic manipulations can be achieved through in utero or postnatal electroporation, as well as via virus-mediated transfer of DNA (Borrell, 2010; Kawasaki et al, 2012; Matsui et al, 2013; Tsunekawa et al, 2016). Thus, it is theoretically possible to disrupt the CRYAB gene in vivo in ferrets to investigate its role in tRG and their progeny, including ependymal cells, and to track the tRG lineage. If the CRYAB gene is essential to form ependymal layers, we will be able to explore how the ventricle contributes to cortical folding and expansion. Despite extensive efforts over a year, we have thus far been unsuccessful in knocking in and/or knocking out the CRYAB gene. Nevertheless, we anticipate that technical advances will surpass our expectations, both in ferret and human organoids. Taken together, these functional studies in ferrets as well as in human organoids hold promising insights into the understanding of the tRG lineage and its contribution to cortical development in the near future”.

      Minor issues:

      2-16. In line 59, the authors state: "cerebral carcinogenesis independently evolved to gain an additional germinal layer (outer SVZ (OSVZ);". Assuming that they mean "cerebral neurogenesis", what is the evidence for this independent evolution? Original publications demonstrating this must be cited.

      Revision plan. We removed the mentioned statement from our manuscript and revised lines 58-59 as follows: “In many mammalian phylogenic states, cerebral cortex evolved to gain an additional germinal layer (Smart et al. 2002; Zecevic et al. 2005; Kriegstein et al. 2006; Reillo et al. 2011)”.

      2-17. Lines 60-61, the third key publication reporting the existence of bRG must be cited together with Hansen 2010 and Fietz 2010: Reillo et al., 2011, Cerebral Cortex.

      We appreciate Reviewer 2’s remark.

      Revision plan. We now added these citations in lines 60-61 and in the Reference list as Reillo I, De Juan Romero C, García-Cabezas MÁ & Borrell V (2011). A role for intermediate radial glia in the tangential expansion of the mammalian cerebral cortex. Cereb Cortex 21: 1674–1694.

      2-18. When introducing ferret as an interesting or important animal model, suitable original studies should be cited.

      Revision plan:

      For ferrets, there is a long history as experimental animals for electrophysiology similarly with cats and monkeys, but this is not a review of ferret biology. We thus added 6 additional references regarding ferret brain morphology and development listed below.

      Jackson, C.A., J.D. Peduzzi, and T.L. Hickey (1989) Visual cortex development in the ferret. I. Genesis and migration of visual cortical neurons. J. Neurosci.9:1242–1253. PMID: 2703875.

      Chapman B & Stryker MP (1992) Origin of orientation tuning in the visual cortex. Curr Opin Neurobiol 2: 498–501.

      Chenn A., and McConnell S.K. (1995) Cleavage orientation and the asymmetric inheritance of Notch1 immunoreactivity in mammalian neurogenesis. Chenn A, et al. Cell PMID: 7664342.

      Noctor SC, Scholnicoff NJ, and Juliano SL. (1997) Histogenesis of ferret somatosensory cortex. J Comp Neurol. 387(2):179-93.PMID: 9336222.

      Reid CB, Tavazoie SF, Walsh CA. (1997) Clonal dispersion and evidence for asymmetric cell division in ferret cortex. Development. 1997 124(12):2441-2450. doi: 10.1242/dev.124.12.2441.PMID: 9199370

      2-19. In Figure 2F-H, layer borders should be labeled. The density of CRYAB+ cells in VZ (?) at P5 seems much greater in Fig 2E,F than in Fig. 2B. Clarifying this discrepancy is important to validate the quantification of Fig 2D.

      Revision plan.

      Layer borders: We now labeled the approximate position of the boundary of the VZ in Figure 2E-G. We have revised the legends as follows; “The border of the VZ is shown with a white line”. For counting, we have determined borders by the distribution of DAPI, and radial glia-specific markers in our hands and determined the approximative distance of the VZ border from the ventricular surface in the antero-posterior axis where we performed the imaging in Figure 2E-G. The distance was approximately determined as 80 µm at P5 and 40 µm at P10.

      Discrepancy in the intensity of CRYAB: We apologize for the unclear statement on how the images were acquired in the legends of Figure 2E-G. We now revised as follows; “Representative images taken with a 100X-objective lens are shown with MAX projection.”. In Figure 2E-G, images were taken as optical sections of 1.5 µm interval for 12 µm-thick sections. Those images were processed as MAX-projection onto the Z plane. On the other hand, In Figure 2B, we have used 20X-objective lens, instead of 100X-objective lens and did not perform any image projection procedure such as a MAX-projection and only 1 z-plane is shown. Therefore, the visual difference in the CRYAB intensity between Figure 2B and Figure 2E-G derives from whether max projection of several consecutive images was done.

      2-20. Co-expression of CRYAB and FOXJ1. In Fig 5B this must be demonstrated with merged channels.

      Revision plan.

      We added the images with merged channels as requested and revised corresponding legends as follows: “Images with merged channels in A are shown with the same color codes, antibodies and scale bars as A.”.

      2-21. Line 247: "near which nuclear line aggregates are observed more frequently (Fig. 2B)". It is very much unclear what the authors refer to. Please, define nuclear line aggregates.

      Revision plan.

      We will revise the cited sentence and will change the referred figure as follows: “These cells often aligned on a line parallel to the ventricular surface (Fig. 5A)”. We show these nuclear rows by arrows.

      2-22. There are a number of typos along the main text and figures, which must be fully checked and corrected. For example, line 59 "cerebral carcinogenesis"; also in Figure S4, Figure 5E. Labeling of graphs in Fig 5C is wrong. The plots present the fraction of CRYAB+ cells that express FOXJ1 (FOXJ1+/CRYAB+ cells), not the reverse.

      Revision plan.

      We thank the Reviewer for their remarks on typos. We corrected the typos indicated by Reviewer 2. We agree with the Reviewer and also modified the title of Figure 5B as suggested by the Reviewer.

      Reviewer #2 (Significance (Required)):

      This manuscript is of interest for being the first ferret single-cell study, and for identifying and characterizing to a great extent a unique population of cortical progenitor cells that so far had only been observed in human. The study is presented as a resource for studies of ferret cortex development, which as such is clearly of interest to a very limited audience. A more appealing perspective might be if this study in ferret is of interest or of use to the more general community studying cortex development, or even maybe cortex evolution.

      We disagree the reviewer’s view that this study is clearly of interest to a very limited audience. This study first enabled a precise comparative analysis in which we could compare rich human single cell transcriptomes and the ferret dataset of single cell transcriptomes, which were based on greatly improved genomic information (especially, gene models). This study is also first to show global temporal patterns of cortical progenitors of a carnivore species, a famous gyrencephalic mammalian model, and have been shown to be similar to a primate species at the single cell transcriptomic level. Indeed, upon uploading this manuscript in BioRxiv, many non-ferret specialists as well as specialists have inquired datasets and requested some collaborations with us. So we believe that this paper has already attract a general interest of brain scientists.

      Advance: it is, so far, the first study of single cell profiling of the ferret cerebral cortex, a well established and highly valued model of gyrencephalic mammals, and a suitable best-alternative to work in primates. In addition to the technical advance, providing a new resource for work in ferret, it shows for the first time the existence of truncated Radial Glia (tRG) in a non-human cortex, and even more importantly in this model, strengthening even more its value.

      This study as is presented will be of most interest to a specialized audience, those directly working with ferret. Nevertheless, it will also be of conceptual interest to the community of cortex development and evolution for the concepts that one can extract on cell type conservation.

      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.

      1-1. The weakest claim in the paper is lines 202: "...tRG cells are formed by apical asymmetric division(s) from unique apical IPC". From my understanding, the main evidence that the tRG parent cells shown in Fig. 3 are not tRGs are the data from Fig. 2E-G showing the low amounts of CRYAB+ cells co-expressing KI67, TBR2, or OLIG2 in P5 and P10. Especially given that these timepoints are after those used in Fig. 3, I believe further evidence is needed to confirm the cell type identity of tRG parent cells in Fig. 3. Such experiments (isolating IPCs from ferret cortex and growing in vitro to determine progeny cells) may be outside of the scope of this paper, in which case I believe the text can be strengthened with either (1) presenting the data from the cited Tsunekawa et al, in preparation that would suggest this claim or (2) rephrasing these claims to omit the mention of IPCs.

      1. We revise the term “IPC” as “mitotic sibling of vRG” and stated that these cells might be tRG (CRYAB+) or non-tRG (CRYAB-) intermediate progenitors. By the term of “intermediate progenitors”, we did not intend to refer to TBR2+ neurogenic IPCs, but rather to an intermediate state of progenitors, in a general sense, with a similar morphology as tRG. To avoid any confusions on this terminology, we revised our manuscript by replacing “IPC” with “a sibling of vRG”.
      2. We delete all statements relevant to Tsunekawa et al. data from the manuscript. We regret that we are not able to include Tsunekawa et al. data because we are planning to submit this data as a separate manuscript, which describes that in ferrets, vRG frequently (30% of apical division) generate non-Tbr2-positive mitotic sibling cells bearing a short basal process during the entire neurogenesis. This study includes a large volume of data with human ones and largely concerns stages that are earlier than that of tRG formation. It is, therefore, not appropriate to combine these data with those described in this manuscript.

      1-2. I also believe the claim in Line 365-366 is overstated: "We found that ferret (and presumably also human) tRG cells differentiate into ependymal cells and astrogenic cells." While I believe the transcriptomic comparisons suggest the presence of uncommitted tRG in both the ferret and human datasets, I would appreciate further analyses to confirm the prevalence of astroglial and ependymal tRG in the humans and/or functional analyses before claiming that human tRG cells make ependymal and astrogenic cells. I appreciate the authors' note that "GW25 is...the latest stages experimentally available" (line 376-377), but their comparative approaches could be applied to existing datasets of the human cortex (Herring et al., 2022, PMID: 36318921) that span later developmental ages. Identifying the presence of astroglial and ependymal tRGs in this and/or similar datasets would provide more convincing evidence of the tRGs' developmental potential. If this computational analysis is outside the scope of the paper, I believe paring the certainty of these claims (especially lines 379 - 383) and recognizing the need for further functional analyses would negate the need for deeper mechanistic validation.

      1. We compared our ferret dataset to the human postnatal dataset recommended by the Reviewer 1 (Herring et al., 2022). As a conclusion of our analyses shown below, we found that Herring et al., (2022) dataset was not favorable for a comparative analysis with our ferret dataset regarding the fates of human tRG, because Herring’s human dataset was derived from the prefrontal cortex; This human dataset does not include neither tRG cell population nor ependymal clusters. We have also elaborated our discussion after analyzing Herring et al. dataset in the discussion.
      2. We, therefore, pare down our claim in lines 365-366, by removing “(and presumably human)” to state that “Our pseudotime trajectory analyses and immunohistochemistry analyses strongly suggested that…”.
      3. We also tone down the statements as for the discussion of the relationship between human and ferrets regarding the tRG progeny fates (originally lines from 372 to the end) and also elaborated our discussion after analyzing Herring et al. dataset in the same paragraph.

      We will describe the details of our analysis of Herring et al. (2022) below.

      https://www.dropbox.com/scl/fi/a0m72orxfsub66dh3hdbg/reviewer1_2ABC.pdf?rlkey=uzrd8ngclp87p5c8v24mqd1j7&dl=0

      As mentioned above, Herring’s human dataset was derived from the prefrontal cortex, and that it did not include a specific subtype defined as tRG nor other HES1-expressing progenitor clusters such as RG in the original cluster annotation. We, therefore, re-clustered the raw dataset from GW22 (the earliest stage available) up to 10-months after birth by using Seurat pipeline with default parameters (B), and found a CRYAB-expressing population in the original “Astrocyte_GFAP” subtype among astrocyte clusters (A), which distribute in the most of collected stages, from late development through the adulthood. We then examined this dataset to find out whether tRG or its progenies are present.

      After reclustering, CRYAB-expressing cells (with more than 1 raw count) represented 0.15% of the dataset and were grouped as a part of cluster 44, which was mostly derived from postnatal stages (among which 4-months was the most enriched one; C). Several astrocyte markers, such as SPARCL1, HOPX, CLU, and GJA1, as well as CRYAB, were enriched in the cluster 44 as revealed by FindMarkers (Methods). FOXJ1 expression was nearly absent overall in this dataset, indicating the absence of the ependymal cell population, a tRG-descendant cell types in ferrets (C).

      To evaluate the similarity between cluster 44 and tRG or astroglial tRG, we next integrated Herring dataset with our ferret subset (about 15,000 cells) and the human GW25 subset from Bhaduri et al. (2021) of approx. 3,000 cells, both of which contained only progenitor cells. As we have done in Figure 7 of our original manuscript; we have removed cells other than progenitors, astrocytes and oligodendrocytes, such as neurons, microglia, endothelial cells. This resulted in about 20,000 cells in Herring dataset.

      https://www.dropbox.com/scl/fi/nz3iulya5199i95ecr1un/reviewer1_2D.pdf?rlkey=kp7lwxtkn562un1uf9l1axn2p&dl=0

      This integration (D) reveals that Herring’s cluster 44 is closely located to Bhaduri’s human and our ferret tRG clusters on UMAP, but does not overlap with these tRG clusters. This result further suggested that tRG population might be lacking or very rare in this neuron- and glia-dominated dataset, which might be due to the sampling method that targeted the enrichment of neuronal layers (Herring et al., 2022). It is also possible that this fragmented information on astrocyte and ependymal lineages could be due to the regional and/or temporal difference of samples between two human datasets.

      1-3. I believe the most significant advance for this paper is the potential to use ferret tRG cells to model those of the human brain. However to support this claim (see Lines 83-84), I believe a comparison of the ferret tRG cells with existing cortical organoid datasets (Bhaduri et al., 2020, PMID: 31996853) would be helpful. If cortical organoids currently lack the presence of tRG cell types, that would strengthen the importance of the ferret model and the findings of this paper - otherwise, I feel that the use of the ferret model needs to be justified in light of the greater accesibility and genetic tractability of the cortical organoid system.

      According to the suggestion of reviewer 1, we analyzed two cortical organoid datasets (Bhaduri et al., 2020; Herring et al., 2022) to examine whether different tRG populations are present in organoids. Our analyses led us to conclude that tRG-like populations seem to be lacking in available organoid datasets; organoids can have CRYAB-expressing astrocyte-like cells in single-cell transcriptome datasets, but the presence of tRG-like cells seem to be unstable and dependent of lines and protocols how organoids are generated. A further assessment on tRGs’ cellular features is required on organoids by immunostaining experiments. In the light of this analysis, we elaborated our discussion by describing observations shown below. Below is our analysis of organoid data.

      https://www.dropbox.com/scl/fi/8mj6u94t3hkzw6q61o7od/reviewer1_3AB.pdf?rlkey=10xiks25nzn9r90guw9l0onqh&dl=0

      Bhaduri dataset contained organoids generated from 4 different lines, which showed a variability in terms of cell distribution on UMAP while overall temporal and differentiation axes were recapitulated (A). While keeping the original cluster annotations except for YH10 line, we performed reclustering. CRYAB was expressed in clusters 26 and 30 enriched in YH10 line, and cluster 29 enriched in 13234 line (B).

      To confirm the identity of these clusters, we integrated organoid dataset with the dataset of primary tissues from the same paper (Bhaduri et al., 2020; C). https://www.dropbox.com/scl/fi/qnqv2e87t74uom2pg836d/reviewer1_3CD.pdf?rlkey=mv370b3dlogwvgh6ig8bdathpdl=0

      As a result of the integration, tRG cells from the primary tissue were not overlapped with organoid-derived CRYAB-expressing cells, although a part of CRYAB-expressing organoid cells were localized in the integrated cluster 16 where primary tRG resided (D). Other cell types that were included in the integrated cluster 16 were “lateRG”, “vRG”, “oRG” from primary tissue dataset, and “glycolyticRG” from organoid dataset. We found that CRYAB-expressing organoid clusters 26 and 30 overlapped with “oRG/astrocyte” clusters of primary tissues.

      1-4. I found the total number of tRG-like cells in the ferret dataset quite small (162), but I understand the difficulty with isolating and sequencing rare cell types from primary tissue sources. I believe most of the transcriptomic analyses were conducted with this low n in consideration, but this caveat is even more reason to pare down the wording for the weaker claims mentioned above.

      As for human dataset, we agree that committed tRG was minor. Thus, we pared down our statements regarding the fates of tRG as mentioned in other comments, both in the Results and Discussion.

      https://www.dropbox.com/scl/fi/aqsg5xlbxyoybzwq0xezp/reviewer1_4.pdf?rlkey=oxhmtko08nhvzkmsqxcjf9qua&dl=0

      1-5. I found it interesting that tRGs persist and even expand in number in postnatal timepoints (Fig. 2C). I'd be interested to know if this is in line with what is known in human developing cortex. If so, it would strengthen the conclusion that ferret tRGs can model that of humans - and if not, this would either be an important finding regarding tRG function or an important caveat in the use of ferret tRGs to model the cell type in humans.

      Our study demonstrated the presence of tRG cells up to P10 by immunohistochemistry and scRNA-seq. P5~P10 is the stage where neurogenesis became dominated by gliogenesis in the dorsal cortex in ferrets, although its timing is delayed in the visual cortex. On the other hand, Nowakowski et al. (2016) originally identified and defined CRYAB-expressing tRG, based on morphology and gene expression on human primary tissues during mid-neurogenic stages, while cortical neurogenesis is mostly declined in human postnatal stages. We have failed to find literatures or textbooks describing the presence of CRYAB-expressing tRG, while an ependymal layer was detected in the postnatal human cortices (Honig et al., 1996; preprint Nascimento et al., 2022). At the moment, the lack of information thus makes it difficult to compare the relationship of birth timing with the period of tRG persistence between ferrets and humans. In the revised manuscript, the “Discussion” will include this argument as well as the following difference between humans and ferrets in the RG scaffold.

      Besides birth timing, Nowakowski et al. also reported that radial glia scaffold spanning from the VZ to the pial surface undergoes a transformation during neurogenic stages; tRG becomes the major RG population in the VZ, disconnecting VZ and OSVZ. In contrast, we did not find a discontinuous scaffold stage over the course of ferret neurogenesis. Instead, we still detected CRYAB-negative vRG with an apical attachment and a basal process extending beyond the OSVZ during stages where the peak of tRG expansion is achieved (such as P5 in Figure 2A, S3A). This appears to be a prominent difference between human and ferret corticogenesis.

      1-6. For Fig. 2A, I would find it helpful to compare the morphology of GFP+/CRYAB+ cells vs GFP+/CRYAB- cells, with the hypothesis that GFP+/CRYAB- cells will have elongated basal processes. I believe this could be done by finding GFP+/CRYAB- cells in the raw images obtained to generate Fig. 2A (or similar), and showing those cells in an adjacent panel. This side-by-side comparison could provide more support that the CRYAB+ cells from the single-cell analyses are indeed specifically linked to tRG-like morphology.

      We prepared the images for GFP+/CRYAB- vRG cells in an adjacent panel in Figure 2A as recommended by the reviewer (below). To better distinguish the morphology of an isolated vRG cell from other labelled cells, we sparsely labeled RG cells with EGFP at P3 by electroporation (Methods), and fixed the samples two days later (right panel). We highlighted the morphology (cell body and basal fiber) of a CRYAB- GFP+ vRG and that of a neighboring CRYAB+ GFP- tRG on the same panel to clarify that vRG did not express CRYAB.

      https://www.dropbox.com/scl/fi/3wrmqdswt69t8pkdy30h7/reviewer1_6.pdf?rlkey=90ixbadan3mxx10m85jnpwphn&dl=0

      2-2. The authors must provide evidence that the cortical area they are examining will give rise to Somatosensory cortex. Their sampling area appears more like Cingulate cortex, while somatosensory may be a bit more lateral. The cingulate cortex is a very unique region, with some unique characteristics including lamination and connectivity. It would be important to provide some justification as to why they chose this particular part of the cerebral cortex, and keep this into consideration when discussing the general value of their findings.

      To avoid such a misleading, we inserted the dotty lines in the revised Figure S1A to demarcate the tissue parts for scRNAseq, which correspond to almost all lateral cortices, mainly including the somatosensory area 1 and 2 with surrounding areas. We accordingly added the following sentence in the legend, “The approximate boundaries of dorsal cortex area used for scRNA sequencing are highlighted with dotty line segments in the dorsal cortex hemisphere above each strip.”.

      We also show actual sampling for single-cell transcriptomics below. As our sampling was not restricted to the somatosensory cortex, we have revised “somatosensory cortex” as “dorsal cortex” in Lines 131 and 1191 of our manuscript.

      https://www.dropbox.com/scl/fi/9gg508iood73zl02836g6/reviewer2_2.pdf?rlkey=lufevala88ihvc1p6mts463as&dl=0

      2-4. Single cell QC methods are incomplete as described in Methods. It is key to consider the relative abundance of mitochondrial RNAs when assessing the integrity and validity of cells, and thus a key criterion to select the cells for clustering analysis. The criteria for the selected choice of clustering resolution is also missing.

      We have now added the mitochondrial QC metrics in the new Figure S2A, and revised the legends as follows: “Violin plots showing the number of genes, mRNAs and the percentage of mitochondrial genes per cell in each sample and time point”. We have computed the percentage of mitochondrial genes for each cell type and found that the majority of cells in each cell type had a value less than 5% while the content value in some cells distributed along the range between 0% and 10%, up to a maximum of 28% (Figure S2A). Despite this, we have decided to include all cells that had less than 30% of mitochondrial genes in our analysis based on the percentage of reads mapped on mitochondrial genome for the following reasons:

      1. The percentage of mitochondrial indicates respiratory activity, rather than apoptosis and the percentage of mitochondrial quite depends on the tissue type and species. For example, in human case, such percentage range from 5%~30% (Mercer et al., 2011 Cell; The human mitochondrial transcriptome).
      2. Unfortunately, unlike human and mouse brains, there is no reference to show the percentage of mitochondrial in ferret brains. Therefore, the suitable way is to keep all of these cells.
      3. These cells showing high percentage of mitochondrial genes are not clustered as an apoptosis cluster in UMAP, instead, these cells are observed in most of clusters (below). Therefore, we believed that these cells are not apoptotic cells and include these cells in further analysis. https://www.dropbox.com/scl/fi/4kp3fczxzo6x4fx8hqt8m/reviewer2_4_1.pdf?rlkey=ypojzbuwgelt51qlf56g883s9&dl=0
      4. After all, we have obtained similar clustering overall after filtering cells with a higher percentage for mitochondrial genes; we set the threshold to 10%. This filtering resulted in 28,686 cells in our dataset. We then performed our workflow from the normalization step with the same settings that we applied to our original ferret dataset (Methods). Below, we show the results comparing newly generated clusters in this filtered subset on UMAP (left), and the original clusters shown in Figure 1B (right). 26 clusters were obtained in both conditions, and both major cell types and subtypes were conserved after filtering.

      https://www.dropbox.com/scl/fi/0mlk69z7hckpiw03ivfjb/reviewer2_4_2.pdf?rlkey=hfvjrifrytmnywc4vchjvf0ms&dl=0

      Clustering resolution: Our choice of the resolution was based on avoiding over- or under-clustering of ferret cells. After trying several resolution values, including 0.6, 0.8, 1.0 and 1.2, we have decided to use the resolution of 0.8 as the separation of cell types was the most reasonable among other resolutions that we have tried, in a similar way to actual known cell types. For example, the resolution of 0.6 did not distinguish “tRG” cells from “late_RG1” cells, as well as “early_RG” subtypes which were distinctly enriched with different cell cycle markers (Figure S2D). On the other hand, the resolution of 1.2 resulted in an over-clustering of IPC, OPC, DL neurons and microglia.

      2-5. When first describing tRGs (line 171), orthogonal views of the image z-stacks must be shown to demonstrate the full morphology of these cells. The basal process might have been cut during tissue sectioning. The same applies to images in Fig. 2C, 2D, S3A.

      We focused on Figure 2A and S3A (2D is a histogram) to show the full morphology of CRYAB+ tRG, because Figure 2A is the initial presentation of tRG in this paper, and Fig. 2A and Fig.S3A images are taken on a 200-micrometer thick section, originally aiming to indicate that CRYAB-positive fiber is short, spanning nearly along the VZ and the SVZ. We made 3D-reconstructions of those images, which are rather better than orthogonal projections, in order to show that CRYAB+ fibers are shorter than those of vRG (terminating at positions around the upper boundary of the SVZ) and that the short basal processes are not due to the cut of long radial fibers during tissue sectioning (we show in below and in the final version as a supplementary figure and movies).

      We show these 3D-reconstruction in below. Please download movie files from the following URLs to look at them clearly.

      Figure 2A

      https://www.dropbox.com/s/qocve596c5xhtlc/%E2%98%85fig2A-Ver02.mp4?dl=0

      Figure S3A

      https://www.dropbox.com/s/v8gqwfi1r8ff5n5/%E2%98%85figS3A-P0%20movie-ver2.mp4?dl=0

      2-7. Still regarding the time-lapse results presented in Figure 3, it is unclear why after first division the authors identify the blue cell as IPC, when it has the exact features of tRG: apical process anchored in VZ surface + short basal process. This is applicable to all three examples shown. For example, the authors describe: "the mother IPC of tRG also possessed both an apical endfoot and a short basal fiber (Fig. 3D)". Why is this identified as IPC, when it looks exactly like vRG, NOT as an IPC? The interpretation of IPCs being the mother cells to tRGs must be changed, to those being vRGs. Or else, more convincing data must be provided.

      In fact, their analyses in Fig 4A contradict their interpretation on tRG mother cells, showing that the transcriptomic trajectory leading to tRGs does not inlcude Eomes+ cells, accumulated in the neurogenic state 2. At the end of this section, the authors indicate: "our data suggest that tRG cells are formed by apical asymmetric division(s) from unique apical IPC with a short basal fiber (Tsunekawa et al, in preparation).". Being as important as this point is, if there is solid supporting data the authors must include it in this study.

      1. We are confident that this blue-labeled cells in Figure 3A and D are not vRG but mitotic sibling cell (of vRG) with a short basal fiber (that we named IPC in the initial manuscript). We now made the morphological features of these cells clearly visible by constructing 3D-views of the images with different snapshot images (we show below and in the final revision as a supplementary figure and movies). In addition, it divides once as time-lapse imaging revealed, hence this cell is still mitotic, instead of a postmitotic cell. Therefore, we used the term that is generally used for this type of cells, namely, intermediate progenitor cells (IPC), by which we did not intend to refer to TBR2+ neurogenic IPC. We plan to include these revised images into our fully revised manuscript.
      2. We agree the reviewer 2 on the point that this blue-labeled cell may express CRYAB (the next comment of reviewer 2 essentially claim the same point), as we also wrote this possibility in line 204-207 of the original manuscript. It could not be technically possible at the moment to examine CRYAB expression in a cell emerging only in the course of time-lapse imaging. If we could label vRG with a transgenic or knock-in fluorescence marker, which mimics CRYAB gene expression, we could have figured out whether blue cells the mitotic vRG sibling cells (or mitotic tRG parental cell) express the CRYAB gene. Indeed, we tried to knock the EGFP gene in the CRYAB gene many times over a year, but have so far failed. Given that tRG is defined as the cell type expressing CRYAB with a short basal fiber at late-neurogenic stage, irrespective of its mitotic activity, this blue labeled vRG sibling cell in Fig. 3A (and/or Fig. 3D) might express CRYAB, hence can be a “mitotic tRG” (although its possibility seems to be low as shown in Fig. 2E). To avoid any possible misleading, we have changed the term of these cells to a “mitotic vRG sibling cell (or mitotic tRG parental cell) with a short basal process”, and add a comment that “this cell might be mitotic tRG with CRYAB expression”.
      3. As for the TBR2 expression, we do not know these cells that appeared in the course of time-lapse imaging express TBR2 or not. As shown in Fig. 2F, 10% (P10) to 30 % (P5) of CRYAB+ cells express TBR2. On the other hand, “intermediate progenitors” do not necessarily express TBR2 in general. Therefore, we disagree on the reviewer 2’s comment “their analyses in Fig 4A contradict their interpretation on tRG’s parent cells”, but “our analyses in Fig 4A is compatible with our interpretation on tRG’s parent cells in time-lapse imaging”, and that is “a mitotic vRG sibling (or mitotic tRG parental cell) with a short basal fiber divides to produce CRYAB+ tRG at the end of timelapse imaging”. However, to avoid any overstatements or misunderstanding on this issue, we have revised related text as described above.
      4. We are not able to include the data taken by Tsunekawa et al.. This is because we are going to submit a separate paper, which includes a large volume of data with human ones in collaboration with another group and largely concerns stages that are earlier than that of tRG formation. It is, therefore, not practical to combine these data with those described in this manuscript. Therefore, we remove all descriptions related with Tsunekawa et al.

      Below we show snapshot images and 3D-reconstructions for Figure 3A and 3D. Please download movie files from the following URLs to look at them clearly.

      @Figure 3A:

      1)A time lapse movie (20 min interval) showing images around time 40:00 at which vRG underwent the second division. https://www.dropbox.com/s/znx3bboxefhj0jt/%E2%98%85Fig_3A%20movies%20around%2040%20h.mp4?dl=0

      2)Snapshot images for time 40:00

      https://www.dropbox.com/s/6y25mk4jhwqy6v7/%E2%98%85E38-fig3A-sRG-2.png?dl=0

      3) 3D-reconstruction images at the same time point (40:00)

      https://www.dropbox.com/s/so8hesjzy63yxmb/%E2%98%853D-reconstruction%20%2840.00%202nd%20div%29.mp4?dl=0

      4) The entire time-lapse movies of time 0:00-84:00; The mitotic sibling cell of the vRG is indicated by a white arrow.

      https://www.dropbox.com/s/ywua95f8fmohsmc/%E2%98%85Fig3A-arrow-time.mp4?dl=0

      @Figure 3D:

      A revised time-lapse snapshots of Figure 3D.

      https://www.dropbox.com/s/xyet4virt3j9u3t/%E2%98%8520211220%EF%BC%8DP0%EF%BC%8Dtimelaps-xt04corrected.psd?dl=0

      The assignment of the cell has corrected to the right one for the same mitotic cell because cell body position at the first two time points were misassigned in the original manuscript (at the following time points, there is no change).

      Snapshot image at time point of 06:20; https://www.dropbox.com/s/hn3v6ao1qkhnfjh/%E2%98%85Fig3D%20sRG%20at%200620.png?dl=0

      Rotating movie of 3D-reconstruction at time point of 06:40:

      https://www.dropbox.com/s/6taqjr0u21x5tn0/%E2%98%853Drotated%20movie%20of%20time%20point%2006.40.mp4?dl=0

      2-8. Alternative interpretation of time-lapse images (lines 196-197): maybe a tRG can generate one tRG CRYAB+, and one IPC CRYAB-.

      We changed the term of IPC to “a mitotic vRG (or mitotic tRG parental cell) sibling cell” and describe the possibility that “This mitotic vRG sibling cell (or mitotic tRG parental cell) can be a mitotic tRG if this cell express CRYAB, and its apical division generates one tRG and one CRYAB-negative climbing cell with an unknown identity, replacing the description of line 196-197.

      2-9. Arrows in Fig 5E are shifted between the top and bottom panels. There is no obvious evidence of mitosis visible. This should be unequivocally labeled with anti-PH3 antibodies.

      We have corrected the shifted position of arrows in Figure 5E. We have removed “mitosis” in the title of Figure 5E since the initial manuscript did not include descriptions on mitosis in the text.

      2-10. Line 277: “Transcriptomic trajectories were homologous across the two species”. What does this refer to? What are these trajectories? Pseudotime? Is this statistically tested?

      We revised our description in this part as “Temporal patterns and variety of neural progenitors during the cortical development were similar to each other between humans and ferrets at the single cell transcriptome level”.

      2-11. When comparing tRG cells between ferret and human, the authors indicate a remarkable similarity between the two species as represented by CRYAB, EGR1, and CYR61 expression. As shown in Fig 6E, EGR1 and CYR61 are not expressed selectively in human tRG as they clearly are in ferret tRG. Hence, this argument is not valid.

      To clarify our statement, we changed this sentence into “tRG cells also showed a remarkable similarity between the two species (Fig. 6C, 6D), as represented by a high level of expression for the combination of CRYAB, EGR1, and CYR61 (Fig. 6E)”

      2-13. Discussion is surprisingly short, given the emphasis that the authors place on the importance of their findings. I would suggest extending it for a better coverage of those findings that have the greatest relevance and interest to a wider readership.

      We added several issues discussed in the responses to the reviewers to Discussion. Please look at our responses to comment 2-14 and 2-15 as well as the preliminary manuscript.

      2-15. In Discussion: “our cross-species analysis highlights the notable role of tRG as progenitors contributing to the formation of the ependyma and white matter”. As mentioned above, this is only based on transcriptomic trajectories, it is not demonstrated in this study. In vivo analyses of cell fate are needed to support this conclusion, and a more extensive videomicroscopy analysis is needed to confirm the cell lineage progression suggested by transcriptomes.

      Taking the comments from reviewer 1 and 2 into account, we largely revised “Discussion” with a more moderate expression, by incorporating comparative analyses with other human datasets, and we also emphasize the importance of in vivo studies as the next step. We just paste the last paragraph of the preliminary revised Discussion. Please see the “Discussion” in the preliminary revision of our manuscript.

      “In ferrets, genetic manipulations can be achieved through in utero or postnatal electroporation, as well as via virus-mediated transfer of DNA (Borrell, 2010; Kawasaki et al, 2012; Matsui et al, 2013; Tsunekawa et al, 2016). Thus, it is theoretically possible to disrupt the CRYAB gene in vivo in ferrets to investigate its role in tRG and their progeny, including ependymal cells, and to track the tRG lineage. If the CRYAB gene is essential to form ependymal layers, we will be able to explore how the ventricle contributes to cortical folding and expansion. Despite extensive efforts over a year, we have thus far been unsuccessful in knocking in and/or knocking out the CRYAB gene. Nevertheless, we anticipate that technical advances will surpass our expectations, both in ferret and human organoids. Taken together, these functional studies in ferrets as well as in human organoids hold promising insights into the understanding of the tRG lineage and its contribution to cortical development in the near future”.

      2-16. In line 59, the authors state: "cerebral carcinogenesis independently evolved to gain an additional germinal layer (outer SVZ (OSVZ);". Assuming that they mean "cerebral neurogenesis", what is the evidence for this independent evolution? Original publications demonstrating this must be cited.

      We removed the mentioned statement from our manuscript and revised lines 58-59 as follows: “In many mammalian phylogenic states, cerebral cortex evolved to gain an additional germinal layer (Smartet al. 2002; Zecevic et al. 2005; Kriegstein et al. 2006; Reillo et al. 2011)”.

      2-17. Lines 60-61, the third key publication reporting the existence of bRG must be cited together with Hansen 2010 and Fietz 2010: Reillo et al., 2011, Cerebral Cortex.

      We now added these citations in lines 60-61 and in the Reference list as Reillo I, De Juan Romero C, García-Cabezas MÁ & Borrell V (2011). A role for intermediate radial glia in the tangential expansion of the mammalian cerebral cortex. Cereb Cortex 21: 1674–1694.

      2-18. When introducing ferret as an interesting or important animal model, suitable original studies should be cited.

      For ferrets, there is a long history as experimental animals for electrophysiology similarly with cats and monkeys, but this is not a review of ferret biology. We thus added 6 additional references regarding ferret brain morphology and development listed below.

      Jackson, C.A., J.D. Peduzzi, and T.L. Hickey (1989) Visual cortex development in the ferret. I. Genesis and migration of visual cortical neurons. J. Neurosci.9:1242–1253. PMID: 2703875.

      Chapman B & Stryker MP (1992) Origin of orientation tuning in the visual cortex. Curr Opin Neurobiol 2: 498–501.

      Chenn A., and McConnell S.K. (1995) Cleavage orientation and the asymmetric inheritance of Notch1 immunoreactivity in mammalian neurogenesis. Chenn A, et al. Cell PMID: 7664342.

      Noctor SC, Scholnicoff NJ, and Juliano SL. (1997) Histogenesis of ferret somatosensory cortex. J Comp Neurol. 387(2):179-93.PMID: 9336222.

      Reid CB, Tavazoie SF, Walsh CA. (1997) Clonal dispersion and evidence for asymmetric cell division in ferret cortex. Development. 1997 124(12):2441-2450. doi: 10.1242/dev.124.12.2441.PMID: 9199370

      2-19. In Figure 2F-H, layer borders should be labeled. The density of CRYAB+ cells in VZ (?) at P5 seems much greater in Fig 2E,F than in Fig. 2B. Clarifying this discrepancy is important to validate the quantification of Fig 2D.

      Layer borders: We now labeled the approximate position of the boundary of the VZ in Figure 2E-G. We have revised the legends as follows; “The border of the VZ is shown with a white line”. For counting, we have determined borders by the distribution of DAPI, and radial glia-specific markers in our hands and determined the approximative distance of the VZ border from the ventricular surface in the antero-posterior axis where we performed the imaging in Figure 2E-G. The distance was approximately determined as 80 µm at P5 and 40 µm at P10.

      2-20. Co-expression of CRYAB and FOXJ1. In Fig 5B this must be demonstrated with merged channels.

      We added the images with merged channels as requested and revised corresponding legends as follows: “Images with merged channels in A are shown with the same color codes, antibodies and scale bars as A.”.

      2-21. Line 247: "near which nuclear line aggregates are observed more frequently (Fig. 2B)". It is very much unclear what the authors refer to. Please, define nuclear line aggregates.

      We revise the cited sentence and will change the referred figure as follows: “These cells often aligned on a line parallel to the ventricular surface (Fig. 5A)”. We show these nuclear rows by arrows.

      2-22. There are a number of typos along the main text and figures, which must be fully checked and corrected. For example, line 59 "cerebral carcinogenesis"; also in Figure S4, Figure 5E. Labeling of graphs in Fig 5C is wrong. The plots present the fraction of CRYAB+ cells that express FOXJ1 (FOXJ1+/CRYAB+ cells), not the reverse.

      We thank the Reviewer for their remarks on typos. We corrected the typos indicated by Reviewer 2. We agree with the Reviewer and also modified the title of Figure 5B as suggested by the Reviewer.

      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.

      2-1. In this report, Bilgic and colleagues study the diversity of progenitor cell types in the developing ferret cerebral cortex, a valuable in vivo model to understand cortex expansion and folding, as in primates including human. Using a single-cell transcriptomics approach, they describe a diversity of progenitor cell types and their interrelation by transcriptomic trajectories, which are conserved but biased as development progresses. Most interestingly, they identify in ferret a type of cell only identified in human before, tRG, which they then characterize throughoutly by transcriptomics. They also identify these cells in histological sections, and via time-lapse videomicroscopy they characterize their cell type of origin. They also provide indirect evidence that tRG may be the source of ependymal cells in the ventricle of the mature cerebral cortex, as well as astroglial progenitor cells. Finally, they extend their analyses to identify oRG in ferret based on previous human single cell data, concluding that they have in ferret a quite different transcriptomic profile than in human.

      We would like to thank the reviewer for carefully reading our manuscript and providing us with valuable feedback. However, we would like to clarify that there might have been a misunderstanding regarding our conclusion about the identification of oRG-like cells in ferrets.

      Our study did not conclude that we have identified oRG cells in ferrets with “a quite different transcriptomic profile than in human”. Instead, our findings indicate that unlike oRG cells in human, ferret oRG-like cells did not exhibit specificity for human oRG markers (such as HOPX and CLU) that would enable us to distinguish them from other late RG cells in ferrets. Despite this, oRG score derived from human oRG marker expression showed higher values in predicted ferret oRG-like cells compared to other ferret RG cells, reflecting a similarity of the transcriptome profile between human oRG and ferret oRG-like cells (Figure 7H-I). We will carefully describe our methodology to reach this conclusion in response to reviewer 2’s comment regarding how we determined ferret oRG in a later comment.

      2-3. It seems that the single cell datasets were collected from only 1 replica at each developmental stage. Current best practice sets the inclusion of several biological replicates. Whereas this represents multiplying the workload (and costs) and re-doing many of the analyses, it is currently highly valued. On the other hand, the authors already have their analysis pipelines defined, and so the time involved should be much shorter than before.

      We disagree with the reviewer 2’s comment. We would like to clarify that we collected brain tissues in two different ways for the same set of developmental stages; one brain tissue by removing cortical plate (T); another independent brain tissue at the same developmental stage by sorting GFP-labelled lineage from neural progenitors that were electroporated at embryonic stages (AG, Methods). Both manipulations of samples aimed to increase progenitor cell populations in scRNAseq. Therefore, we have two sets of samples of the same temporal series, each prepared in a totally different way. All cell types were present in both methods of collection shown as Supplementary Figure 2E’ (section 2) that separates samples by different preparations at each stage (by modifying Supplementary Figure 2E; section 2). We believe that the biological replica (n=2) in this manuscript would be sufficiently reliable, judged by its reproducibility.

      https://www.dropbox.com/scl/fi/levyqy9ngvpyio1yl9oif/reviewer2_3.pdf?rlkey=r4aw0hu9cdn68f1pvhp734vxx&dl=0

      Here, we also cite several examples of papers important in the field of single-cell or bulk transcriptomics of brain tissue, where only a single replicate or pair (replica) was taken for experiments on mice, humans and ferrets:

      mice: Ogrodnik et al., 2021 PMID: 33470505, Hochgerner et al., 2018 PMID: 29335606, Joglekar et al., 2021 PMID: 33469025;

      human: Herring et al., 2022 PMID: 36318921, Polioudakis et al., 2019 PMID: 31303374, Mayer et al., 2019 PMID: 30770253, Fietz et al., 2012 PMID: 22753484;

      macaque: Schmitz et al., 2022 PMID: 35322231;

      ferret: Johnson et al., 2018 PMID: 29643508.

      2-14. In Discussion, the authors state that "ferret (and presumably also human) tRG cells differentiate into ependymal cells and astrogenic cells." Again, this conclusion is purely based on transcriptomic trajectories, which must not be confused with cell lineage. This sentence must be rephrased and toned down accordingly.

      We disagree with the reviewer 2 as for ferrets, because we accessed the relationship of tRG and their progeny cells by not only in silico but also in vivo analyses.

      On the other hand, as for progenies of human tRG, they were predicted certainly depending on the molecular relationship by comparison with ferrets without histochemical evidence, as pointed by reviewer 2, and the populations of these committed tRG are small. Therefore, we removed “(and presumably also human)” and we tone down about the progeny relationship of tRG as a prediction. We also acknowledge that further studies are needed to confirm the lineage relationships among cell types, as we discussed in the Discussion part.

      Reviewer #2 (Significance (Required)):

      This manuscript is of interest for being the first ferret single-cell study, and for identifying and characterizing to a great extent a unique population of cortical progenitor cells that so far had only been observed in human. The study is presented as a resource for studies of ferret cortex development, which as such is clearly of interest to a very limited audience. A more appealing perspective might be if this study in ferret is of interest or of use to the more general community studying cortex development, or even maybe cortex evolution.

      We disagree the reviewer’s view that this study is clearly of interest to a very limited audience. This study first enabled a precise comparative analysis in which we could compare rich human single cell transcriptomes and the ferret dataset of single cell transcriptomes, which were based on greatly improved genomic information (especially, gene models). This study is also first to show global temporal patterns of cortical progenitors of a carnivore species, a famous gyrencephalic mammalian model, and have been shown to be similar to a primate species at the single cell transcriptomic level. Indeed, upon uploading this manuscript in BioRxiv, many non-ferret specialists as well as specialists have inquired datasets and requested some collaborations with us. So we believe that this paper has already attract a general interest of brain scientists.

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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

      Summary: This study by Magalhaes et al sheds light on the molecular underpinnings of the relative resistance of children to severe COVID-19. The authors found that priming of epithelial cells by resident immune cells to express tonic levels PRR receptors MDA-5 and RIG-I predisposes the epithelial cells for a faster and more robust onset of IFN-beta production upon SARS-CoV-2 infection. The study uses a combination of in vitro and ex vivo models, as well as mining of scRNA-Seq datasets from clinical specimens.

      Major comments: The claims and conclusions are supported by the data and therefore no new experiments are needed.

      Optional

      1. The use of primary cells (i.e. human airway epithelial cultures cross talking to immune cells) would make this study more compelling, although I assume that the major findings would be recapitulated in such models.
      2. It is not clear how the use of Yersinia enterocolitica to trigger activation of PBMC is relevant to this story. Using different (commensal) pathogens to achieve PBMC activation may yield different and more physiologically relevant results.
      3. The manuscripts would greatly benefit from improved structure and focus, particularly in the Abstract, Introduction and Results sections. The text is very dense, and makes it difficult for the reader to follow the flow and to distinguish important from less important information. Particularly, the introduction starts very broadly introducing COVID-19, which I think we are by now all familiar with. Directly starting with the burning question why kids get less sick with SARS-CoV-2 would capture the readers' attention better. Figure 1 a is beautiful for a review but much too dense to help the reader as a graphical abstract. In the results section, for each experiment, leading with clearly stating the rationale of the specific question, the gap in knowledge and why the gap is there, then followed by the results, then summarizing the impact of said results, would make this a much more enjoyable read and help the reader evaluate the novelty and impact better, particularly for Figures 1, 2, and 3 (but also all others). The interaction wheel graphs (Figure 4. are amazing, but are not properly explained in the text (do I read this right that in adults, all the crosstalk is basically performed by proliferating T-cells?). In all, these scientific writing issues sell an otherwise beautiful story short.

      Referees cross-commenting

      I agree with reviewers 1 and 2 that the use of primary cells would significantly elevate the story. However, I think this should be "optional", as I do not think it would change the findings.

      Significance

      General assessment:

      The main strength of the study are its topic and clearly relevant question: why do kids rarely get severe COVID-19? The main novelty is the answer to this question, that immune cell-epithelial crosstalk in children elevates the tonic expression of MDA5 and RIG-I via the IRF1 axis, leading to faster onset of IFN production and signaling upon SARS-CoV-2 challenge, which ultimately mounts an antiviral response detrimental to robust SARS-CoV-2 replication. The study uses an innovative combination of in vivo and ex vivo experiments and analysis of clinical specimens.

      The significant advance of this study to the field is clear to this reviewer, although it could be much better stated in the manuscript, as described at length above. The study is of great interest to the field of immunology and virology, and also has clinical and translational impact with respect to risk assessment for severe COVID-19 per age group, as well as epidemiological considerations for infection control.

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      Referee #2

      Evidence, reproducibility and clarity

      In the manuscript entitled "Enhanced airway epithelial response to SARS-CoV-2 infection in children is critically tuned by the crosstalk between immune and epithelial cells" Gonçalves Magalhães et al address the molecular interactions that result in greater antiviral responses to coronavirus infections in children versus adults. The authors have re-analyzed previously reported scRNA data from the nasal passages of healthy donors and found heightened pro-inflammatory responses in the nasal passages of children relative to the signatures in adults. Thus, the authors posit that this could result in priming of epithelial cells subsequently boosting antiviral responses to SARS-CoV-2 infection in a RIG-I and MDA5 dependent manner. Beyond the expected antiviral protection conferred by type I IFNs, the authors demonstrate type II IFN and TNF can also promote antiviral priming. Although epithelial and immune cell crosstalk has been previously demonstrated, this study proposes an age-dependent differences in the magnitude of this crosstalk. Indeed, bacterial stimulation of PBMCs derived from children resulted in greater cytokine [production and A549 priming. The article was well written, the data presented is compelling, and appropriately controlled. Importantly, the authors appropriately acknowledge the study limitations. The mechanisms that govern the increased inflammatory responsiveness of tissues derived from children remain to be addressed.

      Major comments:

      • Although the authors acknowledge this limitation, do primary epithelial cells respond to priming? Is there an age difference in viral detection and/or the response to priming?
      • Optional: Does the deletion of IRF3 phenocopy MAVS deficiency in the context of type I IFN priming and blockade of IFN replication (Fig 2D)? Does priming induced increases in IRF1 and IRF7 and is this sufficient to overcome the loss of IRF3 (PMID: 25520509)?

      Minor comments:

      • Are the differences observed in MDA5 and RIG-I expression after PBMC stimulation across A549 IFNR KO cells significant?
      • Figure 2C could be strengthened by the addition of total IRF3 and STAT2 immunoblot.
      • Figure 3C, include tSTAT2 control.
      • There is one typo on line 610. Change OSA2 to OAS2.
      • Please expand the discussion to include relevant work on IFN and TNF-mediated antiviral priming (PMID: 16537619) and epithelial-immune cell crosstalk (PMID: 36563691).

      Significance

      Advance: This study expands upon previous work by the authors that described that children have higher expression of RLRs and in epithelial and immune cells relative to adults. The current work, provides an incremental but important advance to the previous study by demonstrating that PBMC-mediated priming of epithelial cells (A549). However, it remains to be addressed whether epithelial cells from children have increased capacity to detect SARS-CoV-2 infection or respond to priming.

      Audience: This work is of interest to pediatric clinicians, virologists, immunologists, cell biologists, bioinformaticians.

      Reviewer expertise: viral innate immunity; IFN regulation

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      Referee #1

      Evidence, reproducibility and clarity

      In the manuscript entitled "Enhanced Airway Epithelial Response to SARS-CoV-2 Infection in Children is Critically Tuned by the Cross-Talk Between Immune and Epithelial Cells", the authors report a mechanism how airway epithelium of children is in a primed state to more efficiently defense SARS-CoV-2 infection. They interestingly found that the expression level of MDA5 and RIG-I are essential for this primed stage. In details, they discovered that enhanced immune-epithelial cell interactions through cytokines play a major role in the upregulation of these two RNA sensors, where IRF1 may be the most important regulator. They fully used their single cell sequencing data and established a relative convincing in vitro model to validate their hypothesis. This story is quite attractive to me since they explained the behind mechanisms for a clinical phenomenon. However, there are still some issues need to be addressed.

      Major comments:

      1. A549 is a cancer cell line and cannot support SARS-CoV-2 replication. The authors reply on it too much. Some important experiments need to be performed in primary airway epithelial cells.
      2. MDA5 and RIG-I protein levels need to be detected in some important experiments such as Figure 4D, Figure 5A, Figure 5B, Figure 6B.

      Minor comments:

      1. In figure 1B, the authors claimed that "As expected, pre-treatment alone did not trigger notable IFN-β transcription". But in my mind, pretreatment elevated IFN- β mRNA at least 5 folds compared to mock group (from 10-2.5 to 10-1.8). The authors need to make the statement more accurate.
      2. In figure 2A, the authors used IFNARKO cells. This should be mentioned in the manuscript.
      3. The statement of Figure 2C should be more accurate, "This was also confirmed at the downstream level of STAT2 activation, at which phosphorylation was still observed upon infection of RIG-I or MDA5 single KOs with SARS-CoV- 2, but was fully diminished only in the double KO cells (Fig 2C)." IRF3 phosphorylation is not downstream of STAT2. More downstream event can be shown as STAT1/STAT2/IRF9 nuclear translocation or ISGs transcription. Also, total STAT2 levels should be shown here.
      4. It is quite surprising that none of IRNAR, RIG-I, MDA5 affected SARS-CoV-2 replication in untreated cells.
      5. If we compare the mRNA induction of MDA5 and RIG-I between Figure 4D and Figure 6B, they are not in the same amplitude. There are 10 folds induction in Figure 4D while 100-300 folds in Figure 6B.

      Significance

      This is an interesting study with clear data. They fully used their single cell sequencing data and established a relative convincing in vitro model to validate their hypothesis. They uncovered an important mechanism why airway epithelium of children is in a primed state and more resistant to SARS-CoV-2 infection.

      The limitation here is that they do not have in vivo model to support their conclusions.

      Nevertheless, this manuscript is still able to answer a clinical question. Why children are much more resistant to SARS-CoV-2 infections compared to adults? It has good clinical significance. I believe that a broad audience will be interested in this story.

      I study virus and cancer induced metabolic reprogramming, virus and host interaction and innate immune regulation, and mass spectrometry (metabolomics and proteomics).

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      Reply to the reviewers

      Manuscript number: RC-2023-01901

      Corresponding author(s): Gavin, Sherlock

      We thank the reviewers for their comments and their generally positive reviews – the reviews were constructive, and we have revised the manuscript to deal with all the requested changes and suggestions. We believe the manuscript is improved as a result, and hope that the reviewers agree that it is now suitable for publication. Below with provide a point-by-point reply that explains what revisions we have made. Reviewers’ comments are italicized, while our responses are highlighted.

      • *

      Reviewer #1:

      • *

      *It would be interesting have an idea of the global mutation rates and spectra in the diploid and haploid lineages across the conditions as well. The S. cerevisiae mutational spectrum has been shown to be dependent on the environment and genetic background to an extent but not ploidy. Ploidies differ in terms of just the frequency. How similar/ dissimilar are the overall mutational spectra here? Were there any homozygous mutations in the diploids? *

      • *

      • We have plotted the mutation types in the diploid and haploid lineages across the conditions to compare the frequencies of each type of mutation between ploidies, which is now presented as Supplemental Figure 3. The mutation types between ploidies for each of the conditions look similar. Homozygous diploids are indicated in Table 2.

        *Fitness gains and losses can happen without trade-offs if neutral home mutations are non-neutral in non-home conditions. Can the authors comment on that in this context. Physico-chemically, how different are the home/ non-home environments? How do the fitness effects correlate across the environments in the absence of these adaptive mutations? It would also be useful to know the extent of fitness variance of the populations in the home and away environments, this would aid the reader better grasp the significance of fitness gains/loss. *

      • We agree that trade-offs could occur as a result of mutations that are neutral in the home condition showing trade-offs in the non-home conditions. However, in the newly added Supplemental Table 1, it is clear that most lineages have several passenger mutations, yet for lineages carrying the mutations in the same candidate beneficial mutation, they have largely similar pleiotropic profiles, suggesting that the influence of neutral mutations that arise in the home environment do not play a large role in determining fitness in other environments, at least for those tested. We have not generated strains that only contain the passenger mutations – while that would empirically test the fitness effects of the passenger mutations, it would be extremely time consuming to generate such strains, and the results would be unlikely to change our claims in the paper.

        *A summary table of the pleiotropic effects would be very useful as in Bakerlee et al. 2021. *

      • We have added a summary table (Supplemental Table 3) of the pleiotropic effects as suggested. Reviewer #2

      *The major conclusion of the manuscript state that "mutations in the same genes tend to produce similar pleiotropic effects", suggesting that a number of times this does not occur. For instance, the authors comment on the case of PDR3, which does not always produce 'cost-free' adaptation across environments. I believe that, to strengthen and better define their conclusion, the authors should develop a quantitative analysis of the reproducibility of pleiotropic profiles (that considers how many times genes have been found mutated). The heatmaps provided are compelling, but make it hard to generalize on how often, and to what extent, the gene mutated can predict pleiotropy across various environments. *

      • *

      • We have calculated pairwise correlations between pleiotropic profiles for mutations that arose in the same environment either in the same gene, or in different genes, and added this Supplemental Figure 10. These data show that by and large, correlations between mutations in the same gene are higher than those for different genes.

        *In the concluding sentence of the discussion, it is unclear whether the authors are speculating about a role of the strength of selection in determining pleiotropy based on their results, or if that only represents a suggested hypothesis to test in future studies. *

      • We have modified the concluding sentence to clarify its meaning (it was a suggested hypothesis)

        *The method used to identify putative adaptive mutations should be described in more detail. For instance, I seem to understand that only one mutation per lineage is considered 'adaptive'. However, many lineages seem to have more than one mutation. Based on what reported in the method section, the adaptive mutations have been hand-picked based on previous knowledge of selection in the environments of choice ("the list of genes was curated based on those genes' interactions with other identified genes or pathways known to be involved in the adaptation of that specific condition from previous work"). If this assumption is correct, the criteria for such a curation should be specified in more detail. *

      • We have further clarified our criteria in the text; note, there was not a requirement for there to be only a single beneficial mutation per lineage, though very few lineages had two candidate beneficial mutations.

        *The term 'Pareto front' is technical and left undefined. *

      • We have clarified the meaning of Pareto front

        *The section ' adaptation can be cost-free' only refer to figure 4, (with adaptive mutant lineages from populations evolved in fluconazole), while it comments extensively on mutation isolated in clotrimazole (reported in Sup. Fig10, not mentioned in the section). *

      • We thank the reviewer for noting our oversight – we have also now referenced the supplementary figure too (now Supplementary Figure 11). Reviewer #3

      *It would be helpful if the authors could clearly provide information on the zygosity of the evolved mutations, as the presence of mutations in homozygous or heterozygous states can impact the results of the study. *

      • We have added zygosity information to the genotypes in the text and in Table 2, Summary of Adaptive Mutations

        *Do any of the evolved lineages have multiple adaptive mutations or other potentially adaptive mutations? If so, it would be great if the authors could provide a table listing these lineages and mutations. *

      • We have added Supplemental File 1, which enumerates the adaptive and passenger mutations found in each lineage. Candidate adaptive mutations are in highlighted in red. Of the ~200 adaptive lineages, 4 have two candidate adaptive mutations, while the rest have only one.

        *In the Pooling of the Isolated Clones section of the Methods, the ancestor and subject pools were mixed in different ratios for different types of pools. While not strictly necessary, it would be helpful to provide a brief explanation for this. *

      • We have added a brief explanation

        *The conditions listed in Table 1 and Supplemental Figure 2 do not seem to match perfectly. *

      • We have corrected Supplemental Figure 2 such that it matches Table 1

      • *

      Supplementary Figure 6 demonstrates reproducible fitness estimates across lineages with the same mutations but distinct barcodes, supporting the authors' inference of adaptive mutations. However, it also appears to show no evidence of interactions among these mutations. Can the authors clarify if this is due to the absence of lineages with multiple mutations or if no observable interactions were found?

      • *

      • See response above – there are very few lineages with more than one candidate beneficial mutation. The remaining passenger mutations are thus likely neutral.

      • *

      *In the Pleiotropy is common, strong and variable section of the results, all three conditions were noted to have their evolved lineages tested in other conditions and presented in Supplementary Figure 5. However, due to the rapid dominance of lineages evolved in clotrimazole, there is no comparison data for them in Supplementary Figure 5. *

      • Unfortunately, we were not able to generate robust fitness remeasurements in the clotrimazole condition, due to the rapid takeover by lineages that were evolved in that condition

        *In the Results section on cost-free adaptation, it would be beneficial to include any compositional differences, such as pH, between the two drugs used that could have contributed to the fitness effects of the evolved lineages in pH 7.3. *

      • We are not aware of any such differences – we did not pH any of the media other than the media with a specific pH.

        *Results - Adaptation can be cost-free: While the authors did state "at least across the conditions in which we remeasured fitness" at the end of the paragraph, it may be prudent to exercise caution when stating "cost-free adaptation" as only a few conditions were tested. For instance, an all-beneficial or all-deleterious result can sometimes be obtained solely based on the chosen conditions. *

      • *

      • We have added additional caution in the text based on the reviewer’s suggestion.

      • *

      *Colormaps in Figure 4, Supplemental Figure 6, 10, and 11: The colors for values below -0.2 are uniform, whereas the heatmaps exhibit darker blues. *

      • *

      • We have edited the color scales on Figure 4 and Supplemental Figures 6, 10, and 11 (now Supplemental Figures 6, 11, and 12) such that the scales are uniform.

      • *

      *Results - Pleiotropy varies according to the mutated gene: "For example, haploid lineages adapted in glycerol/ethanol with mutations in IRA1 show the same pattern of fitness effects across conditions (Supplemental Figure 6)." I believe the authors are referring to Supplemental Figure 11. *

      • The reviewer is correct – we have fixed this reference to what is now Supplemental Figure 12

        *On the topic of IRA1, IRA2, and GPB2 in the section "Pleiotropy varies according to mutated gene" in the Results: Although IRA1 mutants exhibit highly similar patterns, it is challenging to ascertain which of the two genes, GPB2 or IRA2, has a more similar pattern. *

      • *

      • We have create a new supplemental figure showing the correlation between mutations in the same gene and mutations in different genes for lineages evolved in the same condition – see response to Reviewer #1 above.

      • *

      Results - Pleiotropy varies according to mutated gene: From "If lineages isolated from the same home environment have similar pleiotropic profiles..." to the end of that paragraph. While it is true that "pleiotropy varies according to target genes and not environment alone," it may be premature to suggest that the environment is the "main driving force" of pleiotropy without some form of statistical analysis.

      • We did not intend to suggest that environment is the main driving force - that section was somewhat poorly worded. We have modified the wording to make that clearer.

        *Discussion - line 5, paragraph 2: "For example, in glycerol/ethanol, the haploid adapted lineages have a trade off at 37{degree sign}C but the diploid adapted lineages do not (Supplemental Figure 11)." I believe the authors are referring to Supplemental Figure 6. *

      • We thank the reviewer for spotting this and have fixed the figure reference.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary

      The authors present an intriguing study on the pleiotropic effects of adaptive mutations in yeast populations evolving in different environments. The study used haploid and diploid barcoded budding yeast populations to understand the pleiotropic effects of adaptive mutations in "non-home" environments where they were not selected. The findings indicate that pleiotropy is common, and most adaptive evolved lineages show fitness effects in non-home environments, which can be beneficial or deleterious. The results also highlight how ploidy influences the observed adaptive mutational spectra in different conditions. The methodology involved whole-genome sequencing and pooled fitness remeasurement assays in 12 environments with various perturbations. The study concludes that pleiotropic effects are unpredictable, but lineages with adaptive mutations in the same genes tend to show similar effects. Overall, the study provides insights into the dynamics of adaptation and the impact of pleiotropy in different environments.

      Major comments

      1. It would be helpful if the authors could clearly provide information on the zygosity of the evolved mutations, as the presence of mutations in homozygous or heterozygous states can impact the results of the study.
      2. Does any of the evolved lineages have multiple adaptive mutations or other potentially adaptive mutations? If so, it would be great if the authors could provide a table listing these lineages and mutations.

      Minor comments

      1. In the Pooling of the Isolated Clones section of the Methods, the ancestor and subject pools were mixed in different ratios for different types of pools. While not strictly necessary, it would be helpful to provide a brief explanation for this.
      2. The conditions listed in Table 1 and Supplemental Figure 2 do not seem to match perfectly.
      3. Supplementary Figure 6 demonstrates reproducible fitness estimates across lineages with the same mutations but distinct barcodes, supporting the authors' inference of adaptive mutations. However, it also appears to show no evidence of interactions among these mutations. Can the authors clarify if this is due to the absence of lineages with multiple mutations or if no observable interactions were found?
      4. In the Pleiotropy is common, strong and variable section of the results, all three conditions were noted to have their evolved lineages tested in other conditions and presented in Supplementary Figure 5. However, due to the rapid dominance of lineages evolved in clotrimazole, there is no comparison data for them in Supplementary Figure 5.
      5. In the Results section on cost-free adaptation, it would be beneficial to include any compositional differences, such as pH, between the two drugs used that could have contributed to the fitness effects of the evolved lineages in pH 7.3.
      6. Results - Adaptation can be cost-free: While the authors did state "at least across the conditions in which we remeasured fitness" at the end of the paragraph, it may be prudent to exercise caution when stating "cost-free adaptation" as only a few conditions were tested. For instance, an all-beneficial or all-deleterious result can sometimes be obtained solely based on the chosen conditions.
      7. Colormaps in Figure 4, Supplemental Figure 6, 10, and 11: The colors for values below -0.2 are uniform, whereas the heatmaps exhibit darker blues.
      8. Results - Pleiotropy varies according to the mutated gene: "For example, haploid lineages adapted in glycerol/ethanol with mutations in IRA1 show the same pattern of fitness effects across conditions (Supplemental Figure 6)." I believe the authors are referring to Supplemental Figure 11.
      9. On the topic of IRA1, IRA2, and GPB2 in the section "Pleiotropy varies according to mutated gene" in the Results: Although IRA1 mutants exhibit highly similar patterns, it is challenging to ascertain which of the two genes, GPB2 or IRA2, has a more similar pattern.
      10. Results - Pleiotropy varies according to mutated gene: From "If lineages isolated from the same home environment have similar pleiotropic profiles..." to the end of that paragraph. While it is true that "pleiotropy varies according to target genes and not environment alone," it may be premature to suggest that the environment is the "main driving force" of pleiotropy without some form of statistical analysis.
      11. Discussion - line 5, paragraph 2: "For example, in glycerol/ethanol, the haploid adapted lineages have a trade off at 37{degree sign}C but the diploid adapted lineages do not (Supplemental Figure 11)." I believe the authors are referring to Supplemental Figure 6.

      Significance

      General assessment:

      The research is well-conducted, utilizing both haploid and diploid barcoded yeast populations, and isolating adaptive clones to determine fitness effects in non-home environments. The double-barcoding system allowed the authors to perform pooled fitness measurements of a large number of lineages coming from different home-environments in a plethora of conditions accurately and efficiently. The inclusion of a multiple evolution conditions followed by fitness measurements in a broader range of conditions allowed the authors to study the effect of environment to pleiotropy.

      The low number of generations used in this study, however, could hamper the discovery of more adaptive mutations, particularly those with smaller effects, and also make the study underpowered for studying epistasis among the evolved mutations. Furthermore, while the definition of pleiotropy in this study is reasonable and practical, it also makes most, if not all, generalist mutations pleiotropic and hence it's not surprising to see pleiotropy to be so common in this study.

      Advance:

      This study is an extension of a few recent publications. Jerison et al. (2020) evolved 20 haploid founder replicates in 11 environments for about 700 generations and measured the fitness of evolved clones - one clone from each replicate - across these conditions. This study provides in-depth analyses of how environments affect pleiotropy and a certain level of analyses for the underlying mutations. Bakerlee et al. (2021) evolved several hundred barcoded haploid and diploid populations in a few environments for 1000 generations and traced not only the fitness changes but also the dynamics of pleiotropy longitudinally. The environments used in this study are similar to each other, and so were the results, with the exception of environments with high (37˚C) and low (21˚C) temperatures. The current study utilized their double-barcoding system to allow for testing both haploids and diploids in a broader range of conditions. Although only three of the starting environments were chosen for further analyses, these environments are more dissimilar, and the putative underlying adaptive mutations in the evolved clones were identified and more thoroughly analyzed.

      The study's findings offer valuable insights into the intricate relationship between adaptation, pleiotropy, and environmental dynamics. While the complexity of pleiotropy and the multitude of factors that influence it make it challenging to comprehensively address all aspects in a single study, the results presented here contribute significantly to our understanding of this phenomenon. Nevertheless, further research of this nature is crucial to deepen our knowledge of the underlying mechanisms and to identify overarching patterns that can be applied across diverse systems. Overall, this study represents a promising step towards advancing our understanding of pleiotropy and its role in adaptive evolution.

      Audience:

      While the current study focuses on yeast as a model organism for evolutionary experiments, the implications of pleiotropy extend far beyond basic research. An understanding of the pleiotropic effects of mutations is crucial for comprehending the mechanisms of evolution and developing effective clinical interventions. As pleiotropy can affect disease outcomes and drug responses, the insights gained from this study can have far-reaching implications in the fields of biology and medicine. Thus, this study contributes not only to our understanding of yeast genetics but also to broader areas of research and application.

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      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript, the authors address the question of pleiotropy (the multiple effects of a single mutation on different traits) of adaptive mutations occurred during evolutionary processes. To do this, they evolve populations of S.cerevisiae strains in 12 environments, they identify the major adaptive mutation occurring in a subset of them, and they use a barcoding system to address their effect on fitness in environments where they were not evolved. The study confirm a number of conclusions from previous studies, such as the frequency of positive and negative pleiotropy of evolved lines when tested in other environments. The major novelty of this work is represented by the focus on single adaptive alleles and the conclusion that mutations in the same genes tend to produce similar pleiotropic effects.

      Major comments:

      1. The major conclusion of the manuscript state that "mutations in the same genes tend to produce similar pleiotropic effects", suggesting that a number of times this does not occur. For instance, the authors comment on the case of PDR3, which does not always produce 'cost-free' adaptation across environments. I believe that, to strengthen and better define their conclusion, the authors should develop a quantitative analysis of the reproducibility of pleiotropic profiles (that considers how many times genes have been found mutated). The heatmaps provided are compelling, but make it hard to generalize on how often, and to what extent, the gene mutated can predict pleiotropy across various environments.
      2. In the concluding sentence of the discussion, it is unclear whether the authors are speculating about a role of the strength of selection in determining pleiotropy based on their results, or if that only represents a suggested hypothesis to test in future studies.
      3. The method used to identify putative adaptive mutations should be described in more detail. For instance, I seem to understand that only one mutation per lineage is considered 'adaptive'. However, many lineages seem to have more than one mutation. Based on what reported in the method section, the adaptive mutations have been hand-picked based on previous knowledge of selection in the environments of choice ("the list of genes was curated based on those genes' interactions with other identified genes or pathways known to be involved in the adaptation of that specific condition from previous work"). If this assumption is correct, the criteria for such a curation should be specified in more detail.

      Minor comments:

      1. The term 'Pareto front' is technical and left undefined.
      2. The section ' adaptation can be cost-free' only refer to figure 4, (with adaptive mutant lineages from populations evolved in fluconazole), while it comments extensively on mutation isolated in clotrimazonle (reported in Sup. Fig10, not mentioned in the section).

      Referees cross-commenting

      I tend to agree with reviewers 1 and 3 that, given the focus on individual mutations of this manuscript, more information about their nature is important. On top of the zygosity, I would be curious to know whether mutations predicted to inactivate the gene (frameshifts, stop codon), have different pleiotropic profiles than AA substitutions.

      To answer the reviewer's 2 second major point, my understanding is that most of the lines have accumulated other mutations (marked with a '+' sign in Fig4 and FigS6,S10,S11). I suspect, however, that none of these mutations have been considered adaptive given the criteria described in the 'identifying adaptive mutations' session (e.g. mutations in coding regions appearing in more than one clone in a given condition, and with median fitness in the original home greater than 0).

      Significance

      The manuscript address a question (the emerge of pleiotropy during evolutionary adaptation) which has been extensively studied. However, it does it in a more comprehensive way that previously achieved, including many environments, both haploid and diploid organisms, and by focusing on single adaptive mutations. Most of the conclusions match the ones of previous studies. Perhaps the only exception is represented by the conclusion that individual genes, more than home environments, are proposed to dictate the pleiotropy profiles. However, the fact that mutations affecting the same genes often produce similar pleiotropy profiles is not necessarily unexpected. Overall, the paper is clearly written and can represent a valuable resource for a rather specialized community interested in the origin of pleiotropy during evolutionary adaptation.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      The objective of the study was to investigate the impact of specific adaptive mutations on trade-offs and pleiotropic effects in haploid and diploid Saccharomyces cerevisiae populations. The authors identified adaptive mutations from strains evolved in three specific home conditions and then conducted fitness assays in up to 12 environments (home/non-home). They highlight that ploidy level plays a condition-specific role in shaping the adaptive mutation spectra. Adaptive mutations showed fitness effects in both home and non-home environments, which were beneficial in some cases and detrimental in others.

      Major comments

      It would be interesting have an idea of the global mutation rates and spectra in the diploid and haploid lineages across the conditions as well. The S. cerevisiae mutational spectra has been shown to be dependent on the environment and genetic background to an extent but not ploidy. Ploidies differ in terms of just the frequency. How similar/ dissimilar are the overall mutational spectra here? Were there any homozygous mutations in the diploids?

      Fitness gains and losses can happen without trade-offs if neutral home mutations are non-neutral in non-home conditions. Can the authors comment on that in this context. Physico-chemically, how different are the home/ non-home environments? How do the fitness effects correlate across the environments in the absence of these adaptive mutations? It would also be useful to know the extent of fitness variance of the populations in the home and away environments, this would aid the reader better grasp the significance of fitness gains/loss.

      Minor comments

      A summary table of the pleiotropic effects would be very useful as in Bakerlee et al. 2021. Citation errors e.g. Consistent with prior work (Jerison, et al., 2021)... is either incorrect or not referenced.

      Significance

      The paper is well written highlighting an interesting question, the take home message is incremental at best in the context of the overall literature. In general, my suggestion would be to tone down the conclusions a bit, as the evidence isn't very clear cut .

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      Point-by-Point Response (author’s replies in plain text)


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

      Summary: Silao et al make the intriguing observation that yeasts that are generally considered less pathogenic are unable to catabolize proline than Candida albicans. They then, in Candida albicans, construct mutants defective for the two key enzymes (Put1, Put2) required to convert proline to glutamate, which they show to be essential for proline utilization as an energy (carbon) and nitrogen source. The authors proceed to untangle the regulatory aspects of proline degradation, including the respective cellular localization of its key enzymes. They then make the important discovery that strains lacking either Put1 or Put2 suffer from a proline-dependent growth defect, which they attribute to resulting defects in mitochondrial metabolism.

      The manuscript then goes on to analyze a broad range of infection models including: reconstituted human epithelial skin model, Drosophila, mouse systemic infections, organ colonization in these mice (kidney, spleen, brain, liver and histochemistry of the kidneys) as well as survival when incubated with cultured human neutrophils. Finally, they use yeast cells constitutively expressing yEmRFP (so that yeasts can be distinguished from other host cells) and coated with FITC before incubation with the host cells (which coats the wall of the original cells, but does not spread to progeny) and they go on to perform an impressive set of analyses of C. albicans growth within mouse kidneys both in vivo and ex vivo, exploiting an implanted window together with intravital imaging with a two photon microscope at different time points. The system is impressive and visualizes tissue invasion by hyphal cells beautifully. Finally, they compare the intra vital images from WT and put2-/- cells and show that, as in vitro, put2-/- cells do not form filaments and do not show extensive invasion of the kidney tissue. While the in vivo aspect of the study includes many different models, it finds defects in virulence for different subsets of put mutants and the relative importance of filamentation vs proline utilization for virulence is not conclusively resolved.

      Overall, this is an important and timely manuscript, which significantly contributes to the understanding of how proline metabolism intersects with yeast fitness in the context of infections. However, there are several major concerns regarding some of the conclusions drawn from the study. In addition, some general recommendations that would improve the manuscript are provided.

      Specifically, the manuscript provides a very detailed description of experiments and observations. However, in several parts it is difficult to follow and the reader needs more guidance about the logic involved in reaching conclusion. Specifically, several aspects of the paper are written for experts in Candida (yeast) metabolism. Here, explaining the rationale for some of the experiments, and providing more background information that is not obvious to a non-expert, is required.

      In particular, writing a clear and measured summary sentence at the end of each paragraph and a conclusion paragraph that summarizes key findings in simple terms would help make the manuscript more digestible for readers.

      In addition, the impressive microscopy and broad range of in vivo experiments is comprehensive but only adds incremental information relevant to proline metabolism-that filamentous growth in vivo and virulence is reduced in cells carrying some mutations in one or more put genes. However, this broad sweep of model systems and the development of the in vivo imagining system might have more impact in a separate paper focused on the real-time in vivo visualization of kidney invasion.

      We thank Reviewer 1 for the extensive list of comments and have endeavored to adjust the manuscript to address all of the major and minor concerns. It is evident that Reviewer 1 clearly understood the significance of the work and we appreciate that the comments are presented in a positive manner intended to improve our manuscript.

      Major comments:

      1. The main finding that impressed this reviewer is that "removing the ability to catabolize proline, in an organism that evolved to catabolize it, leads to (growth) defects". This point could be better highlighted throughout the manuscript.

      Thanks for the comment. We will adjust the text to reflect this suggestion.

      1. The authors show that deletion strains for proline metabolism have defects that are important for in vivo pathogenicity. This is an important finding. However, as the manuscript reads now, it suggests that the main findings are that the ability to use proline in the respective host niche is key. Mechanistically, the manuscript revolves primarily around defects that arise when deleting PUT1 and/or PUT2 (i.e., an "unknown" toxicity of proline in the case of put1-/- (or put1-/- put2-/-) and the additional P5C-dependent toxicity for put2-/- mutants; see below).

      Yes, the reviewer is correct in that we believe that proline catabolism is necessary to initiate and power hyphal growth, which is coupled to virulence. We have previously shown that upon phagocytosis by macrophages, the expression of Put1, Put2 and even Gdh2 are induced in phagocytized C. albicans cells, which is consistent with the analysis shown in Fig. 2D and Fig. S2B. Consequently, proline, or an amino acid that is metabolized via the proline catabolic pathway, must be present in the phagosomal compartment. However, as we now report, proline inhibits growth of cells lacking the capacity to catabolize it. Although we cannot differentiate the cause of reduced virulence in put mutants, i.e., the lack of energy due to the inability to catabolize proline vs proline toxicity, proline catabolism is clearly important and a robust indicator of virulence. As point 1, we have adjusted the text to make this clearer.

      1. In order to claim that catabolizing prolines promotes pathogenicity (as opposed to the alternative hypothesis that the inability to catabolize proline leads to the observed defects), additional experiments would be required. For example, the put mutants would need to be compared with mutants that significantly reduce/impair proline uptake, such as the referenced gnp2 mutant (Garbe et al 2022). While the finding that less pathogenic yeast species are unable to catabolize proline is both intriguing and important, it also remains as is presented as a loose, non-quantitative correlation that only tangentially address the question of whether "proline catabolism is key for pathogenicity".

      We have in fact already shown that proline uptake is required to induce filamentation (Martínez and Ljungdahl 2003, Fig. 6). The main point of our current work, which we believe is important and of general interest, is that C. albicans is adapted to use proline as sole energy source, which reflects the environment (humans) in which it evolved. See the response to point 2. Interestingly, the differences in the expression levels of Put1 (off in the absence of proline, induced robustly by proline) and Put2 (low level of constitutive expression, induced robustly by proline) suggest that cells are primed to decrease the likelihood of becoming inhibited by P5C, i.e., the constitutive expression of Put2 is able to ameliorate the potential toxicity of P5C. Regardless, the finding that put1 and put2 mutants exhibit significantly reduced virulence in two host models provides clear support for proline catabolism being key for C. albicans pathogenicity.

      1. 238 onwards: The conclusion that "the primary growth inhibitory effect of proline is linked to catabolic intermediates formed by Put1 and that are metabolized further by Put2"does not appear to be fully supported by the evidence. Addition of proline to put1 mutants already reduced OD600 by ~50% (Figure 2); and is further reduced to ~10% when put2 is deleted. This implies that there are two inhibitory effects of proline, not one primary one. At the least, this option should be discussed, including why deletion of PUT1 leads to proline toxicity. The latter is not clear-is it that too much proline accumulates in the cell and this accumulation is toxic? If this is the case, the effect would be expected to be proline concentration dependent. Performing a relatively simple experiment as performed for the put2 mutant (Fig. 3 / S3F) may clarify this issue. Particularly, if the experiment would be coupled with intracellular quantification of proline.

      Precisely! Proline toxicity is evident even in put1 mutants, clearly suggesting that proline, without being further catabolized, exerts a growth inhibitory effect (Fig. 3A). We traced this inhibitory effect to decreased mitochondrial respiration (Fig. 3E). There are two parameters to consider regarding the inhibitory effects of proline in put2 mutants. First, the presence of proline induces the expression of Put1 independent of Put2 (Fig. S2C), consequently, the levels of the toxic intermediate P5C increases (Fig. 3B). P5C has previously been postulated to inhibit mitochondrial respiration, which is well-aligned with our analysis (Fig. 3E; see response Point 5). We initially tested whether a proline-P5C cycle, suggested by work in mammalian cells, would play a role in proline-mediated toxicity; however, increasing cytoplasmic pools of proline by supplying high levels of glutamate (which according to work in mammalian cells should efficiently convert to cytoplasmic proline) did not occur; we did not see glutamate-enhanced Put1 expression (Fig. 2D, S2A, S2B). We agree with the reviewer with respect to the suggested experiment, and have monitored growth of put1 in media with different proline concentrations. The results are incorporated in the revised Fig. 3.

      1. The caption "P5C mediates a respiratory block" is misleading, as the evidence is not that compelling: Although P5C increases in put2, but not in put1 mutants, and given that both single mutants experience a proline-dependent respiratory defect (Fig. 3E), the results suggest a more complex relationship.

      Previous work using pure P5C (Ref. 36; Nishimura et al) showed that it targets respiration, hence the caption “respiratory block” in the header. In mammals, PRODH (Put1) physically interacts with mitochondrial respiratory complex II in the inner mitochondrial membrane (line 89-90), while P5CDH (Put2) is in the matrix. The put1 mutation might affect basal activity of the respiratory chain resulting in lowered respiration, which may compound when proline accumulates in the mitochondria. The inhibitory mechanism remains unknown, and in going forward we have begun characterizing various GFP-tagged respiratory complex components in put1 mutants and in strains co-expressing Put1-RFP (for interaction studies). The results are out of the scope of this current work.

      1. The virulence assays and in vivo experiments do not present a unifying view: in Drosophila put2∆∆ is less virulent than put1∆∆, which appears similar to put3∆∆. Given that put2 mutants grow slowly, likely because of P5C inhibition, this seems logical. However, in mice, put3∆∆ remains highly virulent while put1∆∆ and put2∆∆ results for survival are mixed. Furthermore, in 4 mouse organs, put1∆∆ and put2∆∆ are not significantly different from one another but are different from wt, while put3∆∆ has no significant reduction in CFU. Kidney histology shows very little invasion by put1 and put2 and more by put3, but visually put3 appears to invade much less than the WT, and the human neutrophil experiment shows effects of put2 or put3 but not put1. This leaves the reader rather confused. It may be worth discussing the reasons for different results in different models. Is the availability of proline in each of the organisms and organs similar?

      We thank the reviewer for these thoughtful observations, however, we note that all of the diverse assay systems employed provide a clear and consistent indication that the inability to completely catabolize proline significantly reduces virulence. This is well-aligned with our previous data regarding the need for proline catabolism to escape macrophages (Silao et al, 2019). The requirement for Put3 may not be very strict since the Put enzymes are still expressed in the absence of Put3 (Fig. 2D/S2A/S2B), indicating the activity of additional regulatory factors; hence, this may explain why the put3 strain behaves like wildtype in the murine model (Fig. 5B). The dispensability of Put3 in the murine model could be due to a lower neutrophil count and that murine neutrophils exhibit a lower affinity for fungal cells as compared to human blood (Machata et al., 2020, Front Immunol). The more pronounced requirement of Put3 to survive in whole human blood and when co-cultured with human neutrophils could indeed be linked to the need to rapidly derepress PUT1/PUT2 (and even other target genes) as suggested by the global RNASeq analysis that shows that proline catabolism is a core response of C. albicans during neutrophil interaction (Niemiec MJ et al., 2017, BMC Genomics). In Drosophila, a well-established model to study innate immunity, the presence of hemocytes that fulfill the equivalent functions of neutrophils and macrophages could explain the increased requirement for Put3. In summary, although it is impossible to know the precise mechanistic basis underlying the observed differences, we believe it unreasonable to expect that all mutations behave identically in each virulence model. In fact, differences considered trivial such as the use of mouse background can have profound effects on virulence. Presumably the differences we report are due to the specific nutrient composition (proline and metabolites feeding into the proline catabolic network) and physical parameters intrinsic to each model. For instance, Lionakis et al. (2013) suggested that filamentation occurs faster in the kidney compared to other organs, such as the liver/spleen, indicating the presence of kidney-specific cues that drive infections of this organ.

      1. The ex vivo and in vivo analysis of the dynamics of C. albicans growth in the host is visually impressive, but it distracts from the focus of the paper and the metabolic findings. Showing that put mutant cells do not form filaments in vivo (as in vitro) does not add much conceptually to the paper. Furthermore, this lovely advance in in vivo visualization is lost at the end of this paper and the authors should consider whether it might fit better in manuscript that could really highlight the in vivo visualization approach.

      We appreciate this comment. Indeed, our lab is at an advanced stage of completing a manuscript focused on the use of intravital and clearing microscopy to follow the onset of an upper urinary tract infection (UTI) in a murine candidemia model. However, our ability to visualize in 3D the onset of an infection in a living host is not a trivial achievement and we were impressed that it provided a clear answer as to whether a single C. albicans cell can initiate an infection and undergo morphogenesis leading to hyphal growth. Furthermore, we tested a put2 strain, the growth of which is highly sensitive to the presence of proline, and found that it did not exhibit filamentous growth. This clearly shows that cells colonizing the kidney are exposed to an environment that requires a functional proline catabolic network to exhibit filamentous growth, a characteristic of renal infections. Our results are consistent with the kidney being a metabolic hub for arginine/proline biosynthesis, which likely increases the levels of these amino acids in this organ.

      1. The discussion of cells stained with FITC and expressing yEmRFP does not clearly point out that the FITC is only an indicator for those cells that were used to innoculate the tissue and that finding cells without FITC indicates that they are mitotic progeny, indicating that they have been dividing. The authors clearly understand this, but a naive reader may miss this important point if it is not stated explicitly.

      We have adjusted the text to explicitly clarify this.

      Minor comments:

      1. Throughout: what is the distinction between utilization of proline for C or for energy? These terms seem to be used interchangeably.

      C. albicans is heterotroph that can use proline to generate biomass (gluconeogenesis, etc) and its catabolism generates sufficient amounts of ATP to power growth. Thus, when proline is used as sole carbon source, it can also serves as the sole energy source. In the text, we have tried to be consistent using “carbon source” when discussing proline as a component of growth media, and “energy source” when discussing proline catabolism.

      1. Introducing the schematic in Fig. 2A at the beginning of Figure 1, would help explain proline catabolism before delving into the growth experiments that rely upon this framework. This should include an explanation, for readers less familiar with the metabolic issues, of the main limitations to catabolizing proline, and the key issues for being able to use proline for nitrogen, carbon, and energy (potentially indicated in the overview figure, e.g. pointing towards gluconeogenesis etc.).

      We have considered the reviewers suggestion, however, we believe that the placement of the schematic in Fig 2 is appropriate as is, and where it will hopefully enable readers to more readily grasp the strain construction and experiments documented in Fig.2.

      1. Saccharomyces can only grow on proline as a nitrogen source, but not as energy/carbon source. Could the authors briefly mention or discuss why this is the case? This is not clearly apparent after reading the manuscript and it leaves the reader confused and trying to understand if the fact that proline is required for carbon utilization is a new finding of this paper or was already known. Do the authors think this is tied to the presence of complex 1 components in C. albicans that are not found in S. cerevisiae. Is this consistent for the pathogenic, but not the non-pathogenic yeasts analyzed in figure 1?

      We have adjusted the text to clarify our thoughts regarding this. Indeed, we do believe that a major reason for the ability of C. albicans to efficiently grow using proline as a sole energy source is the presence of Complex I. However, C. glabrata appears to be able to grow well using proline as sole energy source despite apparently lacking Complex I. Consequently, alternative NADH dehydrogenases exist in C. glabrata, but how this is coupled to energy metabolism will require additional work that is out of the scope of the present work.

      1. 100: While Gdh2 is apparently an important enzyme for generating ammonium, why is it not necessary for macrophage escape and virulence as shown in reference 18? A recent paper from Garbe et al (ref 12) suggests that Gnp2 is the major proline permease in C. albicans and what is known, and not known, about proline uptake would be good to mention, given that PUT gene functions require that proline enters the cells.

      We have recently shown that ammonia generation by Gdh2 is dispensable for macrophage escape and documented that phagosome alkalinization is not a requisite for the induction of hyphal growth (Silao et al. 2020). We have referred to the work of Garbe et al., which is consistent with our previous work (Martinéz and Ljungdahl, 2004) where we reported that proline-dependent filamentation is dependent on Csh3. Csh3 is an ER membrane-localized chaperone responsible for catalyzing the proper folding of amino acid permeases, in csh3 null mutant strains, amino acid permeases accumulate in the ER as non-functional unfolded aggregates. Consistently, we have tested and found that proline-induced Put2-GFP expression is dependent on Csh3 (unpublished), clearly establishing that the regulatory effects of proline are dependent on its uptake. We have not generated a gnp2-/- strain, but suspect that we could find growth conditions where such a mutant would be refractory to proline induction. We have adjusted the text to include this information.

      1. 116: Is the "low sugar environment of the host" referring to a specific niche, such as the GI tract, or human blood? Compared to most natural environments, glucose is abundant in the host, e.g., at ~5 mM, it is the most abundant metabolite in blood, and similarly, in the GI tract, levels can go beyond 50 mM glucose (see e.g. PMIDs 34371983, 21359215). Or is this comment indicating that the in vivo sugar concentration is lower than that in common lab growth media? Please spell out the niche/concentration for clarification - and compare that to other niches that are considered "high sugar environments".

      We have adjusted the text to clarify our statement. The natural environment of C. albicans is the human host. Virulent infections are not within the GI with high sugar content, but rather result when C. albicans cells successfully cross into the blood with a relatively low glucose (5 mM), which importantly is a level that does not effectively repress mitochondrial function. A major point of our recent work is that laboratory experiments with C. albicans growing on YPD or SD with 2% glucose (111 mM) examine growth of cells with repressed mitochondrial functions.

      1. 123: "proline as sole energy source" - suggest "is the source of carbon, nitrogen, and energy"

      The text is adjusted (see response to Minor Point 1).

      1. 142: it is worth noting to readers that C. neoformans is a basidiomycete and thus VERY distant from the other yeasts studied here-it is in a different major phylum of fungi.

      Again, thanks for this suggestion, the text is adjusted. We included C. neoformans since the role of proline catabolism has been characterized and linked to its pathogenicity (reviewed in Christgen and Becker, 2018, Antioxi Redox Signal, Ref. 1).

      1. 143: Here it is implied that put1 and put2 mutant strains do not grow on SPD, but this is not stated explicitly.

      The put1 and put2 mutants are unable to grow in/on all media containing proline as sole nitrogen source. The phenotype is very tight that we were able to exploit this as a selection phenotype for reconstitution (Fig. 1A). We have adjusted the text to make this clear.

      1. 151: The abbreviation SPG is not explained in main text. This was explained in the methods (1% glycerol as primary carbon source).

      As suggested, we have defined SPG in the main text.

      1. Paragraph 156 onwards: this section is particularly hard to read and very dense. Also, it is difficult to understand the significance of these experiments for the overall findings of the paper. Please at least provide a small conclusion / summary at the end of the paragraph that puts the findings into perspective.

      We have adjusted text to make it more accessible.

      1. Figure 2 C: simplifying the scheme (e.g. lots of redundant information, P2 and Mito - just give it one name) would help. This figure may be better in the supplementary material.

      The schematic of our subcellular fractionation study uses standard designations routinely used by the cell biology community. We believe that its inclusion will help readers judge the how we mapped the intracellular localization of the reporter proteins, which is essential to understand the proline catabolic network.

      1. Figure 2B: It is not directly apparent from the micrographs that Put1-RFP localisation is mitochondrial. Co-localisation of the RFP with a mitochondrial dye (e.g., mitotracker) or something similar is required to validate it.

      We have previously reported that Put2 is a bona fide mitochondrial protein (by confocal microscopy, subcellular fraction, and co-localization with Mitotracker (Far Red) (Silao et al., Ref 17). The fact that the Put1-RFP associated fluorescence exhibits a distinct mitochondrial signature, is spatially exclusive and exhibits no overlap with the cytosolic pattern of Gdh2-GFP, co-fractionates with Put2-HA and the mitochondrial marker Atp1, should suffice to confirm that Put1-RFP is a mitochondrial localized protein.

      1. Throughout the manuscript (figure legends): Suggest using "mean" instead of "Ave."

      We have adjusted the legends.

      1. 175: According to the 'Yeasttract' and 'Pathoyeasttract' databases, Put1 regulates at least 36 and 22 genes, in S. cerev. and C. alb., respectively (based on DNA binding and/or regulatory changes). The only gene in common between these two lists of genes is PUT1. Thus, it is quite likely that Put3 regulates many other processes that explain its function and that its major function may not be only to regulate Put1.

      We assume that the reviewer is referring to Put3 (instead of Put1). Yes, Tebung et al. (2017) suggested that Put3 also regulates other genes. However, their data show that C. albicans put3 mutant was unable to grow in medium (YCB+Pro) compared to SPD (2% glucose as carbon source) where proline is used merely as a nitrogen source (Tebung et al., Fig. 3A). Our data in Fig. 1C shows that a put3 null strain exhibits residual growth on SPD, which aligns well with the expressed levels of PUT enzymes (Fig. 2D). Our conclusion is that despite being essential for rapid proline-dependent derepression of proline catabolic genes, Put3 is not the only transcription factor operating at the promoters of the PUT genes.

      1. 175: Is it clear whether the Put3-independent mechanisms are positive or negative with respect to Put1?

      We have accumulated evidence that an additional transcription factor positively regulates PUT1 expression and have a manuscript in preparation to describe this factors. The manuscript will focus on the Put3-independent regulation of PUT1, PUT2, and GDH2 expression.

      1. 218: Suggestion: "growth was indistinguishable".Unless growth curves or growth rates are provided and if one time-point data are the basis for this point, than "rates" is not a relevant term.

      The reviewer is correct; we will adjust the text accordingly. We have performed growth assays in a multi-well microplate format (Bioscreen) and found that the growth rates are not statistically different between WT, put1, put2, and put1 put2 strains in the presence and absence of proline in SD with 2% glucose. This is consistent with glucose repression of mitochondrial function, i.e., proline toxicity depends on derepression of mitochondrial function.

      1. 256 onwards: did the authors test if the ROS scavenging effectively reduced ROS? i.e. does the luminol-HRP assay yield less ROS in +proline +scavenger treatment? This is necessary to effectively conclude that the growth inhibitory effect of proline is due to blocking respiration.

      Indeed, we used NAC as a control in the luminol-HRP system and we saw reduction in ROS formation. In fact, this is the underlying reason why we used high levels of NAC for growth rescue (in Fig. 3D). We include the control data as Fig S3F.

      1. The Figure captions are extremely lengthy and detailed, making it cumbersome to find the relevant information. Suggest moving some of the information, such as additional experimental details, into the methods section.

      We have streamlined the figure legends.

      1. 277-301: Phloxine is not exclusively a live/dead cell indicator-it is an indicator of metabolic activity. In Scerev. and Calb. it also indicates slower growth, opaque growth, and it has been used as an indicator of aneuploidy in C. glabrata (https://journals.asm.org/doi/10.1128/msphere.00260-22) and of diploids vs haploids in S. pombe. The colonies illustrated aer made up of many live cells, and thus the section "Defective proline utilization is linked to cell death" needs to be presented more carefully. In addition, it appears that this section shifts from using defined medium to using rich medium and 37C instead of 30C. Why was this shift necessary?

      The reviewer is correct that phloxine (PXB) has been used to identify opaque growth (EFG1-dependent). However, the fact that the accumulation of PXB in the put mutants is evident in both SC5314 and cph1 efg1 backgrounds (Fig. 3G and Fig. S4C) suggests that we are not assaying opaque switching. We mention that we have observed an increase in the number of PI+ cells in put mutants under similar conditions, but as we pointed out, we were unable to reliably quantitate this by FACS due to the clumping of put mutants. Zheng et al 2022, the paper cited by the reviewer, used PXB to assess the ploidy of C. glabrata strains, but their assay was developed using 5 μg/ml PXB, half of the concentration we used. The homogenous accumulation of PXB as the macrocolonies grow (Fig. 3G), suggests that the accumulation is not a consequence of spontaneously occurring ploidy variations. Thus, we believe that the accumulation of PXB does indeed reflect enhanced cell death. The point here is to trace the consequences of proline toxicity and to test the dependency on mitochondrial function. We used complex media, which contains multiple nitrogen sources (amino acids, peptides), to specifically highlight the contribution of proline catabolism in the fitness of C. albicans. The put1, put2 and put1 put2 mutants grow normally on YPD+PXB (30 oC) without accumulating the dye; we only observed visible PXB uptake in put2 after 2-3 days in mature macrocolonies. We attribute the gradual increase in PXB accumulation to be a consequence of glucose becoming limiting, derepressing mitochondrial functions, a requisite for proline toxicity. Consistently, the accumulation is more evident in cells grown on non-fermentable C-sources (Fig. 3G and Fig S4C).

      1. 295-301: Related to the point above, these results are hard to interpret due to the switch from defined medium in all prior experiments to rich growth medium here. Also, it is not clear why a 48h old YPD culture was chosen to show that the degree of PI staining correlates with mitochondrial activity - is this due to the culture age? It would be more clear to image cells grown on glucose vs. glycerol/lactate, or under repressive / de-repressive glucose concentrations (e.g., as shown in Fig. S4C where a PI+ difference is apparent for 0.2% glucose vs. 2% glucose at 30 oC).

      See response to Point 19 for our rationale to switch to rich medium. We have adjusted the text to enhance its readability. In liquid YPD, all strains grow, however, we noticed that the put mutants tend to flocculate (sign of stress in yeast) when cells enter stationary phase, giving rise to erratic OD readings, particularly evident in the put1 mutant. At 48h, the cultures become dense and cells experience glucose limitation, derepress mitochondrial functions and exhibit maximal flocculation (Fig. S4D). In put mutants, the derepression of mitochondrial function results in proline sensitivity. We tested the notion that this would also increase cell death, which it does, see Fig. S4E.

      1. 313-14: The statement 'the invasion process was dependent on the ability of cells to catabolize proline' doesn't take into account that put mutant cells are defective in filamentous growth irrespective of their utilization of proline...and like the efg1 cph1 double mutant.

      Proline-induced filamentous growth is dependent on the catabolism of proline, which activates Efg1 and consequently the hyphal growth program. In Fig. 4A we show that put mutants grown on Spider media, initiate filamentation (as evidence by wrinkled colonies) but do not grow invasively (no halo). In Fig. 4B we developed and used a novel invasion assay to assess growth through a collagen plug. Similar to the control cph1 efg1 mutant, the put mutants exhibit drastically reduced capacity to penetrate through the plug, and reach the D10 media in the transwell (D10 = DMEM with 10% FBS). However, it is important to note that although these results are linked to two distinct processes - the filamentation defect of cph1 efg1 is due to the inability respond to multiple filamentation cues (e.g., CO2, 10% FBS, etc.), whereas the filamentation defect of the put mutants is linked to the inability to catabolize proline and to its toxicity. Clearly, the WT strain relies on proline catabolism, coming from one or three possible sources of proline (see response to Reviewer 3): 1) DMEM/F-12 medium used in the PureCol EZ Gel; 2) diffusion of nutrients up through the collagen from the recovery medium DMEM supplemented with 10% FBS; and 3) the proteolytic breakdown of collagen. Also, in contrast to the put mutants, WT cells are refractory to inhibition by proline.

      1. 316-327: The results of the experiment described can only be interpreted as an effect of proline catabolism if the three strains (efg1 cph1; put1; put2) have similar growth rates as yeast cells in vitro. Why weren't the cells competed directly (efg1 cph1 vs put cells)?

      We believe that the relevant comparisons are to WT. We recovered cells from the top of the collagen (see Fig. 4B inset) to monitor their ability to survive and grow on top of the collagen. We found that the ability to catabolize proline enables WT and cph1 efg1 cells to grow equally well (recovered similar ratio as starting input). This was not the case with the put mutants, they did not grow as well and almost 100% of the cells recovered were WT.23.

      Fig 6: The logical order of the experiments, and in the text, is: 1) 4 h window, 2) 26 h window and then 3) ex vivo. The cartoon in 6B should be in this order as well.

      Thanks for bringing this issue up. We have adjusted the figure and text placing the schematic time-lines in proper order.

      1. 337: it is not clear what the 'direct exposure...' is trying to tell us. Can this be made more explicit?

      The direct exposure means that the fungal cells are in contact with the culture media at the edges/border of the 3D skin model (see schematic diagram). Hence, fungal cells are in direct contact with 10% FBS, facilitating the observed filamentous growth. The inability of the put mutants to invade the skin model should be evaluated at the center of the artificial epithelium where there is likely a local increased concentration of proline stemming from the proteolytic activities associated with fibroblasts and keratinocytes.

      1. 340-346: Here proteins with high proline content were used to ask if they could be induce transcription of PUT1 or PUT2 RNA and protein. This experiment is designed only to test the role of these proteins to induce utilization of nitrogen, as glucose is included in the medium. Given that these proline-rich proteins need to be lysed by proteases before they can be imported, and since no import pathways were tested, the results appear to tell us that mucin is more readily digested to peptides that contain proline-but why that is the case is not clear and how it relates to proline utilization is also not clear.

      We thank the reviewer for raising this important point. First, we monitored protein not mRNA levels. We will adjust the text to provide better context for this experiment. Briefly, these experiments were initiated as we were perplexed as to why the wildtype cells took such a long time (14 days) to fully invade the collagen matrix (Fig. 4B); we naïvely assumed that fungal cells would secrete proteases to degrade the collagen and assimilate the liberated proline. In going forward, our experimental strategy was to incubate various proteins with a dense culture of cells in HBSS medium (pH 7.4) supplemented with low glucose (3.8 mM) and lactate (0.83 mM). This condition mimics interstitial fluid, where most broad range proteolytic enzymes are inactive or at least operating suboptimal. The results were clear; with the exception of mucin, the proteins did not stimulate Put1 or Put2 expression. We conclude that host-dependent processes play an important role on the release of the amino acids/peptides from these high-proline content proteins (see line 531-553 for discussion). The capacity of mucin to efficiently induce Put1 expression is interesting since mucin is abundant in the gut where systemic infections are thought to originate. It is important to be cautious here, we used a commercial mucin preparation (Sigma, 2 batches) that may contain degradation products, e.g., proline-rich peptides, that can easily be assimilated by C. albicans. Put1 expression is an excellent readout for proline uptake since its expression responds tightly to the presence of proline derived from exogenous supply or from intracellular conversion (Fig. 2D, S2A, S2B).

      1. 363-369 An alternative is that Put3 induces different proteins important for growth.

      We included this possibility in the revised text.

      1. 379-380-the conclusion for this paragraph is somewhat of an overstatement as there is no analysis of the degree to which proline utilization is a predictor of virulence. It simply shows that put mutants affect the ability to survive in neutrophils.

      We have adjusted the text.

      1. Discussion: The statement that "S. cerevisiae" evolved in high sugar environments is debatable. The natural niche could well be forest soil and tree bark, or insect/wasp guts with arguably little glucose around.

      The reviewer is correct, S. cerevisiae can be isolated from diverse environments with variable sugar contents, but it is the capacity to deal with high sugar environments that makes this yeast stand out in comparison to Candida spp. The unique attribute of S. cerevisiae have been exploited and truly benefited humankind in making alcohol and bread. We have amended the text to state this more accurately.

      1. 469-470-how strong is the 'correlation' between the ability to utilize proline and virulence? Given that different mutants had different effects in different models, this seems like a very loose 'correlation'; it would be good to have some quantitative measures to make this claim.

      We have used directed genetic approaches to determine whether a gene/protein is essential for virulence by testing them in currently available infection models. It is important to note that all virulence assays provided a consistent and clear read-out, namely that the inability to catabolize proline significantly reduced the expression of virulence characteristics. Presumably the differences we report are due to the specific nutrient composition (proline and metabolites feeding into the proline catabolic network) and physical parameters intrinsic to each model. In fact, the expression of virulence factors (i.e., hyphal growth) can significantly differ in different organs within a same mouse model (Lionakis et al., 2013) and that virulence outcomes can change depending on mouse background. We fail to see how this can be viewed as loose. This has not been shown before. Please refer to our response to major point 6.

      1. 500: Was the experiment was done in larvae, and not in adult Drosophila? Fig 5 legend says flies and shows a picture of a fly and larvae are only mentioned much later in the text.

      These experiments were performed using adult flies. We now include a reference regarding the levels of arginine in hemolymph in both larvae and adult Drosophila (Priyankage et al., 2012; Anal Chem).

      1. 512:Why is it presumed that proline accumulates in the mitochondria in put1 mutants? How strong is the presumption?

      Despite a great deal of efforts in many labs, the mechanism of proline transport across the mitochondrial membrane is not known. What has been shown in mammalian and plant systems is that proline can readily enter and accumulate in mitochondria where it is catabolized. (https://link.springer.com/article/10.1007/s00425-005-0166-z; https://www.sciencedirect.com/science/article/pii/0003986177902089). Our presumption that proline accumulates in the mitochondria is based on our finding that proline inhibits mitochondrial respiration when Put1, catalyzing the first oxidation reaction, is absent.

      1. 539: why are MMPs important for digestion of collagen? This is not clear at this point of the Discussion.

      In mammalians cells, some secreted MMPs have collagenase activity (e.g., MMP-1) that degrade proteins comprising the extracellular matrix, which releases proline. We emphasize this since the 3D skin model is comprised of dermal fibroblasts and keratinocytes that are known to secrete MMPs (Ref. 69).

      1. 574: Concluding sentence of this paragraph seems unsubstantiated. There are at least two defects in put2 strains-hyphal growth and growth in general, presumably because of P5C accumulation.

      See response to point 21. Proline-induced filamentous growth is dependent on its catabolism, which activates Efg1 and consequently the hyphal growth program. However, there are many potential cues in hosts that could induce hyphal growth in situ. Our finding that strains unable to catabolize proline do not filament, indicates that proline is a key modulator of virulence.

      1. Fewer abbreviations would make the manuscript easier for non-experts to read. For example, P5C is not defined in the abstract. Furthermore, if an abbreviation is not used more than 3 times, it is not necessary to provide it (e.g., mammalian proteins in the last paragraph).

      We have adjusted the text.

      typos:

      1. 82: should read 'is restricted to the mitoch...'

      2. 102-103: should read 'to evade macrophages'

      3. Fig. S4F is mislabelled as Fig. S4G.

      Thanks!

      **Referees cross-commenting**

      Overall, we stand by our initial assessment of the study. However, we were not aware of previous studies that investigated proline utilization in yeasts, as noted by Rev # 2 (https://onlinelibrary.wiley.com/doi/epdf/10.1002/yea.1845). The current study suggests that using proline as an energy/carbon source is more wide-spread, beyond pathogenic yeasts. Further, the C. albicans strain they used for this study (ATCC 10231) was apparently unable to grow on proline in the quoted paper. In light of this, we think the authors should reference this study, tone down the claims about the clear correlation of pathogenicity and proline utilization, and address this apparent discrepancy with the indicated Candida albicans isolate. We note that our review considered this a paper mostly of interest to specialists.

      Although other non-pathogenic fungi have been shown to use proline as pointed out by Reviewer 2, this metabolic attribute has not been previously tested in members of the pathogenic Candida spp. complex. We have included the reference and included a statement that many fungi, isolated from diverse environmental niches, can use proline as a carbon source.

      Reviewer #1 (Significance (Required)):

      1. The advance in this paper is conceptual for the proline utilization connection to virulence in a range of species and technical for the in vivo microscopy. Limitations are that the conceptual advance is based only on qualitative work in figure 1 and that the animal studies do not provide a conceptual advance, although the technical advance of in vivo visualization of kidney tissue is impressive and (to the knowledge of this reviewer) quite new as the only prior work was in mouse ears.

      In response to the reviewer’s comment regarding Fig. 1, although it is qualitative, it is very reproducible. We even tried several clinical isolates of S. cerevisiae and observed consistent behavior to the standard laboratory strains (i.e., they do not grow on SP medium where proline is used as sole carbon/nitrogen/energy source). We tried to quantify growth of all strain in liquid SP medium at 30 oC using a TECAN microplate reader, but then the results show very erratic reading among species (and replicates) as each behaves differently; C. tropicalis, C. krusei, and C. parapsilosis form pseudohyphae and clump readily, while C. albicans forms hyphae and pseudohyphae.

      2.The work fits well as an extension of the body of work from the corresponding author's lab with additions from the labs with expertise in models of infection.

      1. People interested in yeast metabolism and pathogenic yeast virulence will be the audience for this paper and as written it is for a specialized audience interested in pathogenic yeast metabolism and, perhaps, (although not mentioned at all in the text) for those who want to try PUT gene products as new drug targets.

      This was actually mentioned in the last paragraph of the discussion (line 581-582).

      1. Reveiwer expertise is in pathogenic yeast biology and yeast metabolism. Little expertise in high tech microscopy.

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

      The study is part of the continuous work by the authors to dissect the mechanism of utilization of proline as a carbon source in Candida spp. In particular, this work shows that the inability to process proline leads to accumulation of the toxic intermediate P5C and subsequent inhibition of mitochondrial respiration and toxic effect on the cells. Furthermore, the study demonstrates that proline utilization is important for C. albicans kidney colonization. The experiments are meticulously designed and the study adds to the overall understanding of the metabolic utilization of proline as a carbon source and its potential relevance for infection.

      I find this work interesting, but the role of Put1 and Put2 in proline utilization is not particularly novel. The novelty here is the subcellular localization of the two proteins. Also, the importance of proline utilization for infection is unclear. The host-pathogen interaction assays are ambiguous as each assay gives different result. Lastly, the authors try to generalize the importance of use of proline as a energy source by other Candida spp.. This is not very surprising, given that it has been reported previously by others (example DOI: 10.1002/yea.1845) and that many pathogenic or closely related to C. albicans species use various amino acids, not only proline, as a carbon source.

      Yes, as reviewer 2, we are not surprised that many of the pathogenic members of the Candida spp. complex are able to use proline, but this needed to be checked. The fact that proline can be used as a sole carbon/nitrogen/energy source clearly set them apart from the paradigm yeast S. cerevisiae. A major question is what amino acids are important in the context of the host? To assess this, we have used mutations that specifically block proline utilization. Our past studies demonstrating that proline catabolism is rapidly activated in C. albicans cells phagocytized by macrophages indicates that proline is present in the phagosomal compartment. Furthermore, put mutations clearly affect virulence in flies and murine systems. We are at a loss to understand why the reviewer believes that our data, which consistently shows that proline catabolism is important, is ambiguous.

      The expectation that all three mutant strains, i.e., put1, put2 and put3, would behave identically in the different infection models reflects an unnuanced view of how infection works. In fact, differences considered trivial such as the use of mouse background can have a profound effects on virulence. Consequently, it is striking how the diverse infections models consistently and unequivocally demonstrate that proline catabolism affects virulence. Also, it should be appreciated that we are not testing mutations affecting proteins with many overlapping functions, where it may be appropriate to challenge claims as to their direct role in virulence. Here we tested mutants that lack the enzymes that catalyze proline utilization. A more reasonable expectation is that the virulence is commensurate to the specific nutrient composition of model systems (as asked by reviewer#1), which can fluctuate among models (see our response to the major comment 6 of reviewer 1). As it is not practical to precisely test the proline levels in the models, we have worked to identify and focus on critical phenotypes that can be analyzed in vitro. Our findings provide the basis for understanding the virulence and growth properties of the mutants in the context of the complex infection models.

      Moreover, the authors take C. albicans as an example to demonstrate the role of PUT in invasion and infection. Proline is known stimulus for hyphal growth in this species, but many other Candida spp., including C. auris, do not filament. So how, aside from supporting growth, proline is linked to infection in these species? I think the authors oversell the importance of proline in Candida spp. pathogenesis and should tone this part down or remove completely. A new story that validates the importance of PUT in non-albicans species can bring clarity to why and where proline is critical for survival and infection.

      The fact that proline supports growth in the host environment is one of the critical aspects of our work. The lack of appreciation for this finding represents a common misconception in infection biology. It is not just the ability to gain access to a host and initiate an infection that counts, it is equally important to sustain growth and to thrive within the host. Thus, the adaptation to the host environment is critical. Here we document that proline catabolism not only initiates but sustains an infection acting as a critical carbon/energy source. The inability of the put1 and put2 mutants, which are sensitive to proline, to grow and infect multiple models clearly suggests the substantial quantity of proline is accessible. Also, we have constructed C. glabrata (Fig. S1C) and C. auris (not shown) strains that lack the ability to catabolize proline, and are currently characterizing the virulence properties of these strains. This is out of the scope of the present study.

      Major comments: I am not convinced by the data that proline is important to initiate infection. Candida infections of the kidney occur only at late stages of sepsis. The authors need more compelling data to prove that proline is important for infection in the host.

      Again, not sure why there is such skepticism here, regardless of whether kidney infections occur late, the fact that in contrast to WT, we do not observe put mutants filamenting, clearly suggesting that the capacity to catabolize proline plays a role in the expression of virulence characteristics of C. albicans. Based on our findings using IVM, which provides 3D information, we can at least conclude that a single isolated C. albicans cell can initiate hyphal growth, initiating a point of infection. In addition, our newly added whole human blood data suggests that proline catabolism is required for survival in the blood; human blood contains high amount of proline, arginine, and ornithine that are all catabolized via the proline catabolic network.

      Minor comments: I find the manuscript difficult to read and the discussion part is overly long. Some streamlining and adding a bit more explanation for the rationale of each experiment will make the work easier to follow. Some language/style needs refining as well.

      We have attempted to take this critique into account during the revision of the manuscript and have streamlined the text and added explanations regarding the rationale underlying our experimental approaches.

      **Referees cross-commenting**

      In this manuscripts the authors clarify the cellular compartmentalization of steps in proline catabolism. However, it is not novel that proline is a valuable carbon source. The role of proline utilization for establishing or progression of infection remains ambiguous even after the authors provide different in vivo results. The overall significance of the study is limited.

      Please refer to our comments below. We do not understand that the reviewers apparently question the obvious role of proline utilization facilitating virulence.

      Reviewer #2 (Significance (Required)):

      The strengths of this study are in the experimental design and variety. The data is well presented and visualized. The limitations are as pointed above - I find it especially difficult to figure out where, in a real infection scenario (e.g. breach of the gut barrier and entry into the bloodstream) proline will be the primary energy source. To me the significance of this work is minor.

      C. albicans is the primary human fungal pathogen placed under the “Critical Priority Group” by WHO and yet our understanding of nutrient assimilation in this fungal pathogen is only a fraction of what is known in the model yeast S. cerevisiae, which has proven not to be the best paradigm for understanding the regulatory circuits operating in human fungal pathogens. This manuscript, as well as other recent publications, have revisited and corrected earlier assumptions regarding C. albicans growth, providing novel information that reflect important regulatory differences specifically relevant to the life of C. albicans in the host. For example, had it not been for the recent findings (Ref. 10, 18, 31) that show that proline utilization in C. albicans is not subject to nitrogen catabolite repression (NCR) and that glucose represses mitochondrial function, the perception in the field would remain that C. albicans cannot utilize proline as a carbon and/or nitrogen source in the presence of a “preferred” source of nitrogen, which is applicable in the blood that contains high concentrations of possible sources of carbon and nitrogen. Furthermore, the low but constitutive expression of Put2 and the tight highly responsive Put1 expression in response to proline (Fig. 2D, S2A, S2B), suggest that C. albicans is well equipped to productively anticipate proline availability depending on the host status, entirely consistent with its “opportunistic” character. The many incorrect and previously held assumptions regarding C. albicans, uncritically propagated in several influential reviews, likely have hampered efforts to develop novel antifungal therapies. We do not understand, nor accept the view that a more precise understanding of the proline catabolism is incremental.

      The type of question raised by the reviewer is exactly what we hope to achieve in the future but to get there we have to have correct assumptions in place, and this is only possible if we have a more thorough understanding of the regulatory mechanisms driving proline utilization in C. albicans. The idea that certain proteins are refractory to degradation by C. albicans suggest that other external factors are triggering the release of amino acids from these proteins. This work however, suggest that proline is likely accessible in the gut due to the presence of proline-rich proteins like mucin (Fig. S5A/B).

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

      The manuscript of Silao et al. describes an in-depth investigation of the role of Put1 and Put2 enzymes in proline catabolism and virulence in Candida albicans. This is an extension of previous work in this system. The basic biochemistry and genetics are solid and support the role of these enzymes in the proposed pathway and provide evidence that the build up a toxic intermediate in the absence of Put2 is likely involved in the poor growth of the strain when proline is the only carbon source.

      Note that we observe the toxic effects of proline even when it is not the sole carbon source, however, and importantly, toxicity is dependent on mitochondrial function, which is repressed by high levels of glucose. Proline toxicity is observed when glycerol/lactate are present as carbon sources in addition to proline. Under these conditions, mitochondria are not repressed and exogenous proline impairs growth, particularly evident in put2 cells that accumulate the toxic intermediate P5C.

      The conclusions regarding its role in virulence are less convincing, particularly the data derived from the collagen invasion assay, the ex vivo skin model and the ex vivo/in vivo imaging. The survival and fungal burden assays support a modest role in virulence and a modest reduction in infectivity (although the presented data for survival does not have statistical significance data reported for the kaplan analysis.

      See below for response regarding collagen assay. We have included the significance values derived from Kaplan analysis in the revised Fig. 5B.

      The manuscript is clearly written. The methods are well described.

      **Referees cross-commenting**

      I remain unconvinced of the broad significance of the advances and stand by my assessment that this is for the most part a reasonable study but does not move the field forward. The novel technical aspects are either extensions of previous in vivo imaging or are not well controlled (collagen invasion assay)s.

      See below for response.

      Reviewer #3 (Significance (Required)):

      This is a detailed study of an area that is fairly mature and thus will be of interest to those in the field but does not represent a large advance and is thus truly incremental.

      See below for response.

      Major limitations of the work are as follows. First, the collagen invasion assay may be flawed. The recovery media is made with DMEM which is a medium that lacks proline and is fairly stringent. Control experiments need to be done to be sure that the mutants grow in the recovery medium. Second, the data from the RHE model are hard to interpret since so few cells are present in the tissue. It is hard to see if there are few filaments of if there are just too few cells to assess in the tissue. Third, in vitro experiments assessing the filamentation of the mutants in the medium in which these assays are preformed need to be done as controls. Candida albicans filaments in many conditions such as tissue culture medium. Spider medium is a strong inducer of filamentation but is very different than in vivo/ ex vivo conditions.

      Related to the collagen invasion assay, there is a misunderstanding. The reviewer appears to confuse the put mutations with proline auxotrophy. The put mutants are proline prototrophs and can synthesize proline as they possess a full repertoire of biosynthetic enzymes. In contrast, the put mutants cannot utilize proline to obtain nitrogen or energy. In fact, the presence of excess proline imposes toxicity to the put mutants. There are three possible sources of proline. 1) PureCol EZ Gel is a ready-to-use collagen solution that forms a firm gel when warmed to 37 °C. It contains purified Type I bovine collagen (5 mg/ml) dissolved in DMEM/F-12 medium, which has multiple amino acids, including a substantial amount of arginine. 2) The recovery medium DMEM supplemented with 10% FBS. The presence of FBS provides amino acids and induces filamentous growth. As the reviewer points out, C. albicans grows in this media and exhibits filamentous growth. 3) The proteolytic breakdown of collagen is expected to liberate proline. Consequently, the poor growth of the mutants clearly demonstrate the importance of proline catabolism. Also, the fact that we recovered put mutants surviving on top of the collagen (Fig. 4B, inset) suggests that they remain viable but simply are unable to efficiently invade the collagen. Consistently, microscopic inspection of the wells of the put mutants showed extremely few or even complete absence of invading cells in the recovery medium. We will adjust the text and provide a more detailed description of the experimental set-up. In summary, the main concern of the reviewer with respect to lack of proline is not relevant.

      Regarding the 3D-skin model, equal numbers of fungal cells were applied on top of the RHE. To avoid overgrowth, only low numbers (100 C. albicans cells) can be applied for the WT strain, and consequently for all other strains. In contrast to WT, which clearly proliferates, the apparent low level of put1 and put2 cells at the center of the 3D skin model is the consequence of poor growth. The upper layer of the RHE consists of stratified keratinocytes. To grow, WT fungal cells obtain proline either directly from the keratinocyte, from secreted proteases that liberate proline from keratin (proline not as abundant in keratin as in collagen, the main component of the dermis), or from the medium that basolaterally feeds the RHE. At the border of the model leakage from the medium can occur. Our results, showing poor growth of the mutants in the center of the 3D-skin model, entirely consistent with the collagen plug experiments, indicates that proline catabolism plays a determinant role to enable invasive growth.

      Lastly, the imaging experiments are highly problematic. First, reference must be made to previous ex vivo imaging reported by the Lionakis lab in 2013. Second, the number of cells imaged is so low that there is no power to make any conclusions. At 24 hr, the mutants may be delayed in filamentation or they may be delayed in establishing infection. There is no way to know what is causing the apparent lack of filaments. This technique as presented is not any higher resolution than traditional histology and in fact histology would provide a more convincing case for reduced filamentation.

      These considerations significantly reduce the overall significance of the work.

      I work on Candida albicans.

      We thank the reviewer for highlighting the beautiful study by Lionakis et al which document the host response, specifically the role of macrophages in mitigating C. albicans infection of the kidney. However, the reviewer apparently failed to recognize that their method is completely differed from ours. Lionakis et al. performed ex vivo imaging of kidney slices using regular confocal imaging, and the authors express an awareness regarding the limitations of this approach. In fact, these authors even state in their discussion that intravital microscopy should be pursued in the future to further investigate Candida-macrophage interactions in the kidney. Also, they point out that kidney-specific factors seem to facilitate rapid filamentous growth of C. albicans. In our work, we have experimentally addressed both of these astute statements. To our knowledge, our work is the first report of imaging a Candida cell infecting a kidney in a living mouse, which on its own is a major development and achievement considering the complexity of the kidney microenvironment. The finding that the put2 mutant does not exhibit filamentous growth in the kidney of a living mouse (24 h) is striking and strongly suggests that a substantial quantity of proline, or amino acids (e.g., arginine) that are metabolized via the proline catabolic network, is present in the kidney. This is clear based on finding that WT C. albicans cells respond accordingly to initiate hyphal growth. Consistent to this, it is well documented that the kidney is a major metabolic hub for arginine and proline metabolism. The work by Lionakis aligns remarkably well with our previous and current work in that put mutants exhibit greatly reduced survivability in co-culture with macrophages and do not evade these primary immune cells due to their inability to induce filamentous growth within the phagosome (Silao et al., 2019). We have adjusted the text to include a discussion that places our work in the context of the Lionakis work.

      We have added a Fig. 6C showing an example of the scanned area of the kidney. Further we added the following in the revised legend to indicate that large areas of kidneys were imaged in our assessment of fungal growth and filamentation:

      “Sites of colonization where localized using a spiral scan in the Las-X Navigator-module in the FITC channel. The entire area of the renal surface attached to the glass imaging window was scanned; circles highlight examples of regions of interest (ROI) exhibiting stronger and deviating fluorescence from the background. Each ROI was examined in detail using FITC, yEmRFP and autofluorescence. Scale bar, 500 µm.”

      CONCLUDING STATEMENT – SUMMARY RESPONSE:

      Our current work is based our previous discovery that proline metabolism provides energy to induce and support filamentous growth (PLoS Genetics, 2019). This turned out to be important since we also discovered that C. albicans cells depend on mitochondrial proline metabolism to evade engulfing macrophages, implicating this process as being an important virulence determinant. Consistently, using time-lapse microscopy, we subsequently found that proline catabolic enzymes are rapidly induced in C. albicans cells upon phagocytosis by macrophages. These results demonstrated that proline is present within phagosomes. As exciting as these findings are, they focused on a single phenotype, i.e., filamentation, and were obtained using in vitro experimental approaches. These results demanded that we pursue additional avenues to further characterize and test the in vivo relevance and merely provide a solid background for the current work.

      In contrast to reviewer 2 and 3, we do not believe that our finding that proline catabolism plays such a critical role in virulence as being merely “incremental”. We also could not have foreseen that the ability to use proline as an energy source is a common feature of multiple fungal pathogens capable of causing human disease. This is conceptionally very important in that human fungal pathogens, unlike the well-studied yeast Saccharomyces cerevisiae, are not readily found out in nature, and thus have evolved to use a similar spectrum of nutrients as host cells, including cancer cells. It is important for the fungal pathogen community to realize that regulatory switches operating in C. albicans are wired substantially differently to those in S. cerevisiae, and are likely optimized to reflect the actual condition in the host environment. The growing appreciation that diverse cancers are able to shift metabolism to exploit proline as an energy source is strikingly and fascinatingly similar to our findings with pathogenic fungi. This represents a conceptual advance in that it points to the wealth of proline stored within extracellular matrix proteins as providing a potential and significant source of energy for virulent fungal and cancerous growth.

      Finally, we strongly believe it is improper to extrapolate virulence properties based on in vitro findings, and that it is essential to actually test host-microbial pathogen interactions using refined in vivo models. Our successful use of advanced intravital microscopy goes beyond traditional and accepted murine infection models and has provided us with a unique state-of-the-art vantage point. Our findings that a single C. albicans cell is able to initiate and establish a site of infection in a kidney within a living mouse is itself important, and coupled to the novel finding that hyphal development at sites of infection depends on the ability of the fungal cells to catabolize proline must reflect the physiological conditions in the kidney. This is not an incremental finding, and we do not understand that reviewers 2 and 3 diminish the significance of these findings. Clearly, our manuscript provides a strong foundation for more detailed and advanced studies.

    2. 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

      The manuscript of Silao et al. describes an in-depth investigation of the role of Put1 and Put2 enzymes in proline catabolism and virulence in Candida albicans. This is an extension of previous work in this system. The basic biochemistry and genetics are solid and support the role of these enzymes in the proposed pathway and provide evidence that the build up a toxic intermediate in the absence of Put2 is likely involved in the poor growth of the strain when proline is the only carbon source.

      The conclusions regarding its role in virulence are less convincing, particularly the data derived from the collagen invasion assay, the ex vivo skin model and the ex vivo/in vivo imaging. The survival and fungal burden assays support a modest role in virulence and a modest reduction in infectivity (although the presented data for survival does not have statistical significance data reported for the kaplan analysis.

      The manuscript is clearly written. The methods are well described.

      Referees cross-commenting

      I remain unconvinced of the broad significance of the advances and stand by my assessment that this is for the most part a reasonable study but does not move the field forward. The novel technical aspects are either extensions of previous in vivo imaging or are not well controlled (collagen invasion assay)s.

      Significance

      This is a detailed study of an area that is fairly mature and thus will be of interest to those in the field but does not represent a large advance and is thus truly incremental.

      Major limitations of the work are as follows. First, the collagen invasion assay may be flawed. The recovery media is made with DMEM which is a medium that lacks proline and is fairly stringent. Control experiments need to be done to be sure that the mutants grow in the recovery medium. Second, the data from the RHE model are hard to interpret since so few cells are present in the tissue. It is hard to see if there are few filaments of if there are just too few cells to assess in the tissue. Third, in vitro experiments assessing the filamentation of the mutants in the medium in which these assays are preformed need to be done as controls. Candida albicans filaments in many conditions such as tissue culture medium. Spider medium is a strong inducer of filamentation but is very different than in vivo/ ex vivo conditions.

      Lastly, the imaging experiments are highly problematic. First, reference must be made to previous ex vivo imaging reported by the Lionakis lab in 2013. Second, the number of cells imaged is so low that there is no power to make any conclusions. At 24 hr, the mutants may be delayed in filamentation or they may be delayed in establishing infection. There is no way to know what is causing the apparent lack of filaments. This technique as presented is not any higher resolution than traditional histology and in fact histology would provide a more convincing case for reduced filamentation.

      These considerations significantly reduce the overall significance of the work.

      I work on Candida albicans.

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

      Learn more at Review Commons


      Referee #2

      Evidence, reproducibility and clarity

      The study is part of the continuous work by the authors to dissect the mechanism of utilization of proline as a carbon source in Candida spp. In particular, this work shows that the inability to process proline leads to accumulation of the toxic intermediate P5C and subsequent inhibition of mitochondrial respiration and toxic effect on the cells. Furthermore, the study demonstrates that proline utilization is important for C. albicans kidney colonization. The experiments are meticulously designed and the study adds to the overall understanding of the metabolic utilization of proline as a carbon source and its potential relevance for infection. I find this work interesting, but the role of Put1 and Put2 in proline utilization is not particularly novel. The novelty here is the subcellular localization of the two proteins. Also, the importance of proline utilization for infection is unclear. The host-pathogen interaction assays are ambiguous as each assay gives different result. Lastly, the authors try to generalize the importance of use of proline as a energy source by other Candida spp.. This is not very surprising, given that it has been reported previously by others (example DOI: 10.1002/yea.1845) and that many pathogenic or closely related to C. albicans species use various amino acids, not only proline, as a carbon source. Moreover, the authors take C. albicans as an example to demonstrate the role of PUT in invasion and infection. Proline is known stimulus for hyphal growth in this species, but many other Candida spp., including C. auris, do not filament. So how, aside from supporting growth, proline is linked to infection in these species? I think the authors oversell the importance of proline in Candida spp. pathogenesis and should tone this part down or remove completely. A new story that validates the importance of PUT in non-albicans species can bring clarity to why and where proline is critical for survival and infection.

      Major comments: I am not convinced by the data that proline is important to initiate infection. Candida infections of the kidney occur only at late stages of sepsis. The authors need more compelling data to prove that proline is important for infection in the host.

      Minor comments: I find the manuscript difficult to read and the discussion part is overly long. Some streamlining and adding a bit more explanation for the rationale of each experiment will make the work easier to follow. Some language/style needs refining as well.

      Referees cross-commenting

      In this manuscripts the authors clarify the cellular compartmentalization of steps in proline catabolism. However, it is not novel that proline is a valuable carbon source. The role of proline utilization for establishing or progression of infection remains ambiguous even after the authors provide different in vivo results. The overall significance of the study is limited.

      Significance

      The strengths of this study are in the experimental design and variety. The data is well presented and visualized. The limitations are as pointed above - I find it especially difficult to figure out where, in a real infection scenario (e.g. breach of the gut barrier and entry into the bloodstream) proline will be the primary energy source.

      To me the significance of this work is minor.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      Silao et al make the intriguing observation that yeasts that are generally considered less pathogenic are unable to catabolize proline than Candida albicans. They then, in Candida albicans, construct mutants defective for the two key enzymes (Put1, Put2) required to convert proline to glutamate, which they show to be essential for proline utilization as an energy (carbon) and nitrogen source. The authors proceed to untangle the regulatory aspects of proline degradation, including the respective cellular localization of its key enzymes. They then make the important discovery that strains lacking either Put1 or Put2 suffer from a proline-dependent growth defect, which they attribute to resulting defects in mitochondrial metabolism.

      The manuscript then goes on to analyze a broad range of infection models including: reconstituted human epithelial skin model, Drosophila, mouse systemic infections, organ colonization in these mice (kidney, spleen, brain, liver and histochemistry of the kidneys) as well as survival when incubated with cultured human neutrophils. Finally, they use yeast cells constitutively expressing yEmRFP (so that yeasts can be distinguished from other host cells) and coated with FITC before incubation with the host cells (which coats the wall of the original cells, but does not spread to progeny) and they go on to perform an impressive set of analyses of C. albicans growth within mouse kidneys both in vivo and ex vivo, exploiting an implanted window together with intravital imaging with a two photon microscope at different time points. The system is impressive and visualizes tissue invasion by hyphal cells beautifully. Finally, they compare the intra vital images from WT and put2-/- cells and show that, as in vitro, put2-/- cells do not form filaments and do not show extensive invasion of the kidney tissue. While the in vivo aspect of the study includes many different models, it finds defects in virulence for different subsets of put mutants and the relative importance of filamentation vs proline utilization for virulence is not conclusively resolved.

      Overall, this is an important and timely manuscript, which significantly contributes to the understanding of how proline metabolism intersects with yeast fitness in the context of infections. However, there are several major concerns regarding some of the conclusions drawn from the study. In addition, some general recommendations that would improve the manuscript are provided.

      Specifically, the manuscript provides a very detailed description of experiments and observations. However, in several parts it is difficult to follow and the the reader needs more guidance about the logic involved in reaching conclusion. Specifically, several aspects of the paper are written for experts in Candida (yeast) metabolism. Here, explaining the rationale for some of the experiments, and providing more background information that is not obvious to a non-expert, is required.

      In particular, writing a clear and measured summary sentence at the end of each paragraph and a conclusion paragraph that summarizes key findings in simple terms would help make the manuscript more digestible for readers.

      In addition, the impressive microscopy and broad range of in vivo experiments is comprehensive but only adds incremental information relevant to proline metabolism-that filamentous growth in vivo and virulence is reduced in cells carrying some mutations in one or more put genes. However, this broad sweep of model systems and the development of the in vivo imagining system might have more impact in a separate paper focused on the real-time in vivo visualization of kidney invasion.

      Major comments:

      1. The main finding that impressed this reviewer is that "removing the ability to catabolize proline, in an organism that evolved to catabolize it, leads to (growth) defects". This point could be better highlighted throughout the manuscript.
      2. The authors show that deletion strains for proline metabolism have defects that are important for in vivo pathogenicity. This is an important finding. However, as the manuscript reads now, it suggests that the main findings are that the ability to use proline in the respective host niche is key. Mechanistically, the manuscript revolves primarily around defects that arise when deleting PUT1 and/or PUT2 (i.e., an "unknown" toxicity of proline in the case of put1-/- (or put1-/- put2-/-) and the additional P5C-dependent toxicity for put2-/- mutants; see below).
      3. In order to claim that catabolizing prolines promotes pathogenicity (as opposed to the alternative hypothesis that the inability to catabolize proline leads to the observed defects), additional experiments would be required. For example, the put mutants would need to be compared with mutants that significantly reduce/impair proline uptake, such as the referenced gnp2 mutant (Garbe et al 2022). While the finding that less pathogenic yeast species are unable to catabolize proline is both intriguing and important, it also remains as is presented as a loose, non-quantitative correlation that only tangentially address the question of whether "proline catabolism is key for pathogenicity".
      4. 238 onwards: The conclusion that "the primary growth inhibitory effect of proline is linked to catabolic intermediates formed by Put1 and that are metabolized further by Put2"does not appear to be fully supported by the evidence. Addition of proline to put1 mutants already reduced OD600 by ~50% (Figure 2); and is further reduced to ~10% when put2 is deleted. This implies that there are two inhibitory effects of proline, not one primary one. At the least, this option should be discussed, including why deletion of PUT1 leads to proline toxicity. The latter is not clear-is it that too much proline accumulates in the cell and this accumulation is toxic? If this is the case, the effect would be expected to be proline concentration dependent. Performing a relatively simple experiment as performed for the put2 mutant (Fig. 3 / S3F) may clarify this issue. Particularly, if the experiment would be coupled with intracellular quantification of proline.
      5. The caption "P5C mediates a respiratory block" is misleading, as the evidence is not that compelling: Although P5C increases in put2, but not in put1 mutants, and given that both single mutants experience a proline-dependent respiratory defect (Fig. 3E), the results suggest a more complex relationship.
      6. The virulence assays and in vivo experiments do not present a unifying view: in Drosophila put2∆∆ is less virulent than put1∆∆, which appears similar to put3∆∆. Given that put2 mutants grow slowly, likely because of P5C inhibition, this seems logical. However, in mice, put3∆∆ remains highly virulent while put1∆∆ and put2∆∆ results for survival are mixed. Furthermore, in 4 mouse organs, put1∆∆ and put2∆∆ are not significantly different from one another but are different from wt, while put3∆∆ has no significant reduction in CFU. Kidney histology shows very little invasion by put1 and put2 and more by put3, but visually put3 appears to invade much less than the WT, and the human neutrophil experiment shows effects of put2 or put3 but not put1. This leaves the reader rather confused. It may be worth discussing the reasons for different results in different models. Is the availability of proline in each of the organisms and organs similar?
      7. The ex vivo and in vivo analysis of the dynamics of C. albicans growth in the host is visually impressive, but it distracts from the focus of the paper and the metabolic findings. Showing that put mutant cells do not form filaments in vivo (as in vitro) does not add much conceptually to the paper. Furthermore, this lovely advance in in vivo visualization is lost at the end of this paper and the authors should consider whether it might fit better in manuscript that could really highlight the in vivo visualization approach.
      8. The discussion of cells stained with FITC and expressing yEmRFP does not clearly point out that the FITC is only an indicator for those cells that were used to innoculate the tissue and that finding cells without FITC indicates that they are mitotic progeny, indicating that they have been dividing. The authors clearly understand this, but a naive reader may miss this important point if it is not stated explicitly.

      Minor comments:

      1. Throughout: what is the distinction between utilization of proline for C or for energy? These terms seem to be used interchangeably.
      2. Introducing the schematic in Fig. 2A at the beginning of Figure 1, would help explain proline catabolism before delving into the growth experiments that rely upon this framework. This should include an explanation, for readers less familiar with the metabolic issues, of the main limitations to catabolizing proline, and the key issues for being able to use proline for nitrogen, carbon, and energy (potentially indicated in the overview figure, e.g. pointing towards gluconeogenesis etc.).
      3. Saccharomyces can only grow on proline as a nitrogen source, but not as energy/carbon source. Could the authors briefly mention or discuss why this is the case? This is not clearly apparent after reading the manuscript and it leaves the reader confused and trying to understand if the fact that proline is required for carbon utilization is a new finding of this paper or was already known. Do the authors think this is tied to the presence of complex 1 components in C. albicans that are not found in S. cerevisiae. Is this consistent for the pathogenic, but not the non-pathogenic yeasts analyzed in figure 1?
      4. 100: While Gdh2 is apparently an important enzyme for generating ammonium, why is it not necessary for macrophage escape and virulence as shown in reference 18? A recent paper from Garbe et al (ref 12) suggests that Gnp2 is the major proline permease in C. albicans and what is known, and not known, about proline uptake would be good to mention, given that PUT gene functions require that proline enters the cells.
      5. 116: Is the "low sugar environment of the host" referring to a specific niche, such as the GI tract, or human blood? Compared to most natural environments, glucose is abundant in the host, e.g., at ~5 mM, it is the most abundant metabolite in blood, and similarly, in the GI tract, levels can go beyond 50 mM glucose (see e.g. PMIDs 34371983, 21359215). Or is this comment indicating that the in vivo sugar concentration is lower than that in common lab growth media? Please spell out the niche/concentration for clarification - and compare that to other niches that are considered "high sugar environments".
      6. 123: "proline as sole energy source" - suggest "is the source of carbon, nitrogen, and energy"
      7. 142: it is worth noting to readers that C. neoformans is a basidiomycete and thus VERY distant from the other yeasts studied here-it is in a different major phylum of fungi.
      8. 143: Here it is implied that put1 and put2 mutant strains do not grow on SPD, but this is not stated explicitly.
      9. 151: The abbreviation SPG is not explained in main text.
      10. Paragraph 156 onwards: this section is particularly hard to read and very dense. Also, it is difficult to understand the significance of these experiments for the overall findings of the paper. Please at least provide a small conclusion / summary at the end of the paragraph that puts the findings into perspective.
      11. Figure 2 C: simplifying the scheme (e.g. lots of redundant information, P2 and Mito - just give it one name) would help. This figure may be better in the supplementary material.
      12. Figure 2B: It is not directly apparent from the micrographs that Put1-RFP localisation is mitochondrial. Co-localisation of the RFP with a mitochondrial dye (e.g., mitotracker) or something similar is required to validate it.
      13. Throughout the manuscript (figure legends): Suggest using "mean" instead of "Ave."
      14. 175: According to the 'Yeasttract' and 'Pathoyeasttract' databases, Put1 regulates at least 36 and 22 genes, in S. cerev. and C. alb., respectively (based on DNA binding and/or regulatory changes). The only gene in common between these two lists of genes is PUT1. Thus, it is quite likely that Put3 regulates many other processes that explain its function and that its major function may not be only to regulate Put1.
      15. 175: Is it clear whether the Put3-independent mechanisms are positive or negative with respect to Put1?
      16. 218: Suggestion: "growth was indistinguishable".Unless growth curves or growth rates are provided and if one time-point data are the basis for this point, than "rates" is not a relevant term.
      17. 256 onwards: did the authors test if the ROS scavenging effectively reduced ROS? i.e. does the luminol-HRP assay yield less ROS in +proline +scavenger treatment? This is necessary to effectively conclude that the growth inhibitory effect of proline is due to blocking respiration.
      18. The Figure captions are extremely lengthy and detailed, making it cumbersome to find the relevant information. Suggest moving some of the information, such as additional experimental details, into the methods section.
      19. 277-301: Phloxine is not exclusively a live/dead cell indicator-it is an indicator of metabolic activity. In Scerev. and Calb. it also indicates slower growth, opaque growth, and it has been used as an indicator of aneuploidy in C. glabrata (https://journals.asm.org/doi/10.1128/msphere.00260-22) and of diploids vs haploids in S. pombe. The colonies illustrated aer made up of many live cells, and thus the section "Defective proline utilization is linked to cell death" needs to be presented more carefully. In addition, it appears that this section shifts from using defined medium to using rich medium and 37C instead of 30C. Why was this shift necessary?
      20. 295-301: Related to the point above, these results are hard to interpret due to the switch from defined medium in all prior experiments to rich growth medium here. Also, it is not clear why a 48h old YPD culture was chosen to show that the degree of PI staining correlates with mitochondrial activity - is this due to the culture age? It would be more clear to image cells grown on glucose vs. glycerol/lactate, or under repressive / de-repressive glucose concentrations (e.g., as shown in Fig. S4C where a PI+ difference is apparent for 0.2% glucose vs. 2% glucose at 30{degree sign}C).
      21. 313-14: The statement 'the invasion process was dependent on the ability of cells to catabolize proline' doesn't take into account that put mutant cells are defective in filamentous growth irrespective of their utilization of proline...and like the efg1 cph1 double mutant.
      22. 316-327: The results of the experiment described can only be interpreted as an effect of proline catabolism if the three strains (efg1 cph1; put1; put2) have similar growth rates as yeast cells in vitro. Why weren't the cells competed directly (efg1 cph1 vs put cells)?
      23. Fig 6: The logical order of the experiments, and in the text, is: 1) 4 h window, 2) 26 h window and then 3) ex vivo. The cartoon in 6B should be in this order as well.
      24. 337: it is not clear what the 'direct exposure...' is trying to tell us. Can this be made more explicit?
      25. 340-346: Here proteins with high proline content were used to ask if they could be induce transcription of PUT1 or PUT2 RNA and protein. This experiment is designed only to test the role of these proteins to induce utilization of nitrogen, as glucose is included in the medium. Given that these proline-rich proteins need to be lysed by proteases before they can be imported, and since no import pathways were tested, the results appear to tell us that mucin is more readily digested to peptides that contain proline-but why that is the case is not clear and how it relates to proline utilization is also not clear.
      26. 363-369 An alternative is that Put3 induces different proteins important for growth.
      27. 379-380-the conclusion for this paragraph is somewhat of an overstatement as there is no analysis of the degree to which proline utilization is a predictor of virulence. It simply shows that put mutants affect the ability to survive in neutrophils.
      28. Discussion: The statement that "S. cerevisiae" evolved in high sugar environments is debatable. The natural niche could well be forest soil and tree bark, or insect/wasp guts with arguably little glucose around.
      29. 469-470-how strong is the 'correlation' between the ability to utilize proline and virulence? Given that different mutants had different effects in different models, this seems like a very loose 'correlation'; it would be good to have some quantitative measures to make this claim.
      30. 500: Was the experiment was done in larvae, and not in adult Drosophila? Fig 5 legend says flies and shows a picture of a fly and larvae are only mentioned much later in the text..
      31. 512:Why is it presumed that proline accumulates in the mitochondria in put1 mutants? How strong is the presumption?
      32. 539: why are MMPs important for digestion of collagen? This is not clear at this point of the Discussion.
      33. 574: Concluding sentence of this paragraph seems unsubstantiated. There are at least two defects in put2 strains-hyphal growth and growth in general, presumably because of P5C accumulation.
      34. Fewer abbreviations would make the manuscript easier for non-experts to read. For example, P5C is not defined in the abstract. Furthermore, if an abbreviation is not used more than 3 times, it is not necessary to provide it (e.g., mammalian proteins in the last paragraph).

      Typos: 1. 82: should read 'is restricted to the mitoch...' 2. 102-103: should read 'to evade macrophages' 3. Fig. S4F is mislabelled as Fig. S4G.

      Referees cross-commenting

      Overall, we stand by our initial assessment of the study. However, we were not aware of previous studies that investigated proline utilization in yeasts, as noted by Rev # 2 (https://onlinelibrary.wiley.com/doi/epdf/10.1002/yea.1845). The current study suggests that using proline as an energy/carbon source is more wide-spread, beyond pathogenic yeasts. Further, the C. albicans strain they used for this study (ATCC 10231) was apparently unable to grow on proline in the quoted paper. In light of this, we think the authors should reference this study, tone down the claims about the clear correlation of pathogenicity and proline utilization, and address this apparent discrepancy with the indicated Candida albicans isolate. We note that our review considered this a paper mostly of interest to specialists.

      Significance

      1. The advance in this paper is conceptual for the proline utilization connection to virulence in a range of species and technical for the in vivo microscopy. Limitations are that the conceptual advance is based only on qualitative work in figure 1 and that the animal studies do not provide a conceptual advance, although the technical advance of in vivo visualization of kidney tissue is impressive and (to the knowledge of this reviewer) quite new as the only prior work was in mouse ears.
      2. The work fits well as an extension of the body of work from the corresponding author's lab with additions from the labs with expertise in models of infection.
      3. People interested in yeast metabolism and pathogenic yeast virulence will be the audience for this paper and as written it is for a specialized audience interested in pathogenic yeast metabolism and, perhaps, (although not mentioned at all in the text) for those who want to try PUT gene products as new drug targets.
      4. Reviewer expertise is in pathogenic yeast biology and yeast metabolism. Little expertise in high tech microscopy.
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      Reply to the reviewers

      Comment 1.

      The impact of this study would be greatly enhanced if the authors could provide electrophysiological results validating that intra-VTA infusion of Grin2c siRNA affects the excitability or discharge regularity of different populations of VTA neurons (at least dopaminergic and GABA neurons).

      *Reply. The reviewer is suggesting an additional experiment that constitutes a next logical step to further determining the role of NMDA receptors (NMDARs) containing the GluN2C subunit(s) in induction of EPSP in VTA TH+ and TH- neurons; such an experiment could be performed in vivo in behaviorally tested animals, but the recordings would have to be done in anesthetized animals 24h after VTA siRNAs microinjections; an alternative would be an in vitro experiment with slices that would maintain a functional link between dorsal raphe (DR) glutamatergic reward neurons and VTA neurons; we agree that this would constitute an important step forward, and that can be performed once our findings are published. *

      Comment 2.

      The strategy of targeting Grin2c transcripts with siRNAs is appropriate and the authors implemented a western blot validation to control the effectiveness of their approach. However, due to the large volume of siRNA injected into the VTA (500 nl), the authors should consider extending their western blot validation experiments to structures anatomically close to the VTA and rich in GluN2C subunit such as RMTg. The DRN would also be an interesting structure to show that GluN2C subunit expression is not affected by the siRNA approach.

      *Reply. Optogenetic studies have shown that the reward signal initiated by glutamatergic neurons in the dorsal raphe is transmitted to VTA neurons (Liu et al., 2014; McDevitt et al., 2014 Qi et al., 2014;), findings that are consistent with, and that were predicited by, our previous findings (Rompré and Miliaressis, 1985; Boye and Rompré, 2000; Ducrot et al 2013). Moreover, a large body of research carried out with local (VTA) drug injections generated data confirming the role of VTA neurons in reward (See for instance Wise and McDevittt, 2018). Thus, it is reasonable to hypothesizes that it is the reduction of GluN2c (NMDARs that contain this subunit) in VTA neurons that is responsible of the attenuation of reward. Such a hypothesis is further reinforced by our findings that GluN2c and its gene are expressed in VTA neurons. Why should it be within the RMTg? The reviewer likely knows many different studies that have shown that RMTg neurons play a key role in aversion; they provide a strong tonic inhibitory input to reward-relevant VTA neurons and more specifically to VTA DA neurons (see Zhou et al). RMTg neurons receives a strong excitatory input from the lateral habenula; activation of these neurons strongly inhibit reward. Consequently, a logical hypothesis is that a reduction of the glutamatergic excitation of RMTg caused by a reduction of NMDARs that contain GluN2Csubunits would have produce an enhancement of the reward signal, not an attenuation. *

      *The DR, site at which the electrical stimulation was delivered is located near 2 mm behind and 3 mm above the injection sites, it is very unlikely that the small volume injected hit the DR. *

      Comment 3.

      The authors should consider discussing or re-evaluating their findings from their 2015 study which suggested that the effect on reward induced by DRN stimulation was controlled by GluN2A-containing NMDARs most likely located on afferent terminals.

      Reply. We cannot understand why we should re-evaluate or re-discuss these findings. It has been repeatedly shown that blockade of GluN2A containing NMDARs by local VTA PPPA and R-CPP microinjections enhances the reward signal initiated by DR electrical stimulation (Bergeron and Rompré, 2013; Ducrot et al., 2013; Hernandez et al., 2015; and the present study (control group); this enhancement effect was not attenuated by a decrease in NMDARs that contain GluN2A subunit(s) (Hernandez et al 2015). Our discussion is reasonable in view of these findings, and the hypothesis that these GluN2A containing NMDARs are located on afferent terminal explains the results.

      Comment 4.

      In Figure 2, the authors should quantify the results of the colocalization levels of Grin2c and TH in dopaminergic neurons of the substantia nigra pars compacta.

      Reply. As mentioned in our reply to comment 2, the quantification of VTA neurons was highly justified by a large body of the literature which is not the case for the substantia nigra pars compacta neurons.

      Comment 5.

      The results should be presented differently in figure 5 in order to be able to compare on the same graph and with the appropriate statistical analyses (two-way ANOVA), the impact of PPPA infusion or solvent in the SCRGluN2C or siRNA groups.

      Reply. Unfortunately, this is not possible because not all subjects were tested with PPPA.

      Comment 6. The authors should clarify the N=12 per condition in Figure 3, especially since 11 values for the control conditions are plotted on the histogram.

      *Reply. The reviewer is correct there are 11 Subjects in the control group and 12 in the active sirna group. We made the changes to the methods section. *

      Comment 7.

      Authors should standardize the way they cite literature throughout the manuscript (number or authors).

      Reply. Thanks for the suggestion changes have been made to standardize the citations.

      Comment 8.

      The authors should clarify sentences 102-105 of their introduction, which seem to conflict but ultimately describe similar results.

      Reply. Reviewer is correct the following sentences at the end the paragraph were deleted:

      *This hypothesis predicts that activation of a given subtype(s) potentiates DA burst firing and DA release, whereas activation of different subtype(s) increases the inhibitory drive to DA neurons. This idea is supported by data mentioned above and others showing that both activation and blockade of VTA NMDARs increase DA burst firing (French et al., 1993), accumbens DA release (Karreman et al., 1996; Westerink et al., 1996; Mathé et al., 1998; Kretschmer, 1999), and stimulate forward locomotion (Kretschmer, 1999; Cornish et al., 2001). Rodents also readily learn to directly self-administer the non-selective NMDAR antagonists, AP-7, into the VTA (David et al., 1998), showing that NMDAR blockade can have positive rewarding properties on its own. *

      Comment 9.

      The expression of different GluN2 subunits across different regions of the brain has been known since the early 90's as the authors acknowledged. In the abstract, the authors state that GluN2C is "the most abundant subunit of the NMDA receptor expressed in the VTA" (line 67). This idea that GluN2C is "the most abundantly expressed in DR and VTA compared to other Grin2 subunit transcripts" (line 425), is repeated throughout the paper. However, in the Results section they state that GluN2C is present at the same level as GluN2B; something that is also clearly visible in figure 1, where is also clear that GluN2A is also present almost at the same level. The emphasis that GluN2C has a larger representation over 2A and 2B in VTA is not necessary and misleading.

      *Reply. The point here was to bring attention to the reader to the expression of GLuN2C, the main target of the current study. As shown in Figure 1, GluN2c is indeed the most abundant in the VTA. *

      Comment 10

      3) Performing an immunoblot in tissue obtained with a tissue punch of the VTA, the authors confirmed that the GluN2C mRNA detected is translated into protein. Unfortunately, this important data is not showed, and it should be shown. Moreover, immunocytochemistry of GluN2C could help to identify the cellular type where the protein is expressed, something that could be key to better understand the role of NMDARs in the reward pathway. Are 2A/2B expressed in different cells that 2C? What type of cells express 2C? These are just a few of the question that a better and more detailed analysis of 2C expression could provide. Without this, the interpretation of results presented here, as well as previous results, regarding the role of NMDARs continuous being confusing.

      Reply. Because we measured GluN2C proteins within the VTA, we infer that some Grin2C detected in VTA TH+ and TH- neurons is translated into proteins, and reduction of the protein expression resulted into a selective attenuation of reward We added a supplementary fig showing the GluN2c protein in different brain regions.

      Comment 11.

      4) The largest number of 2C positive cells do not express TH complicating the interpretation that 2C is necessary to convey reward information in the DR-VTA circuit. Other effects due to downregulation of 2C could be responsible of the behavior changes observed. Although the authors offer an explanation for this, is not enough. They suggest that 2C maybe involved in a reduction of excitatory inputs into inhibitory interneurons that when downregulated should produce an opposite effect to what is observed. However, without knowing the identity of those GluN2C expressing cells this comment is only speculation and does not rule out a role for other GluN2C expressing cells that are not TH positive.

      *Reply. We do consider the hypothesis that the attenuation of reward is due to a reduction of GluN2C in TH- neurons, in fact we discuss both hypothesis, TH+ and TH-. Characterization of the reward-relevant neuronal pathway has been an important aim since Olds and Milner discovery. Our findings constitute, as mentioned in reply to comment 1, and important step forward, and indeed identification of the specific VTA cells that convey the reward signal is another important question that should be addressed. Our findings provide a strong ground to focus on GluN2C but not the other subunits. *

      Comment 12.

      5) In this line, there is no good explanation why treatment of animals with GluN2A blocker enhances the reward pathway only in animals treated with control siRNA. Two possibilities could explain this. 1) there is some sort of relationship between 2C and 2A that when 2C is absent, PPPA has no effect. Again, it could be important to know if 2C and 2A are expressed in the same cellular type; 2) the control siRNAs are not completely innocuous and may produce unknown effects that alter the functionality of VTA.

      *Reply. We believe that our data are strong and valid because the methods we used have been validated and the results with PPPA in control group are similar to those previously published. Previously we have shown that a reduction of VTA GluN2A proteins has no impact on reward per se nor on the enhancement of reward by PPPA (Hernandez et al., 2015). The hypothesis raised by the reviewer that 2C and 2A interact is incompatible with the findings that we obtained. Could it be a non-specific effects like tissue damage. In such a case however we would have observed a decrease in 2A subunits as well which is not the case. *

      Comment 13- 16

      6) Downregulating 2C suggests that this subunit is vital to relay a reward signal in VTA neurons. The following are comments regarding the analysis of the data of Fig 4 and 5. 7) It is not clear how the maximum and minimum are estimated in order to fit a sigmoidal curve to the data. Are they average of the stable part? Where the error bars on each data point come from? What is the actual value and standard deviation of M50 values?

      Reply: As described in the self-stimulation training in the materials and method section

      “The data relating to the rate-frequency was fitted to a sigmoid described by the following equation y=Min+((Max-Min) )/(1+[10]^((x50-x)*p) ) where Min is the lower asymptote, Max is the upper asymptote, x50 is the position parameter denoting the frequency at which the slope of the curve is maximal, and p determines the steepness of the sigmoid curve. The resulting fit was used to derive an index of reward defined as the pulse-frequency sustaining a half-maximal rate of responding (M50). Self-stimulation behavior was considered stable when the M50 values varied less than 0.1 log unit for three consecutive days”

      The Max, Min and all the free parameters of the equation are the determine by the best-fit parameters by minimizing a chosen merit function. A merit function, also known as a figure-of-merit function, is a function that measures the agreement between data and the fitting model for a particular choice of the parameters. By convention, the merit function is small when the agreement is good. To optimize the merit function, it is necessary to select a set of initial parameter estimates and then iteratively refine the merit parameters until the merit function does not change significantly between iterations. The Levenberg-Marquardt algorithm has been used for nonlinear least squares calculations in the current implementation.

      As described in the self-stimulation training and material section.

      “.. Four stimulation sweeps were run daily, and the first sweep was considered a warm-up and discarded from the analysis”. The remaining 3 sweeps were fitted to a sigmoid and the parameters were obtained. The error bars correspond to the difference across each sweep”.

      • What is the actual value of the M50 SD? Don’t understand why this is relevant? We already provide the SEM.*

      8) This type of data is better analyzed by nonlinear regression analysis followed by ANOVA and some post hoc multiple comparison test.

      Reply: We totally agree with the reviewer that is why the analysis was done fitting a sigmoid line. In fact, the fitting uses non-linear regression to compare the data points to the function, which in this case is a sigmoid function defined by the equation y=Min+((Max-Min) )/(1+[10]^((x50-x)*p) ) where Min is the lower asymptote, Max is the upper asymptote, x50 is the position parameter denoting the frequency at which the slope of the curve is maximal, and p determines the steepness of the sigmoid curve. In fact, intracranial self-stimulation data has been analysed using non-linear regression models since the seminal work by Coulombe and Miliaresis 1986 [1]

      Indeed, after obtaining the results of the parameters, we follow it up with traditional statistics like t test and Anovas

      9) Given the large variance of individual data points in Figure 4 and 5, a stricter statistical analysis than a t-test is necessary.

      *Reply: Thanks for the suggestion, but we do not fully understand what the reviewer is suggesting. In general, the condition to apply or not a specific statistical test assumes about the underlying distribution the conditions required to conduct a t-test include: the measured values are in ratio scale or interval scale, simple random extraction, homogeneity of variance (i.e., the variability of the data in each group is similar), and normal distribution of data. The normality assumption means that the collected data follows a normal distribution, which is essential for parametric assumption. In all the data presented in figure 4-5 the assumptions are respected and checked (the data is measure in a continues scale, the group assignation was performed randomly, the data does not violate the normality assumption and the variance between the groups is similar. In the only case where the variance assumption was not held the Welsh correction was applied. *

      Comment 17

      10) Minor comments include the need to refer to figure panels in ascending order in the same sequence as they are described in the text.

      Reply: Thanks for the suggestion we made the required changes. Now the figure panels are in the same sequence as they are described.

      Comment 18

      The role of NMDARs in VTA are explained in a rather confusing manner in the introduction. Lines 104 to 106 need some rewording since it conveys that blockade of NMDARs stimulates reward and that an opposite effect is observed following the blockade of NMDARs.

      Reply. We simply report data from the literature. Each statement is supported by the relevant literature.

      [1] Coulombe and Miliaressis, “Fitting Intracranial Self-Stimulation Data with Growth Models.”

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      Given the importance of glutamatergic synaptic transmission in the reward pathway from the dorsal raphe to VTA, the authors set to study the role of GluN2 subunits in a reward behavior. First, using qRT-PCR they quantify the amount of GluN2 subunits in different brain regions. They highlight the presence of GluN2C as the most abundant species of GluN2 subunits in VTA. They also identify that ~50% of TH positive neurons are also positive for GluN2C mRNA. Finally, they downregulate GluN2C in VTA using a commercially available siRNA. Knockdown of GluN2C reduces reward-seeking behavior as it increases the M50 and reduces the maximum response. The authors conclude that "VTA glutamate neurotransmission relays a reward signal initiated by DR stimulation by acting on GluN2C NMDA receptors".

      Major comments

      1. The expression of different GluN2 subunits across different regions of the brain has been known since the early 90's as the authors acknowledged. In the abstract, the authors state that GluN2C is "the most abundant subunit of the NMDA receptor expressed in the VTA" (line 67). This idea that GluN2C is "the most abundantly expressed in DR and VTA compared to other Grin2 subunit transcripts" (line 425), is repeated throughout the paper. However, in the Results section they state that GluN2C is present at the same level as GluN2B; something that is also clearly visible in figure 1, where is also clear that GluN2A is also present almost at the same level. The emphasis that GluN2C has a larger representation over 2A and 2B in VTA is not necessary and misleading.
      2. Previous reports showed that pharmacological blockade on GluN2B or downregulation of GluN2A, the most common GluN2 subunits in the brain, do not affect the nose-poke behavior the authors use here. The biophysical properties of GluN2C are very different from those of 2B and 2A, therefore the fact that 2C downregulation does affect the behavior observed makes an interesting case for the subunit.
      3. Performing an immunoblot in tissue obtained with a tissue punch of the VTA, the authors confirmed that the GluN2C mRNA detected is translated into protein. Unfortunately, this important data is not showed, and it should be shown. Moreover, immunocytochemistry of GluN2C could help to identify the cellular type where the protein is expressed, something that could be key to better understand the role of NMDARs in the reward pathway. Are 2A/2B expressed in different cells that 2C? What type of cells express 2C? These are just a few of the question that a better and more detailed analysis of 2C expression could provide. Without this, the interpretation of results presented here, as well as previous results, regarding the role of NMDARs continuous being confusing.
      4. The largest number of 2C positive cells do not express TH complicating the interpretation that 2C is necessary to convey reward information in the DR-VTA circuit. Other effects due to downregulation of 2C could be responsible of the behavior changes observed. Although the authors offer an explanation for this, is not enough. They suggest that 2C maybe involved in a reduction of excitatory inputs into inhibitory interneurons that when downregulated should produce an opposite effect to what is observed. However, without knowing the identity of those GluN2C expressing cells this comment is only speculation and does not rule out a role for other GluN2C expressing cells that are not TH positive.
      5. In this line, there is no good explanation why treatment of animals with GluN2A blocker enhances the reward pathway only in animals treated with control siRNA. Two possibilities could explain this. 1) there is some sort of relationship between 2C and 2A that when 2C is absent, PPPA has no effect. Again, it could be important to know if 2C and 2A are expressed in the same cellular type; 2) the control siRNAs are not completely innocuous and may produce unknown effects that alter the functionality of VTA.
      6. Downregulating 2C suggests that this subunit is vital to relay a reward signal in VTA neurons. The following are comments regarding the analysis of the data of Fig 4 and 5.
      7. It is not clear how the maximum and minimum are estimated in order to fit a sigmoidal curve to the data. Are they average of the stable part? Where the error bars on each data point come from? What is the actual value and standard deviation of M50 values?
      8. This type of data is better analyzed by nonlinear regression analysis followed by ANOVA and some post hoc multiple comparison test.
      9. Given the large variance of individual data points in Figure 4 and 5, a stricter statistical analysis than a t-test is necessary.
      10. Minor comments include the need to refer to figure panels in ascending order in the same sequence as they are described in the text.
      11. The role of NMDARs in VTA are explained in a rather confusing manner in the introduction. Lines 104 to 106 need some rewording since it conveys that blockade of NMDARs stimulates reward and that an opposite effect is observed following the blockade of NMDARs.

      Overall, the data and analysis still leave too many open questions and the role of GluN2C, vs the other subunits, is not clearly established.

      Significance

      The study is limited in its scope and possible interpretations. The role of GluN2 subunits in the relay of reward information is only incrementally advanced and still continuous to be confusing.

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      Referee #1

      Evidence, reproducibility and clarity

      This study by Hernandez et al provides a series of molecular, anatomical, and behavioral experiments exploring the distribution and the function of the NMDA Glun2C subunit in the ventral tegmental area (VTA). The authors demonstrate that reward signals originating from the dorsal raphe (DR) are carried to VTA neurons through the activation of GluN2C NMDA receptors. this study did not address the specific role of VTA dopaminergic neurons in mediating the reward signal. The major strengths of this paper are the quality of the FISH experiments and the well-established model of Brain Stimulation Reward targeting the Dorsal Raphe. This study is a follow-up of a previous study published by the same group (2015), which explored the expression of GluN2A/2D subunits in the VTA and their role in reward induced by dorsal raphe stimulation. This is an interesting and original manuscript. However, several issues, concerning the design of the experiment and the interpretation of the data, reduce my enthusiasm.

      The impact of this study would be greatly enhanced if the authors could provide electrophysiological results validating that intra-VTA infusion of Grin2c siRNA affects the excitability or discharge regularity of different populations of VTA neurons (at least dopaminergic and GABA neurons).

      The strategy of targeting Grin2c transcripts with siRNAs is appropriate and the authors implemented a western blot validation to control the effectiveness of their approach. However, due to the large volume of siRNA injected into the VTA (500 nl), the authors should consider extending their western blot validation experiments to structures anatomically close to the VTA and rich in GluN2C subunit such as RMTg. The DRN would also be an interesting structure to show that GluN2C subunit expression is not affected by the siRNA approach.

      The authors should consider discussing or re-evaluating their findings from their 2015 study which suggested that the effect on reward induced by DRN stimulation was controlled by GluN2A-containing NMDARs most likely located on afferent terminals.

      In Figure 2, the authors should quantify the results of the colocalization levels of Grin2c and TH in dopaminergic neurons of the substantia nigra pars compacta.

      The results should be presented differently in figure 5 in order to be able to compare on the same graph and with the appropriate statistical analyses (two-way ANOVA), the impact of PPPA infusion or solvent in the SCRGluN2C or siRNA groups.

      The authors should clarify the N=12 per condition in Figure 3, especially since 11 values for the control conditions are plotted on the histogram.

      Authors should standardize the way they cite literature throughout the manuscript (number or authors).

      The authors should clarify sentences 102-105 of their introduction, which seem to conflict but ultimately describe similar results.

      Significance

      This study is a follow-up of a previous study published by the same group (2015), which explored the expression of GluN2A/2D subunits in the VTA and their role in reward induced by dorsal raphe stimulation. This is an interesting and original manuscript.

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      Reply to the reviewers

      We thank the reviewers for their insights and comments on this manuscript. Specific responses to reviewer concerns are detailed below. We made a couple of significant changes based on the feedback. First, we performed more experiments to increase biologic replicates and then quantified image data for multiple figures. The new quantitative information added to Figure 3 fully supports our original conclusions about changes to the ONH in Hes-TKO mutants. The quantification of Atoh7, Otx2, Rbpms and Crx expressing cells among the different genotypes revealed interesting differences in Notch intracellular gene requirements for both RGC and cone development. The most startling outcome is that changes in both cell types correlate with significant changes in Otx2, but not Atoh7. This singular finding suggests interesting future work is needed, well beyond the scope of this paper about the molecular mechanisms underlying these cell fates. Second, our data presentation was reorganized with new information added to Fig 1 that clarifies the relationships between Hes1, Hes5, Foxg1 and Pax2; old Figs 6 & 7 about neurogenesis were merged; and some data moved to new Suppl Figs 2 and 5. The numbering for multiple figures changed and a new summary model (now Fig 8) is provided. In addition, the manuscript was completely rewritten to improve clarity. We hope this revised manuscript is acceptable for publication.

      Reviewer #1 Summary:

      In this study, the authors employed an impressive set of mouse mutant or Cre lines to investigate the complexity of Notch signaling across different stages of retinal development. These comprehensive analyses led to two main findings: 1. Sustained hes1 in the OHS/OS is Notch-independent; 2. Rbpj and Hes1 exhibited opposing roles in cone photoreceptor development. Although the study is potentially interesting, the current manuscript needs the essential research background and quantification, a lack of which significantly reduced the clarity of the manuscript and the credibility of the major conclusions. Also, how the authors organized the results is quite confusing, making the manuscript very difficult to follow.

      Response: We agree with all reviewers concerning incomplete quantification of the data. We directly addressed this shortcoming in revised Figs 3 and 6 (the latter combines old Figs 6 +7). To do this, we repeated some IHC experiments to add more replicates and reorganized all of the neurogenesis phenotypic data figures. Our quantifications uncovered several surprising outcomes that clarify our model. For these reasons, the manuscript was exhaustively rewritten. We merged E13 neurogenesis data into revised Figure 6 and moved the most relevant E16 analyses to new supplemental data Fig 5. All changes made should make the paper easier to understand for retinal development, neurogenesis, and Notch pathway aficionados, in addition to readers lacking such expertise.

      Major comments: 1. The authors needed to make the quantification for many analyses to strengthen the conclusions, such as Fig. 1F, 1G, and etc.

      Response: We quantified optic nerve head (ONL) immunohistochemistry data in the revised Fig 3. We also quantified neurogenesis markers Atoh7, Otx2, Rbpms (RGCs), and Crx at E13 in revised Fig 6 (former Figs 6 and 7). Older stages were moved to a new Suppl Fig 5.

      Respectfully, Hes5 mRNA expression in old Fig 1F and 1G shows that Hes5, like other retinal progenitor cell (RPC) markers, expanded in Rax-Cre deletion but not Chx10-Cre deletion conditions. This is analogous to Pax6 and Rax expansion in Rax-Cre;Hes1 CKO eyes and Pax2 mutants (doi: 10.1523/JNEUROSCI.2327-19.2020) (1). In revised Fig 1, we now show analogous expansion of Hes5 mRNA in Pax2 mutant retinas (compare Figs 1F-1I). Because Hes5 RNA in situ hybridization experiments are nonquantitative, we do not discuss the possibility of Hes5 mRNA level changes in labeled cells.

      The authors reported many exciting results. However, further mechanistic insights are largely missing. They may focus on one of these exciting findings and give some mechanistic insights. For example, hes1 suppresses hes5 expression as the ONH boundary forms; hes1 expression in the ONH is Notch independent; differential influences of Rbpj and Hes1 on cone development. It is better for the authors to select one of these exciting findings and provide a deeper mechanistic study.

      Response: This revision brings fresh focus to Notch regulation of RGC and photoreceptor development, particularly differential influences for Rbpj versus Hes1. We also better support our interpretation of image data in Fig 1. We include new data about the spatial relationships between Hes5-GFP/Pax2 and Hes5-GFP/Foxg1. In summary, we find that as Pax2 becomes restricted to the nasal optic cup prior to the onset of RGC genesis, it becomes mutually exclusive with Hes5-GFP, at the same time that Hes5-GFP+ cells coexpress Hes1. This is consistent with Hes1 indirectly regulating Hes5-GFP as a marker of neurogenic RPCs at the forming ONH. Furthermore, it emphasizes the importance of genetically teasing apart the separate and potentially compensatory roles for Hes1 versus Hes5 undertaken here. These relationships remain poorly resolved during vertebrate CNS development.

      Some analyses lack an explanation of the rationale. For example, "To understand if the loss of multiple Hes genes is more catastrophic than Hes1 alone..."(PAGE 7). Please explain its significance.

      Response: We assume the reviewer is referring to the first sentence of the last paragraph on this page. We analyzed Hes triple mutant mice (TKO) to understand if removing multiple Hes genes reveals redundant functions. This is an open question, given that Hes1 is expressed in the ONH/OS, which is normally devoid of Hes5 by the time retinal neurogenesis begins. These questions have only been explored in a handful of tissues throughout the body. Also see response to point 2 above. In general, we have expanded the rationale for all of the experiments throughout the revised manuscript.

      Significance: In general, many results are quite interesting. However, the significance of these findings is largely hampered in the following aspects: 1. The authors were unable to provide the sufficient research contexts that are essential for understanding many results.2. Many conclusions were solely based on descriptive images but lacked statistical quantification, which significantly weakened many conclusions. 3. Many interesting findings are quite descriptive, and some mechanistic understandings of one of these exciting findings will be beneficial to improve the focus and significance of the study. Current format of the manuscript fits more specialized audience.

      Response: During in vivo development, we wished to understand which particular Notch pathway genes can interact in a Notch-dependent versus a Notch-independent manner. Genetic (phenotypic) studies produce extremely rigorous datasets, in our opinion. This revision now extensively quantifies key findings. Here we dissected the "receipt" of a Notch signal by identically testing the functional requirements of particular pathway members. For Mastermind (Maml), there are 3 paralogues, double mutants for Maml1 and Maml3 are early lethal, and no floxed alleles exist, so it was logical to employ the ROSA-dnMaml mouse strain, particularly since it has been discussed throughout the Notch literature as "analogous" to removing either a Notch receptor or Rbpj. Our finding that the dnMAML allele does not function like a Rbpj null in the retina is important for researchers in the broad Notch field to consider when designing and interpreting experiments.

      Reviewer #2: Hes genes are effectors of the Notch signaling pathway but can also act down-stream of other signaling cascades. In this manuscript the authors attempt to address the complexity of Hes effectors during optic cup development and retinal neurogenesis. To do so, they compared optic cup patterning and retinal neurogenesis in seven germline or conditional mutant mouse embryos generated with two spatio-temporally distinct Cre drivers. These lines allowed for the analysis of the consequences of perturbing the Notch ternary complex and multiple Hes genes alone or in combination. The authors show that the optic disc/nerve head is regulated by Notch independent Hes1 function. They also confirm that perturbation of Notch signaling interferes with cell proliferation enhancing the production of differentiated ganglion cells, whereas photoreceptor genesis requires both Rbpj and Hes1 with Notch dependent and independent mechanisms. This is a rather complex study that dissects further the role of the Notch pathway and Hes proteins during eye development, a topic that has been addressed in many previous studies but perhaps not with the details that the authors have used here. In this respect, this study adds to current literature but will likely be of interest to retina aficionados. The manuscript reads well and the figures are of very good quality. However, many of the statements are based on qualitative rather than on quantitative analysis. This should be, at least in some cases, remediated, despite the effort that this may require given the number of mouse lines used in the study.

      Response: As described in the response to Reviewer 1, we agree and present considerably more quantification data. We extensively reorganized and rewrote this manuscript to emphasize that Hes1 in the ONH/OS is fully Notch-independent and highlight branchpoints in Notch-dependent signaling, for Rbpj versus Hes,1 during early retinal neurogenesis. It is too simplistic that the ternary complex (Rbpj-NICD-Maml) simply activates Hes1 (and/or multiple Hes genes) to regulate downstream signaling targets. This paradigm has been portrayed in the literature numerous times for many processes throughout vertebrate development, homeostasis or relative to particular diseases. By focusing on one tissue and a narrow window of development, our phenotypic studies delved more deeply to show the greater complexity and molecular cross-talk that we think underlie the modulation of signaling levels with in vivo context. Thus, our results are of broad interest and impact to the greater Notch field.

      1. The title is somewhat misleading. The authors have explored mostly the role of Hes1, 3 and5. Although these are Notch effectors, there is already evidence that they participate in other pathways This is confirmed by the data present here. I would suggest to eliminate Notch from the title and use instead "Hes" to better reflect the findings. Furthermore, it is unclear why there is a reference to "mutations" or what are the Notch branchpoints to which the authors refer at the beginning of the discussion.

      Response: We appreciate the reviewer’s viewpoint but disagree this paper is mostly about Hes genes, as there is a critical direct, comparable evaluation with Rbpj and dn-Maml. Direct comparison of 7 genotypes highlights where each pathway member exhibits idiosyncratic phenotypes. We are striving for a clear, simple title about a very complex topic, involving the in vivo genetic dissection of a signaling pathway. We modified the title to: "Notch pathway mutations do not equivalently perturb mouse embryonic retinal development "

      1. "Although the Pax6-Pax2 boundary is intact in Rax-Cre;RbpjCKO/CKO eyes, ONH shape was attenuated compared to controls (Fig 3I)". This statement is arguable as the difference seems subtle. Perhaps some kind of quantification would help.

      Response: We quantified Pax2+ cells (ONH domain) using the adjacent proximal terminus of the retinal pigmented epithelium (RPE) to indicate a transition from ONH to optic stalk (OS). We also quantified the number of Pax2+Pax6+ double positive cells where the 2 domains abut (boundary cells). Some higher magnification examples are now provided in Fig 3H';3K';3N'. Grossly, the imaging data support that the Pax2+ ONH is expanded in Chx10-Cre;TKO eyes, while boundary cells are most affected in Rax-Cre;HesTKO eyes, due to an expansion of retinal tissue. This is supported by our quantitative data (Fig 3O,3P). We observed even in controls that Pax2-expressing cells show some numerical variability. We attributed this to the position of the section through the ONH, which is a 3-dimsenional ring (torus). Therefore, we quantified additional wild-type controls and mutant samples in the new Fig 3O,3P graphs, improving statistical power, and allowing us to detect quantitative differences.

      Page 12 first paragraph. "....but all other genotypes were unaffected". This statement is unclear. All lines in which the Rax-Cre has been used seem to have an increased number of apoptotic cells. This should be better explained

      Response: Respectfully, only one genotype, Rax-Cre;Rbpj mutants contain a statistically significant increase in apoptotic cells (Fig 5P). This is demonstrated by one-way ANOVA analyses that included all pairwise comparisons. To ensure that the quantification was not misleading due to changes in tissue morphology, data in Figs 5, 6, and 7 were normalized to optic cup area. The area was traced in FIJI, creating a polygon whose area was determined in square microns. For every section image, the marker+ cells were divided by the square micron area of the retina (excluding the opening for the optic nerve). Such a method is critical for comparison across this allelic series, given the morphologic changes, differences in cell clustering where rosettes form, and reduced proliferation whenever Notch signaling is lost or reduced.

      Page 12, end of second paragraph: "E13.5 Chx10-Cre;HesTKO eyes had a milder RGC phenotype (Figs 6G, 6N, 6U), but all other mutants were unaffected (Figs 6E, 6F, 6L, 6M, 6S, 6T). This statement is also rather subjective. The phenotype of Chx10-Cre;HesTKO is quite strong and the other mutants seem to have a phenotype. Some quantifications here will help.

      Response: We agree and provide quantification for both Atoh7 and Rbpms positive cells in the revised Figure 6. This is now in the same figure with quantification of Otx2+, Otx2+Atoh7+ and Crx+ cells. The reviewer is correct that both ROSA-dnMaml and both HesTKO mutants have a statistically significant increase in RGCs. Surprisingly, neither of the Rbpj CKO mutants have this outcome (Fig 6Y).

      1. Page 13, toward the bottom..."...but noted that Chx10-Cre RbpjCKO/CKO eyes were not different from controls (Figs 7E, 7AA)". Again, this statement is questionable as staining for both CRX and Rbpms seem reduced as compared to controls as quantifications in 7AA seems also to indicate (about half?). Did the authors calculate whether there is a statistical difference between controls and Chx10-Cre RbpjCKO/CKO ?

      Response: Rbpms+ RGCs and Crx+ photoreceptor precursors were colabeled and quantified on sections for all genotypes. All counts were normalized to area as described above. Upon quantification and ANOVA with pairwise comparisons, there was no statistical difference in Crx+ or Rbpms+ cells between control and Chx10-Cre;Rbpj mutants (new Fig 6Y and Z).

      In Fig 7CC the authors should make the effort of including at least one additional sample, 2 biological replicates seem insufficient to draw a conclusion.

      Response: The Rax-Cre;Hes1CKO/+ X Hes1CKO/CKO matings stopped producing litters in late 2022. While this manuscript was out for review, we obtained younger mice, from which new control and Rax-Cre; Hes1 mutant littermates were collected, stained, imaged and quantified. Upon adding samples, we found that the outcome was unchanged, but the data better support the lack of a statistical difference in rods between genotypes at E17. These data were moved to revised Suppl Fig 5.

      Significance: This is a rather complex study that dissects further the role of the Notch pathway and Hes proteins during eye development, a topic that has been addressed in many previous studies but perhaps not with the details that the authors have used here. In this respect, this study adds to current literature but will likely be of interest to retina aficionados. The manuscript reads well and the figures are of very good quality. However, many of the statements are based on qualitative rather than on quantitative analysis. This should be, at least in some cases, remediated, despite the effort that this may require given the number of mouse lines used in the study.

      Response: To increase the impact of our manuscript, we quantified all markers except Tubb3, since its localization in cell bodies and axons make it impossible to assign to individual cells. We feel that this additional quantification strongly improves the quality of our findings and allowed us to make well-supported and novel conclusions. While we certainly believe that the retinal development community will find this paper of interest, it will also be of value to the broader Notch pathway scientific community. In this manuscript, we simultaneously compared phenotypes for Notch pathway genes in signal receiving cells. We could find essentially no studies like this for the mouse CNS and only a few from the Kopan lab about the kidney and immune system. Interestingly, one of us (NLB) is a coauthor on a recent paper about Notch signaling in the cortex, in which ROSA-dnMaml behaves analogously to Notch1CKO or RbpjCKO. This emphasizes that findings in one organ may not recapitulate the "rules" for this pathway for other cell types or tissues (doi: 10.1242/dev.201408)(2). Deeper understanding of how the Notch pathway in the retina functions, analogously or differently, is important. We feel our revised study advances when and where there are "branchpoints" in canonical signaling that may be overlooked in other developing tissues and organs.

      Reviewer #3: I have reviewed a manuscript submitted by Bosze et al., which is entitled "Not all Notch pathway mutations are equal in the embryonic mouse retina". The authors focused on Notch signaling pathway. Notch signaling is deeply conserved across vertebrate and invertebrate animal species: in general, two transmembrane proteins, Delta and Notch, interact as a ligand and a receptor, respectively, which induces proteolytic cleavage of Notch receptors to generate Notch intracellular domain (NICD). NICD is translocated into nucleus, then forms the transcription factor complex including Rbpj (also referred to as CBF1) and Mastermind-like (Maml), and activates the transcription of Hes family transcription factors. Three Hes proteins, Hes1, 3, and 5, are important for nervous system development. In the vertebrate developing retina, these Hes proteins inhibit neurogenesis to maintain a pool of neural progenitor cells. In addition to their primary role in neurogenesis, the authors recently reported that Hes1 promotes cone photoreceptor differentiation. In the later stages of development, Hes proteins also promote Müller glial differentiation. In addition, Hes1 is highly expressed in the boundary between the neural retina and optic stalk and required for this boundary maintenance. To understand precise regulation of Notch component-mediated signaling network for retinal neurogenesis and cell differentiation, the authors compared retinal phenotypes in the knockdown of three Notch pathway components, that is (1) Hes1/3/5 cTKO, (2) Rbpj KO, and (3) dominant-negative Maml (dnMaml) overexpression, under the control of two Cre derivers; Rax-Cre and Chx10-Cre. First, the authors found that Hes1 expression in the boundary between optic stalk and neural retina is lost in Rax-Cre; Hes1/3/5 cTKO, but still retained in Rax-Cre; Rbpj KO and Rax-Cre; dnMaml overexpression, suggesting that Delta-Notch interaction is not required for Hes1 expression in the boundary between optic stalk and neural retina. Furthermore, Hes1 expressing boundary region expands distally at the expense of the neural retina in Chx10-Cre; Hes1/3/5 cTKO. Maintenance of ccd2 expression in this expanded boundary area suggests that Hes1 normally maintains a proliferative state in the optic stalk, which may allow these cells to differentiate into astrocyte in later stages. Second, in addition to precocious RGC differentiation in all the Notch component KO, the authors found that, as compared with wild-type, cone and rod photoreceptor genesis is highly enhanced in Rax-Cre; Rbpj KO and Rax-Cre; dnMaml overexpression and mildly enhanced in Chx10-Cre; dnMaml overexpression. On the other hand, in Rax-Cre; Hes1/3/5 cTKO, cone and rod photoreceptor genesis is not enhanced but similar to wild-type level. Since the authors previously reported that cone genesis is reduced in Rax-Cre; Hes1 cKO and Chx10-Cre; Hes1 cKO, so Rax-Cre; Hes1/3/5 cTKO may rescue decrease in cone genesis in single Hes1 cKO. The authors raise the possibility that elevated Hes5 expression in single Hes1 cKO may suppress cone photoreceptor genesis. The authors also found that amacrine cell genesis is significantly suppressed in Rax-Cre; Rbpj KO but not changed in Rax-Cre; dnMaml overexpression and Rax-Cre; Hes1/3/5 cTKO, suggesting that Rbpj is specifically required for amacrine cell genesis. From these observations, the authors propose that there are at least two branchpoints for photoreceptor and amacrine cell genesis in Notch component-mediated signaling network. Their findings are very interesting and provide some new insight on how Notch signaling components are integrated into other signaling pathways and promote to generate diverse but well-balanced retinal cell-types during retinal neurogenesis and cell differentiation, in addition to conventional classic view of Notch signaling pathway. However, one weak point is that, although the authors figured out what kinds of phenotypic difference appear in the KO retinas between these Notch components, the research result is descriptive and less analytical. Most of their conclusions may be supported by their previous works or others; it is still hypothetical. So, it is important to show more analytical data to support their interpretation and more clearly show what is new conceptual advance for Notch signaling pathways.

      For example, sustained Hes1 expression in the boundary region between optic stalk and neural retina may be reminiscent to brain isthmus situation. I would like to request the authors to show more direct evidence that Hes1 regulation in optic stalk/retina boundary is independent of Delta-Notch interaction. One possible experiment is whether DAPT treatment phenocopies Rax-Cre; Rbpj KO and Rax-Cre; dnMaml overexpression (Hes1 in optic stalk boundary is normal?).

      Response: Usage of the gamma secretase inhibitor DAPT is an interesting experiment as it can phenocopy the loss of Notch signaling in developing tissues. However, the reviewer's proposed DAPT experiment is problematic for two major reasons. First, DAPT blocks the gamma secretase complex, which has more than 90 protein targets in the cell membrane (3). Therefore, DAPT may not be informative for Hes1 regulation given the myriad of expected off-target effects. Second, it would be difficult to treat embryos at the relevant stages with DAPT. Injections into pregnant mice are lethal and we cannot localize drug to the relevant area during in vivo development. Our direct phenotypic comparisons with two Cre drivers strongly indicate that Hes1 is independent of canonical Notch signaling in the developing optic stalk.

      We include an extra related data figure (Reviewer Fig 1) showing anti-Hes1 immunolabeling of E13.5 Rax-Cre;Notch1CKO/CKO (n=2) and E13.5 Rax-Cre;Notch2CKO/CKO eyes (n=3). The Notch1 mutant lost oscillating Hes1 expression in retinal progenitors, but the uniform Hes1 ONH domain remains. Interestingly, the Notch2 mutant had essentially no effect on Hes1 (oscillating or sustained), or Hes5 mRNA expression. A Notch2 RNA in situ hybridization demonstrates that Notch2 mRNA was lost in the E13 optic cup and RPE (Rax-Cre expressing tissues). These data emphasize: A) the Notch1-specific dependency of oscillating Hes1 expression in retinal progenitors is absent from the ONH; B) although coexpressed in the same tissue, Notch receptors have unequal activities.

      Does Rax-Cre; Rbpj KO; Hes1-cKO phenocopy Rax-Cre; Hes1-cKO (or Rax-Cre; Hes1/3/5 cTKO)?

      Response: This is a good question! The first author tried very hard to produce Rax-Cre; Rbpj CKO;Hes1 CKO double mutant embryos. However, these progeny could not be recovered from E10-E13 embryos, despite collecting more than 10 litters. Thus, it is likely that this genotype is lethal before eye formation.

      Could the authors identify an enhancer element that drives Hes1 transcription in optic stalk/retina boundary, which should be not overlapped with that of NICD/ Rbpj binding motif? Such additional evidence will make their conclusion more convincing.

      Response: Another interesting question. We have been working for >3 years on Hes1 cis regulatory enhancers, but the pandemic greatly delayed progress. The proximal Hes1 600bp upstream region is a generic enhancer that contains Hes1 binding sites for repressing its own expression (4) and has a pair of Rbpj consensus sites for Notch ternary complex activation of Hes1 expression (5,6). Nearby is a binding site occupied by Gli2 in the E16 mouse retina (7). Recently, it was shown that Ikzf4 binds slightly farther away (8). The upstream 1.8 kb region (including the 600bp just described) can drive destabilized GFP or dsRed reporters in early postnatal retinal explants (9). However, this sequence was used to make and analyze a classic Hes1-GFP transgenic reporter mouse, in which GFP was not expressed in the early embryonic mouse optic vesicle or cup (10). Therefore, any early eye-specific enhancer(s) are located farther upstream, in an intron, or downstream (or combination thereof). Public domain epigenetic and chromatin accessibility datasets support this idea. Identifying the gene regulatory logic for Hes1 expression in the eye will be an exciting future story, well beyond this manuscript. We are excited to use live imaging of enhancer reporters to discern oscillating versus sustained activity patterns during early ocular development.

      Regarding the conclusion on new branchpoints on photoreceptor and amacrine cell genesis, a model shown in Figure 9 is still hypothetical. Figure 9B indicate a model in which the increase of Otx2+ cells and Crx+ cells in Rax-Cre; Rbpj KO is mediated by Hes1, which is presumed to be activated in Notch-independent signaling. However, Hes1 expression in the neural retina is markedly reduced in Rax-Cre; Rbpj KO (Fig. 2I), which does not fit in with the model.

      Response: We removed Fig 9B and now present new models about the Notch-dependent versus -independent roles for both Rbpj and Hes1. The new summary is Fig 8.

      So, I would like to request the authors to examine whether the increase of Otx2+ cells and Crx+ cells in Rax-Cre; Rbpj KO, (or Rax-Cre; dnMaml overexpression and Chx10-Cre; dnMaml overexpression) is inhibited by Hes1 KO.

      Response: If we understand this correctly, it would mean generating double mutants, some of which we determined are not viable (see the response above, and Suppl Table 2). Given there is only a partial knockdown of Hes1 or Hes5 in either dnMaml mutant we do not believe repeating this in the Hes1 CKO genetic background to be informative and it would take 3 generations to perform.

      Second, the authors concluded that both cone and rod genesis are enhanced in Rax-Cre; Rbpj KO by showing the data on Crx/Nr2e3 labeling in Rax-Cre; Hes1 cKO in Fig. 7BB. However, as the authors mentioned in the manuscript, Hes5 expression is elevated in Rax-Cre; Hes1 cKO (Fig. 1G). So, since Rax-Cre; Hes1 cKO has residual Hes activity in the retina, Fig. 7BB should be replaced with labeling of Crx/Nr2e3 in Rax-Cre; Hes1/3/5 cTKO.

      Response: Unfortunately, Rax-Cre;HesTKO embryos do not live past E13 (Suppl Table 2). Thus, we cannot evaluate rods, whose genesis starts around E13.5. Revised Fig 1G shows the Hes5 domain is shifted with the expansion of retinal tissue in E13.5 Hes1 single mutants, but importantly, also analogously shifted in Pax2 mutants (Fig 1H). We do not conclude that mRNA levels are "elevated" since mRNA in situ hybridization is not a quantitative technique. Our initial examination of rods in E17 Rax-Cre;Hes1 CKO mutants tested the idea of a fate shift from cones to rods. However, deeper quantification (Suppl Fig 5) do not support such a fate change.

      Furthermore, possibly, it is best to examine labeling of the retinas of Rax-Cre; Rbpj KO with rod and cone-specific markers and confirm that the number of both rods and cones is significantly increased. Third, as for defects in amacrine cells genesis in Rax-Cre; Rbpj KO, I would like to request the authors to show the data on Crx10-Cre; Rbpj KO. Although Rbpj KO is mosaic in Crx10-Cre; Rbpj KO, we can distinct Rbpj KO cells by GFP expression (Fig. S2C, C', C'). So, the authors can confirm that amacrine cell genesis is inhibited in a cell-autonomous manner in Crx10-Cre; Rbpj KO retinas but not in Crx10-Cre; dnMaml overexpression. Addition of such data will make the authors' conclusion is more convincing.

      Response: Suppl Table 1 lists multiple references (two from the NLB lab) that demonstrated both a rod and cone increase in Rbpj loss-of-function conditions. Chx10;Rbpj CKO animals were evaluated by Zheng et al., who showed an amacrine loss phenotype in these mutants (11). This is equivalent to what we see in our Rax-Cre;Rbpj CKO data, but without the complications of Chx10 mosaic Cre expression upon Rbpj deletion.

      Other comments: 1) Title of this manuscript is "Not all Notch pathway mutations are equal in the embryonic mouse retina". However, this title is quite obscure in what is research advancement of their findings. I suggest the authors to include more concrete and conclusive sentence in the title, for example "Hes and Rbpj differentially promotes retina/optic stalk boundary maintenance and photoreceptor genesis, in parallel with neurogenic inhibition by Notch signaling pathway".

      Response: We appreciate the reviewer's perspective. We are striving for a relatively simple title about a very complex topic, involving the in vivo genetic dissection of a signaling pathway. We modified the title to "Notch pathway mutations do not equivalently perturb mouse embryonic retinal development ".

      2) The "Results" section is a bit difficult to follow logics without detailed knowledge on roles of Notch signaling in mouse retinal development. I suggest the authors to improve a writing style of "Results" section for readers without such detailed knowledge on mouse Notch mutant phenotypes to follow logical flow more easily. There are many additional descriptions on research background before start to mention results. Such introductory sentences should be moved to the "Introduction" section, by which logical flow in the Results section should be simpler. In addition, the authors should show a concrete question at the beginning of each result subsection. Furthermore, the authors sometimes jump over from one result subsection and suddenly move to cite another figure panel in a far ahead subsection whose data has not been explained. Such a back-and-forth citation of figure data generally makes it difficult to follow logical flow.

      Response: We now present a considerable amount of new quantified data, reorganized multiple figures, and extensively rewrote the paper. We significantly revised the summary figure to improve clarity. In addition, Suppl Table 1 provides a wealth of background information to orient the reader on this topic. We feel that this extensive revision has greatly improved the quality, logical flow, and readability of the manuscript.

      3) In addition, figure configuration is not well organized. Each figure compared some particular marker expression in wild-type, Rax-Cre; HesTKO, Rax-Cre; Rbpj cKO, Rax-Cre; dn-Maml-GFP, Chx10-Cre; HesTKO, Chx10-Cre; Rbpj cKO, Chx10-Cre; dn-Maml-GFP. For example, Fig. 2 shows Hes1 for inhibition of neurogenesis, Fig. 3 shows Vsx2; Mitf and Pax2; Pax6 for retinal pigmented epithelium and optic stalk, Fig. 6 shows Atoh7, Rbpms, and Tubb3 for retinal ganglion cells. Fig. 7 shows Crx, Otx2, and Thrb2 for photoreceptor differentiation. Fig. 8 shows Prdm1, and Ptf1a for photoreceptors and amacrine cells. Although this figure configuration is convenient to show phenotypic difference between different genetic mutations, it is difficult to know how each differentiation steps are spatially and temporally coordinated during development. At least, I recommend the authors to show one summary figure, which shows spatio-temporal expression profile of retinal markers in wild-type mouse retinas.

      Response: We recognize this point and completely reorganized and combined Figs 6 and 7 to improve clarity. New Figure 6 presents E13 quantification for Atoh7, Otx2, Atoh7/Otx2, Rbpms and Crx expressing retinal populations. E16-E17 data were condensed and moved to a new Suppl Fig 5.

      4a) Page 7, line 7-10 "With earlier deletion using Rax-Cre, hes5 mRNA abnormally extended into the optic stalk": I wonder how the authors define the optic stalk. It is likely that optic stalk area (Pax2+, Vax1+ area) is shifted to more proximal (depart from the optic cup and move toward the brain), and neural retina is expanded accordingly (Fig. 4B, 4F), resulting in expansion of hes5 expression. Thus, it may be better to mention that optic stalk/neural retina boundary is abnormally shifted toward the brain.

      Response: The retina, including the optic nerve head, ends where the adjacent RPE terminates. This is conspicuous morphologically in our sections. We also defined this by colabeling for Pax2 and Pax6, which is now quantified in revised Fig 3. To clarify this further, we added the words " in all panels the brain is to the right" in the Fig 4 legend.

      4b) Page 8, line 14-15, "ONH/OS cells still express it (Hes1), demonstrating that sustained Hes1 is independent of Notch": I presume that Cre-Rax drives Cre in neural retina as well as optic stalk and pigmented epithelium. However, it is likely that Rbpj is not expressed in optic stalk/neural retina boundary area in wild type (Fig. S2A). No expression of Rbpj in optic stalk/neural retina boundary may support that Hes1 expression in this boundary area is Notch-independent. However, Rbpj expression is retained in some vitreal cells near optic nerve head in Rax-Cre; Rbpj-CKO retinas (Fig. S2B). What are these Rbpj+ cells? I would like to request the authors to confirm that Rbpj expression is completely absent in both neural retina and optic stalk in Rax-Cre; Rbpj-CKO mice. Otherwise, this conclusion is still not fully supported.

      Response: We show the Rax-Cre lineage in Suppl Fig 2 via the Ai9 (tomato) reporter. The results are striking, with all of the optic cup derivatives (retina, RPE, ONH, optic stalk, and presumptive ciliary tissue and iris) being tomato positive, while the well-described population of vascular cells in the hyaloid space lack tomato expression. Furthermore, our figure shows that Rbpj expression is only absent from the optic cup derivates, rather than the vascular structures in the vitreous. Vascular cells also depend on the Notch pathway and express Rbpj. Based on considerable evidence from the literature and our lineage experiments, the population of cells the reviewer highlights represents the hyaloid vasculature and associated cell types. It does not represent any population that derives from neuroectoderm.

      4c) Page 9, line 16-18, "Foxg1 had spread into the nasal optic stalk": Is Foxg1 expanded nasal area really "OS" rather than expanded retina? I suggest the authors to confirm molecular markers Pax2 expression is overlapped with Foxg1. Otherwise, it is difficult to conclude that foxg1 is expanded into the optic stalk territory, because foxg1 is normally a marker of retina. Indeed, Fig. 3K shows pax2 expression is shifted into more inside towards the brain, suggesting that neural retina is expanded. Please explain the situation.

      Response: Foxg1 (BF-1) mRNA and protein are found in the nasal retina and are expressed in other brain tissues. Multiple studies show Foxg1 in the nasal side of the E10 optic cup/retina/optic stalk and developing hypothalamus (See extra data figure Reviewer Fig 2; top row figure is data from Smith et al., 2017 (12) with Foxg1 mRNA in purple. Also see our new manuscript panel Fig 1C. We include here for reviewers (extra data Reviewer Fig 2 showing E13 ocular cryosections colabeled for Foxg1 and Pax2, highlighting their relationship in the retina, optic stalk and adjacent forming hypothalamus. On page 9 the text now reads "At E13.5 Rax-Cre;HesTKO eyes, the Foxg1 nasal retinal domain was contiguous with the nasal optic stalk (Suppl Fig 4D). This is reminiscent of younger stages (Fig 1C), since normally at E13.5, Foxg1 in the nasal optic cup/retina is separated from expression in the ONH/OS (Suppl Fig 4A). Based on the expansion of Pax6, Vsx2 and Hes5 RPC domains into the optic stalk, we conclude that the change in Foxg1 similarly reflects an extension of retinal tissue."

      4d) Page 10, line 4-5, In Rax-Cre; Hes1/3/5 cTKO eye, this tissue (RPE) extended into the optic stalk": This description seems to be incorrect. A part of Pax2 area, which is adjacent to the neural retina, contacts with RPE in wild type (Fig. 3AH), so most of RPE covers the neural retina even in Fig. 3DK.

      Response: We disagree with the reviewer’s interpretation. Fig 3D shows Mitf labeling of RPE nuclei. Figure 3K shows the adjacent section labeled with Pax2 and Pax6 (labels both retina and RPE). As the retina extended "towards the brain", the RPE analogously extends and surrounds the retinal domain. We also added higher magnification data panels 3H, 3K and 3N, showing merged and single channels.

      4e) Page 10, line 22-23, "For Chk10-Cre; Hes1/3/5 cTKO, there was a unique presence of ectopic Pax2 within the retinal territories": I wonder if this description is correct. I suspect that proliferative Pax2+ cells expand into regressing territory of Hes KO retinal cells, which undergo precocious neurogenesis and lose proliferative activity, in Chk10-Cre; HesTKO. In this case, it is possible that the Pax2/Pax6 interface may be maintained. Please show red and green channel panels for Fig. 3N to confirm that there is ectopic pax2 and pax6 double positive cells.

      Response: New quantification in revised Fig 3 (see panels O,P) fully supports our original conclusion. Only Chx10-Cre;HesTKO mutants have a statistically significant increase in Pax2+ cells. There are not more Pax2+Pax6+ double labeled cells. Only this particular genotype has an increase in Pax2+ single labeled cells.

      5a) Page 11, line 20-25. There seems to be inconsistency between result description and image data of Fig. 5A-G, and histogram Fig. 5O. Authors mentioned that a modest loss of pH3+ cell fraction in Chx10-Cre; Hes1/3/5 cTKO but not in Rax-Cre; Hes1/3/5 cTKO. However, Fig. 5D indicates severe reduction of pH3+ cell fraction in Rax-Cre; Hes1/3/5/ cTKO, which is similar to reduction of pH3+ cell fraction in Rex-Cre; Rbpj (Fig. 5B), but histogram data is different (Fig. 5O). Furthermore, pH3+ cell fraction is severely reduced in Chx10-Cre; ROSA(dn-Maml-GFP) (Fig. 5F) and modestly reduced in Chx10-Cre; Hes1/3/5 cTKO (Fig. 5G). However, pH3+ cell fraction seems to be normal in Chx10-Cre; Rbpj (Fig. 5E). These Chx10-Cre image data do not match the histogram of Fig. 5O. Please check their situation.

      Response: Images in old Figs 5-8 were normalized using area measurements, see methods and above comments (note: old Figs 6&7 were combined into new Fig 6). One-way ANOVA with pairwise comparisons for each mutant genotype compared to control were calculated using Prism. All genotypes except two have a statistically significant loss of M phase cells and we discuss possibilities for this outcome (Fig 5O). A normalization method for the sampled area is an essential component of these studies since morphologic differences are apparent for particular genotypes. The quantitative data are consistent with our original conclusions.

      5b) Fig. 5H-N, P: I wonder if the stage E13 is appropriate to evaluate cell death and survival because optic cup already becomes smaller in Rax-Cre; Rbpj, Hes1/3/5 cTKO, or ROSA(dn-MAML-GFP) than in wild-type control. I suggest the authors examine more earlier stage.

      Response: While an earlier effect is possible, we only observed size differences in a subset of the genotypes. Thus, E13 serves as a critical timepoint to examine early developmental phenotypes across the totality of our mutant conditions. It is also first age when the ONH is fully formed.

      5c) Page 12, line 19-20, "all other mutants (Chx10-Cre; Rbpj, and Chx10-Cre; ROSA(dn-MAML-GFP) were unaffected (Fig. 6EF, LM, ST)": It is likely that atoh7 expressing cells are mildly decreased and neuronal marker, Tubb3 and Rbpms-expressing cells are increased in Chx10-Cre; Rbpj, and Chx10-Cre; ROSA(dn-MAML-GFP). I requested the authors to evaluate the fraction of these markers in retinal area statistically in all the cases.

      Response: As described above, we quantified Atoh7 and Rbpms nuclear expression by immunohistochemistry. We do not believe that Tubb3+ cells can be reliably quantified. Nonetheless, it is useful to qualitatively show the extent of excess neuron formation. Importantly, we observed that it is not the Atoh7 status that matters for RGC formation, rather it is the Otx2 expression status. This is in good agreement with single cell-RNA transcriptomics data from Wu et al 2021 showing that Atoh7 mRNA in all early transitional RPCs remains fairly constant and its loss does not block the formation of early RGC cell states (13). By contrast Otx2 fluctuates but remains expressed in transitional RPCs that progress to photoreceptor lineages.

      6a) Page 7, line 19 "Ectopic blood vessels protruded from the ONH (Fig. 1K, 1L)": It is difficult to see blood vessel structures in these panels (Fig. 1I-L). Please show some molecular marker of blood vessels to confirm how blood vessel is organized in Hes1/3/5 cTKO.

      Response: These vascular structures are highly conspicuous by morphology in the H&E insets. Nonetheless, we used adjacent P21 sections to immunolabel for Endomuscin (14) and Tubb3 antibodies. This colabeling confirms the morphology and position of ectopic blood vessels in the abnormal tissue masses in Chx10-Cre;HesTKO mutant eyes. Ectopic tissue contains only rare Tubb3+ cells or cell processes suggesting it is overwhelmingly nonneural. All P21 data were moved to a new Suppl Fig 2. A full detailing of vascular phenotypes is beyond the scope of this manuscript and, interestingly, would be potentially attributable to non-autonomous effects of perturbing the Hes genes in the adjacent retina.

      6b) Fig. 5: Increase of pH3 fraction indicates several possibilities, for example (1) increased fraction of mitotic cells due to precocious neurogenesis, (2) increased fraction of mitotic cells due to activated cell proliferation of retinal progenitor cells, (3) increased cell-cycle arrest in M phase due to some stress response of progenitor cells. So, I suggest the authors to examine (1) BrdU percentage of retinal section area, (2) the percentage of pH3+ cells in PCNA+ retinal cells.

      Response: The data listed in Suppl Table 1 presents a unified picture that disrupting Notch signaling reduced proliferation. This paradigm extends to other model organisms (e.g., Drosophila, chick, frog, zebrafish and even to nonneural tissues). We included the phospho-histone H3 staining so readers would see how the six mutants evaluated in this study align with this paradigm, providing confidence for the novel findings in other figures. A full evaluation of cell cycle kinetics is interesting, but beyond the scope and focus of this manuscript.

      6c) Fig. 5: It is better that cell death fraction will be evaluated by TUNEL and labeling with anti-activated caspase 3 antibody.

      Response: We disagree. The DNA repair enzyme PARP is inactivated upon cleavage by activated caspase 3. There are currently ~3,600 citations that use it as a marker of apoptosis. PARP also has a separate and very specific role in maintaining the integrity of sperm DNA. This antibody works on all metazoans and is amenable to many tissue preparations and fixatives, making it easy to use, robust and quantifiable.

      7a) Please show red channel (Hes1) image in Fig1BC.

      Response: This was added to Revised Fig 1 (Fig 1A).

      7b) Fig. 1DH should be shown in neighbor. Fig. 1H should be assigned as Fig. 1E.

      Response: The new Fig 1 layout addresses this point.

      7c) Fig. S2D, F, H, J: Please show GFP green channel as well. Otherwise, it is difficult to see non-overlapping expression in optic stalk area.

      Response: In the revision, this is Suppl Fig 3. Chx-10-Cre is not expressed by ONH-OS cells (1). The green and fuchsia overlap (coexpression) in RPCs is white, we feel this is fairly clear. If needed, all readers can turn on and off the green channel in the final PDF version of this figure to compare GFP with Hes1 expression for those panels.

      7d) Fig. 9B: It is better to show Rax-Cre: Hes1/3/5 TKO rather than Rax-Cre: Hes1 cKO. 7e) Fig. 9B: Lettering "Rbpj mutant" should be revised as "Rax-Cre: Rbpj KO".

      Response: Fig 9B was removed so these terms are now irrelevant. Our models are presented in new Fig 8.

      Significance: The senior author of this manuscript, Dr. Nadean Brown, is an expert scientist who has investigate the role of Notch signaling pathway in vertebrate ocular tissue, including the neural retina and lens. In general, Notch signaling pathway consists of signaling stream from the interaction of Delta and Notch, Notch receptor activation by proteolytic cleavage, translocation of Notch intracellular domain (NICD) into nucleus, formation of transcription factor complex consisting of NICD/Rbpj/Maml, to the transcriptional activation of Notch target genes, Hes family transcription factors. Finally, Hes suppresses neurogenic program and maintain a pool of neural progenitor cells. Therefore, Notch is a key factor to regulate the balance between neurogenesis and progenitor proliferation. In this manuscript, the authors investigated retinal phenotypes in the knockout mice of different Notch signaling components, including Rbpj, Maml, and Hes. They found that functions of these three factors are not always equal in retinal cell differentiation; rather, they specifically regulate a particular step of retinal development. The authors propose the possibility that each of Notch signaling components may be modified by other signaling pathways and achieve some new roles beyond the conventional frame of classic Notch signaling pathway. In this point, this work has a potential to provide a new conceptual advance in the field of developmental and cell biology.

      We fully agree this work is a significant advance for the fields of developmental and cell biology. Our findings provide new information and stimulate fresh ideas for anyone working on signal transduction and signal integration.

      References cited:

      1. Bosze et al., 2020 Journal of Neuroscience Vol 40:1501-13; Bosze et al. 2021 Dev Biol Vol 472:18-29.
      2. Han et al., 2023 Development Vol 150 dev201408.
      3. Kopan and Ilagan, 2004 Nat Rev Cell Biol. Vol 5:499-504
      4. Hirata et al., 2002 Science Vol 298:840-3
      5. Friedmann and Kovall, 2010 Protein Sci. Vol 19:34-46
      6. Ong et al., 2006 JBC Voll24:5106-19
      7. Wall et al., 2009 J Cell Biol. Vo 184: 101-12.
      8. Javed et al., 2023 Development Vol 150:dev200436
      9. Matuda and Cepko 2007 PNAS Vol 104: 1027-1032
      10. Ohtsuka et al., 2006 Mol. Cell Neurosci. Vol 31:109-22
      11. Zheng et al., 2009 Molecular Brain Vol 2:38
      12. Smith et al., 2017 Journal of Neuroscience Vol 37:7975-93.
      13. Wu et al., 2021 Nature Communications Vol 12:1465: doi 10.1038/s41467-021-21704-4
      14. Saint-Geniez et al., 2009 IOVS Vol 50: 311-21.
    2. Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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      Referee #3

      Evidence, reproducibility and clarity

      I have reviewed a manuscript submitted by Bosze et al., which is entitled "Not all Notch pathway mutations are equal in the embryonic mouse retina". The authors focused on Notch signaling pathway. Notch signaling is deeply conserved across vertebrate and invertebrate animal species: in general, two transmembrane proteins, Delta and Notch, interact as a ligand and a receptor, respectively, which induces proteolytic cleavage of Notch receptors to generate Notch intracellular domain (NICD). NICD is translocated into nucleus, then forms the transcription factor complex including Rbpj (also referred to as CBF1) and Mastermind-like (Maml), and activates the transcription of Hes family transcription factors. Three Hes proteins, Hes1, 3, and 5, are important for nervous system development. In the vertebrate developing retina, these Hes proteins inhibit neurogenesis to maintain a pool of neural progenitor cells. In addition to their primary role in neurogenesis, the authors recently reported that Hes1 promotes cone photoreceptor differentiation. In the later stages of development, Hes proteins also promote Müller glial differentiation. In addition, Hes1 is highly expressed in the boundary between the neural retina and optic stalk and required for this boundary maintenance.

      To understand precise regulation of Notch component-mediated signaling network for retinal neurogenesis and cell differentiation, the authors compared retinal phenotypes in the knockdown of three Notch pathway components, that is (1) Hes1/3/5 cTKO, (2) Rbpj KO, and (3) dominant-negative Maml (dnMaml) overexpression, under the control of two Cre derivers; Rax-Cre and Chx10-Cre.

      First, the authors found that Hes1 expression in the boundary between optic stalk and neural retina is lost in Rax-Cre; Hes1/3/5 cTKO, but still retained in Rax-Cre; Rbpj KO and Rax-Cre; dnMaml overexpression, suggesting that Delta-Notch interaction is not required for Hes1 expression in the boundary between optic stalk and neural retina. Furthermore, Hes1 expressing boundary region expands distally at the expense of the neural retina in Chx10-Cre; Hes1/3/5 cTKO. Maintenance of ccd2 expression in this expanded boundary area suggests that Hes1 normally maintains a proliferative state in the optic stalk, which may allow these cells to differentiate into astrocyte in later stages.

      Second, in addition to precocious RGC differentiation in all the Notch component KO, the authors found that, as compared with wild-type, cone and rod photoreceptor genesis is highly enhanced in Rax-Cre; Rbpj KO and Rax-Cre; dnMaml overexpression and mildly enhanced in Chx10-Cre; dnMaml overexpression. On the other hand, in Rax-Cre; Hes1/3/5 cTKO, cone and rod photoreceptor genesis is not enhanced but similar to wild-type level. Since the authors previously reported that cone genesis is reduced in Rax-Cre; Hes1 cKO and Chx10-Cre; Hes1 cKO, so Rax-Cre; Hes1/3/5 cTKO may rescue decrease in cone genesis in single Hes1 cKO. The authors raise the possibility that elevated Hes5 expression in single Hes1 cKO may suppress cone photoreceptor genesis. The authors also found that amacrine cell genesis is significantly suppressed in Rax-Cre; Rbpj KO but not changed in Rax-Cre; dnMaml overexpression and Rax-Cre; Hes1/3/5 cTKO, suggesting that Rbpj is specifically required for amacrine cell genesis. From these observations, the authors propose that there are at least two branchpoints for photoreceptor and amacrine cell genesis in Notch component-mediated signaling network.

      Their findings are very interesting and provide some new insight on how Notch signaling components are integrated into other signaling pathways and promote to generate diverse but well-balanced retinal cell-types during retinal neurogenesis and cell differentiation, in addition to conventional classic view of Notch signaling pathway. However, one weak point is that, although the authors figured out what kinds of phenotypic difference appear in the KO retinas between these Notch components, the research result is descriptive and less analytical. Most of their conclusions may be supported by their previous works or others; it is still hypothetical. So, it is important to show more analytical data to support their interpretation and more clearly show what is new conceptual advance for Notch signaling pathways.

      For example, sustained Hes1 expression in the boundary region between optic stalk and neural retina may be reminiscent to brain isthmus situation. I would like to request the authors to show more direct evidence that Hes1 regulation in optic stalk/retina boundary is independent of Delta-Notch interaction. One possible experiment is whether DAPT treatment phenocopies Rax-Cre; Rbpj KO and Rax-Cre; dnMaml overexpression (Hes1 in optic stalk boundary is normal?). Does Rax-Cre; Rbpj KO; Hes1-cKO phenocopy Rax-Cre; Hes1-cKO (or Rax-Cre; Hes1/3/5 cTKO)? Could the authors identify an enhancer element that drives Hes1 transcription in optic stalk/retina boundary, which should be not overlapped with that of NICD/ Rbpj binding motif? Such additional evidence will make their conclusion more convincing.

      Regarding the conclusion on new branchpoints on photoreceptor and amacrine cell genesis, a model shown in Figure 9 is still hypothetical. Figure 9B indicate a model in which the increase of Otx2+ cells and Crx+ cells in Rax-Cre; Rbpj KO is mediated by Hes1, which is presumed to be activated in Notch-independent signaling. However, Hes1 expression in the neural retina is markedly reduced in Rax-Cre; Rbpj KO (Fig. 2I), which does not fit in with the model. So, I would like to request the authors to examine whether the increase of Otx2+ cells and Crx+ cells in Rax-Cre; Rbpj KO, (or Rax-Cre; dnMaml overexpression and Chx10-Cre; dnMaml overexpression) is inhibited by Hes1 KO. Second, the authors concluded that both cone and rod genesis are enhanced in Rax-Cre; Rbpj KO by showing the data on Crx/Nr2e3 labeling in Rax-Cre; Hes1 cKO in Fig. 7BB. However, as the authors mentioned in the manuscript, Hes5 expression is elevated in Rax-Cre; Hes1 cKO (Fig. 1G). So, since Rax-Cre; Hes1 cKO has residual Hes activity in the retina, Fig. 7BB should be replaced with labeling of Crx/Nr2e3 in Rax-Cre; Hes1/3/5 cTKO. Furthermore, possibly, it is best to examine labeling of the retinas of Rax-Cre; Rbpj KO with rod and cone-specific markers and confirm that the number of both rods and cones is significantly increased. Third, as for defects in amacrine cells genesis in Rax-Cre; Rbpj KO, I would like to request the authors to show the data on Crx10-Cre; Rbpj KO. Although Rbpj KO is mosaic in Crx10-Cre; Rbpj KO, we can distinct Rbpj KO cells by GFP expression (Fig. S2C, C', C'). So, the authors can confirm that amacrine cell genesis is inhibited in a cell-autonomous manner in Crx10-Cre; Rbpj KO retinas but not in Crx10-Cre; dnMaml overexpression. Addition of such data will make the authors' conclusion is more convincing.

      Other comments are shown below.

      1. Title of this manuscript is "Not all Notch pathway mutations are equal in the embryonic mouse retina". However, this title is quite obscure in what is research advancement of their findings. I suggest the authors to include more concrete and conclusive sentence in the title, for example "Hes and Rbpj differentially promotes retina/optic stalk boundary maintenance and photoreceptor genesis, in parallel with neurogenic inhibition by Notch signaling pathway".
      2. The "Results" section is a bit difficult to follow logics without detailed knowledge on roles of Notch signaling in mouse retinal development. I suggest the authors to improve a writing style of "Results" section for readers without such detailed knowledge on mouse Notch mutant phenotypes to follow logical flow more easily. There are many additional descriptions on research background before start to mention results. Such introductory sentences should be moved to the "Introduction" section, by which logical flow in the Results section should be simpler. In addition, the authors should show a concrete question at the beginning of each result subsection. Furthermore, the authors sometimes jump over from one result subsection and suddenly move to cite another figure panel in a far ahead subsection whose data has not been explained. Such a back-and-forth citation of figure data generally makes it difficult to follow logical flow.
      3. In addition, figure configuration is not well organized. Each figure compared some particular marker expression in wild-type, Rax-Cre; HesTKO, Rax-Cre; Rbpj cKO, Rax-Cre; dn-Maml-GFP, Chx10-Cre; HesTKO, Chx10-Cre; Rbpj cKO, Chx10-Cre; dn-Maml-GFP. For example, Fig. 2 shows Hes1 for inhibition of neurogenesis, Fig. 3 shows Vsx2; Mitf and Pax2; Pax6 for retinal pigmented epithelium and optic stalk, Fig. 6 shows Atoh7, Rbpms, and Tubb3 for retinal ganglion cells. Fig. 7 shows Crx, Otx2, and Thrb2 for photoreceptor differentiation. Fig. 8 shows Prdm1, and Ptf1a for photoreceptors and amacrine cells. Although this figure configuration is convenient to show phenotypic difference between different genetic mutations, it is difficult to know how each differentiation steps are spatially and temporally coordinated during development. At least, I recommend the authors to show one summary figure, which shows spatio-temporal expression profile of retinal markers in wild-type mouse retinas.
      4. There are several logically incorrect sentences or inconsistent sentences in the results section. Please respond my comment below.
        • a) Page 7, line 7-10 "With earlier deletion using Rax-Cre, hes5 mRNA abnormally extended into the optic stalk": I wonder how the authors define the optic stalk. It is likely that optic stalk area (Pax2+, Vax1+ area) is shifted to more proximal (depart from the optic cup and move toward the brain), and neural retina is expanded accordingly (Fig. 4B, 4F), resulting in expansion of hes5 expression. Thus, it may be better to mention that optic stalk/neural retina boundary is abnormally shifted toward the brain.
        • b) Page 8, line 14-15, "ONH/OS cells still express it (Hes1), demonstrating that sustained Hes1 is independent of Notch": I presume that Cre-Rax drives Cre in neural retina as well as optic stalk and pigmented epithelium. However, it is likely that Rbpj is not expressed in optic stalk/neural retina boundary area in wild type (Fig. S2A). No expression of Rbpj in optic stalk/neural retina boundary may support that Hes1 expression in this boundary area is Notch-independent. However, Rbpj expression is retained in some vitrial cells near optic nerve head in Rax-Cre; Rbpj-CKO retinas (Fig. S2B). What are these Rbpj+ cells? I would like to request the authors to confirm that Rbpj expression is completely absent in both neural retina and optic stalk in Rax-Cre; Rbpj-CKO mice. Otherwise, this conclusion is still not fully supported.
        • c) Page 9, line 16-18, "Foxg1 had spread into the nasal optic stalk": Is Foxg1 expanded nasal area really "OS" rather than expanded retina? I suggest the authors to confirm molecular markers Pax2 expression is overlapped with Foxg1. Otherwise, it is difficult to conclude that foxg1 is expanded into the optic stalk territory, because foxg1 is normally a marker of retina. Indeed, Fig. 3K shows pax2 expression is shifted into more inside towards the brain, suggesting that neural retina is expanded. Please explain the situation.
        • d) Page 10, line 4-5, In Rax-Cre; Hes1/3/5 cTKO eye, this tissue (RPE) extended into the optic stalk": This description seems to be incorrect. A part of Pax2 area, which is adjacent to the neural retina, contacts with RPE in wild type (Fig. 3AH), so most of RPE covers the neural retina even in Fig. 3DK.
        • e) Page 10, line 22-23, "For Chk10-Cre; Hes1/3/5 cTKO, there was a unique presence of ectopic Pax2 within the retinal territories": I wonder if this description is correct. I suspect that proliferative Pax2+ cells expand into regressing territory of Hes KO retinal cells, which undergo precocious neurogenesis and lose proliferative activity, in Chk10-Cre; HesTKO. In this case, it is possible that the Pax2/Pax6 interface may be maintained. Please show red and green channel panels for Fig. 3N to confirm that there is ectopic pax2 and pax6 double positive cells.
      5. There seems to be some mismatch descriptions between image data and histogram (or text in the result section). Please respond my comments below.
        • a) Page 11, line 20-25. There seems to be inconsistency between result description and image data of Fig. 5A-G, and histogram Fig. 5O. Authors mentioned that a modest loss of pH3+ cell fraction in Chx10-Cre; Hes1/3/5 cTKO but not in Rax-Cre; Hes1/3/5 cTKO. However, Fig. 5D indicates severe reduction of pH3+ cell fraction in Rax-Cre; Hes1/3/5/ cTKO, which is similar to reduction of pH3+ cell fraction in Rex-Cre; Rbpj (Fig. 5B), but histogram data is different (Fig. 5O). Furthermore, pH3+ cell fraction is severely reduced in Chx10-Cre; ROSA(dn-Maml-GFP) (Fig. 5F) and modestly reduced in Chx10-Cre; Hes1/3/5 cTKO (Fig. 5G). However, pH3+ cell fraction seems to be normal in Chx10-Cre; Rbpj (Fig. 5E). These Chx10-Cre image data do not match the histogram of Fig. 5O. Please check their situation.
        • b) Fig. 5H-N, P: I wonder if the stage E13 is appropriate to evaluate cell death and survival because optic cup already becomes smaller in Rax-Cre; Rbpj, Hes1/3/5 cTKO, or ROSA(dn-MAML-GFP) than in wild-type control. I suggest the authors examine more earlier stage.
        • c) Page 12, line 19-20, "all other mutants (Chx10-Cre; Rbpj, and Chx10-Cre; ROSA(dn-MAML-GFP) were unaffected (Fig. 6EF, LM, ST)": It is likely that atoh7 expressing cells are mildly decreased and neuronal marker, Tubb3 and Rbpms-expressing cells are increased in Chx10-Cre; Rbpj, and Chx10-Cre; ROSA(dn-MAML-GFP). I requested the authors to evaluate the fraction of these markers in retinal area statistically in all the cases.
      6. Some experiments are necessary to improve their design. Please respond my comments below.
        • a) Page 7, line 19 "Ectopic blood vessels protruded from the ONH (Fig. 1K, 1L)": It is difficult to see blood vessel structures in these panels (Fig. 1I-L). Please show some molecular marker of blood vessels to confirm how blood vessel is organized in Hes1/3/5 cTKO.
        • b) Fig. 5: Increase of pH3 fraction indicates several possibilities, for example (1) increased fraction of mitotic cells due to precocious neurogenesis, (2) increased fraction of mitotic cells due to activated cell proliferation of retinal progenitor cells, (3) increased cell-cycle arrest in M phase due to some stress response of progenitor cells. So, I suggest the authors to examine (1) BrdU percentage of retinal section area, (2) the percentage of pH3+ cells in PCNA+ retinal cells.
        • c) Fig. 5: It is better that cell death fraction will be evaluated by TUNEL and labeling with anti-activated caspase 3 antibody.
      7. Panel configuration of Figures should be revised as below.
        • a) Please show red channel (Hes1) image in Fig1BC.
        • b) Fig. 1DH should be shown in neighbor. Fig. 1H should be assigned as Fig. 1E.
        • c) Fig. S2D, F, H, J: Please show GFP green channel as well. Otherwise, it is difficult to see non-overlapping expression in optic stalk area.
        • d) Fig. 9B: It is better to show Rax-Cre: Hes1/3/5 TKO rather than Rax-Cre: Hes1 cKO.
        • e) Fig. 9B: Lettering "Rbpj mutant" should be revised as "Rax-Cre: Rbpj KO".

      Significance

      The senior author of this manuscript, Dr. Nadean Brown, is an expert scientist who has investigate the role of Notch signaling pathway in vertebrate ocular tissue, including the neural retina and lens. In general, Notch signaling pathway consists of signaling stream from the interaction of Delta and Notch, Notch receptor activation by proteolytic cleavage, translocation of Notch intracellular domain (NICD) into nucleus, formation of transcription factor complex consisting of NICD/Rbpj/Maml, to the transcriptional activation of Notch target genes, Hes family transcription factors. Finally, Hes suppresses neurogenic program and maintain a pool of neural progenitor cells. Therefore, Notch is a key factor to regulate the balance between neurogenesis and progenitor proliferation. In this manuscript, the authors investigated retinal phenotypes in the knockout mice of different Notch signaling components, including Rbpj, Maml, and Hes. They found that functions of these three factors are not always equal in retinal cell differentiation; rather, they specifically regulate a particular step of retinal development. The authors propose the possibility that each of Notch signaling components may be modified by other signaling pathways and achieve some new roles beyond the conventional frame of classic Notch signaling pathway. In this point, this work has a potential to provide a new conceptual advance in the field of developmental and cell biology.

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      Referee #2

      Evidence, reproducibility and clarity

      Hes genes are effectors of the Notch signaling pathway but can also act down-stream of other signaling cascades. In this manuscript the authors attempt to address the complexity of Hes effectors during optic cup development and retinal neurogenesis. To do so, they compared optic cup patterning and retinal neurogenesis in seven germline or conditional mutant mouse embryos generated with two spatio-temporally distinct Cre drivers. These lines allowed for the analysis of the consequences of perturbing the Notch ternary complex and multiple Hes genes alone or in combination. The authors show that the optic disc/nerve head is regulated by Notch independent Hes1 function. They also confirm that perturbation of Notch signaling interferes with cell proliferation enhancing the production of differentiated ganglion cells, whereas photoreceptor genesis requires both Rbpj and Hes1 with Notch dependent and independent mechanisms.

      This is a rather complex study that dissects further the role of the Notch pathway and Hes proteins during eye development, a topic that has been addressed in many previous studies but perhaps not with the details that the authors have used here. In this respect, this study adds to current literature but will likely be of interest to retina aficionados. The manuscript reads well and the figures are of very good quality. However, many of the statements are based on qualitative rather than on quantitative analysis. This should be, at least in some cases, remediated, despite the effort that this may require given the number of mouse lines used in the study. Specific comments are listed below:

      1. The title is somewhat misleading. The authors have explored mostly the role of Hes1, 3 and5. Although these are Notch effectors, there is already evidence that they participate in other pathways This is confirmed by the data present here. I would suggest to eliminate Notch from the title and use instead "Hes" to better reflect the findings. Furthermore, it is unclear why there is a reference to "mutations" or what are the Notch branchpoints to which the authors refer at the beginning of the discussion.
      2. "Although the Pax6-Pax2 boundary is intact in Rax-Cre;RbpjCKO/CKO eyes, ONH shape was attenuated compared to controls (Fig 3I)". This statement is arguable as the difference seems subtle. Perhaps some kind of quantification would help.
      3. Page 12 first paragraph. "....but all other genotypes were unaffected". This statement is unclear. All lines in which the Rax-cre has been used seem to have an increased number of apoptotic cells. This should be better explained
      4. Page 12, end of second paragraph: "E13.5 Chx10-Cre;HesTKO eyes had a milder RGC phenotype (Figs 6G, 6N, 6U), but all other mutants were unaffected (Figs 6E, 6F, 6L, 6M, 6S, 6T). This statement is also rather subjective. The phenotype of Chx10-Cre;HesTKO is quite strong and the other mutants seem to have a phenotype. Some quantifications here will help.
      5. Page 13, toward the bottom..."...but noted that Chx10-Cre RbpjCKO/CKO eyes were not different from controls (Figs 7E, 7AA)". Again, this statement is questionable as staining for both CRX and Rbpms seem reduced as compared to controls as quantifications in 7AA seems also to indicate (about half?). Did the authors calculate whether there is a statistical difference between controls and Chx10-Cre RbpjCKO/CKO ?
      6. In Fig 7CC the authors should make the effort of including at least one additional sample, 2 biological replicates seem insufficient to draw a conclusion.

      Significance

      This is a rather complex study that dissects further the role of the Notch pathway and Hes proteins during eye development, a topic that has been addressed in many previous studies but perhaps not with the details that the authors have used here. In this respect, this study adds to current literature but will likely be of interest to retina aficionados. The manuscript reads well and the figures are of very good quality. However, many of the statements are based on qualitative rather than on quantitative analysis. This should be, at least in some cases, remediated, despite the effort that this may require given the number of mouse lines used in the study.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary: In this study, the authors employed an impressive set of mouse mutant or Cre lines to investigate the complexity of Notch signaling across different stages of retinal development. These comprehensive analyses led to two main findings: 1. Sustained hes1 in the OHS/OS is Notch-independent; 2. Rbpj and Hes1 exhibited opposing roles in cone photoreceptor development. Although the study is potentially interesting, the current manuscript needs the essential research background and quantification, a lack of which significantly reduced the clarity of the manuscript and the credibility of the major conclusions. Also, how the authors organized the results is quite confusing, making the manuscript very difficult to follow.

      Major comments:

      1. The authors needed to make the quantification for many analyses to strengthen the conclusions, such as Fig. 1F, 1G, and etc.
      2. The authors reported many exciting results. However, further mechanistic insights are largely missing. They may focus on one of these exciting findings and give some mechanistic insights. For example, hes1 suppresses hes5 expression as the ONH boundary forms; hes1 expression in the ONH is Notch independent; differential influences of Rbpj and Hes1 on cone development. It is better for the authors to select one of these exciting findings and provide a deeper mechanistic study.
      3. Some analyses lack an explanation of the rationale. For example, "To understand if the loss of multiple Hes genes is more catastrophic than Hes1 alone..."(PAGE 7). Please explain its significance.

      Significance

      In general, many results are quite interesting. However, the significance of these findings is largely hampered in the following aspects: 1. The authors were unable to provide the sufficient research contexts that are essential for understanding many results.2. Many conclusions were solely based on descriptive images but lacked statistical quantification, which significantly weakened many conclusions. 3. Many interesting findings are quite descriptive, and some mechanistic understandings of one of these exciting findings will be beneficial to improve the focus and significance of the study.

      Current format of the manuscript fits more specialized audience.

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

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      Referee #3

      Evidence, reproducibility and clarity

      Schiano and colleagues present data on two mouse knock-in models with a missense mutation in uromodulin (C171Y and R186S). A strength of the paper is that the mutations are found in patients with autosomal dominant tubulointerstitial kidney disease (ADTKD) but lead to divergent disease progression. The mouse models are characterized in detail examining changes in uromodulin processing, plasma and urine biochemistry and transcript levels by RNA-sequencing. These findings combined with studies in collecting duct lines provide evidence that the extent of uromodulin aggregate formation is related to the severity of the disease and mechanisms are provided to explain these findings including clearance pathway which might be targeted in the future. Overall, there is a large quantity of good data in the manuscript which moves our understanding of uromodulin mutations forward. However, there are some issues that need to be addressed as outlined below.

      Major Comments

      1. In the Introduction, the authors state that the current mouse models have only provided limited information warranting this new study. More information is required here to provide a stronger rationale. What are the specific weaknesses of the prior approaches and what precise questions remain unanswered and how is this hindering therapeutic development. Subsequently, how does this study fill these gaps in our knowledge? This narrative of highlighting the new aspects of this study should also run through the Abstract of the paper more prominently.
      2. The authors have selected two missense mutations from the Belgo-Swiss ADTKD Registry to subsequently model in mice. Are these mutations also present at a high prevalence in other genetic studies of ADTKD? The authors indicate that the patients with a Arg185Ser mutation have a faster progression than Cys170Tyr. One caveat here is that in Supplementary Table 1-2, the patients with Arg185Ser are predominately male and those with Cys170Tyr predominately female. Therefore, is gender playing a role here with males more susceptible to renal disease. Taking this concept forward, if the generated mice are separated by gender are comparable results seen in pathology and renal function parameters than if the animals are grouped together as presented in the paper.
      3. In Figure 1D, an examination of kidney biopsies is undertaken. Can the authors provide any quantification across multiple samples/sections/cells to strengthen this data? The authors measure CD3+ cells in their mouse models - any evidence of these cells in the human biopsies.
      4. In Figure 2C, the quantification presented does not seem to fully reflect the pattern of the blot shown, for example, increase in total signal seen in homozygous mice versus heterozygous C171Y mice. As one of the focuses of the paper is the formation of uromodulin aggregates, perhaps there is a rationale for the core and HMW proteins to be quantified separately, rather than the ratio between them.
      5. The authors use electron microscopy (Figure 2F) to conclude that expansion and hyperplasia of the ER occurs in their mutant mice. A representative snapshot is shown, but can quantification be provided to strengthen this data.
      6. A detailed assessment of plasma and urine biochemistry has been made. As highlighted above, separating this data by sex could be helpful. It is stated that the C171Y mice have a progressive increase in BUN at 4 months, but this statement requires clarification. Are the authors referring to a progressive change over time or with respect to gene dosage? An additional measurement of creatinine clearance might also be useful here. Are there any changes in glomerular function? Significant changes are also found in the urine of C171 heterozygous mice (in sodium and creatinine) but not in the homozygous animals. Any explanation for these findings which are not mentioned in the text? Some of the data is not reported corrected, for example it is stated that uric acid excretion is reduced at 1 month, but this has not been measured then. The conclusion that there are strong gene-dosage effects in both models seems strong. The reviewer agrees this holds for BUN but is not so clear cut for other parameters such as diuresis and osmolarity in C171Y mice. This should be refined.
      7. An interesting analysis is presented on the effect of partial and total denaturation treatments of uromodulin. The reproducibility of these experiments is unclear. Please clarify. Do the authors have any information on how the protein structure of uromodulin might change due to these mutations, for example by structural modelling?
      8. Next, the authors delete a wild-type allele in the R186S mice and examine the severity of disease. In Figure 4D and E it would be more informative to also present the specific changes in HMW and core proteins separately. Is there really a pronounced reduction in premature uromodulin in Figure 4E? Why have the authors focused on CD3+ cells as a marker of inflammation, how about other cell types such as macrophages? The rationale needs to be provided here. Are there changes in fibrosis by histology? Importantly, there appears to be no changes in clinical parameters when the wild-type allele is deleted, so is the main conclusion of this part that the deletion of the wild-type allele has no effect on disease severity, despite some of the gene changes observed.
      9. In Figure 5, the relationship between the amount of uromodulin aggregates and the UPR pathway, fibrosis and inflammation is examined. As highlighted above, the methodology to determine the number of uromodulin aggregates needs to be considered. It is unclear in Figure 5C how this parameter has been generated. Can the authors present the data in this panel as individual mice of all six groups rather than the grouped analysis currently done. This would distinguish if the individual mice with greatest uromodulin aggregates also had the most fibrosis and inflammation and strengthen the presentation of this data.
      10. In your RNA-sequencing data, please clarify if the mice were of the same sex. Interesting changes are found, but the final conclusion is that the transcription signals recapitulate severe ADTMD. This seems an overinterpretation and to strengthen this section the authors could go back to their biopsy samples and examine some of the expression patterns of the novel genes they have identified. Similarly, can any of the novel transcripts identified in the RNA-seq be examined (and/or) altered in the cell lines they have generated with the same mutations in uromodulin.
      11. Using their cells the authors show the autophagy may be involved in the clearance of uromodulin in R185S mutants. However, this pathway is not explored in vivo, an assessment of autophagy in these mice would strengthen this connection.

      Minor

      1. The authors should present full Western blots in their Supplementary data
      2. Figure 2C (and others). Please clarify and label clearly the blots from 1 month and 4-month-old mice.

      Significance

      Schiano and colleagues present data on two mouse knock-in models with a missense mutation in uromodulin (C171Y and R186S). A strength of the paper is that the mutations are found in patients with autosomal dominant tubulointerstitial kidney disease (ADTKD) but lead to divergent disease progression. The mouse models are characterized in detail examining changes in uromodulin processing, plasma and urine biochemistry and transcript levels by RNA-sequencing. These findings combined with studies in collecting duct lines provide evidence that the extent of uromodulin aggregate formation is related to the severity of the disease and mechanisms are provided to explain these findings including clearance pathway which might be targeted in the future. Overall, there is a large quantity of good data in the manuscript which moves our understanding of uromodulin mutations forward. However, there are some issues that need to be addressed; in particular the authors should (i) precisely outline the novelty of their study compared with the prior literature; (ii) clarify the reproducibility of their experiments; (iii) refine areas of overinterpretation in the manuscript; (iv) consider the potential role of gender in their findings and (v) complete the circle in some of their findings, for example examining the novel genes identified in their RNA-sequencing in their human biopsy samples and examining autophagy in their mouse models. These changes will considerably strengthen their article.

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      Referee #2

      Evidence, reproducibility and clarity

      UAKD, a subtype of ADTKD, is extensively studied, although it is an rare inherit kidney disease. Using a knock-in strategy, the authors raised a novel concept that the differences in allelic and gene dosage of Umod mutation triggered distinct protein catabolic pathways, yielding distinct phenotypes and prognosis. The functional mechanisms include that UmodR186S mutation caused insoluble uromodulin aggregates resulting in activation of autophagy, and UmodC171Y mutation led to uromodulin misfolding and touched off ubiquitin-dependent ERAD pathway. Accordingly, the authors tested whether enhancing autophagy attenuates the accumulation of UmodR186S protein in cell cultures. Based on these observations, the authors suggested a strategy to improve clearance of mutant uromodulin. This study was carried out by a team with strong reputation in this area. However, the story appears to be incomplete and in vivo testing of their therapeutic strategy is needed to improve this research.

      Specific comments

      1. Figure 1D: Images at low magnification do not show DAPI, therefore there is no information on the total number of cells in the selected field. Nephron loss (represented by glomeruli) did not appear to differ between UMOD p C170Y and UMOD p R185S, which is inconsistent with the overall conclusions. In addition, PAS staining should be added in Figure 1D.
      2. Figure 2E: in image of C171Y/+, this is no corresponding tubules which is represented by the insert. Figure 2F lower panel, the bars in EM fields are same, indicating a hypertrophy of nuclei in R186S? Figure 2G: how about serum creatinine in these mice? In addition, signs of catabolism (e.g., loss of body weight) are associated with these KI mice?
      3. Figure 3C: what is rationale of using two high speed centrifuges. Please state briefly in method.
      4. Figure 4: histologic assessment of progression is missing here, please add images of PAS, Masson staining at low magnification
      5. Figure 5: Can the authors provide low magnification images (40X) for each condition? A histological evaluation of kidney damage is critical to support the conclusion.
      6. Figure 6: Why are no ubiquitin-related catabolic processes or pathways enriched in C171Y? The authors should perform GSEA analysis to determine whether defined gene sets have significant differences between C171Y and R186S.
      7. Following the experiments in Figures 7 and 8, the authors should assess whether administration of autophagy agonists could improve kidney injury and function in R186S mice.

      Significance

      Although ADTKD is an rare inherit kidney disease, the authors provide new insight into its pathogenesis. As nephrologist, I agreed with the observations and conclusions provided by the study. However, sufficient histological assessment and in vivo validation of the proposed therapeutic strategy would significantly improve this study.

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      Referee #1

      Evidence, reproducibility and clarity

      Uromodulin (Tamm-Horsfall protein) is the most abundant protein excreted in human urine.<br /> It plays role in protection against urinary tract infections and renal stones. Mutations in UMOD gene encoding uromodulin cause Autosomal Dominant Tubulointerstitial Disease (ADTKD) that slowly progresses to chronic kidney disease.

      In this manuscript, Schiano et al. isolate 12 missense UMOD mutations, which they classify into two groups by age occurrence. They then proceed to study two of these mutations: one from the earlier-onset - Arg185Ser - and the second from the later-onset - Cys170Tyr.

      The authors generate UmodC171Y and UmodR186S knock-in mice with distinct dynamic pathways impacting on ADTKD progression. These mutations are equivalent with UMOD mutations (C170Y and R185S) in patients. UmodC171Y and UmodR186S knock-in mice show impaired uromodulin biogenesis, with strong allelic and gene-dosage effects. The trafficking problem of ADTKD-UMOD mutants, involving ER retention, ER stress, and activation of the UPR is recapitulated in mIMCD-3 cells, where the R185S mutant reveals more aggregates that are triggering PERK and IRE1 pathways and ER stress responses.

      The manuscript is well written, experiments are in general well described and performed, results offer important insights on cellular events eventually leading to organ damage in ADTKD resulting from missense mutation in the UMOD gene.<br /> The part of the work investigating the degradation mode of two different UMOD mutants, one relying on proteasomal and one relying on lysosomal clearance, is the most interesting for a general audience. Unfortunately, this last part of the work is too preliminary to be accepted as it is.

      Comments/Suggestions:

      • Selection of the UMOD variants, page 5: "R185S and C170Y are the most prevalent mutants in the clusters" please document/add reference.
      • Fig. 1D: please show the position of the insets in the UMOD and BiP panels. Please separate the IF panels from the Picrosirius red panels (these are not the same samples that are shown),<br /> Formally, the BiP panels in Fig. 1D reveal that there is more BiP in cells expressing R185S. That this correlates with UPR induction (as confirmed in Fig. x) should be written at the end of page 5 to make this issue clear for non-experts.<br /> In Fig. 1D, the signal of BiP is not visible in WT and C170Y tissue/cells, which is odd because BiP is abundant protein. Moreover, the differences in BiP levels quantified in WB (semi-quantitative analyses) are not that dramatic in the mouse model (SFig. 3). Which panel in SFig. 3 (mouse) should be representative of the IF shown in Fig. 1D (patients)?<br /> Fig. 1D: Magnification of these images is not sufficient to conclude that R185S accumulates in the ER, and that WT and C170Y are at the apical cell's membrane as written (page 5). Authors should refer to Suppl Fig 1C, where individual cells are visible.<br /> Authors should briefly explain at the end of page 5 how the P. red staining in Fig. 1D informs on fibrosis.
      • In the analyses of misfolded UMOD mutants (e.g., Fig. 2, 3, 4, ...) one would expect a test showing that BiP associates with R185S>C170Y>WT.
      • Fig. 2F: in R186S there is a dramatic enlargement (at least 2x) of nuclei. Can the authors comment on that?
      • Fig. 7E: Shouldn't one expects apical signal for C170Y?
      • Fig. 7F: Why there is apical signal for R185S (and not for C170Y)?

      • The part covering the degradation of the two UMOD variants would be of great interest for a wide audience of cell biologists. However, these data are too preliminary and, in this form, inconclusive.<br /> Few examples: MG132 is a non-specific inhibitor of the proteasome, which may enhance endogenous and trans-gene expression (check in Pubmed "mg132 promoter" for relevant literature). Thus, an increase in the intracellular level of C170Y on MG132 treatment does not necessarily indicate inhibition of the protein's proteasomal turnover. It could also, at least in part, be caused by an increased synthesis of UMOD. The authors should show that MG132 does not increase synthesis of mutant UMOD (or use the more selective proteasome inhibitor PS-341 in their experiments); similarly, the data on R185S do not prove that this protein is client of autophagy. They rather show that autophagy removes the protein when cells are under nutrient restriction (note that starvation activates bulk autophagy, the non-selective lysosomal clearance of cellular components). To show that misfolded R185S is removed from cells by misfolded protein-induced ER-phagy (i.e., ER-to-lysosome-associated degradation), the authors should monitor in WB the accumulation of R185S in the presence of BafA1 and/or in IF the accumulation of R185S within lysosomes in the presence of BafA1.

      Minor comments

      • Figure 1B: dotted lines should be defined in the legend.
      • Figure 1C: "phenotypes are denoted as indicated". The color-code used for the phenotype is unclear to me. For example, what is the phenotype of the V.2 (grey square)?
      • The meaning of "Unlike in UMOD R185S cells, higher SQSTM1 puncta colocalizing with uromodulin were initially present in C170Y mutant cells and further accumulated in MG132-treated cells (Supplementary Figures 10A, B). These data suggest that mutant cells respond differently to UPS inhibition, with C170Y mutant uromodulin being mainly targeted to this pathway." (page 14) and the interpretation of the results shown in 10A and 10B is unclear to me.
      • Page 7: "The UmodC171Y mice showed a progressive increase in BUN at 4 months" please define BUN.
      • Please, provide a complete list of primary antibodies used for immunoblotting, immunohistochemistry, and immunofluorescence staining.

      Significance

      The manuscript is well written, experiments are in general well described and performed, results offer important insights on cellular events eventually leading to organ damage in ADTKD resulting from missense mutation in the UMOD gene.<br /> The part of the work investigating the degradation mode of two different UMOD mutants, one relying on proteasomal and one relying on lysosomal clearance, is the most interesting for a general audience. Unfortunately, this last part of the work is too preliminary to be accepted as it is.

      My expertise: protein quality control, ER-phagy

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      Reply to the reviewers

      1. General Statements [optional]

      This study represents a detailed analysis of the mechanistic bases of Atg15 function in autophagy, which relates back to our initial studies of autophagy published by our group 30 years ago in JCB (10.1083/jcb.119.2.301), where we reported autophagy by disrupting vacuolar protease function. We report that Atg15 is the sole vacuolar lipase in yeast, and that it exhibits broad activity on a range of lipids. Following submission of our study to Review Commons, we have received favorable feedback from all three reviewers. We plan to perform the revisions below within one month, following which we will submit a full revision to JCB with complete point-by-point responses to the reviewers. We are confident that this study will be of interest to the broad readership of JCB and trust that you will find it worthy of further consideration for publication.

      2. Description of the planned revisions

      Reviewers 1 and 3 suggested that we should confirm whether Atg15 is indeed the sole vacuolar lipase using lipids other than NBD-PE. While we have already shown that the kinase-dead S332A variant is non-function in vacuolar lysates, we will further address this comment by determining whether vacuolar lysates isolated from _atg15_Δ cells are able to process other lipid species. We will also collect replicates and quantify data for all figures to address comments made by the reviewers.

      We also received a comment from reviewer 2 asking us to determine the function and expression level of vector-borne ATG15 and ATG15-Flag expressed in the _atg15_Δ background strain. We will provide these data, along with a comparison with results from WT cells, in our revision.

      Reviewer 1 indicated that we need to address the localization of Atg15 in more detail. We plan to better explain the results of our initial analyses, as well as collecting more detailed data using super-resolution microscopy, and these data will be analyzed and discussed in further detail.

      Regarding the text, we will update the results, discussion and methods to make these easier to follow, as pointed out by reviewer 1.

      A complete point-by-point response to reviewer comments will be provided in the full revision.

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

      No revisions yet carried out.

      4. Description of analyses that authors prefer not to carry out

      Reviewer 3 suggested that when considering Atg15 lipase activity we provide information about the lipid makeup of autophagic body membranes. While we agree that this is an interesting suggestion, our pilot experiments have indicated to us that this analysis is complex and will generate a very large amount of additional data and technical details that would need to be supplemented, clearly exceeding the scope of this study. Further, while we are currently performing these analyses, it will take significant additional time to bring these experiments to a conclusion in line with the reviewer’s comment.

      On a related note, reviewer 3 suggested that we provide more detailed analyses of the degradation products arising from Atg15 lipase activity to provide some context into our finding that Atg15 acts on purified autophagic body membranes. We have collected initial lipidomic data for vacuolar extracts following the induction of autophagy (see attached figure), and have confirmed that we detect lysophospholipids in a manner that quantitatively depends on the amount of Atg15. However, as we feel that these data require further careful and time-consuming lipidomic analyses, as well as a nuanced discussion of results arising, we plan to publish these data in a separate, detailed paper and not in the present study.

      With regard to reviewer 1’s comment about the vulnerability of Atg15 to the presence of detergent, we agree that this is an important point, but we can not eliminate the possibility that a very small amount of Atg15 exhibits lipase activity that is detected by this assay. The key message of our study is that Atg15 is activated by proteases in the vacuole to function as a broad-activity lipase; we feel that a detailed investigation of the active fragment of Atg15 is secondary to this finding and would unfortunately be very difficult to determine using currently available techniques, especially when considering the expression of Atg15. While it would be very nice to have these data, we therefore cannot provide these data in the full revision.

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      Referee #3

      Evidence, reproducibility and clarity

      This manuscript by Kagohashi and colleagues provide evidence of the enzymatic activity of ATG15 as a phospholipase B with a broad substrate specificity and dissect the mechanisms by which this protein is activated in the vacuole to promote the hydrolysis of autophagic bodies. The manuscript is very clear and well written, the scientific and experimental concepts are clearly presented and easy to follow. This is excellent biochemistry with very well-thought concepts and well-designed experiments. I am convinced by the authors' results and conclusions which, for the most part, are justified by appropriate experiments and conclusive data.

      Main points:

      • The authors claim that they show that ATG15 is the sole vacuolar phospholipase (see abstract). This conclusion is based on the in vitro analysis of the hydrolysis of NBD-PE (a fluorescent, thus not physiological, phospholipid). Fig. 1 indeed show the absence of NBB-PE hydrolysis in cells lacking ATG15 or expressing a catalytic dead version of the protein, which supports the authors' conclusion. But (1) this does not reflect the hydrolysis of physiological phospholipids in the vacuole, (2) this is only based on the analysis of one phospholipid (PE), (3) this is not consistent with results presented in Fig.5 C-F, in which, in the absence of ATG15 or when the catalytic dead version of the protein is expressed, there is clearly still some hydrolysis of PC, PG and PI. Concerning Fig. 4, the authors state that 'the commercially available NBD-PC and NBD-PG had been somewhat decomposed' and it is unclear to me if the lanes marked by an oblique line correspond to the lysate of atg15D cells or to NBD-PL alone. If this corresponds to the NBD-PL alone, I suggest that the authors perform the experiment presented in Fig.1 (at least Fig. 1C), with additional NBD-PL to actually test for residual phospholipases activity in the absence of ATG15.

      I also suggest that the authors perform lipid analyses of purified vacuoles including engulfed organelles (autophagic bodies, MVBs etc.) to detect changes when put in contact with vacuolar lysates from WT, ATG15D cells and ATG15S332A, which will be more physiological that the use of NBD-PL, and could therefore support their conclusion.<br /> - The use of NBD-PL is very powerful and support the authors conclusions, but the paper lacks from physiological results as stated above. For instance, concerning the efficiency and substrate specificity of ATG15: what is the actual lipid composition of autophagic bodies ? How efficient is ATG15 in regard to the lipids that mainly compose the membrane of autophagic bodies ? Can the authors quantitatively compare the activity of ATG15 from one phospholipid to the other ? Here the experiments are performed on lipids in solution, what about hydrolysis activity on lipids in membranes ? As mentioned in my comment above, I suggest that the authors perform tests with purified autophagy bodies, or, at least, on reconstituted vesicles with a composition similar to what is found in autophagic bodies to assess the activity of ATG15 in physiological conditions.<br /> - The results and data presented by the authors are clear and seem unequivocal for the main parts but none of the results are quantified and there is no statistical analyses provided. How many times were the experiments repeated and how consistent are the results ? The authors must provide quantitative information and stats for all the figures.

      Significance

      Although it has been long known that ATG15 is required for the degradation of autophagic bodies, how this protein which transits to the vacuole through the MVB pathway can be activated in the vacuole to specifically target autophagic bodies and/or its cargo, remained completely unknown. The results presented here, pending their confirmation with additional experiments, thus fill an important gap in knowledge to understand the last crucial steps of the autophagy pathway which had remained largely elusive across organisms. Autophagy is critical for the physiology and development of all eucaryotes with major implication in human diseases. This manuscript will thus be of interest not only for the autophagy community but also for a broader general scientific community with potential applications in medical sciences. The results presented here also provide crucial elements to understand lipid hydrolysis in the vacuole and how this is finely regulated to ensure proper disruption of autophagic bodies, and thus, to the support the finality of autophagy degradation, while maintaining the integrity of the vacuolar membrane. In that context, this paper will influence all cell biologists by providing knowledge of the function, activities and homeostasis of the vacuole. This work raises the question of how ATG15 is specifically addressed to the membrane of autophagic bodies in the vacuole which will be the subject for future exciting research.

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      Referee #2

      Evidence, reproducibility and clarity

      Atg15, a membrane-bound phospholipase targeted to the vacuole via the MVB pathway, has been studied in recent years. It is synthesized as an inactive proenzyme activated by vacuolar enzymes and is responsible for the degradation of the autophagic bodies' membranes. While its physiological function is well characterized, the biochemical details of its activation and overall activity remain largely unknown.

      In the present study, Kagohashi et al conducted a comprehensive investigation to understand the mechanisms involved in the disruption of autophagic bodies (ABs) membranes. They focused on the activities of Atg15 and Pep4/Prb1 and employed primarily in vitro methods to elucidate the functional mechanism of Atg15. Purified Atg15 and ABs were used in the experiments. According to the proposed model, Pep4/Prb1 processes and activates Atg15 during its localization to the AB membrane, and this activation is necessary for Atg15's lipase activity. To support this model, the authors performed in vitro assays using purified proteins, vacuole and ABs purification, genetics, mutations, lipase activity assays, and morphological examination of autophagic bodies using a super-resolution fluorescence microscope. Their findings demonstrated that the activity of Pep4/Prb1 is required for Atg15's lipase activity, and Atg15 functions as a vacuolar lipase. The importance of the lipase motif for Atg15's activity was confirmed through the use of hydrolase mutants and purified Atg15 from both wild-type and mutant samples. Overall, the manuscript provides a comprehensive and solid analysis of Atg15 that will most likely interest the cell biology community. The experiments are well-controlled, and the conclusions are based on solid experimental data.

      There are only a couple of minor issues that deserve the authors' attention:

      Figure 1c shows a lower level of NBD-LPE in cells expressing ATG15 from a plasmid compared to the wild type, indicating that the plasmid did not fully restore the lipase activity. Additionally, the exact details for Atg15 expression should be explicitly described.

      To ensure transparency and reproducibility, the authors should provide information about the specific expression vector(s) used for the plasmids in the study.

      Significance

      As indicated above, the study provides elaborate and solid characterization of an important enzyme that has been previously mainly functionally characterized.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      The rupture of single membrane-bound autophagic bodies is essential to release and catabolize contents of autophagosomes deposited in the vacuole. The phospholipase Atg15 has been thought to play an important role in this process. This study establishes methods to analyze phospholipase activity in isolated Saccharomyces cerevisiae vacuoles and elucidates the mechanisms that activate Atg15. Using an elegant cell-free assay the authors demonstrate that vacuolar extracts can cleave phosphatidyl ethanolamine in an Atg15 and Pep4/Prb-dependent manner. Atg15 is cleaved in the presence of Pep4/Prb, likely causing the release of Atg15 cytosolic domain in the vacuole. An Atg15 construct lacking the transmembrane anchor retains its lipase activity and when artificially targeted to vacuole using CPY tag localizes to autophagic bodies. The authors also establish the minimum construct of Atg15 that is sufficient to execute lipase function. The authors then isolate Atg15 from vacuolar extracts using a FLAG tag-based pulldown and show that the FLAG eluate is sufficient to cleave a range of phospholipids. Finally, using a protease-protection assay the authors show that Atg15 isolated using FLAG resin can cause disruption of isolated autophagic bodies.

      Major comments:

      1. Throughout the manuscript, TLC data and Ape1 maturation data are not quantified. The authors should include data on replicates and quantitation for all TLC and Ape1 processing data.
      2. The conclusion that Atg15 is the sole source of phospholipase activity is based on cleavage of NBD-PE alone. It is not clear why specifically PE was chosen to test lipase activity of Atg15. It is possible that Atg15 has a higher preference for PE as has been shown previously (Ramya and Rajsekaran 2016). Have the authors tested to see if other phospholipids can be cleaved by vacuolar lysates derived from Atg15 knockout cells? This should be investigated further before concluding that Atg15 is the sole source of all lipase activity in vacuolar extracts.
      3. Atg15 overexpressed and purified from Saccharomyces cerevisiae is shown to be sufficient to catalyze the cleavage of PE (among other phospholipids). How do the authors reconcile this finding with their observations on the requirement of Pep4 and Prb? This information should be included in the discussion.
      4. Regarding Figure 3 and movie EV3, especially the lower panel, the overlap of cherry-Atg8 (autophagic bodies) and CPY(1-50)-Atg15(DN35)-mNG is not very clear. There appear to be several CPY(1-50)-Atg15(DN35)-mNG rings that do not surround Atg8.
      5. a. Are these images from a single stack or represent the entire volume of the cell? This result could be better represented as a line profile and through a correlation analysis.
      6. b. The finding that CPY(1-50)-Atg15(DN35) binds autophagic bodies is interesting, but it should be demonstrated with native/wild type protein. This can be achieved by expressing lipase deficient Atg15-mNG in rapamycin-treated cells, which should have intact accumulated autophagic bodies.
      7. c. Atg15-mNG also localizes to a ring-like structure outside the vacuole. The authors should comment on the potential impact of this finding.
      8. The rationale for using detergent solubilized and FLAG-eluted Atg15 to test lipase activity with other phospholipids (LPC, PI, PC and PG) is not clear. Detergent solubilized and FLAG-eluted Atg15 is degraded (Figure4C). Does this mean that degraded forms of Atg15 exhibit broader lipase activity? The authors should test for breakdown of other phospholipids with whole vacuolar extracts or vacuolar pellet fraction that has intact membrane bound Atg15. If only degraded forms of Atg15 show broad phospholipid lipase activity, then this will be informative about regulation of Atg15 function.
      9. Figure6B: ProteinaseK is a broad-spectrum protease. It is unclear why it would specifically cleave GST-GFP and prApe1 to produce single bands (and not a smear) corresponding to free-GFP and dApe1. This result can be explained better.

      Minor comments:

      1. Fig1E legend states, "Each vacuolar lysates were added at a volume ratio of 1:5:25". It's not clear what this means or what this ratio is for. In general figure legends need to be more descriptive on how the experiment was performed.
      2. It's not clear what processed Atg15 (pcrAtg15) refers to in Figure4C. Is it indicating the smear around the 75kDa band? This should be explained clearly in the figure legend and the results section.

      Significance

      The phospholipase Atg15 is known to play a crucial role in the degradation of autophagic bodies within the vacuole. However, the regulatory mechanisms that prevent detrimental lipase activity of Atg15 have remained unclear. This study shows that proteolytic processing and membrane binding could activate Atg15, thereby providing important insights into the mechanism of Atg15 regulation.<br /> Using isolated autophagic bodies and vacuolar extract, the results here show direct disruption of autophagic bodies by Atg15. The cell-free assay to assess lipase activity can be further utilized to analyze vacuolar function. These finding will be of interest to a audience interested in various forms of autophagy and vacuolar degradation.

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      Reply to the reviewers

      1. General Statements [optional]

      Reply to general assessment of referee #2:

      1. General assessments: The current study adds some to these observations…some of these observations are incremental…biological significance is limited. While this reviewer does not suggest additional experimentation, this manuscript would be suitable as a resource paper.

      Reply: It appears we were not clear enough in explaining the novel aspects of our study.

      The starting points are two published studies from our lab demonstrating a global increase of ISGF3 association with ISG promoters in IFNγ-treated cells and a remarkable similarity of IFN-γ and type I IFN-induced early transcriptome changes. These findings challenge the notion in the field (as mentioned by the referee) that IFNγ specificity is produced by the predominant deployment of STAT1 homodimers. We thus tested the hypothesis that the specificity of the IFNγ-induced transcriptome is generated over time, rather than during the early response, and relies on secondary responses to transcription factors such as IRF1. In contrast, IRF1 plays no or only a small role in the type I IFN response that utilises ISGF3 and/or unknown secondary factors in the delayed response. We tested this hypothesis with PRO-seq technology to rule out confounding effects of mRNA processing over a 48h period. The data are clear in showing that many genes associated with the antibacterial or anti parasite profile of activated macrophages are indeed much more abundant in late-stage rather than briefly IFNγ-treated macrophages and these delayed changes are to a large extent dependent on IRF1. Our findings are based on the best available technologies, a combination of nascent transcript analysis with genetics and protein interaction studies. In addition, our findings rule out alternative models of sustained or secondary ISG transcription, such as the employment of alternative ISGF3 complexes (such as STAT2-IRF9) or of ISGF3 complexes formed with unphosphorylated STAT1 and STAT2. We provide evidence for higher order waves of transcription caused by unknow transcription factors that are produced by transcriptional activation of ISGF3 or IRF1 target genes and identify candidates among the AP1 and Ets transcription factor families. We agree that some of the data are confirmatory rather than novel (i.e. some of the genes we describe were known from previous literature to be IRF1 targets), but it is the systems approach of our study, and particularly the delineation of conditions under which the largely neglected delayed response diverts the IFNβ and IFNγ-induced transcriptomes, that generates a comprehensive and conclusive view of IFNγ acting predominantly as a macrophage activating factor, and IFNβ being an essential antiviral cytokine. We do think this main outcome is immunologically meaningful and not incremental. For this reason, we would prefer to publish the paper as a relevant contribution to innate immunology rather than a resource. Emphasizing our point, a paper appeared in ‘Cell’ while our study was under review, showing that human IRF1 mutations cause mendelian susceptibility to mycobacterial disease (MSMD), a term coined by JL Casanova and colleagues for immunological defects that reduce the ability of macrophages to cope with intracellular bacteria (new ref. 65). This important study emphasizes the main conclusions of our study about the relevance of IRF1 for macrophage activation. We discuss this paper on p. 14 lines 9-14.

      Revision: We tried to better explain the scientific motivation for this study and the significance of the results (p. 4, lines, lines 12-25).

      Revision plan: n. a.

      2. Description of the planned revisions

      Referee #3; major comment 1:

      In Fig. 1d is difficult to interpret and misleading for many reasons. First, the cluster numbering is disconnected from the cluster order; why not numbering them based on the hierarchical clustering and writing the cluster number besides the cluster itself? Second, having a 2-color gradient is misleading; negative values shouldn't be in the same color tone than the positive values. Third, the authors did not provide adequate rationale behind using only the top 1,000 most expressed gene? Why not using all the differentially expressed genes in at least one of the condition to provide a comprehensive analysis? Could this potentially lead to bias in the data, and is there any information lost by not using the - lower - expressed genes fraction? Fourth, it is not clear what the color scale is representing and how the data was transformed. Was a mean centering of the expression values of the log2FC applied to the RNA-seq data to facilitate clustering? Mean centering and z-scoring is a common technique used to adjust expression data, but it can potentially exaggerate differences between samples. More information about the data and analysis should be provided, as it is difficult to determine whether this was a valid approach or not.

      Reply:

      • To create the heatmap, we used the pheatmap package from R and the cutree_rows option to separate 11 clusters with strikingly different patterns of gene expression based on visual exploration. The numbering was autogenerated by the program.
      • The data is now shown in red-blue.
      • We restricted our list to only 1000 genes from each comparison as we aimed to analyze the prominent patterns of gene expression across timepoints. Considering all differentially expressed genes based on a padj value would also include genes expressed at very low levels as evident from the low baseMean values obtained from DESeq2. Hence, we applied a selection of 1000 genes which effectively represented the major patterns of gene expression across timepoints.
      • Variance stabilized transformation was applied on read counts obtained from PRO-seq using the DESeq2 package. The transformed reads were z-score normalized and used for performing hierarchical clustering by the “Ward.D2” method using the pheatmap package in R. A total of 3126 genes were used for this analysis. 11 distinct clusters were defined using cutree_rows option. The color scale represents z-score normalized counts. The genes represented in the heatmap were selected based on the following criteria: each timepoint of interferon treatment was compared to the homeostatic condition (untreated sample) in wildtype BMDMs. The differentially expressed genes from each comparison were selected based on the filtering criteria: absolute log2FoldChange >=1 and adjusted p value <0.01 by Wald test. Following the differential analysis, the first 1000 differentially expressed genes in each treatment condition (ordered based on adjusted p values) were selected for both IFN types and combined and selected for creating a list which consisted of 3126 unique genes. The scale in the heatmap represents z-scores of variance-stabilized reads, calculated across all genotype and treatment conditions, separately for each IFN type.

      Revision plan: We will label the clusters with the cluster number next to it in addition to the color codes.

      Referee #3; major comment 3:

      The large standard deviation bars in the claim that ChIP data confirmed the binding of ISGF3 components to the promoter of Mx2 cast doubt on the validity of the results and conclusions. The authors should consider additional experiments or complementary analyses to validate their findings. Or alternative, to adjust their claims accordingly.

      Reply: To demonstrate sufficient quality of the data the ratio of Stat1/ Stat2 was calculated for early (1.5hrs) and late (48h) separately. The unpaired two-tailed t test comparing this ratio between 1.5 hrs and 48hs, shows that they are not significantly different. This indicates that all ISGF3 components are associated with ISG during both early and delayed responses, i. e., that STAT2/IRF9 complexes are unlikely to contribute to delayed ISG control. However, we agree with the referee that the standard deviations of the kinetic ChIP experiment are high and that it would be good to generate additional data.

      Revision plan: We will perform additional ChIP experiments to improve the statistical power of the results in fig. S2c.

      Referee #3, major comment 6:

      The authors interpret their ATAC-seq and ChIP-seq results based on a 2kb window to the TSS of genes, not considering relatively close enhancers or longer range cis-regulatory interactions in their interpretation. For example, they mention on p.7 "Contrasting the strong binding of IRF9 and IRF1 to the Mx2 (cluster 2) and Gbp2 (cluster 9) promoters, respectively, we saw no evidence for direct binding to Lrp11 (cluster 3) and Ptgs2 (cluster 10)", but on Fig 3d they show only the proximal regions. No scale bars are shown either. Moreover, exploring the same published IRF1 ChIP-seq dataset, there is a clear IRF1 binding site at the promoter of Ptgs2, while the authors report none.

      Reply:

      • According to the literature (e. g. refs. 11, 27), most IFN-induced accessibility changes occur in the vicinity of the TSS of ISG. This is further strengthened by the data shown in this manuscript. In addition, most functionally validated GAS and ISRE sequences are in the DNA interval chosen for our analysis. While distal ISG enhancers have been reported (e. g. DOI: 10.26508/lsa.202201823), an analysis beyond the placement of most control regions increases the risk of wrong assignments between ISG and their regulatory elements, hence the causality between transcription factor binding and accessibility changes.
      • We extended the regions for the analysis of the Lrp11 and Ptgs2 regulatory regions and found no evidence for the binding of ISGF3 or IRF1. We find no evidence for a clear peak in the Ptgs2 promoter. There is a peak called by the Macs2 algorithm, but visual inspection of the track (bigwig file) shows it consists of a minor increase in reads above background that does not suggest a bona fide IRF1 binding site (see below). This view is supported by our inability to find an IRF binding site in the vicinity of the peak.

      IRF1 binding indicated by bigWig browser tracks and corresponding peakfiles detected at the locus. We identified the peakfile from Langlais et al., 2016 and identified peaks using MACS2, however using mm10 genome as the analysis in the original paper was done with mm9 genome. The peak identified here appears to be an artefact of the MACS2 program as there is no evident enrichment at the gene promoter region upon inspection of the bigWig files.

      Revision plan: Scales will be added to the browser tracks as requested.

      Referee #3, major comment 7:

      Lack of statistical analysis on chromatin accessibility claims: The authors claim that ATAC-seq data in BMDMs stimulated with IFNβ or IFNγ for a short (1.5 hours) or long (48 hours) period reveals a striking similarity between transcription and the general trends of chromatin accessibility at regions up to 1000 bp upstream of the TSS (Fig. 2a), suggesting continuous chromatin remodeling during the transcriptional response. However, I would like to know if this conclusion is well-supported by the correlation between the chromatin accessibility from ATAC-seq data from only one sample and the PRO-seq data.

      Reply: See revision plan.

      Revision plan: We will analyze single experiments whether they support the conclusions derived from the z-score of the triplicate samples.

      Referee #3, major comment 8:

      The need for additional experiments to verify claims such as the dependence of Ifi44 on IRF1 for gaining ATAC signal, as stated in the claim, "Expression required IRF1 for both, but accessibility of the Ifi44 regulatory region depended upon IRF1 whereas that of Gbp2 acquired an open structure independently of IRF1 (Fig. 5c).

      Reply: We think the lack of clarity might be related to the size of figures 5a and 5b and the density of the dots in some areas of the plot. We agree it is very difficult to assign our gene labels unambiguously to a single dot.

      Fig. 5a combines ATACseq data in wt and IRF1 knockout cells with the expression data from the Pro-seq experiment, Fig. 5b is the same set-up, but IRF9-deficient macrophages are analyzed.

      Blue dots show ATACseq signals induced by IFN treatment. Violet dots represent genes that require IRF1 (Fig. 5a) or IRF9 (Fig. 5b) for transcriptional induction. Yellow dots mark genes such as IFI44 requiring IRF1 (Fig. 5a) or IRF9 (Fig. 5b) for both expression and the accessibility change in the promoter region. Fig. 5c visualizes representative examples of genes whose accessibility is coupled to the transcription factor dependence of the transcriptional induction (IFI44), or not (Gbp2). Thus Fig. 5c must be interpreted based on the dot color code in fig. 5a and we admit this has been difficult with the figure in its present form.

      Revision plan: We will improve the clarity of figs 5a and 5b in several ways:

      • We will label the panels to better indicate the intersected data sets.
      • We will increase the size of the panels and figure legends and make sure that the correspondence between gene names and dots are unambiguous.
      • We will include trend lines of the Ifi44 and Gbp2 genes to visualize their induction and IRF1 dependence.

      Referee #3, major comment 13 (see also section 3):

      The authors have not adequately addressed the methodological limitations in their discussion, which extends beyond the aforementioned comments. It is suggested they include a comprehensive discussion of the claims made pertaining to the necessity of IRF1 for accessibility and the potential biases in the interactomes, along with their associated consequences.

      Reply: The contribution of IRF1 to the accessibility of ISG promoters emerges from the data in figures 5a, whose clarity will be improved (see reply to point 8). We do not interpret the impact of IRF1 beyond the data, in fact we state a relatively minor effect of IRF1 in the control of promoter accessibility (p. 10, lines 20-22) and we have added a reference in agreement with an impact of IRF1 on basal expression of antiviral genes (ref. 39, as suggested by the referee).

      We have added discussion on potential limitations of the TurboID approach (p. 11, lines 22-24 and p. 15, lines 3-11).

      Revision plan: Improvement of fig 5a (see ref. #3, point 8).

      Referee #3, minor comment 2

      Fig 1e. The color scales on the GO enrichment graphs are misleading since they use the same blue-to-red gradient for adj p-values ranging from 10-25 to 10-49 and 0.008 to 0.016, which could be considered non significant.

      Reply: We agree that this is confusing. It results from automated assignments of the color gradients by the software.

      Revision plan: We will investigate possibilities to change color codes for different ranges of p values.

      Referee #3, minor comment 4

      The incomplete schema in Figure 1a, which only focuses on PRO-seq and does not include the ATAC-seq element.

      Reply: We will add a new figure to visualize the set-up of the ATAC seq experiments and their intersection with the Pro-seq data.

      Revision plan: We will add a new figure in accordance with the referee’s request.

      Referee #3, minor comment 6

      The clearer labeling of Figure 5a and 5b.

      Reply: Please refer to our reply to major point 8.

      Referee #3, minor comment 10

      Fig S1b, S3b. The PRO-seq was generated in triplicates, hence these graphs should include the Log2FC for the individual data points.

      Reply: The Log2FC from DESeq2 were calculated from the triplicates, the software does not compute Log2FC from individual replicates.

      Revision plan: We mention the p-values for the Log2FC to show the degree of consistency (figure legends). We will provide a table with log2FC and corresponding padj values of the genes represented at each timepoint (table_showing_padj_values_and_log2fc).

      Referee #3, minor comment 12

      In the genomic snapshot shown, only bars or fading triangles are shown in place of the gene body. The authors should provide an accurate gene structure; i.e., exons and introns.

      Reply: We will try to include the exon-intron structure wherever the size of the figure allows this.

      Revision: n. a.

      Revision plan: If figure size permits, we will add the exon-intron structure of the genes in browser tracks as requested.

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

      Referee #1, major comment 1

      Figure 2. Difficult to interpret data as it is presented. Consider quantifying figure 2C in order to make "changes in Pol II pausing were more pronounced during IFNb signaling" statement more apparent.

      Reply: We presented the pausing data in two different graphic representations (figures 2c and S2) to make the understanding of the information content easier. In hindsight we may have generated more confusion than clarity.

      Revision: We removed the original figure 2c and replaced it with original figure S2. This representation is quite intuitive as the graphs represent a direct quantitative logarithmic display whether and how much the relative amount of paused polymerase changes when comparing IFN-treated and untreated cells. The calculation of these ratios is now explained better in the legend to figure 2.

      Referee #1, major comment 2

      How are you distinguishing autocrine signaling in the BMDMs driven by IFN treatment from late transcripts (for example, at 48 hours are differential genes due to autocrine cytokine signaling or are they truly late transcripts)?

      Reply: We do not exclude autocrine effects. In case of ISG, the most likely autocrine factor would be secreted interferon. According to our Proseq data, the differentially expressed genes do not include any interferon genes. That being said, it is possible that the transcription factors from the AP1 family we hypothesize as drivers of secondary or tertiary waves of transcription are activated by non-IFN cytokines secreted from IFN-treated cells (see also reply to comment 3).

      Revision: We now mention that enhanced IFN production is not sustaining ISG responses (p.5 lines 18/20). We mention the possibility that secreted factors may drive secondary or tertiary waves of ISG transcription (p. 8, lines 21/23).

      Referee #1, major comment 3

      Figure 3D. Authors choose Gbp2 (as positive control for IFNg driven gene), but don't show that Gbp2 is a IFNb independent gene. Consider using IRF1 KO BMDMs in this data as well.

      Reply: This is a misunderstanding. Gbp2 is not shown as an IFNγ-specific gene (it’s induction by both IFN types has been shown previously and emerges from our Pro-seq analysis, see also response to minor issue no. 2). It represents the cluster of genes that are sustained specifically after IFNγ treatment in an IRF1-dependent manner. The purpose of fig. 3D is to show that not all ISGF3/IRF9-dependent genes have promoter binding sites for ISGF3 and not all IRF1-dependent genes have binding sites for IRF1. This suggests indirect effects of both transcription factors in sustaining IFN-induced transcription (in line with the referee’s comment 1).

      Previous figure S3e (now S2f) confirms binding of IRF1 to the GBP2 promoter by ChIP with kinetics correlating to its transcriptional effect. This experiment is normalized with an IgG control. IRF1 knockout cells did not produce a ChIP signal with IRF1 antibody, as expected (data not shown).

      Revision: We better explain the rationale behind the experiments shown in figure 3D (text on p8, lines 12-16). In addition, we show the trend line of Gbp2 expression in WT vs IRF1KO as well as that of additional genes showing delayed/sustained responses in the new Figure S3.

      Referee #1, minor comment 2

      Define known IFNg and IFNb driven genes when they are introduced in figure 2 rather than in discussion.

      Reply: Following the referee’s suggestion we provide the examples of IFNβ and IFNγ-controlled genes and the characteristics of their regulation in the context of our description of the results displayed by fig. 2 (p.6 lines 15-21). This includes Gbp2 (see major issue no. 3).

      Revision: The text on p. 6 lines 15-21 has been modified in accordance with the request.

      Referee #1, minor comment 4

      Unclear whether IRF1 expression in figure 3A is from whole cell lysate or nuclear fraction.

      Reply: We indicate in the figure legend that whole cell lysates were used.

      Revision: We added a sentence with the relevant information in the legend of figure 3.

      Referee #1, minor comment 5

      Authors suggest IFNb treatment induces less IRF1 at later time points, however loading control also seems slightly lower than other considerations. Is it possible that IFNb treated cells are dying at later time points, given that type I IFN signaling can be pro-apoptotic.

      Reply: The graph below the blot represents quantified IRF1 signals, normalized to the loading control. It shows that the differences are not generated by unequal loading of the blotted gel. We and others have shown that IFNβ may indeed enhance macrophage death, however only when the cells are simultaneously infected with an intracellular pathogen (e.g. new ref. 25). These studies also show that treatment with IFNβ alone over periods used in the present study does not affect macrophage viability.<br /> Revision: We added a sentence about the viability of IFN-treated macrophages (p. 4, lines 31-32).

      Revision plan: n. a.

      Referee #2, major comment 3

      The sequencing and BioID data are not submitted to public databases.

      Reply: An accession number has been added.

      Revision: The accession number was added on p.29, line 25.

      Referee #3, major comment 1 (see also revision plan, section 2):

      Revision: The rationale for using the top 1.000 genes is explained (p.5, lines 7-9). The description of the pro-seq read count processing has been extended in accordance with our reply to the referee in the legend of figure 1d and in the methods section (p. 33, lines following line 10.)

      Referee #3, major comment 2

      Fig 2c. The authors claim that RNA Pol II pausing is a major factor in controlling the dynamics of ISG transcription. However, they did not provide sufficient explanation of the results, and in all fairness there is not much variation between the clusters to sustain the claim that this is a major factor in ISG transcriptional control.

      Reply: We agree with the referee that we cannot posit RNA pol II pausing as a major factor for the differences of transcriptional control of ISG in individual clusters. We have made sure to remove any statements suggesting this possibility. We also try to better integrate our findings with RNA pol II pausing into the existing literature.

      Revision: We added relevant literature on p. 6 lines 28-30 and p. 7, lines 4-6.

      Referee #3, major comment 4

      On p.5, the authors mention "Representative browser tracks from the Gbp2 and Slfn1 genes further validate this observation" but they are simply referring to genome browser snapshot, i.e., specific genomic examples, extracting from the same single dataset. Without using an independent dataset, this can not "further validate" the initial findings.

      Reply: We agree the wording is incorrect.

      Revision: We changed the paragraph describing this experiment (p. 6, lines 15-21).

      Referee #3, major comment 5

      IRF1 was successfully pulled down with STAT1 bait but not in the reciprocal experiment. The author should discuss this point as it is important for the conclusions. Could it potentially indicate issues with the technique used, and if this could introduce any bias into the results. The statement, "In contrast, interactors of the IRF1 bait did not include STAT1. This discrepancy could result from steric constraints of the tagged proteins due to the limitation of the 10nm distance reached by the biotin ligase," does not seem to be sufficient to explain this discrepancy.

      Reply: STAT1 was present in the IRF1 pull-down and the interaction increased significantly after IFN treatment but after normalization to the NLS control it did not conform to our criterium of a 95% confidence interval for the FDR. To be consistent we did not include it in the list of IRF1 interactors. We have observed on several occasions that the significance of proximity is not reciprocal, even for well- documented physical interactions. A prime example for this is the interaction between STAT1 and IRF9 in IFN-treated cells which is recorded in the STAT1 pull-down, but not that with IRF9 (ref. 10). Apart from steric reasons the lack of reciprocity may result from different signal/noise ratios in pull downs with different baits.

      Revision: We mention that IRF1 was a STAT1 interactor below the statistical cut-off (p. 11, lines 26-28) as well as the possibility of different signal/noise ratios in the IRF1 and STAT1 pull-downs on p.11, lines 22-24.

      Referee #3, major comment 9

      In the figure legends, there is missing information about the number of times experiments were replicated, suggesting that some were done a single time. Moreover, some graphs are missing statistical analysis, e.g., in Fig S3cS3e, S3f, the ChIP-qPCR experiments were done on biological triplicates, there is no mention of statistical test performed, it is not mentioned what the error bars represents (SD, SEM, etc.) and the variance is large, but the authors still interpret these results as significant enrichment of the transcription factors to the Mx2 promoter.

      Reply: Where missing the relevant information has been added to figure legends. In brief, all experiments represent at least three biological replicates. The only exception is the western blot shown in figure S3a, (no S2a) which represents two independent replicates. Here, the clarity of the difference of IRF1 expression and the fact that the only purpose is to show that Raw264.7 macrophages behave like bone marrow-derived macrophages in fig. 3a justifies the omission of another replicate (please see also answer to point 3).

      Revision: The relevant information has been added to figure legends where necessary (figs. 1, a, 3a, 6a-f, S1, S4, S5).

      Referee #3, major comment 10

      Another example are the RNA Pol II pausing index ratios, which show minor variations and not are supported by statistics to support a possible significance. Proper description, replication and statistical analyses of the results are critical.

      Reply: We agree.

      Revision: Statistics underlying the RNA Pol II pausing data are included in supplementary data 2.

      Referee #3, major comment 11

      The authors used CRISPR-Cas9 genome editing to generate knockout cell lines. However, they did not verify the knockouts at the protein level. Further experiments could confirm that the targeted proteins are not expressed in the knockout cell lines.

      Reply: We included a western blot showing the lack of IRF1 and STAT1 expression in the respective cell lines.

      Revision: New figure S6.

      Referee #3, major comment 12

      On p.9, it is mentioned "IRF1 affects chromatin structure ...". Here chromatin structure is related to minor changes in chromatin accessibility, this can not be qualified as changes in chromatin structure.

      Reply: ‘structure’ has been changed in accordance with the request.

      Revision: ‚structure‘ has been replaced with ‘accessibility’. (p. 10, lines 19 and 21).

      Referee #3, major comment 13 (see also section 2, revision plan, major comment 8)

      The authors have not adequately addressed the methodological limitations in their discussion, which extends beyond the aforementioned comments. It is suggested they include a comprehensive discussion of the claims made pertaining to the necessity of IRF1 for accessibility and the potential biases in the interactomes, along with their associated consequences.

      Reply: The contribution of IRF1 to the accessibility of ISG promoters emerges from the data in figures 5a, whose clarity will be improved (see reply to point 8). We do not interpret the impact of IRF1 beyond the data, in fact we state a relatively minor effect of IRF1 in the control of promoter accessibility (p. 10, lines 20-22) and we have added a reference in agreement with an impact of IRF1 on basal expression of antiviral genes (ref. 39, as suggested by the referee).

      We have added discussion on potential limitations of the TurboID approach (p. 11, lines 22-24 and p. 15, lines 3-11).

      Revision: Change of the discussion section (p. 11, lines 22-24 and p. 15, lines 3-11).

      Revision plan: Improvement of fig 5a (see ref. #3, point 8).

      Referee #3, major comment 15

      The work should be discussed in the context of the demonstrated physiopathological evidence of the IRF1 and IRF9 functions. IRF9 (Hernandez et al., JEM 2018) and more recently IRF1 (Rosain et al Cell, 2023) were identified as causing non overlapping phenotypes in human patients carrying loss-of-function mutations for these genes. The authors must interpret their results in this context.

      Reply: We thank the referee for reminding us about the importance of these papers for our work.

      Revision: The papers have been mentioned and discussed (p. 13 lines 19-28 and p.14, lines 9-14).

      Referee #3, minor comment 3

      The inconsistency in the title referring to IFNb as Type 1 but using IFNg instead of Type 2 nomenclature, perhaps consistency is best.

      Reply: We agree about the importance of consistency but find ourselves in yet another quandary. While the use of ‘type I IFN’ is clearly indicated and widely used as a collective name for this group of cytokines, the use of ‘type II IFN’ for IFNγ is rare because it is the only member of this type. Hence, we decided for sticking with convention at the expense of a bit of consistency. We agree about the title, though, and have changed type I IFN to IFNβ.

      Revision: We adapted the title in agreement with the referee’s comment.

      Referee #3, minor comment 5

      Figure 6d includes a color scale of -1 to +3, but it is unclear what these values represent and how they were calculated per interactor. The figure legend should be revised to clarify this information.

      Reply: We agree. The relevant information has been added to the figure legend.

      Revision: We added information (log2FC with regard to the NLS control) to the legend of fig. 6d.

      Referee #3, minor comment 9

      Fig 1e, S1c. Graphs having circles of varying sizes in function of a value are named "bubble plots" and not "dot plots".

      Reply: Thank you for pointing this out, we corrected our mistake.

      Revision: We changed dot plot to bubble plot in legend to figure S1c.

      Referee #3, minor comment 11

      Fig S3c legend. It is mentioned "Graph represents RT-qPCR of genomic Mx2". RT-qPCR usually stands for reverse transcription quantitative PCR, hence we suggest to change to "ChIP-qPCR" or qPCR. Confusingly, in the literature the term "RT-PCR" is used for real-time PCR and "qPCR" for quantitative PCR. Also, the authors should be specific about the "genomic" region targeted; the graphs mention "promoter", hence it would be appropriate to use the same designation in the legend.

      Reply: We agree and thank the referee for correction of the terminology.

      Revision: We changed RT-PCR to qPCR throughout the manuscript. Moreover, we specifically refer to ‘promoter region’ as the amplified DNA.

      Referee #3, minor comment 12

      Fig S3e. The y-axis names are missing.

      Reply: Thanks for spotting this.

      Revision: The y axis in the figure received its proper label.

      Referee #3, minor comment 14

      Raw cells are sometimes spelled as "Raw" and other times as "RAW". Please choose one for consistency.

      Revision: This inconsistency has been corrected

      Referee #3, minor comment 15

      In p.10 l.20, the figure number is missing.

      Revision: We corrected this mistake.

      4. Description of analyses that authors prefer not to carry out

      Referee #1, minor comment 1

      Simplify figure 4B- consider focusing on most differentially expressed genes between clusters

      Reply: The purpose of fig. 4B is to provide a visual overview of the kinetics of eRNA transcription in response to both IFN types and of the effects of IRF9 and IRF1 knockouts. This information needs to be given to demonstrate the similarities and differences between the control of eRNA and the corresponding ISG transcripts in the different regulatory clusters (as shown in figs. 1d and 2a).

      Simplifying the figure would mean to separate it according to time point, IFN type treatment or knock-out effect. We think this would require to mentally reassemble the figure to understand the interrelationships between these parameters. To our opinion the visual display of the data interrelationship in fig. 4B facilitates the impropriation of the information content.

      Revision: n. a. - we hope our reasoning has become sufficiently clear.

      Revision plan: n. a.

      Referee #1, minor comment 3

      Clarify which cell types (IRF1 KO vs IRF9 KO) are used in figure 5 A/B.

      Reply: The cell type (bone marrow-derived macrophages) is mentioned in the first sentence of the figure legend. Since all experiments except the Bio-ID experiment were performed with this cell type we decided not to label each figure.

      Revision: n. a.

      Revision plan: n. a.

      Referee #2, major comment 2 and referee #3, major comment 14

      Ref #2: Biological significance is limited as this study is largely descriptive and they do not test the hits obtained from BioID.

      Ref #3: Although the TurboID experiments identify known STAT1 and IRF1 interactors, the proposed new interactors are numerous, and none are validated through independent co-IP experiments. Moreover, the results are very noisy, with little differences between untreated BMDMs (where IRF1 is barely expressed) and IFN-treated conditions.

      Reply: The big advantage of BioID or TurboID is the ability to score proximity and very transient interactions. Validating BioID hits with technologies such as coIP is not particularly useful as the two technologies will obviously produce different interactomes. In fact, we show in this manuscript that IRF1 and STAT1 show proximity, but they do not form a stable complex under co-IP conditions. This leaves genetic approaches (LOF or GOF) as alternatives. However, apart from the workload (> 100 genes would have to be knocked out or their products overexpressed), most of our hits are expected to produce very broad effects in such experiments, hard to interpret regarding ISGF3 and IRF1 activities.

      In view of this situation, we publish exclusively the high confidence nuclear interactors identified in our screen: biological replicates were performed in triplicate, a stringent internal control (TurboID-NLS) was used, and a stringent statistical cut-off for high-confidence interactors (95% FDR between groups) was applied. We further account for the experimental situation by limiting interpretation of the data to confirmed molecular events. For example, STAT1 dimers and the ISGF3 complex are required for histone acetylation in response to IFN, and ISGF3 is known to contribute to the exchange of the H2AZ histone variant (refs 11, 14, 71, 72). Our data show that IRF1 contributes to promoter accessibility changes and this is in line with its proximity to a remodelling complex. Thus, the BioID data indeed validate previous findings. However, in agreement with the referee’s comment, some of the data remain descriptive (such as the intriguing proximity of both STAT1 and IRF1 to nuclear products of ISG). To determine the importance of this molecular proximity is a major undertaking and beyond the scope of this study.

      Revision: We added discussion to state the difficulty of validating TurboID-based interactions and the limitations of the TurboID experiments (p.15 lines 3-11).

      Referee #3, minor comment 1

      In most graphs the expression values or log2FC are shown separately for IFNb and IFNg, however in the heatmaps (Fig 1d, S1d) the IFNb and IFNg results are intercalated keeping them side-by-side for each time point, which makes them more difficult to interpret.

      Reply: We are in a quandary about the design of the figure. On the one hand our goal is to visualize gene clusters with distinct behaviors for each IFN type. For this purpose, it would be advantageous to separate the IFN types. On the other hand, we aim at showing similarities and differences between genes induced by each IFN type, for this purpose it is better to maintain the current sample order. While understanding the referee’s point, we prefer to keep the figure as it is, because the suggested change will not increase its overall clarity.

      Revision: n. a.

      Revision plan: n. a.

      Referee #3, minor comment 7

      The statement that "IFN-I are the more important mediators of antiviral immunity" is not entirely accurate and may be an oversimplification, as there are certainly articles which suggest a larger role for type ll IFN elements than type l (ref: Yamane D et al., 2019 Nature microbiology). While yes, IFN-I plays a critical role in the innate immune response to viral infections, IFNγ also has antiviral activity and is involved in the adaptive immune response to viral infections, and in some instances to a larger extent than IFN l.

      Reply: The Yamane et al study (now mentioned on p 10, lines 22-25 and referenced) agrees with our findings because it shows that IRF1 contributes to the basal expression of an ISRE-driven ISG subset. Our statement about the predominant role of type I IFN versus IFNγ refers to genetic data in both humans (mainly Casanova’s work including effects of autoantibodies against type I IFN, see also the paper about human STAT2 deficiency in the June 15th issue of the JCI, https://doi.org/10.1172/JCI168321) and mice (hundreds of papers) showing that disruption of type I IFN synthesis or response causes profound effects of antiviral immunity (i.e. resulting susceptibilities are first and foremost to viral pathogens) whereas susceptibilities as a consequence of disrupting the IFNγ pathway are first and foremost to intracellular nonviral pathogens such a mycobacteria. In fact, the term mendelian susceptibility to mycobacterial disease (MSMD) was coined by Casanova and colleagues to describe a variety of human mutations that include those of the IFNγ, but not the type I IFN pathway.

      Maybe more importantly, the Rosain et al. paper mentioned by the referee which appeared in ‘Cell’ while our study was under review, shows that human IRF1 mutations also fall into the MSMD category (new ref. 65). In contrast, the authors did not observe diminished antiviral immunity. This emphasizes the main conclusions of our study about the relevance of IRF1 for macrophage activation. We discuss this paper on p 14. lines 9-14.

      Obviously, this does not exclude a role of type I IFN in nonviral infection or of IFNγ in viral infection, in fact much of our own work has been dedicated to a role of type I IFN in infections with L. monocytogenes. Nevertheless, we think that in a generic statement about the difference between type I IFN and IFNγ it is correct to label the former as predominantly antiviral and the latter predominantly as a macrophage activating factor against nonviral, intracellular pathogens.

      Revision: We added discussion of Rosain et al. (ref. 65) on p 14. lines 9-14.

      Referee #3, minor comment 8

      The authors claim that a significant portion of ISG promoters is associated with ISGF3 upon IFNγ receptor engagement and that the transcriptomes of macrophages treated briefly with IFNβ or IFNγ exhibit remarkable similarity and sensitivity to Irf9 deletion. However, I am uncertain about the extent of consensus on this claim.

      Reply: The data were surprising but supported by ChIP-seq and RNA-seq in wt and IRF9 ko macrophages (ref 10). Data in a follow-up study (ref. 11) and in this manuscript support our original conclusion by demonstrating the impact of the IRF9 ko on IFNγ responses. Importantly, we don’t claim this is true in all cell types, it may well depend on STAT/IRF9 expression levels and tonic IFN signaling.

      Revision: n. a.

      Revision plan: n. a.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      This manuscript by Geetha et colleagues addresses the differences and similarities in gene expression control between Type I IFN (IFNβ) and Type II IFN (IFNγ) signaling. The authors aim to determine the factors responsible for the partitioning of IFNβ and IFNγ-induced transcriptomes and their propagation of diverse biological responses. The authors mention the JAK-STAT paradigm of IFN signaling, which posits that ISGF3 dominates transcriptional responses to IFN-I, whereas GAF is critical for the generation of an IFNγ-specific transcriptome. However, recent investigations suggest that the ISGF3 complex may also play a role in IFNγ signaling. The authors investigate the contributions of IRF1, ISGF3, and noncanonical versions of the ISGF3 complex to the transcriptome divergence produced by IFN-I or IFNγ signaling. They used nascent transcript sequencing to determine how ISGs expression are temporally controlled by the ISGF3 complex or IRF1 and show that temporal control of ISG expression includes transcription factor recruitment, enhancer activation, changes of chromatin accessibility, and control of RNA pol II pausing. The authors also investigate cooperativity between STAT1 and IRF1 correlates with different nuclear interactomes. In its current form, the manuscript suffers from a lack of independent experiment replications and nuance in the interpretation of the results.

      Major comments:

      1. In Fig. 1d is difficult to interpret and misleading for many reasons. First, the cluster numbering is disconnected from the cluster order; why not numbering them based on the hierarchical clustering and writing the cluster number besides the cluster itself? Second, having a 2-color gradient is misleading; negative values shouldn't be in the same color tone than the positive values. Third, the authors did not provide adequate rationale behind using only the top 1,000 most expressed gene? Why not using all the differentially expressed genes in at least one of the condition to provide a comprehensive analysis? Could this potentially lead to bias in the data, and is there any information lost by not using the - lower - expressed genes fraction? Fourth, it is not clear what the color scale is representing and how the data was transformed. Was a mean centering of the expression values of the log2FC applied to the RNA-seq data to facilitate clustering? Mean centering and z-scoring is a common technique used to adjust expression data, but it can potentially exaggerate differences between samples. More information about the data and analysis should be provided, as it is difficult to determine whether this was a valid approach or not.
      2. Fig 2c. The authors claim that RNA Pol II pausing is a major factor in controlling the dynamics of ISG transcription. However, they did not provide sufficient explanation of the results, and in all fairness there is not much variation between the clusters to sustain the claim that this is a major factor in ISG transcriptional control.
      3. The large standard deviation bars in the claim that ChIP data confirmed the binding of ISGF3 components to the promoter of Mx2 cast doubt on the validity of the results and conclusions. The authors should consider additional experiments or complementary analyses to validate their findings. Or alternative, to adjust their claims accordingly.
      4. On p.5, the authors mention "Representative browser tracks from the Gbp2 and Slfn1 genes further validate this observation" but they are simply referring to genome browser snapshot, i.e., specific genomic examples, extracting from the same single dataset. Without using an independent dataset, this can not "further validate" the initial findings.
      5. IRF1 was successfully pulled down with STAT1 bait but not in the reciprocal experiment. The author should discuss this point as it is important for the conclusions. Could it potentially indicate issues with the technique used, and if this could introduce any bias into the results. The statement, "In contrast, interactors of the IRF1 bait did not include STAT1. This discrepancy could result from steric constraints of the tagged proteins due to the limitation of the 10nm distance reached by the biotin ligase," does not seem to be sufficient to explain this discrepancy.
      6. The authors interpret their ATAC-seq and ChIP-seq results based on a 2kb window to the TSS of genes, not considering relatively close enhancers or longer range cis-regulatory interactions in their interpretation. For example, they mention on p.7 "Contrasting the strong binding of IRF9 and IRF1 to the Mx2 (cluster 2) and Gbp2 (cluster 9) promoters, respectively, we saw no evidence for direct binding to Lrp11 (cluster 3) and Ptgs2 (cluster 10)", but on Fig 3d they show only the proximal regions. No scale bars are shown either. Moreover, exploring the same published IRF1 ChIP-seq dataset, there is a clear IRF1 binding site at the promoter of Ptgs2, while the authors report none.
      7. Lack of statistical analysis on chromatin accessibility claims: The authors claim that ATAC-seq data in BMDMs stimulated with IFNβ or IFNγ for a short (1.5 hours) or long (48 hours) period reveals a striking similarity between transcription and the general trends of chromatin accessibility at regions up to 1000 bp upstream of the TSS (Fig. 2a), suggesting continuous chromatin remodeling during the transcriptional response. However, I would like to know if this conclusion is well-supported by the correlation between the chromatin accessibility from ATAC-seq data from only one sample and the PRO-seq data. The need for additional experiments to verify claims such as the dependence of Ifi44 on IRF1 for gaining ATAC signal, as stated in the claim, "Expression required IRF1 for both, but accessibility of the Ifi44 regulatory region depended upon IRF1 whereas that of Gbp2 acquired an open structure independently of IRF1 (Fig. 5c)."
      8. In the figure legends, there is missing information about the number of times experiments were replicated, suggesting that some were done a single time. Moreover, some graphs are missing statistical analysis, e.g., in Fig S3cS3e, S3f, the ChIP-qPCR experiments were done on biological triplicates, there is no mention of statistical test performed, it is not mentioned what the error bars represents (SD, SEM, etc.) and the variance is large, but the authors still interpret these results as significant enrichment of the transcription factors to the Mx2 promoter. Another example are the RNA Pol II pausing index ratios, which show minor variations and not are supported by statistics to support a possible significance. Proper description, replication and statistical analyses of the results are critical.
      9. The authors used CRISPR-Cas9 genome editing to generate knockout cell lines. However, they did not verify the knockouts at the protein level. Further experiments could confirm that the targeted proteins are not expressed in the knockout cell lines.
      10. On p.9, it is mentioned "IRF1 affects chromatin structure ...". Here chromatin structure is related to minor changes in chromatin accessibility, this can not be qualified as changes in chromatin structure.
      11. The authors have not adequately addressed the methodological limitations in their discussion, which extends beyond the aforementioned comments. It is suggested they include a comprehensive discussion of the claims made pertaining to the necessity of IRF1 for accessibility and the potential biases in the interactomes, along with their associated consequences.
      12. Although the TurboID experiments identify known STAT1 and IRF1 interactors, the proposed new interactors are numerous and none are validate through independent co-IP experiments. Moreover, the results are very noisy, with little differences between untreated BMDMs (where IRF1 is barely expressed) and IFN-treated conditions.
      13. The work should be discussed in the context of the demonstrated physiopathological evidence of the IRF1 and IRF9 functions. IRF9 (Hernandez et al., JEM 2018) and more recently IRF1 (Rosain et al Cell, 2023) were identified as causing non overlapping phenotypes in human patients carrying loss-of-function mutations for these genes. The authors must interpret their results in this context.

      Minor comments:

      • In most graphs the expression values or log2FC are shown separately for IFNb and IFNg, however in the heatmaps (Fig 1d, S1d) the IFNb and IFNg results are intercalated keeping them side-by-side for each time point, which makes them more difficult to interpret. Suggestion to show the IFNb data first and followed by the IFNg results.
      • Fig 1e. The color scales on the GO enrichment graphs are misleading since they use the same blue-to-red gradient for adj p-values ranging from 10-25 to 10-49 and 0.008 to 0.016, which could be considered non significant.
      • The inconsistency in the title referring to IFNb as Type 1 but using IFNg instead of Type 2 nomenclature, perhaps consistency is best.
      • The incomplete schema in Figure 1a, which only focuses on PRO-seq and does not include the ATAC-seq element.
      • Figure 6d includes a color scale of -1 to +3, but it is unclear what these values represent and how they were calculated per interactor. The figure legend should be revised to clarify this information.
      • The clearer labeling of Figure 5a and 5b.
      • The statement that "IFN-I are the more important mediators of antiviral immunity" is not entirely accurate and may be an oversimplification, as there are certainly articles which suggest a larger role for type ll IFN elements than type l (ref: Yamane D et al., 2019 Nature microbiology). While yes, IFN-I plays a critical role in the innate immune response to viral infections, IFNγ also has antiviral activity and is involved in the adaptive immune response to viral infections, and in some instances to a larger extent than IFN l.
      • The authors claim that a significant portion of ISG promoters is associated with ISGF3 upon IFNγ receptor engagement and that the transcriptomes of macrophages treated briefly with IFNβ or IFNγ exhibit remarkable similarity and sensitivity to Irf9 deletion. However, I am uncertain about the extent of consensus on this claim.
      • Fig 1e, S1c. Graphs having circles of varying sizes in function of a value are named "bubble plots" and not "dot plots".
      • Fig S1b, S3b. The PRO-seq was generated in triplicates, hence these graphs should include the Log2FC for the individual data points.
      • Fig S3c legend. It is mentioned "Graph represents RT-qPCR of genomic Mx2". RT-qPCR usually stands for reverse transcription quantitative PCR, hence we suggest to change to "ChIP-qPCR" or qPCR. Confusingly, in the literature the term "RT-PCR" is used for real-time PCR and "qPCR" for quantitative PCR. Also, the authors should be specific about the "genomic" region targeted; the graphs mention "promoter", hence it would be appropriate to use the same designation in the legend.
      • Fig S3e. The y-axis names are missing.
      • In the genomic snapshot shown, only bars or fading triangles are shown in place of the gene body. The authors should provide an accurate gene structure; i.e., exons and introns.
      • Raw cells are sometimes spelled as "Raw" and other times as "RAW". Please choose one for consistency.
      • In p.10 l.20, the figure number is missing.

      Significance

      Nature and significance of the advance:

      The paper presents an investigation of the transcriptional response to IFNβ and IFNγ in mouse bone marrow-derived macrophages and identifies key factors controlling the dynamics of interferon-stimulated gene (ISG) expression. The study employs cutting-edge technologies such as PRO-seq and ATAC-seq to assess transcriptional and chromatin accessibility changes, respectively. The results can potentially provide new insights into the transcriptional regulation of ISGs and the factors controlling their expression, which have significant implications for understanding the immune response to viral infection and cancer. Overall, the work could represent a conceptual advance in the field of immunology and epigenetics surrounding the transcriptional regulation of IFN, but validations and further mechanistic results are required.

      Contextualization of the work:

      The study builds on previous research on the transcriptional response to interferons but provides a more detailed and comprehensive investigation of the underlying mechanisms. Some of the key references that the authors build on include studies on the role of IRF9 in interferon signaling, the regulation of chromatin accessibility during immune activation, and the characterization of interferon-stimulated gene expression. However, the current study goes beyond these previous studies by integrating multiple approaches to examine the transcriptional and epigenetic changes that occur during interferon signaling of two types, I and II.

      Audience and potential impact:

      The findings of the study are likely to be of interest to a wide range of researchers in the fields of immunology, molecular biology, and epigenetics, as well as those interested in the transcriptional regulation. The study may also be of interest to clinical researchers investigating the use of interferons as therapies for viral infections and cancer. The identification of factors controlling ISG expression may have implications for the development of new interferon-based therapies, as well as for understanding the mechanisms of resistance to interferon treatment in patients.

      Field of expertise:

      Overall, the study is contributing to our understanding of the differiential transcriptional response to interferons and the factors controlling ISG expression. Upon provide further mechanistic demonstrations and validations, the work could have significant implications for both basic and clinical research.

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      Referee #2

      Evidence, reproducibility and clarity

      • This interesting study addresses underlying molecular mechanisms that distinguish transcriptomes induced by type I and II IFNs. It is widely accepted that they induce distinct and overlapping genes. The IFN field has shown that type I IFNs can induce both ISGF3 (STAT1/STAT1/IRF9) and GAS (STAT1/STAT1) activation, while type II IFNs only induce GAS elements. The current study adds to some of these observations, including the cooperation of ISGF3 and IRF1 at later time points. They show that ISGF3 and IRF1 can affect enhancers and modify chromatin accessibility. While some of these observations are incremental, this study would significantly interest the interferon community.
      • Biological significance is limited as this study is largely descriptive and they do not test the hits obtained from BioID.
      • The sequencing and BioID data are not submitted to public databases.

      Significance

      This interesting study addresses underlying molecular mechanisms that distinguish transcriptomes induced by type I and II IFNs. It is widely accepted that they induce distinct and overlapping genes. The IFN field has shown that type I IFNs can induce both ISGF3 (STAT1/STAT1/IRF9) and GAS (STAT1/STAT1) activation, while type II IFNs only induce GAS elements. The current study adds to some of these observations, including the cooperation of ISGF3 and IRF1 at later time points. They show that ISGF3 and IRF1 can affect enhancers and modify chromatin accessibility. While some of these observations are incremental, this study would significantly interest the interferon community.

      While this reviewer does not suggest additional experimentation, this manuscript would be suitable as a resource paper.

      My expertise is in innate immunity.

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      Referee #1

      Evidence, reproducibility and clarity

      The authors examine the differences between the genes induced by type I IFNs and IFNg by examining nascent transcripts over a prolonged period of time. The overall question being asked is very broad and the authors generate a massive amount of data that is hard to understand and interpret. The authors also fail to take into consideration the secondary genes induced by products of primary genes. For example, IFNb will induce other type I IFNs that will act in cis or trans to induce secondary ISGs. Also, it is not clear what effect cell death has on gene expression especially at later time points. The differential roles of ISFG3 (IRF9) and IRF1 are interesting but the biological meaning or outcomes of differences in gene expression at 24 and 48 hours is not entirely clear to this reviewer.

      Major Issues:

      • Figure 2. Difficult to interpret data as it is presented. Consider quantifying figure 2C in order to make "changes in Pol II pausing were more pronounced during IFNb signaling" statement more apparent.
      • How are you distinguishing autocrine signaling in the BMDMs driven by IFN treatment from late transcripts (for example, at 48 hours are differential genes due to autocrine cytokine signaling or are they truly late transcripts)?
      • Figure 3D. Authors choose Gbp2 (as positive control for IFNg driven gene), but don't show that Gbp2 is a IFNb independent gene. Consider using IRF1 KO BMDMs in this data as well.

      Minor Issues:

      • Simplify figure 4B- consider focusing on most differentially expressed genes between clusters
      • Define known IFNg and IFNb driven genes when they are introduced in figure 2 rather than in discussion
      • Clarify which cell types (IRF1 KO vs IRF9 KO) are used in figure 5 A/B.
      • Unclear whether IRF1 expression in figure 3A is from whole cell lysate or nuclear fraction.
      • Authors suggest IFNb treatment induces less IRF1 at later time points, however loading control also seems slightly lower than other considerations. Is it possible that IFNb treated cells are dying at later time points, given that type I IFN signaling can be pro-apoptotic.

      Significance

      I accepted the request to review the paper based on the abstract and the idea that this was mechanistic investigation of the role of ISGF3 and IRF1 in regulation of genes induced by type I IFN and IFNg. However, after thorough reading of the paper, I feel that I am not aptly qualified to evaluate all aspects of the manuscript. Many of the data are bioinformatic analysis and I do not have sufficient expertise to either understand the analysis or offer my interpretation of the conclusions drawn by the authors. I suggest that the best path forward is to find another suitable reviewer.. Hopefully, other reviewers have already offered you their suggestions to make an informed decision.

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      Reply to the reviewers

      1. General Statements

      We would like to thank the reviewers for their careful reading of our manuscript and constructive comments.

      2. Description of the planned revisions

      Reviewer 1) “Indeed, the manuscript describe the alteration of total brain O-GlcNAc levels, but understanding pathways or protein specific changes would allow to identify the mechanisms potentially at the basis of the development of intellectual disability.”

      Response: While finding the pathways involved in phenotypes described here is beyond the scope of the present manuscript, we plan to include RNAi experiments elucidating cell types responsible for the sleep phenotype observed in sxc mutant flies.

      Reviewer 3) “2) Lacing fly food with compounds can sometimes lead to phenotypes not actually caused by the drug. There are reports I have previously seen where the compound can make the food more aversive or attractive, both leading to results not due to the drug. Specifically, it has been previously reported that starved flies (if the compound leads to aversion from the food and causes starvation) will reduce the bouts of sleep in Drosophila ( Masek et al J Exp Biol 2014; Figure 4). Do the authors know if the TMG treated food eaten at the same level as normal food? Is there the potential for a starvation phenotype?”

      Response: We appreciate this insight and we plan to perform this control experiment. Briefly, this will entail measuring male adult ingestion of Thiamet G laced food by adding Blue No. 1 dye and measuring absorbance of lysed flies, as previously described in Wong et al. 2009 (PMID: 19557170).

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

      Reviewer 1) “In figure 1C the blot show a different MW range compared to blots 1A and 1B, author should correct. “ and “For figure 1 and 2 the dot graph are too small and difficult to read”

      Response: The figures have been amended to address this.

      Reviewer 3) “In the methods - Neuromuscular Junction Immunohistochemistry - which muscles and which types of boutons were imaged was not denoted in this section - it is described in results (lines 210-211) but should be in methods for ease to the reader.”

      Response: The methods section has been amended.

      “The statistics and data analyses are some of the best I have seen to date. One concern is the removal of a single outlier data point described at line 575. Was this necessary? Does it change the data? If not, I would recommend leaving it in. If it does, I would further recommend additionally biasing toward the alternative hypothesis by additionally removing the data point that lies furthest from the outlier. This would reduce bias.”

      Response: Removal of an outlier does indeed change the results of the data. Following the suggestions of the reviewer, we re-analyzed our data removing the minimum for the group for which we previously removed an outlier (the maximum).

      “1) line 391 mentions that feeding higher doses of TMG results in a non-rescue phenotype. Is there any data to support this statement (maybe supplementally) to give the reader the full picture of the availability of this compound? For example, how far above 250 uM does this happen?”

      Response: This statement refers to adult Thiamet G feeding experiments, and the data to support this statement can be found in figures 2B and S2A. This statement has been amended for clarity and to include the caveat that even higher doses of TMG were not trialed.

      4. Description of analyses that authors prefer not to carry out

      Reviewer 1) “Authors employed RL2 antibody for O-GlcNac detection, however it recognized mainly high MW proteins and it would be nice to obtain the alteration profile of low MW proteins at the same conditions.”

      Response: We agree that the use of a single method for detecting O-GlcNAcylation is limited, however, there is no reason to believe that immunoblotting using this antibody would bias the interpretation of the effects of mutations studied here on global O-GlcNAcylation. Specifically, there is no reason to believe low molecular weight proteins are recognized and modified by OGT differently to high molecular weight proteins. While gaining insight into substrate specific alterations in O-GlcNAcylation is of great interest to us, this is technically very challenging and beyond the scope of this study.

      Reviewer 2) “… would it be possible for the authors to overexpress specifically in neurons wildtype OGT postnatally on a mutant background and quantify the effects on neuro-muscular synapse number and morphology? It would be interesting to compare these data with a similar experiment where they overexpress wildtype OGT in the corresponding muscle.

      Response: While temporal control of transgene expression is possible in Drosophila, it is not a technique that we routinely use and would require extensive optimization to include in the present manuscript.

      _Reviewer 3) “In Figure 3D the authors show sxcWT compared with OgaKO with no significant difference at ~20 boutons in the count. Other work done by [47] in their reference list (ref 47: Figure 2D) shows an increase in OgaKO boutons vs WT and also shown in [50] (ref 50; Figure 4B) where # of boutons in 1B muscle 4 is increased in OgaKO significantly. There appears to be a difference in what was found with OgaKO vs controls in the authors' results vs these two manuscripts and it should be noted and explained to the reader.” _

      Response: This is indeed an inconsistency we have observed, however, looking at reference Fenckova et al. 2022 (47 in our manuscript) we find that in figure legend 2 the following is stated: “None of the parameters is significantly affected in the OgaKO larvae (N = 30, in purple; OgaKO experiments were performed simultaneously and first published here [53] with significantly increased bouton counts (p <0.05) without multiple testing correction)” Reference [53] in the quote refers to Muha et al. 2020 (reference 50 in our manuscript). Therefore, it appears that this effect is too weak to withstand multiple correction testing, which we employ in our analysis.

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      Referee #3

      Evidence, reproducibility and clarity

      The manuscript written by Czajewski et al. "Rescuable sleep and synaptogenesis phenotypes in a Drosophila model of O-GlcNAc transferase intellectual disability" is a novel approach to examining genetic missense mutations representing a patient derived OGT mutation in quantifiable phenotypes coupled with genetic and pharmacological manipulation. The authors find novel contradictory results in synaptic bouton parameters than previous work leading to increased interest in these results. The authors also use pharmacological intervention to reverse the phenotypes derived from the OGT mutations creating an interesting path forward for these types of studies. The manuscript was well written, the experiments are sounds, and the analyses are extremely well done. The manuscript would benefit from addressing a few concerns:

      Minor:

      In the methods - Neuromuscular Junction Immunohistochemistry - which muscles and which types of boutons were imaged was not denoted in this section - it is described in results (lines 210-211) but should be in methods for ease to the reader.

      The statistics and data analyses are some of the best I have seen to date. One concern is the removal of a single outlier data point described at line 575. Was this necessary? Does it change the data? If not, I would recommend leaving it in. If it does, I would further recommend additionally biasing toward the alternative hypothesis by additionally removing the data point that lies furthest from the outlier. This would reduce bias.

      Major:

      In Figure 3D the authors show sxcWT compared with OgaKO with no significant difference at ~20 boutons in the count. Other work done by [47] in their reference list (ref 47: Figure 2D) shows an increase in OgaKO boutons vs WT and also shown in [50] (ref 50; Figure 4B) where # of boutons in 1B muscle 4 is increased in OgaKO significantly. There appears to be a difference in what was found with OgaKO vs controls in the authors' results vs these two manuscripts and it should be noted and explained to the reader.

      The results working with Thiamet G (TMG) is very interesting and needs a bit more clarification. I tried to find other research where TMG is fed to Drosophila, and could not find this, and I suspect this is novel and very interesting, especially as a tool. However, I do have concerns about the details for this feeding and would like to further understand a few things that came up in the manuscript that need to be addressed:

      1. line 391 mentions that feeding higher doses of TMG results in a non-rescue phenotype. Is there any data to support this statement (maybe supplementally) to give the reader the full picture of the availability of this compound? For example, how far above 250 uM does this happen?
      2. Lacing fly food with compounds can sometimes lead to phenotypes not actually caused by the drug. There are reports I have previously seen where the compound can make the food more aversive or attractive, both leading to results not due to the drug. Specifically, it has been previously reported that starved flies (if the compound leads to aversion from the food and causes starvation) will reduce the bouts of sleep in Drosophila ( Masek et al J Exp Biol 2014; Figure 4). Do the authors know if the TMG treated food eaten at the same level as normal food? Is there the potential for a starvation phenotype?

      Significance

      There is a novel technique in this manuscript that could enhance OGT research in Drosophila, which is significant.

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      Referee #2

      Evidence, reproducibility and clarity

      The authors of this manuscript describe the effect on neuronal development and function in drosophila of OGT mutations derived from patients with intellectual disability. OGT is an enzyme that adds the posttranslational modification O-GlcNAc to proteins. Once added, O-GlcNAc can be removed by the enzyme OGA. O-GlcNAc cycling on and off proteins has been associated not only with intellectual disability but a range of other brain-dependent disorders. However, molecular mechanisms by which O-GlcNAc cycling may affect brain development and function are largely unclear. It is equally unclear whether and how disorders associated with OGT mutations may be treated. This manuscript presents evidence that it is possible to rescue neurological phenotypes dependent on patient-derived OGT mutations using genetic or pharmacological manipulations of OGA. These results strongly suggest that at least some aspects of the phenotype of patient-derived OGT mutations depend on O-GlcNAc cycling rather than other mechanisms. Excitingly, they also suggest that it may be possible to treat patients suffering from OGT mutations with drugs that target OGA after the baby has been born. This last point is critical not only for the field of OGT-associated disorders but for the whole field of intellectual disability.

      Minor comment:

      While the manuscript delivers its message clearly with a simple and concise language, the manuscript would become even stronger if the observation that some aspects of intellectual disability can be treated postnatally is substantiated with additional methods that are more specific. The current data are also somewhat difficult to interpret because the pharmacological and genetic manipulations of OGA used so far may not be a direct rescue of the OGT mutations, which the authors also point out. For example, would it be possible for the authors to overexpress specifically in neurons wildtype OGT postnatally on a mutant background and quantify the effects on neuro-muscular synapse number and morphology? It would be interesting to compare these data with a similar experiment where they overexpress wildtype OGT in the corresponding muscle. These experiments would both strengthen their finding that it is possible to rescue neurodevelopmental conditions postnatally and give further evidence to the molecular mechanism by which OGT affects neurodevelopment.

      Significance

      In summary, while it is a short manuscript and on a topic studied previously, its data are novel, clearly presented and would appeal to researchers within and outside the field of O-GlcNAc. It is ready for publication as it has been submitted but including more experiments along the lines suggested above would help it reach one level higher.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript from Czajewski and colleagues demonstrates that patients-derived OGT mutation can lead to reduced O-GlcNAc levels, which can be rescued by genetic OGA ablation or pharmacological OGA inhibition. Several studies in the last decade demonstrated that O-GlcNac homeostasis is crucial for brain development and function and that its alteration is deeply involved in neurodegeneration and cognitive decline. Therefore rescuing protein O-GlcNAcylation by targeting OGT/OGA cycling could represent a valuable therapeutic approach for intellectual disability. Results obtained by the authors are very promising and support the notion of a mutual interplay between OGT and OGA in regulating brain O-GlcNAc levels. Furthermore, the partial rescue of synaptogenesis and sleep stability support the efficacy of OGA reduction in rescuing O-GlcNAc levels.<br /> I do not find Major flaws in the manuscript structure or in the experimental approach, however I believe that the manuscript would benefit of the analysis of the molecular target that lead to brain defects under OGT mutation and that are rescued after OGA inhibition. Indeed, the manuscript describe the alteration of total brain O-GlcNAc levels, but understanding pathways or protein specific changes would allow to identify the mechanisms potentially at the basis of the development of intellectual disability . Furthermore, it would be also interesting to understand if the mutation of OGT has direct or indirect effects on Ser/Thr phosphorylation levels.

      Minor comments:

      Authors employed RL2 antibody for O-GlcNac detection, however it recognized mainly high MW proteins and it would be nice to obtain the alteration profile of low MW proteins at the same conditions.

      In figure 1C the blot show a different MW range compared to blots 1A and 1B, author should correct.

      For figure 1 and 2 the dot graph are too small and difficult to read

      Significance

      General assessment: I believe that the study is well executed and interesting since it nicely demonstrate the influence of OGT mutation on O-GlcNAc levels and the efficacy of OGA reduction in rescuing the process and in improving synaptogenesis and sleep stability. However, I also believe that a better understanding of the molecular mechanisms involved could substantially improve the study.

      Advance: the present study provide further knowledge about the physio/pathological role of OGT/OGA cycling in the brain.

      Audience: basic researchers

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      Reply to the reviewers

      1. General Statements [optional]

      Here we describe the experiments that will be performed as specifically requested by the reviewers to strengthen the main conclusions of the manuscript, and we also justify the utilization of a specific shRNA for KIS knockdown.

      2. Description of the planned revisions

      Reviewer 1

        • Building on the experiments they perform in a KIS knock-down context (e.g. Fig. 3B, or previously described spine phenotype), the authors should investigate whether inhibiting PTBP2 in this context (through shRNA or expression of a phospho-mimetic construct) might suppress the phenotypes observed when inactivating KIS.*

      We will use shRNAs against PTBP2 to test the functional interactions with KIS on CamK2b splicing as in Fig. 3B and we will also assess possible effects on spine morphogenesis. Since it does not show dominant negative effects, the phosphomimetic mutant of PTBP2 will not be included in these experiments.

      • Based on Figures 1E and 3A, it seems that KIS downregulation affects both exon inclusion and exon skipping, and that its function in exon usage is only partly explained by modulation of PTBP2-dependent exons. Have the authors analyzed the populations of PTBP2-dependent exons that are regulated by KIS in an opposite manner? This may point to specific classes of transcripts (in terms of expression pattern, function, molecular signature) important in the context of endogenous neuronal differentiation.*

      We will extend our analysis to exons that are regulated by KIS and PTBP2 in the same direction. We fully agree with the reviewer that these data may uncover gene sets with specific functional implications different from those in which KIS and PTBP2 counteract each other.

      Reviewer 2

        • FigEV4 (also introductory text on p3): RRMs 3 and 4 of PTBP1/2/3 fold as a single back to back packed didomain - with the so-called linker contributing to the didomain fold (e.g. PMID: 24688880, PMID: 16179478) and also extending the RNA binding surface by creating a positive patch (e.g. PMID:20160105 PMID: 24957602). AlphaFold successfully predicts the didomain in full length PTBP2 (https://alphafold.ebi.ac.uk/entry/A0A7I2RVZ4). The authors should therefore use AlphaFold2 to predict the RRM3-4 di-domain structure of wt and phosphomimetic mutant PTBP2s. Phosphorylation of S434 or S434D, which is on the C-terminal end of RRM3 may have no predicted effect on RRM3 alone (FigEV4), but it could conceivably disrupt didomain packing, which could itself have important knock-on consequences for RNA binding. In addition, the introduction of negative charges at S434 might affect the ability of R438, K440 & K441 to interact with RNA. An image of the didomain charge density of WT and mutant PTBP2 would be useful to address this.*

      As suggested by the reviewer, we have considered the di-domain structure of RRM3 and RRM4, and AlphaFold2 predicted no effects by the phosphomimetic residues. We will add these data to the revised version of the manuscript.

      • Figure 4 could also easily go further in experimentally testing the effects of individual phosphomimetic mutations upon protein-protein interactions (Alphafold predicts that S178D, but not S308 or S434D, should affect Y244 mediated interactions, such as MATR3). The co-IP approach in Fig 4A could readily be used with FLAG-PTBP2 mutants. Likewise, consequences of individual mutations upon RNA binding (Fig 4D) could be tested. The use of a Y244N mutant here would test whether the loss of RNA binding is a consequence of the loss of protein-protein interactions. Such experiments are not essential, but they are readily carried out and have the potential to unravel the consequences of the individual phosphorylation events (more correctly of phosphomimetic mutants).*

      Extending the analysis to this residue will be a very interesting contribution to the article. After building the Y244N mutant we will test PTBP2 interactions with protein partners and the splicing reporter RNA as in Fig. 4A-D.

      Reviewer 3

      • Part of the reported splicing changes might reflect an indirect consequence of an altered differentiation contributing to the correlation observed in figure 1F. It would be interesting to confirm splicing changes using shorter incubation times with the shRNA compared to the 11 days used in this study.*

      The levels of splicing regulators such as PTBP1 and PTBP2 change quite markedly during the initial phases of neuronal differentiation (Zheng et al 2012). However, we observed no change in their levels when comparing KIS knockdown to control conditions, suggesting no major upstream effects on the differentiation program per se. In any event, we will analyse expression levels of transcription factors key to neuronal differentiation.

      • Previous papers of the group described a function of KIS in translation (Cambray et al 2009, Pedraza et al 2014). This is not discussed here. For example, the possibility that RBPs are regulated by KIS at the translation level is not excluded by the analysis in Fig EV2a.*

      In our experiments coexpressing KIS with splicing factors in HEK293 cells (Fig. 4A) we did not observe any reduction in their levels. We will include the corresponding immunoblots from input samples in the revised version of the paper. We will also measure PTBP2, Matrin3 and hnRNPM levels by immunoblots in KIS-knockdown cortical neurons to test this possibility further.

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

      (None at this stage)

      4. Description of analyses that authors prefer not to carry out

      Reviewer 3

        • To minimize possible off target problems, the RNAseq analysis would be more convincing if replicated with a second shRNA to knockdown KIS.*

      The efficiency of the selected shRNA had been validated both by the supplier (Sigma) and in our previous work, which also included a complementation assay (see Fig. 4 in Pedraza et al 2014).

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      Referee #3

      Evidence, reproducibility and clarity

      In this manuscript, the authors explored the function of the protein kinase KIS in splicing regulation associated with neuronal differentiation in vitro. KIS is a serine threonine kinase known to phoshorylate splicing factors such as SF1 and SUGP1, and to be preferentially expressed in adult brain in mammals. Using an shRNA based approach, the authors characterize cassette exon usage upon partial KIS depletion in cultured mouse cortical neurons. In parallel using mass spectrometry of proteins in KIS overexpressing HEK293 cells, they identify potential KIS substrates including the splicing regulator PTBP2. They confirm that recombinant KIS can phosphorylates PTBP2 in vitro. They show a correlation between KIS-activated and PTBP2-inhibited exons using published data for this factor. They report opposite effects of KIS and PTBP2 on CamKIIB splicing and Finally, coimmunoprecipitation and FRET experiments suggest that KIS inhibits the interactions of PTBP2 with known protein binders, hnRNPM and Matrin3 as well as with RNA. Altogether these data suggest that KIS downregulates PTBP2 during neuronal differentiation.

      Major comments:

      Overall the manuscript is well written and the data are interesting. However several points could have been more extensivelly studied or discussed to achieve a stronger demonstration of the role of KIS in PTBP2 phosphorylation and neuronal differentiation.

      1. To minimize possible off target problems, the RNAseq analysis would be more convincing if replicated with a second shRNA to knockdown KIS.
      2. Part of the reported splicing changes might reflect an indirect consequence of an altered differentiation contributing to the correlation observed in figure 1F. It would be interesting to confirm splicing changes using shorter incubation times with the shRNA compared to the 11 days used in this study.
      3. Standard deviation is more relevant to describe data dispersion in all figures.
      4. Previous papers of the group described a function of KIS in translation (Cambray et al 2009, Pedraza et al 2014). This is not discussed here. For example, the possibility that RBPs are regulated by KIS at the translation level is not excluded by the analysis in Fig EV2a.

      Minor comments:

      Figure 1:

      The authors state that : "KIS...accumulates in nuclear sub-structures adjacent to those formed by splicing factors". As the figure presents in fact GFP-KIS, it should be mentioned, and how this localisation is relevant for endogenous KIS should be adressed.

      Fig EV1: SI range in pannel D is very different from that in pannel C and Fig1E.

      On page 4 "KIS expression reached maximal levels in hippocampal cultures (Fig 1B)." However the figure legend indicate that this analysis was performed with cortical neurons. The use of cortical or hippocampal neurons along the manuscript should be clarified.

      page 4 " KISK54A, a point mutant without kinase activity" The authors should indicate the reference.

      Figure EV2C: It is not clear whether the Coomassie staining and autoradiography do correspond to the same gel.

      Figure 3C The authors use a dual fluorescence reporter to analyse PSD95 exon 18 splicing. However the well to well variability in such experiments might be elevated. Not only the cells number in a single well but also the number of replicates should be indicated and well to well variability reported.

      Figure 3D. The precise timing for the transfection and culture of cells before staining is unclear

      Figure 4A. The input should be loaded to evaluate the coIP efficiencies and ascertain that KIS does not downregulate Matrin3 and hnRNPM levels.

      Figure EV4A. No difference of Matrin3 binding is to be seen on the gel. In addition, the authors should confirm that PTBP2 or binders are phosphorylated by recombinant KIS. The preparation of GST-KIS is not described. Page 6: "We found that PTBP2-inhibited exons are significantly (FDR=0.001) enriched in KIS knockdown neurons, supporting the notion that KIS acts on AS, at least in part, by inhibiting PTBP2 activity." This should be rephrased as in fact PTBP2-inhibited exons are enriched among KIS activated exons. Page 10: "SUGP1 is one of the most enriched proteins in our KIS phosphoproteome (see Fig 2A)". Phosphorylation and interaction with KIS was already reported by Arfelli and coll. 2023 supplementary figure 2.

      " It forms part of the spliceosome complex, interacts with the general splicing factor U2AF2 and has been reported to play an important role in branch recognition by its association with SF3B1." A reference is needed there.

      The authors previously reported a differentiation defect in cultured neurons 'Cambray et al, 2008' that was not observed by another group (Manceau et al., PLOS One 2012). This should be discussed in view of these more recent results. Is there any differentiation defect in the experiments reported there?

      Statistical values are difficult to read in the figures. Please use larger fonts.

      Significance

      This manuscript brings new elements supporting the function of the protein kinase KIS in splicing regulation in neurons. In particular it identifies for the first time the splicing regulator PTBP2 as a substrate for KIS.

      It will be of interest to a specialized audience of researchers interested in splicing regulators in neuronal differentiation.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      The manuscript by Moerno-Aguilera et al. shows that the brain enriched protein kinase KIS targets the well known neuronal splicing regulator PTBP2 and several of its interaction partners. As a consequence, PTBP2 activity is down-regulated. Using cultured primary immature neurons they show that KIS expression increases during differentiation and that shRNA knockdown of KIS alters the splicing of many alternative exons. Phosphoproteomic anlaysis of HEK293 cells transfected with KIS or a kinase dead mutant (K545A) show that it phosphoryates both PTBP2 as well as a cluster of proteins that are known to interact with PTBP2 or its paralog PTBP1. By comparing the new data on KIS-dependent splicing with previous data-sets on PTBP2-dependent splicing targets they show that KIS appears to act antagoniostically with PTBP2 when it acts as a repressive regulator, but not when it is an activator. Using combinations of wt and kinase-dead KIS with PTBP2 mutants in the 3 main phsophorylation sites (3SA - non-phosphorylatable, S3D - phosphomimetic) to look at the effects on a known PTBP2 functional target, PSD95, they show that the likely effect of KIS is to antagonise PTBP2 function by phosphorylation at one or more of three residues (S178, S308, S434). Finally, they show that transfected KIS (but not K54A) reduces known protein-protein interactions of PTBP2 and that the triple phosphomimetic PTBP2 mutant shows reduced binding to RNA. Alphafold2 predictions show that the S178 phosphomimetic mutant might alter the conformation of the RRM2 domain, in particular altering the environment of Y244, which has been shown in PTBP1 to be critical for interaction with MATR3 and other coregulators.

      Major points

      In general, the conclusions drawn are consistent with the data. I have a few suggestions where the authors could either extend their findings with a few straightforward additional experiments, or clarify some of the existing data.

      FigEV4 (also introductory text on p3): RRMs 3 and 4 of PTBP1/2/3 fold as a single back to back packed didomain - with the so-called linker contributing to the didomain fold (e.g. PMID: 24688880, PMID: 16179478) and also extending the RNA binding surface by creating a positive patch (e.g. PMID:20160105 PMID: 24957602). AlphaFold successfully predicts the didomain in full length PTBP2 (https://alphafold.ebi.ac.uk/entry/A0A7I2RVZ4). The authors should therefore use AlphaFold2 to predict the RRM3-4 di-domain structure of wt and phosphomimetic mutant PTBP2s. Phosphorylation of S434 or S434D, which is on the C-terminal end of RRM3 may have no predicted effect on RRM3 alone (FigEV4), but it could conceivably disrupt didomain packing, which could itself have important knock-on consequences for RNA binding. In addition, the inrtoduction of negative charges at S434 might affect the ability of R438, K440 & K441 to interact with RNA. An image of the didomain charge density of WT and mutant PTBP2 would be useful to address this.

      Figure 4 could also easily go further in experimentally testing the effects of individual phosphomimetic mutations upon protein-protein interactions (Alphafold predicts that S178D, but not S308 or S434D, should affect Y244 mediated interactions, such as MATR3). The co-IP approach in Fig 4A could readily be used with FLAG-PTBP2 mutants. Likewise, consequences of individual mutations upon RNA binding (Fig 4D) could be tested. The use of a Y244N mutant here would test whether the loss of RNA binding is a consequence of the loss of protein-protein interactions. Such experiments are not essential, but they are readily carried out and have the potential to unravel the consequences of the individual phorphoryation events (more correctly of phosphomimetic mutants).

      Minor

      Do KIS regulated exons show enrichment of motifs associated with PTBP2, consistent with the proposed model - particularly CU-rich motifs upstream of exons that are more repressed upon KIS shRNA treatment.

      For the splicing analysis pipeline, how were exon-exon junction reads treated? If "only exons with more than 5 reads in all samples" were considered, will this not exclude highly regulated exons that are completely skipped under one condition?

      The Introduction mentions U2AF homology (UHM) domains, but neglects to discuss their known binding partners - ULMs (UHM ligand motifs), which contain an essential tryptophan. It would be useful for the discussion to highlight whether any direct KIS interactors possess ULMs and how this relates to the phospho-targets identified here. The authors may wish to draw the parallel with the structurally analagous way that PTBP1 (and presumably PTBP2) interact with their short peptide ligand motifs.

      Figure EV2C. The S3A and S308A mutations clearly reduce phosphorylation. However, the effects of S178A and S434A are far less clear. Presumably the quantitation shown in the lower panel of EV2C relies on normalization to PTBP2 protein input, which appears quite variable in the Coomassie gel. It might be better to repeat the experiment with uniform protein inputs. Minimally, details of the quantitation approach should be added to Materials and Methods.

      Fig 3D shows PTBP2 overexpression, but the main text (p7) states KIS overexpression.

      Fig 4B should have a scale bar for the FRET signal

      Fig 4E should indicate the location of S178

      Significance

      This interesting, clear and concise manuscript provides important new insights into the way that a neuron specific kinase can regulate neuronal splicing networks by phosphorylating and thereby downregulating the known neuronal splicing regulator PTBP2. Alternative splicing is known to play a particularly important role in neurons, so this demonstration of an additional layer of regulation by post-translational modification should make the manuscript of wide interest to investigators of splicing regulation, neuronal differentiation and maturation.

      Issues that are not addressed in the manuscript include; i) how does KIS specifically target PTBP2 and related proteins? The UHM domain can mediate interaction with ULM containing splicing factors (such as U2AF2, SF3B1), but none of the identified targets have known ULMs. ii) the consequences of individual phoshomimetic mutants upon protein-protein interactions and RNA binding could readily be explored further using computational and experimental methods already used in the manuscript.

      For context, this reviewer has a direct interest in the mechanisms of regulation of alternative splicing, but not in the context of neurons (though I am familiar with a lot of the relevant literature), and I do not have expertise in neuronal cell biology.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      In this manuscript, the authors characterized the molecular function of the brain-enriched kinase KIS by combining transcriptome-wide approaches with molecular and functional studies. They uncover that KIS regulates isoform selection of genes involved in neuronal differentiation and inhibits through phosphorylation the capacity of the splicing regulator PTB2 to interact with both target RNAs and protein partners.

      Major comments

      • This is a very clear and well-written manuscript presenting high-quality and carefully controlled experimental results. The authors used an impressive range of approaches (transcriptome-wide exon usage, phospho-proteomic, imaging, biochemical assays..) to profile exon usage alterations upon KIS knock down and provide a mechanistic understanding of how KIS regulate the splicing activity of PTBP2. Specifically, they convincingly demonstrate that the phosphorylation of PTBP2 by KIS leads to both dismantling of PTBP2 protein complexes and impaired RNA binding. My only main concerns relate to the understanding of the biological context in which the mechanism studied may be at play. That KIS can counteract PTB2 activity through direct phosphorylation has been very clearly shown by the authors using overexpression of KIS and /or PTB constructs in different contexts (HEK293T cells, N2A cell line, hippocampal neurons). Whether this occurs endogenously in the context of neuronal differentiation, and how much this contributes to the overall phenotypes induced by KIS inactivation, is less clear. While fully investigating the interplay between KIS and PTB2 in the context of neuronal differentiation is beyond the scope of this study, the three following points could be addressed to provide some evidence in this direction.

      • Building on the experiments they perform in a KIS knock-down context (e.g. Fig. 3B, or previously described spine phenotype), the authors should investigate whether inhibiting PTBP2 in this context (through shRNA or expression of a phospho-mimetic construct) might suppress the phenotypes observed when inactivating KIS.

      • Based on Figures 1E and 3A, it seems that KIS downregulation affects both exon inclusion and exon skipping, and that its function in exon usage is only partly explained by modulation of PTBP2-dependent exons. Have the authors analyzed the populations of PTBP2-dependent exons that are regulated by KIS in an opposite manner? This may point to specific classes of transcripts (in terms of expression pattern, function, molecular signature) important in the context of endogenous neuronal differentiation.
      • The authors should better discuss when and where they think PTBP2 phosphorylation by KIS might be relevant. Is there evidence that this process (or PTBP2 complex assembly) might be regulated upon differentiation or plasticity?

      Minor comments

      1. Figures and associated legends are overall very clear and well-organized. Addressing the following points would however help improving the clarity of some Figures:
        • In Figure 2EV2C legend, the characteristics of the 3SA constructs are not described
        • the difference between Figure EV1A and Figure 1H classifications is unclear, nor the interpretation regarding the different GO classes identified
      2. Whether PTBP2 is endogenously the major target of KIS explaining transcriptome-wide changes in exon selection is a possibility that remains to be demonstrated. Thus, the authors should correct and tune down the following sentences: "KIS phosphorylation counteracts PTBP2 activity and thus alters isoform expression patterns ..." (end of introduction) "PTBP2 being one of the most relevant phosphotargets" (results, end of the second section)

      Significance

      • The splicing regulator PTBP2 is a known master regulator of neuronal fate whose tightly controlled expression drives the progenitor-to-neuron transition as well as the establishment of neuronal differentiation programs. How this protein is regulated at the post-translational level has so far remains poorly investigated. In this manuscript, the authors provide a thorough mechanistic understanding of how KIS-mediated phosphorylation of PTB2 impacts on its regulation of exon usage. They also provide a transcriptome-wide view on the function of the brain-enriched KIS kinase in exon usage, uncovering its broad functions in alternative splicing. If the physiological context in which KIS-mediated phosphorylation of PTB2 is induced remains to be precisely defined, this work opens interesting new perspectives on regulatory mechanisms at play during neuronal differentiation. Providing extra lines of evidence indicating that KIS acts on neuronal functions through PTBP2 phosphorylation will help further strengthen this aspect.
      • This manuscript will be of interest to different large communities interested on one hand on the regulation of gene expression programs underlying neuronal differentiation and on the other hand on the molecular regulation of major complexes involved in alternative splicing and isoform selection. It opens new perspectives related to the spatiotemporal regulation of neuronal isoform selection.
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      Reply to the reviewers

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

      * Srinivasan et al. present a comprehensive study on systematizing the structure-dynamics-function relation of lipid transfer proteins (LTPs), combining extensive molecular simulations and complementary experiments. Indeed, the current state-of-the-art in the field is quite chaotic and fractional, and such systematic studies are necessary to advance our general and conceptual understanding of the mechanisms of action of LTPs. The selected techniques and research strategies are all suitable, their description is sufficient and enables reproducibility; the obtained results are carefully presented and discussed; the conclusions are adequately supported by the data.

      Given my primarily computational background, I evaluated mainly the simulation part of the manuscript. Considering experiments, I do not see any significant flows or deficiencies that could diminish the value of the data and following conclusions given in the manuscript. I would even suggest improving the abstract by more explicitly saying that this work includes experimental measurements because it currently reads like purely computational work was performed. *

      We thank Reviewer #1 for the positive evaluation of our work. The abstract has now been updated to include that our work allows us to interpret existing data but also to design and perform new experimental measurements.

      * Major comments: *

      1) Although I like the central message of the paper and have no objections, I am curious whether the conclusion "a more "dynamic" or/and "mobile" part of the protein interacts with the membrane or any other (macro)(bio)molecule" makes sense globally and is not limited to LTPs. For example, it is a reasonable assumption that a more flexible part of the protein, i.e., capable of adopting necessary binding configurations, would be a more likely interacting spot. Locking in a less flexible and more specific configuration upon binding with a target molecule is also anticipated and quite typical, e.g., when ligands interact with target proteins, thereby blocking their function. The authors themselves recognize this paradigm as referring to the enzymes' dynamics. It would be great if authors could comment more on dynamics-function relation, referring to the existing literature, where such observations were/were not observed for different protein families. Performing simulations on proteins that do not exhibit such a feature and do not belong to LTPs, but, e.g., structurally similar to some of the studied LTPs, would be an excellent addition too, highlighting this signature characteristic of LTPs.

      We have now added a discussion comparing the mechanism we observe with those described for other proteins such as membrane transporters and receptors. Since those proteins are very different and have been already thoroughly characterized (including with molecular simulations) we don’t think that additional simulations are required. Also, concerning protein binding dynamics, we refer to the excellent review of Wade and coworkers: "Acc. Chem. Res. 2016, 49, 5, 809–815"

      "____Notably, the conformational plasticity we observe for LTPs is reminiscent of other, previously described, functional protein mechanisms, including enzyme dynamics during catalysis (____DOI: 10.1126/science.1066176____), the alternating-access model of membrane transporters (____https://doi.org/10.1038/nsmb.3179____) or GPCR dynamics (____https://doi.org/10.1021/acs.chemrev.6b00177____). In all these cases, protein dynamics is strongly coupled to ligand binding (____https://doi.org/10.1021/acs.accounts.5b00516____) and protein function, be it for signaling, transport or enzymatic activity. Unlike for these fields, however, the contribution of structural and spectroscopic studies to uncover LTP dynamics remains quite limited, and our simulations provide an important contribution to fill this gap. We hope that our results will motivate researchers to increase efforts to experimentally quantify LTPs conformational plasticity, e.g. by structural determination of LTPs in different states (or bound to different lipids) or by single-molecule spectroscopy studies."

      *Minor comments: *

      *

      1) Fig 1d. What is so special in Lysine compared to Arginine? Is there any disbalance in their presence in studied proteins? Any correlations between the binding affinity of certain amino acids and their overall presence on the protein surface? *

      Indeed, there is disbalance in the presence of lysine and arginine residues in our proteins. The relation between the number of these residues in our dataset is Lys:Arg = 1.6:1. On top of that, and as described in (Tubiana T et al PLoS Comput Biol. 2022 ;18(12):e1010346) lysine is preferred over arginine in peripheral membrane proteins, likely because it induces fewer perturbations in the lipid bilayer. Our data also agree with Tubiana et al, concerning the correlation between abundance of specific residues on the protein surface and membrane binding.

      * 2) Fig S1. GM2A and TTPA seem to be irreversibly adsorbed to the membrane on the microsecond timescale in most replicas. Is anything special in these proteins? Did this affect the sampling of a claimed membrane-binding interface?*

      Our interpretation of the different adsorption profile of GM2A and TTPA is that these two proteins appear to have higher membrane affinity in our computational assay in comparison with the other proteins in our dataset. However, this has no effect on the membrane-binding interface as the proteins are still able to undergo significant tumbling before binding to the lipid bilayer, as demonstrated by the angle between the two main protein axes and the bilayer normal before membrane binding (Fig. S8 in Supplementary Information).

      * 3) A related follow-up question. Multiple replicas were performed to identify the membrane-binding interface. However, if I understand well, the initial orientation of the protein with respect to the membrane was always the same. I found it a pity since performing multiple replicas starting from different initial geometries (e.g., rotating the protein in a somewhat systematic way) would likely result in a more efficient exploration of the conformation space. Can the authors comment on whether this predefined initial configuration could negatively affect the results? Performing a few additional simulations for the most problematic proteins I mentioned earlier (GM2A and TTPA) could be a nice opportunity to apply this strategy. *

      In our protocol, all proteins start from the same initial orientation but undergo significant tumbling in solution before interacting with the lipid bilayer, including for the two most extreme cases, GM2A and TTPA (Fig. R1). Hence, we think that there is no bias for what pertains to the final membrane interacting region. We have added the Fig. R1 in Supplementary Information (Fig. S8) and added the following text in the Methods Section:

      "____Despite starting from a single orientation, all proteins undergo extensive tumbling before binding to the bilayer, as illustrated by the angle between the two principal protein axes and the membrane normal for the two proteins that display the highest binding propensity, GM2A and TTPA (Fig. S8)."

      * 4) How was the volume of the cavity affected by mutations in STARD11 and Mdm12? Do these data somehow correlate with the experimentally observed reduced efficiency of the lipid transfer? *

      Our data on the volume of the cavity in STARD11 and Mdm12 are inconclusive. However, we caution from such a simplistic interpretation, since it completely neglects the lipid-bound conformation that normally has a much larger cavity than the apo form (Fig. 3).

      *5) I would appreciate it if the authors considered playing with the templates of the main Figures at later stages because in the current version, and when printed on A4 paper, the readability of certain graphs and pictures is uncomfortable and sometimes even impossible. Obviously, the final schematics would depend on the journal and its formatting. *

      We will modify the templates of the main Figures to improve readability according to journal formatting.

      * **Referees cross-commenting** *

      * I would like to acknowledge the thoughtful and detailed reviews provided by other reviewers. I do like their reports, and I believe that by addressing the reviewers' comments and incorporating their revisions, the article will significantly improve in terms of scientific rigor and contribution to the field. *

      *Reviewer #1 (Significance (Required)):

      This manuscript is a solid scientific work addressing gaps in our knowledge about Lipid Transfer Proteins by employing state-of-the-art methods. It advances the field on conceptual and fundamental levels. This study is of interest to both computational biophysicists and physical chemists (to whom I belong myself) as well as experimentalists, who seek a rational explanation of the experimental observations. *

      We thank the reviewer again for the positive evaluation of the significance of our work.

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

      * Summary:

      In a combined computational and experimental study, the authors provide insights into general features of lipid transfer proteins (LTPs), which play key roles in lipid trafficking: Through molecular dynamics simulations of a diverse set of 12 shuttle-like LTPs, they demonstrate that LTPs consistently exist in an equilibrium between two or more conformations, whose populations are modulated by a bound lipid, and that residues significantly involved in these collective conformational changes typically interact with a membrane. Their simulations indicate that conformational plasticity is a general feature of LTPs, leading them to suggest that the ability to change conformations is essential for LTP function. They test the generality of this hypothesis through in cellulo assays of two LTPs (STARD11 and Mdm12) that were not originally simulated. While experiments of STARD11 support their hypothesis, those presented for Mdm12 provide ambiguous results. *

      *

      Major comments: *

      * Throughout the manuscript, it's stated that common 'dynamical features' correlate with LTP function. The accuracy of this statement is unclear since 'dynamical features' are never precisely defined and, while equilibrium conformational ensembles are characterized, dynamics (ie kinetics or time-dependent observables) are not. Please clarify.*

      We plan to improve the scholarly presentation of our article to clarify this issue. In short, two distinct properties modulate protein function: 1. Conformational plasticity, i.e. the (thermodynamic) ability of the protein to adopt different conformations (and with different populations depending on the bound substrate). 2. Conformational “dynamics”, i.e. the propensity to exchange between these different thermodynamic states. This ability depends on the free energy barriers between different states and it is intrinsically a kinetic (rather than thermodynamic) property.

      *More importantly, further evidence is needed to determine a correlation with *function*. LTPs are suggested to have faster transfer rates (a measure of function) if the apo form adopts a substantial population of holo-like conformations, akin to enzyme preorganization. This is further tested by rationally mutating STARD11 and Mdm12. However, the support for this conclusion and if these mutations alter the LTPs conformational ensembles as desired is unclear: *

      In our opinion, the interpretation suggested by Reviewer #2 that there is a “correlation” between transfer rates and the overlap of apo-like and holo-like conformations, though fascinating, cannot be derived from the available data at this stage, and we did not mean to imply as such. Rather, lipid transport is a complex phenomenon that involves several steps (membrane binding/unbinding, lipid uptake/release,…). Our simulations indicate that protein conformational plasticity, including potentially the overlap between apo-like and holo-like conformations, also influences lipid transfer rates. We will clarify this aspect in the text.

      * Is there a quantitative correlation between the overlap of apo and holo conformational distributions (as could be quantified by KL divergence or Wasserstein distance, for example) and difference in transfer rates as suggested by Fig S6?*

      We plan to compute quantitative correlation between apo and holo conformational distribution for Fig.S6 and for mutant simulations (see answer below) but, as discussed above, we are skeptical that we will observe a clear correlation.

      * The conclusion and the generality of the findings would be greatly strengthened if a correlation can be shown for other LTPs through additional simulations of mutants whose transfer rates have been previously characterized experimentally in the literature. (For example: Ryan 2007 PMID 17344474, Grabon 2017 PMID 28718450, Iaea 2015 PMID 26168008, among many others)*

      We are currently running simulations of several mutants to address this point and provide additional data/context.

      * While differences in the apo conformational ensembles of the WT and mutants are observed in Fig S7b and d, if these mutations reduce overlap with holo-like conformations is not determined. Simulations of the WT holo forms are needed to properly test this hypothesis. *

      We are currently performing these simulations.

      • For Mdm12, mutations are specifically made to "lock the protein in the apo-like state;" however, the mutant adopts conformations distinct from the apo form as show in Fig S7d. How do the authors interpret the results of the cellular assays considering this and could it help explain why the mutant has similar kinetics to WT? What may explain the puzzling results of similar transfer kinetics but differing mitochondrial morphology? *

      As discussed above, interpretation of lipid transport rates based exclusively on apo and holo conformational population is premature, as this is a complex mechanism that depends on many variables. For what concerns the experimental results, we think three explanations are possible: 1. Mitochondrial morphology could be more sensitive to small variations in lipid composition than our METALIC assay. 2. Our assay only quantifies transport of unsaturated PC and PE species, and we can’t quantify variations in transport of other lipid species that are likely to also be transported by ERMES, such as PS and PA. 3. According to a recent structural model (Wozny et al, Nature 618, 88–192, 2023), Mdm12 might be part of a tunnel-like LTP complex in which it doesn't establish direct interactions with nearby organellar membranes. As such, its mechanism might be different from the one described here for other shuttle-like lipid transport domains. We will discuss these possibilities in the main text.

      • Confounding factors potentially complicate the interpretation of the in cellulo experiments. Simpler in vitro experiments may be better suited to determine if altering LTP's biophysical properties, namely rationally altering the population of apo- vs holo-like configurations, quantitatively affects transport rates as suggested.*

      We agree with Reviewer #2 that this information could be useful. However, this is beyond our technical abilities, and it would require lengthy and expensive experiments that are unlikely to be completed within a reasonable time framework for a revision (3 months). We have rather opted to better discuss our model in the context of published in vitro lipid transport experiments.

      • The abstract, intro, and title highlight that the manuscript's findings are indicative of and correlated with *function* but on p. 12 it's foreseen "that future studies will focus on the functional consequence of such observation." Please reconcile these conflicting statements and ensure connections to function are accurately described. The current title is rather bold. *

      We will rewrite and clarify the extent of our hypotheses and validations.

      * All mentions of "correlation" throughout the manuscript need to be quantitatively evaluated or properly qualified. In addition to that mentioned above regarding Fig S6, what is the correlation coefficient between residues' contribution to PC1 and membrane interaction frequency (Fig 2)? *

      To address this point, we will quantify the correlation between residues' contribution to PC1 and membrane interaction frequency. However, we expect a low correlation between residues' contribution to PC1 and membrane interaction frequency for at least two main reasons. __ First, not all residues contributing to PC1 interact with membranes, but only a subset, as discussed above. Second, our methodology to compute membrane binding, based on the geometric distance between residues and bilayer, is intrinsically quite noisy (since residues in proximity of bona fide membrane binding regions will also appear as involved in membrane binding), thus making quantification of correlations somewhat inaccurate. Rather, we will try to explain in the text that our observations are not of "correlation" but rather of dependence/association, and we will use quantitative measures to quantify these properties (such as rank correlation coefficients or multivariate analyses).__

      * Residue's contributions to collective conformational changes are found to be indicative of membrane binding. Yet, membrane interacting residues are identified from CG simulations that cannot capture such collective conformational changes due to the use of an elastic network. Given that the CG simulations agree with previous experimental findings, this suggests that collective conformational changes are not important for membrane binding. *

      We disagree with this interpretation by Reviewer #2 of our data: we do not claim that residue's contributions to collective conformational changes is indicative of membrane binding. Rather, membrane binding happens at protein regions displaying high contribution to collective conformational changes. This distinction is subtle but important: protein motion does not determine membrane binding regions. Rather, it appears that, for LTPs, membrane binding regions are also characterized by collective motions (suggesting function). We will clarify this in the main text.

      *Are similar conclusions drawn from residues' RMSFs? In other words, are local conformational fluctuations just as indicative of membrane binding? *

      We will compute protein residues’ RMSFs and compare it with the membrane binding data. However, given that RMSF is representative of thermal fluctuations, we again expect a bad correlation between RMSF and membrane binding. On the other hand, we indeed observe that most membrane binding regions are protein loops, but this is not unexpected (e.g. Tubiana et al, PLoS Comput Biol. 2022 Dec; 18(12): e1010346.). However, such observation does not provide any information on lipid transport, but only on the mechanism of membrane binding. Rather, the observation of a relationship between membrane binding and global motion is more interesting, since the latter is often indicative of protein function.

      *The stated correlation may in fact be spurious and instead arise because residues at the entrance to LTP's hydrophobic cavities need to be positioned at the membrane surface for productive lipid uptake and these same residues must undergo significant conformational changes to allow lipid entry. *

      This is exactly what we think it is happening and what our data suggest. However, one must remember that our simulations allow us to predict the membrane binding interface, that is often difficult to determine experimentally (and often via indirect evidence). Hence our data provide novel evidence in this direction.

      *Is proximity to cavity entrance more or less correlated with membrane binding than 'dynamics'? *

      If we consider that, as discussed before, dynamics does not correlate with membrane binding (there are many dynamical regions that are not at the membrane interface), it is safe to assume that proximity to cavity entrance would correlate more with membrane binding. However, we have to consider that often we do not know where the cavity entrance in LTPs is located simply based on structure alone, and hence our approach provides important clues into this process.

      p.12 speculatively suggests "the high degree of protein dynamics we observed in membrane proximal regions could potentially facilitate the energetically unfavorable reaction that involves the extraction of a lipid from a membrane." Yet, the logic behind this idea does not make sense since a free energy barrier, an equilibrium thermodynamic quantity, cannot be lowered by changes in dynamics. Please explain.*

      Our current understanding of the mechanism of lipid extraction is quite poor. However, both using chemical intuition and following a recent MD study on one LTP (Rogers et al, 2023, Plos Comp Biol), it is safe to assume that the hydrophobic environment around the lipid is important for its stabilization in the lipid bilayer. Hence, reducing the number of hydrophobic contacts between the lipid and its environment could facilitate transport. A highly dynamic protein, by cycling between different conformations, could “stir” the bilayer, and hence decrease the number of contacts between the lipid and its environment favoring transport. We will clarify this point in the text.

      *Examining how the LTPs impact membrane properties would offer insight into the functional relevance of such residues for lipid extraction. *

      Indeed, our point above is connected to this one. We are performing simulations to compute hydrophobic contacts in bilayer as proposed in (Rogers et al, 2023, Plos Comp Biol).

      The authors highlight that a bound lipid alters LTPs' conformational ensembles akin to "conformational selection" or "induced fit." How sensitive are these findings to the bound lipid species? Do LTPs with multiple known substrates exhibit an increasing diversity of holo conformations and are different conformations stabilized by different substrates? Would similar observations (Fig 3) be made with a lipid that is not known to be transferred by a given LTP? An interesting future direction would be to examine if lipid substrate specificity could be assessed by comparing conformational ensembles to that of a known substrate and/or by overlap with the apo ensemble.

      We deem that the role of lipid specificity on LTP conformational plasticity is beyond the scope of the current work. While this topic is certainly worth future investigations, we must point out that (i) not all proteins bind/transport multiple lipids (at least according to current knowledge) and (ii) only few LTPs have been structurally characterized bound to different lipids (Osh4, Osh6, …). This limitation prevents a wide generalization, and we prefer not to speculate on this topic. So far, we have tested our approach for Osh4 bound to cholesterol or PI(4)P and found that indeed the protein exhibits different holo conformations (in agreement with the experimental data) when bound to different substrates. We have added a short comment on this topic in the Discussion section.

      "____We foresee that future studies will focus on the functional consequence of such observation, and most notably to the characterization of the extent to which such conformational changes affect multiple steps of protein function, including membrane binding or lipid extraction and release, and whether these are further modulated when different lipids are being transported."

      For LTPs to transfer lipids between membranes, transitions between apo and holo forms ought to occur when LTPs are membrane bound. How does membrane binding influence the conformational ensembles observed in solution? Does it promote conformational changes between apo- and holo-like structures, as suggested to regulate lipid uptake and release by previous studies of Osh/ORP, Ups/PRELI, and START family members? (For example: Miliara 2019 PMID 30850607, Watanabe 2015 PMID 26235513, Grabon 2017 PMID 28718450, Iaea 2015 PMID 26168008, Kudo 2008 PMID 18184806, Dong 2019 PMID 30783101) While answering these questions would require further computational effort, doing so will allow more accurate assessment of the role of conformational changes in LTP function.

      We can’t unfortunately currently quantify how membrane binding influences the conformational ensembles observed in solution, as the slowdown in diffusion at the water-membrane interface makes this task computationally challenging (and certainly not feasible within the time framework of a review). We have so far tested two different proteins and have not succeeded in converging their conformational distribution when membrane-bound despite long MD simulations that lasted several months (even though the non-converged data indicate sampling of both “open” and “closed” conformations). Interestingly, our observations are in qualitative agreement with a recent study on CPTP (Rogers et al, PLOS Comp Biol, 2023), where membrane-bound CPTP is able to sample different conformations (“open” and “closed”) but not to transition between the two states in 300 ns-long MD simulations.

      * The authors motivate the study with the *assumption* that a common molecular mechanism of LTP function exists. Yet LTPs have evolved diverse sequences, structures, and substrate preferences; thus there seems to be no a priori requirement (or even necessarily a benefit) for a single molecular mechanism. What evidence then supports this premise? While previous studies are limited to individual LTPs, when viewed altogether retrospectively, they suggest features that could be shared among LTPs. Synthesizing previous studies and more thoroughly referencing them (only 5 are cited in the intro on p. 3) would strengthen both the premise and findings of the manuscript. *

      Indeed, despite having different structures, substrates and the ability to target distinct organelles, previous evidence on LTPs seem to suggest a potential role for protein conformational plasticity for function, e.g. for Osh/ORP (Jun Im et al, Nature 2005; Canagarajah et al, JMB 2008; Moser von Filseck et al, Nat Comm, 2015; Lipp et al, Nat Comm. 2019,...), StART (Arakane et al, PNAS, 1996; Feng et al, Biochemistry, 2000; Grabon et al, JBC, 2017; Khelashvili et al, eLife, 2019;...) and PITP domains (Tremblay et al, Archives of Biochemistry and Biophysics, 2005; Ryan et al, MBOC, 2007; …). Our simulations provide additional evidence in this direction and allow for generalizing these observations, allowing to draw parallelisms with “enzyme-like” or transporter-like” features that could be exploited for further design of testable hypotheses. We will rewrite our text to better contextualize/acknowledge previous findings and to clarify these points.

      *The LTPs investigated are known to target distinct membranes. Should they then be expected to share structural or sequence-based features predictive of membrane binding interfaces, as motivates the analysis in Fig 1d, 1e, and S3? Or is it beneficial for LTPs to recognize membranes in different ways? *

      Since membrane binding is membrane/organelle-specific, it is possible that residue’s diversity in membrane binding interfaces could indeed be beneficial for this diversity. We will add this comment as a potential explanation of our finding of a lack of conserved sequence-based features for membrane binding interfaces.

      *

      Minor comments:*

      * 2 "making lipid transfer across the cytoplasm a potentially energetically favorable process": Is it meant that it is less energetically costly than transfer without a LTP? Why it would be energetically favorable is unclear (and would indicate that the LTP sequesters lipids away from membranes instead of transferring them between membranes). *

      Yes, this is what we meant. We will rewrite this appropriately.

      * 3 "The excellent agreement between the membrane interface determined from the simulations and the experimentally-proposed one available for... Osh6" is missing a citation. *

      We have now added the relevant citation.

      * The plots in Fig 1d and S3 are difficult to interpret. Bar plots, for example, would allow easier comparison and evaluation. Currently, it seems that most proteins individually exhibit some of the same trends observed among the whole set, counter to the conclusion on p 5. *

      We will improve the presentation of our Figures.

      * Negatively charged residues engage in a number of membrane interactions (Fig 1d and S3). What is a potential explanation for this unconventional observation? *

      One possible interpretation is that negatively charged residues could interact with positively charged moieties (ethanolamine, choline) of PC and PE lipids.

      * How much variance is captured by PC1, and how many PCs are needed to capture most of the variance in the conformations? *

      PC1 explains 38 % of the total variance, by average, whereas PC2 accounts for 17 % of it. Therefore, PC1 and PC2 capture most of the variance in almost all cases.

      We have also added this to the text:

      "____We specifically focused on PC1 as it explains most of the variance in the dynamics (38% on average for all the proteins in our dataset, see Supplementary Table 2).____ "

      We have computed this variance and we have added this analysis in Supplementary Information.

      * Plots in Fig 3, especially panels c and d are difficult to see. Please make the panels larger (perhaps a 3 x 4 layout instead of 2 x 6 would work better). *

      We will improve the presentation of our Figures.

      * 8 "these conformational changes are localized in protein regions that interact with the lipid bilayer" is contradicted by the results in Fig 2b showing that all residues with large contributions to PC1 do not interact with the membrane and discussed on p 5. *

      As discussed above, we don’t observe “correlation” between membrane binding and conformational plasticity, but we rather observe that membrane binding regions display high conformational plasticity (the opposite is not true). We will further clarify in the text.

      *

      8 "in the absence of bound lipids, it is able to sample multiple conformations" is not supported by the orange distributions in Fig 3d that appear unimodal. Is it instead meant that the apo form exhibits larger variance in cavity volume? *

      Yes, this is what we meant. We’ll clarify.

      *

      Please clarify if the elastic network was constructed to maintain the holo or apo structures of each protein and if a bound lipid was used in the CG simulations. *

      For membrane binding CG simulations, we used the apo structure and no bound lipid was used in the simulations. However, analogous simulations in the holo form (not shown) have essentially identical membrane binding interfaces.

      *

      Was *CHARMM* TIP3P used? *

      Yes.

      * Please clarify how membrane interacting residues were defined and how interaction frequency was calculated from the longest duration of interaction. *

      We will add this explanation in the Methods. The method is identical to (Srinivasan et al, Faraday Discussion, 2021).

      * Refs 16 and 45 refer to the same paper. *

      Thanks, it is now corrected!

      * Reviewer #2 (Significance (Required)): *

      * General assessment: *

      * The work aims to tackle a grand question regarding membrane homeostasis mechanisms-what are universal principles underlying LTP function-and offers initial insights; however, further evidence is needed to support the conclusions as written, and some key results require further investigation and explanation. *

      *Advance and audience: *

      *

      By concurrently investigating the largest number of lipid transfer proteins to-date, the authors provide data invaluable for uncovering general mechanisms of non-vesicular lipid transport and advancing our understanding of membrane homeostasis mechanisms. By illuminating the wide-spread importance of conformational plasticity among lipid transfer proteins, the work presents a conceptual advance in our understanding of lipid transfer mechanisms and unifies previous studies. Because the manuscript emphasizes common biophysical principles and draws connections to enzyme biophysics, it ought to be of interest not only to membrane biologists but biochemists and molecular biologists more broadly.*

      We thank Reviewer #2 for the very positive evaluation of the significance of our work and for the in-depth analysis provided that will certainly help improve the quality of our work.

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

      *The article "Conformational dynamics of lipid transfer domains provide a general framework to decode their functional mechanism." by Sriraksha Srinivasan, Andrea DiLuca, Arun Peter, Charlotte Gehin, Museer Lone, Thorsten Hornemann, Giovanni D'Angelo and Stefano Vanni study the interaction of Lipid transport Domains with membranes. This is done mainly by molecular modelling but also with selected experimental validations. *

      * Major comments: *

      * - The key conclusions are generally well supported by the analysis. - The authors could however analyze in more details some aspects in which specific cases appear. For example, p3 "multiple binding and unbinding events, as shown by the minimum distance curves" does not give an entire description of the variability seen in Fig S1, e.g. LCN1 versus GM2A.*

      We now discuss in more detail the variability seen in Fig. S1 and attribute it to different membrane binding affinities of the proteins in our dataset. We also discuss how this variability could reflect the diversity of organellar membranes to which these proteins bind in vivo.

      "____Notably, the proteins in our dataset display distinct binding affinities, with some proteins showing very transient binding while others remain membrane-bound for most of the simulation trajectory (Fig. S1). This behavior could be, in part, attributed to the wide diversity of organellar membranes to which the LTDs in our dataset bind to in vivo, and to the comparative simplicity of our in silico model DOPC lipid bilayers."

      • Later the "excellent agreement" for the data in Fig S2 is not quantified which does not allow the reader to know whether it better than would have been with other methods (SASA, OPM, DREAM). *

      We have explicitly quantified this agreement by providing a direct comparison between the experimental results and our in silico assay, and we further compared it against two alternative methods: OPM and DREAMM. In detail, we have identified 12 experimentally-characterized spots suggested to be involved in membrane binding in our protein dataset (see shaded blue regions in Fig. S2). Of those 12, our method identifies all of them (100%), while DREAMM identifies 7 of them (58 %) and OPM 4 out of 8 (50 %), since of the 12 proteins we tested, only 7 are available in the OPM database. Overall, even if our approach is much noisier than the others, and thus suggesting multiple binding regions that are not currently supported by experimental observations, using physics-based methodologies appears to remain a preferable strategy to characterize the binding of peripheral proteins to lipid bilayers. Given the limited size of our dataset, we prefer not to make a direct comparison between our assay and OPM/DREAMM in the main text as this won't be representative of the various methodologies.

      *p5 commenting on Fig2b the case of Osh6 that appears to disagree should probably be mentioned. *

      We now discuss this case, and attribute to this disagreement to insufficient sampling for the peculiar case of Osh6:

      "____One interesting exception in our database appears to be Osh6, where the experimentally determined membrane-binding region at the N-terminus (https://doi.org/10.1038/s41467-019-11780-y) is only marginally binding to the lipid bilayer in silico and it also appears to have limited contribution to PC1. However, our simulations are unable to sample the large conformational changes that the N-terminal lid of Osh6 has been proposed to undergo from its lipid-bound to its apo state, indicating that insufficient sampling could be the reason for this apparent discrepancy."

      *

      -The data and the methods are generally well presented allowing to be reproduced.

      • The experiments adequately replicated with adequate statistical analysis. *

      * Minor comments: *

      * - When presenting the dataset the authors could probably detail a bit more the protocol undertaken to chose the cases. In particular it is unclear whether the chosen proteins have any membrane selectivity, which in principle could be affected by the choice of lipid used here.*

      We have now added in Table 1 a column with a list of potential organelles the different LTPs have been shown to localize to (source: UniProt). As model membrane bilayer, we opted to use a pure DOPC bilayer, for both simplicity and to compare membrane binding in a uniform setting. We foresee that future studies investigating the membrane specificity of the various proteins will shed further light into the molecular mechanism of LTPs. Finally, we also indicate that our choice of proteins was mainly driven by the availability of lipid-bound structures in the protein data bank. We have added the following sentences in the main text:

      "____Specifically, we selected all LTPs for which a crystallographic structure in complex with a lipid was available at the start of our project, plus two additional proteins (GM2A and LCN1) to increase the structural diversity of our dataset (Fig. 1a)"

      and

      "____Notably, the proteins in our dataset display distinct binding affinities, with some proteins showing very transient binding while other remain membrane-bound for most of the simulation trajectory (Fig. S1). This behavior could be, in part, attributed to the wide diversity of organellar membranes to which the LTDs in our dataset bind to in vivo, and to the comparative simplicity of our in silico model DOPC lipid bilayers."

      *- The authors could probably give some indication of how much of the variance is explained by PC1 and comment briefly on the choice to ignore other PCs. *

      PC1 explains 38 % of the total variance, on average. This means that PC1 has a large contribution to the variance, especially in comparison to the other PCs. For instance, PC2 only accounts for 17 % of the total variance. This is the reason we limited our discussion to PC1. We have added a table in supplementary Information quantifying the variance explained by PC1 and PC 2 and added the following sentence in the main text:

      "____We specifically focused on PC1 as it explains most of the variance in the dynamics (38% on average for all the proteins in our dataset)____. "

      * - When analyzing the residues involved in the interaction with the membrane the results could probably be compared with that of the systematic analysis performed recently: Tubiana, T., Sillitoe, I., Orengo, C., & Reuter, N. (2022). Dissecting peripheral protein-membrane interfaces. PLOS Computational Biology, 18(12), e1010346. *

      We have added in the text a reference to the work by Tubiana et al and we have further stressed that our results agree with previous observations (including theirs). This includes the preference for Lys over Arg and the importance of protruding hydrophobes:

      "____Concomitant analysis of all LTDs (Fig. 1d) indicates that the membrane binding interface of LTDs is enriched in the positively charged amino acid Lysine, as this amino acid is less membrane-disruptive than Arginine22, and aromatic/hydrophobic ones (Phe, Leu, Val, Ile). This confirms previous observations, as (i) binding of negatively charged lipids via positively charged residues and (ii) hydrophobic insertions are two of the main mechanisms involved in membrane binding by peripheral proteins22-27."

      * - In the discussion on allostery/conformational selection might not be centered so much on enzymes. *

      We thank the reviewer for this important observation. We have now included in the Discussion the following paragraph that provides additional references and discussion of membrane transporters and receptors.

      "____Notably, the conformational plasticity we observe for LTPs is reminiscent of other, previously described, functional protein mechanisms, including enzyme dynamics during catalysis (____DOI: 10.1126/science.1066176____), the alternating-access model of membrane transporters (____https://doi.org/10.1038/nsmb.3179____) or GPCR dynamics (____https://doi.org/10.1021/acs.chemrev.6b00177____). In all these cases, protein dynamics is strongly coupled to ligand binding and protein function, be it for signaling, transport or enzymatic activity. Unlike for these fields, however, the contribution of structural and spectroscopic studies to uncover LTP dynamics remains quite limited, and our simulations provide an important contribution to fill this gap. We hope that our results will motivate researchers to increase efforts to experimentally quantify LTPs conformational plasticity, e.g. by structural determination of LTPs in different states (or bound to different lipids) or by single-molecule spectroscopy studies."

      * Reviewer #3 (Significance (Required)): *

      *

      The article shows convincing results on the debated issue of the mechanism of lipid transport by lipid transfer proteins. *

      First the study employs molecular modelling to allow a rather large test on 12 cases. The molecular dynamics experiments allow the authors to draw clear hypotheses on role of protein dynamics on the interaction with membranes and the effect on bound lipids on the modification of this dynamics.

      *Then the authors use this knowledge to design experiments that largely confirm those hypotheses. The results should therefore be interesting for a large audience of biochemists and cell biologists interested in lipid transport in the cell. *

      We thank Reviewer #3 for its very positive evaluation and contextualization of our work.

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      Referee #3

      Evidence, reproducibility and clarity

      The article "Conformational dynamics of lipid transfer domains provide a general framework to decode their functional mechanism." by Sriraksha Srinivasan, Andrea DiLuca, Arun Peter, Charlotte Gehin, Museer Lone, Thorsten Hornemann, Giovanni D'Angelo and Stefano Vanni study the interaction of Lipid transport Domains with membranes. This is done mainly by molecular modelling but also with selected experimental validations.

      Major comments:

      • The key conclusions are generally well supported by the analysis.
      • The authors could however analyze in more details some aspects in which specific cases appear. For example, p3 "multiple binding and unbinding events, as shown by the minimum distance curves" does not give an entire description of the variability seen in Fig S1, e.g. LCN1 versus GM2A. Later the "excellent agreement" for the data in Fig S2 is not quantified which does not allow the reader to know whether it better than would have been with other methods (SASA, OPM, DREAM). p5 commenting on Fig2b the case of Osh6 that appears to disagree should probably be mentioned.
      • The data and the methods are generally well presented allowing to be reproduced.
      • The experiments adequately replicated with adequate statistical analysis.

      Minor comments:

      • When presenting the dataset the authors could probably detail a bit more the protocol undertaken to chose the cases. In particular it is unclear whether the chosen proteins have any membrane selectivity, which in principle could be affected by the choice of lipid used here.
      • The authors could probably give some indication of how much of the variance is explained by PC1 and comment briefly on the choice to ignore other PCs.
      • When analyzing the residues involved in the interaction with the membrane the results could probably be compared with that of the systematic analysis performed recently: Tubiana, T., Sillitoe, I., Orengo, C., & Reuter, N. (2022). Dissecting peripheral protein-membrane interfaces. PLOS Computational Biology, 18(12), e1010346.
      • In the discussion on allostery/conformational selection might not be centered so much on enzymes.

      Significance

      The article shows convincing results on the debated issue of the mechanism of lipid transport by lipid transfer proteins.

      First the study employs molecular modelling to allow a rather large test on 12 cases. The molecular dynamics experiments allow the authors to draw clear hypotheses on role of protein dynamics on the interaction with membranes and the effect on bound lipids on the modification of this dynamics. Then the authors use this knowledge to design experiments that largely confirm those hypotheses.

      The results should therefore be interesting for a large audience of biochemists and cell biologists interested in lipid transport in the cell.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In a combined computational and experimental study, the authors provide insights into general features of lipid transfer proteins (LTPs), which play key roles in lipid trafficking: Through molecular dynamics simulations of a diverse set of 12 shuttle-like LTPs, they demonstrate that LTPs consistently exist in an equilibrium between two or more conformations, whose populations are modulated by a bound lipid, and that residues significantly involved in these collective conformational changes typically interact with a membrane. Their simulations indicate that conformational plasticity is a general feature of LTPs, leading them to suggest that the ability to change conformations is essential for LTP function. They test the generality of this hypothesis through in cellulo assays of two LTPs (STARD11 and Mdm12) that were not originally simulated. While experiments of STARD11 support their hypothesis, those presented for Mdm12 provide ambiguous results.

      Major comments:

      Throughout the manuscript, it's stated that common 'dynamical features' correlate with LTP function. The accuracy of this statement is unclear since 'dynamical features' are never precisely defined and, while equilibrium conformational ensembles are characterized, dynamics (ie kinetics or time-dependent observables) are not. Please clarify.

      More importantly, further evidence is needed to determine a correlation with function. LTPs are suggested to have faster transfer rates (a measure of function) if the apo form adopts a substantial population of holo-like conformations, akin to enzyme preorganization. This is further tested by rationally mutating STARD11 and Mdm12. However, the support for this conclusion and if these mutations alter the LTPs conformational ensembles as desired is unclear:

      • Is there a quantitative correlation between the overlap of apo and holo conformational distributions (as could be quantified by KL divergence or Wasserstein distance, for example) and difference in transfer rates as suggested by Fig S6?
      • The conclusion and the generality of the findings would be greatly strengthened if a correlation can be shown for other LTPs through additional simulations of mutants whose transfer rates have been previously characterized experimentally in the literature. (For example: Ryan 2007 PMID 17344474, Grabon 2017 PMID 28718450, Iaea 2015 PMID 26168008, among many others)
      • While differences in the apo conformational ensembles of the WT and mutants are observed in Fig S7b and d, if these mutations reduce overlap with holo-like conformations is not determined. Simulations of the WT holo forms are needed to properly test this hypothesis.
      • For Mdm12, mutations are specifically made to "lock the protein in the apo-like state;" however, the mutant adopts conformations distinct from the apo form as show in Fig S7d. How do the authors interpret the results of the cellular assays considering this and could it help explain why the mutant has similar kinetics to WT? What may explain the puzzling results of similar transfer kinetics but differing mitochondrial morphology?
      • Confounding factors potentially complicate the interpretation of the in cellulo experiments. Simpler in vitro experiments may be better suited to determine if altering LTP's biophysical properties, namely rationally altering the population of apo- vs holo-like configurations, quantitatively affects transport rates as suggested.
      • The abstract, intro, and title highlight that the manuscript's findings are indicative of and correlated with function but on p. 12 it's foreseen "that future studies will focus on the functional consequence of such observation." Please reconcile these conflicting statements and ensure connections to function are accurately described. The current title is rather bold.

      All mentions of "correlation" throughout the manuscript need to be quantitatively evaluated or properly qualified. In addition to that mentioned above regarding Fig S6, what is the correlation coefficient between residues' contribution to PC1 and membrane interaction frequency (Fig 2)?

      Residue's contributions to collective conformational changes are found to be indicative of membrane binding. Yet, membrane interacting residues are identified from CG simulations that cannot capture such collective conformational changes due to the use of an elastic network. Given that the CG simulations agree with previous experimental findings, this suggests that collective conformational changes are not important for membrane binding. Are similar conclusions drawn from residues' RMSFs? In other words, are local conformational fluctuations just as indicative of membrane binding? The stated correlation may in fact be spurious and instead arise because residues at the entrance to LTP's hydrophobic cavities need to be positioned at the membrane surface for productive lipid uptake and these same residues must undergo significant conformational changes to allow lipid entry. Is proximity to cavity entrance more or less correlated with membrane binding than 'dynamics'?

      p. 12 speculatively suggests "the high degree of protein dynamics we observed in membrane proximal regions could potentially facilitate the energetically unfavorable reaction that involves the extraction of a lipid from a membrane." Yet, the logic behind this idea does not make sense since a free energy barrier, an equilibrium thermodynamic quantity, cannot be lowered by changes in dynamics. Please explain. Examining how the LTPs impact membrane properties would offer insight into the functional relevance of such residues for lipid extraction.

      The authors highlight that a bound lipid alters LTPs' conformational ensembles akin to "conformational selection" or "induced fit." How sensitive are these findings to the bound lipid species? Do LTPs with multiple known substrates exhibit an increasing diversity of holo conformations and are different conformations stabilized by different substrates? Would similar observations (Fig 3) be made with a lipid that is not known to be transferred by a given LTP? An interesting future direction would be to examine if lipid substrate specificity could be assessed by comparing conformational ensembles to that of a known substrate and/or by overlap with the apo ensemble.

      For LTPs to transfer lipids between membranes, transitions between apo and holo forms ought to occur when LTPs are membrane bound. How does membrane binding influence the conformational ensembles observed in solution? Does it promote conformational changes between apo- and holo-like structures, as suggested to regulate lipid uptake and release by previous studies of Osh/ORP, Ups/PRELI, and START family members? (For example: Miliara 2019 PMID 30850607, Watanabe 2015 PMID 26235513, Grabon 2017 PMID 28718450, Iaea 2015 PMID 26168008, Kudo 2008 PMID 18184806, Dong 2019 PMID 30783101) While answering these questions would require further computational effort, doing so will allow more accurate assessment of the role of conformational changes in LTP function.

      The authors motivate the study with the assumption that a common molecular mechanism of LTP function exists. Yet LTPs have evolved diverse sequences, structures, and substrate preferences; thus there seems to be no a priori requirement (or even necessarily a benefit) for a single molecular mechanism. What evidence then supports this premise? While previous studies are limited to individual LTPs, when viewed altogether retrospectively, they suggest features that could be shared among LTPs. Synthesizing previous studies and more thoroughly referencing them (only 5 are cited in the intro on p. 3) would strengthen both the premise and findings of the manuscript.

      The LTPs investigated are known to target distinct membranes. Should they then be expected to share structural or sequence-based features predictive of membrane binding interfaces, as motivates the analysis in Fig 1d, 1e, and S3? Or is it beneficial for LTPs to recognize membranes in different ways?

      Minor comments:

      p. 2 "making lipid transfer across the cytoplasm a potentially energetically favorable process": Is it meant that it is less energetically costly than transfer without a LTP? Why it would be energetically favorable is unclear (and would indicate that the LTP sequesters lipids away from membranes instead of transferring them between membranes).

      p. 3 "The excellent agreement between the membrane interface determined from the simulations and the experimentally-proposed one available for... Osh6" is missing a citation.

      The plots in Fig 1d and S3 are difficult to interpret. Bar plots, for example, would allow easier comparison and evaluation. Currently, it seems that most proteins individually exhibit some of the same trends observed among the whole set, counter to the conclusion on p 5.

      Negatively charged residues engage in a number of membrane interactions (Fig 1d and S3). What is a potential explanation for this unconventional observation?

      How much variance is captured by PC1, and how many PCs are needed to capture most of the variance in the conformations?

      Plots in Fig 3, especially panels c and d are difficult to see. Please make the panels larger (perhaps a 3 x 4 layout instead of 2 x 6 would work better).

      p. 8 "these conformational changes are localized in protein regions that interact with the lipid bilayer" is contradicted by the results in Fig 2b showing that all residues with large contributions to PC1 do not interact with the membrane and discussed on p 5.

      p. 8 "in the absence of bound lipids, it is able to sample multiple conformations" is not supported by the orange distributions in Fig 3d that appear unimodal. Is it instead meant that the apo form exhibits larger variance in cavity volume?

      Please clarify if the elastic network was constructed to maintain the holo or apo structures of each protein and if a bound lipid was used in the CG simulations.

      Was CHARMM TIP3P used?

      Please clarify how membrane interacting residues were defined and how interaction frequency was calculated from the longest duration of interaction.

      Refs 16 and 45 refer to the same paper.

      Significance

      General assessment:

      The work aims to tackle a grand question regarding membrane homeostasis mechanisms-what are universal principles underlying LTP function-and offers initial insights; however, further evidence is needed to support the conclusions as written, and some key results require further investigation and explanation.

      Advance and audience:

      By concurrently investigating the largest number of lipid transfer proteins to-date, the authors provide data invaluable for uncovering general mechanisms of non-vesicular lipid transport and advancing our understanding of membrane homeostasis mechanisms. By illuminating the wide-spread importance of conformational plasticity among lipid transfer proteins, the work presents a conceptual advance in our understanding of lipid transfer mechanisms and unifies previous studies. Because the manuscript emphasizes common biophysical principles and draws connections to enzyme biophysics, it ought to be of interest not only to membrane biologists but biochemists and molecular biologists more broadly.

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      Referee #1

      Evidence, reproducibility and clarity

      Srinivasan et al. present a comprehensive study on systematizing the structure-dynamics-function relation of lipid transfer proteins (LTPs), combining extensive molecular simulations and complementary experiments. Indeed, the current state-of-the-art in the field is quite chaotic and fractional, and such systematic studies are necessary to advance our general and conceptual understanding of the mechanisms of action of LTPs. The selected techniques and research strategies are all suitable, their description is sufficient and enables reproducibility; the obtained results are carefully presented and discussed; the conclusions are adequately supported by the data.

      Given my primarily computational background, I evaluated mainly the simulation part of the manuscript. Considering experiments, I do not see any significant flows or deficiencies that could diminish the value of the data and following conclusions given in the manuscript. I would even suggest improving the abstract by more explicitly saying that this work includes experimental measurements because it currently reads like purely computational work was performed.

      Major comments:

      1. Although I like the central message of the paper and have no objections, I am curious whether the conclusion "a more "dynamic" or/and "mobile" part of the protein interacts with the membrane or any other (macro)(bio)molecule" makes sense globally and is not limited to LTPs. For example, it is a reasonable assumption that a more flexible part of the protein, i.e., capable of adopting necessary binding configurations, would be a more likely interacting spot. Locking in a less flexible and more specific configuration upon binding with a target molecule is also anticipated and quite typical, e.g., when ligands interact with target proteins, thereby blocking their function. The authors themselves recognize this paradigm as referring to the enzymes' dynamics. It would be great if authors could comment more on dynamics-function relation, referring to the existing literature, where such observations were/were not observed for different protein families. Performing simulations on proteins that do not exhibit such a feature and do not belong to LTPs, but, e.g., structurally similar to some of the studied LTPs, would be an excellent addition too, highlighting this signature characteristic of LTPs.

      Minor comments:

      1. Fig 1d. What is so special in Lysine compared to Arginine? Is there any disbalance in their presence in studied proteins? Any correlations between the binding affinity of certain amino acids and their overall presence on the protein surface?
      2. Fig S1. GM2A and TTPA seem to be irreversibly adsorbed to the membrane on the microsecond timescale in most replicas. Is anything special in these proteins? Did this affect the sampling of a claimed membrane-binding interface?
      3. A related follow-up question. Multiple replicas were performed to identify the membrane-binding interface. However, if I understand well, the initial orientation of the protein with respect to the membrane was always the same. I found it a pity since performing multiple replicas starting from different initial geometries (e.g., rotating the protein in a somewhat systematic way) would likely result in a more efficient exploration of the conformation space. Can the authors comment on whether this predefined initial configuration could negatively affect the results? Performing a few additional simulations for the most problematic proteins I mentioned earlier (GM2A and TTPA) could be a nice opportunity to apply this strategy.
      4. How was the volume of the cavity affected by mutations in STARD11 and Mdm12? Do these data somehow correlate with the experimentally observed reduced efficiency of the lipid transfer?
      5. I would appreciate it if the authors considered playing with the templates of the main Figures at later stages because in the current version, and when printed on A4 paper, the readability of certain graphs and pictures is uncomfortable and sometimes even impossible. Obviously, the final schematics would depend on the journal and its formatting.

      Referees cross-commenting

      I would like to acknowledge the thoughtful and detailed reviews provided by other reviewers. I do like their reports, and I believe that by addressing the reviewers' comments and incorporating their revisions, the article will significantly improve in terms of scientific rigor and contribution to the field.

      Significance

      This manuscript is a solid scientific work addressing gaps in our knowledge about Lipid Transfer Proteins by employing state-of-the-art methods. It advances the field on conceptual and fundamental levels. This study is of interest to both computational biophysicists and physical chemists (to whom I belong myself) as well as experimentalists, who seek a rational explanation of the experimental observations.

  3. Jun 2023
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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

      Chakraborty et al describe the biochemical and structural characterization of Spiroplasma FtsZ and report that the protein has unusual properties compared to other FtsZ. Sedimentation and GTPase measurements showed that whereas the wild-type protein has a high critical concentration and low GTPase activity, a mutant predicted to facilitate FtsZ cleft opening (F224M) exhibited lower critical concentration and higher GTPase activity. In addition, the crystal structures of both wild-type and F224M SmFtsZ revealed a unique domain-swapped dimer configuration in which one of the monomers in each dimer exhibited an R/T hybrid or intermediate conformation, with the NTD in the T state and the CTD in the R state. The T state of FtsZ has only been observed before when the protein crystallizes as filaments. Thus, the crystal structure of SmFtsZ - which is not assembled in filaments - was interpreted as capturing a conformational state that could explain the kinetic polarity of FtsZ (preferential addition of subunits to the CTD-exposed end of FtsZ filament).

      This is a good quality manuscript overall, but which could still be improved by the suggestions below. In terms of significance, it provides new data to support current models for FtsZ assembly mechanism but no major new insights. The findings are interesting for a more specialized audience.

      Major points

      1. The peculiar biochemical properties of SmFtsZ (high CC, low GTPase) are well documented and interesting but deserve further critical assessment to rule out artifacts. The EM in Fig 1B suggests abundant aggregated protein (not monomers), in addition to filament bundles, which suggests that SmFtsZ is not stable under the experimental conditions used. There are reports that some FtsZ will lose nucleotide during purification and become partially unfolded and unstable (doi.org/10.1111/febs.15235). Figure S1E suggests that the same may be happening here, as the amount of GDP released by SmFtsZ seems to be lower than expected if all the protein had nucleotide. Perhaps the authors should repeat their experiments with SmFtsZ purified in the presence of GDP, which should stabilize the protein, to confirm that the biochemical properties of the protein stay the same.
      2. Another unexpected observation is that the SmFtsZ bundles are quite short despite the low GTPase activity of the protein, whereas mutant F224M forms much longer bundles and is a stronger GTPase. In general, filament length correlates inversely with GTPase activity, if measurements are being made at steady state. However, no kinetic (light scattering or fluorescence) experiments seem to have been done to ensure measurements were done in steady state. The authors do try to explain the odd behavior of SmFtsZ but the idea that the increase in GTPase reflects a faster kinetics of nucleation and elongation is not necessarily true. GTP turnover is usually limited by the kinetics of filament disassembly not by assembly. However, it is possible that in reactions with a mutant that is much better at nucleation there will be many more filaments than with a poorly nucleating protein and, thus, more filament ends for subunit turnover. A complicator to these experiments is that they were carried out in high magnesium and at pH 6.5 which favor bundling, and bundling affects subunit and GTP turnover in ways that are hard to account for. Ideally, experiments aimed at properly determining the kinetic properties of FtsZ should be carried out under conditions that avoid bundling (pH 7.4-7.7, 2-5 mM Mg2+) and include proper kinetic measurements, such as light scattering. Thus, before any hard conclusions can be drawn about the properties of SmFtsZ, the authors may wish to revisit some of their biochemical experiments in light of the caveats pointed out here.
      3. A central part of the paper is the description of the intermediate R/T conformation but that was a bit confusing and perhaps could be improved. The first thing would be to more clearly define what are the structural changes of the NTD in the T conformation. From other publications, it seems that the NTD undergoes little alteration upon switching to the T conformation, the main one being the flipping of the guanine of the bound nucleotide. But if the NTD structure remains essentially the same, what causes the flipping of the guanine? My impression was that guanine flipping was caused by the downward movement of H7 but if H7 and its attached elements (H6, S6) are moving, why is this not manifested as a significant structural change in the NTD in the T state? Moreover, from Figs. 3C and 5A we conclude that the relative position of H7 in the R/T structures is the same as in R structures. If H7 has not changed in the R/T structure, can you call this a T structure? Also, if there is no H7 movement, what caused the change in guanine angle?
      4. The observation that the intermediate conformation was detected in a swapped-dimer is always a matter of some concern, as domain swapping imposes additional constraints on the conformational freedom of a protein and generates structures that are often different from their non-swapped counterparts. This seems to be the case for other FtsZ domain-swapped structures, which were outliers in the extensive comparisons made by Wagstaff et al (doi.org/10.1128/mBio.00254-17) and also stand out in the analysis in Fig. 3BC. Perhaps the authors should discuss more thoroughly why this structure must reflect a natural conformation of FtsZ.
      5. Still regarding the structural basis of kinetic polarity, it would be desirable to present a more complete view of the debate in the field about this issue. For example, Ruiz et al, (doi.org/10.1371/journal.pbio.3001497) recently provided structural arguments for the NTD being the face used for monomer addition without detecting the same intermediate form reported in this manuscript. How do their data and arguments differ from your findings? More generally, isn´t the fact that the NTD does not change substantially as FtsZ transitions from R to T already an argument for the NTD being the surface used for monomer addition?
      6. l. 74 "led us to propose a structural basis for the kinetic polarity of FtsZ, where transition of the NTD to the T-state conformation driven by GTP binding is sufficient to add a GTP-bound monomer to the bottom interface of the FtsZ filament." This statement suggests that GTP is necessary for the intermediate conformation but this is not supported by the data, as the GDP bound 7YSZ structure also has one monomer in the intermediate conformation. As far as I can tell, there is no structural evidence to suggest that the nucleotide gamma phosphate plays any role in the R-T transition. Even the role of the gamma phosphate in organizing the T3 loop in an assembly-conducive conformation seems to still be a controversial matter in the field. According to Matsui 2014 (doi.org/10.1074/jbc.M113.514901) "based on the results of the present study as well as on the structures deposited previously by other groups (PDB codes 2RHL, 2RHO, 2Q1X, and 2Q1Y) (43, 44), nucleotide exchange appears not to directly induce a structural change in the monomer, including the T3 loop."
      7. The experiments with the reciprocal cleft mutation in E. coli are not very informative as it is difficult to correlate the division defect in vivo with specific kinetic defects of the mutant FtsZ. The authors should have at least done a basic characterization of the E. coli mutant in vitro to demonstrate that it is altered in its CC alone. In fact, the dominant negative effect of the mutation in vivo is not something one expects from a poorly nucleating protein, which, if anything, should have a hard time poisoning the endogenous protein. The effect on ring compaction also suggests that the mutation must affect the protein in a broader way, perhaps including filament geometry. I would suggest that this part of the manuscript could be excluded without any loss for the SmFtsZ conclusions.

      Minor points

      1. l. 14 "CTD of the nucleotide-bound monomer cannot bind to the NTD-exposed end of the filament unless relative rotation of the domains leads to cleft opening." This is not accurate. There is no steric impediment to this reaction. Monomers in R conformation should be able to add to the NTD end of the filament as well, even if this is slower than the opposite reaction. The absence of growth from the NTD end is because the rate of addition/conformational change is slower than the rate of GTP hydrolysis.
      2. The comparison between the 7YOP (B) structure and the S. aureus 3WGN structure to show the effect of the gamma phosphate on T3 loop structure should be presented in a single figure, instead of being split between Fig. 4 and Fig. S2, and preferably using similar poses of the two structures. In the current state, it is quite hard to visualize the similarities mentioned by the authors.
      3. In contrast to what´s in the main text (l. 130), the chain with continuous density in Figure 2 is assigned as B, not A. Please clarify which is correct.
      4. l. 271 pBAD is the plasmid name, not the promoter. The promoter is PBAD(subscript).

      Significance

      This is a good quality manuscript overall, but which could still be improved by the suggestions below. In terms of significance, it provides new data to support current models for FtsZ assembly mechanism but no major new insights. The findings are interesting for a more specialized audience.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      This is a study of cell division protein SmFtsZ from Spiroplasma melliferum, a cell wall-less Mollicutes bacterium where FtsZ may provide the primary force for division. Using X-ray crystallography, biochemical and microbiological experiments, the authors provide insight into how FtsZ's relaxed (R) to tense (T) conformational switching and its GTPase activity explain the kinetic polarity of FtsZ filament treadmilling. They propose: 1) an intermediate R/T state of FtsZ that facilitates preferential binding of N-terminal domain (NTD) of a monomer to C-terminal domain (CTD) of the terminal subunit at the filament bottom end; 2) that R to T switching is the rate-limiting step of FtsZ polymerization; 3) a T3 loop mechanism for GTP gamma phosphate triggering FtsZ polymerization.

      All comments and criticisms below are made for the sake of this interesting study.

      General Comments

      The study is well thought, carefully executed, and the manuscript is well written. However, the first conclusion is not convincing, because it is based on a misleading analysis; and the third conclusion is complicated by the use of an unqualified analog of GTP. SmFtsZ crystallizes as a dimer with the NTD and CTD domains swapped and a NTD-NTD contact. Mechanistic conclusions drawn from this unusual structural context are largely speculative, as they may not hold for normal FtsZ assembly. The NTD structure changes really very little between R-FtsZ and T-FtsZ, so that it is probably incorrect to define a T-NTD/R-CTD intermediate conformation from the guanine ring angle, as will be reasoned below. In addition, the GTP analog GMPPNP employed to investigate the effects of the gamma phosphate is not demonstrated to promote FtsZ assembly as GTP does; in fact, its beta-gamma phosphate geometry in the SmFtsZ structure is clearly different from GTP in other FtsZ structures. This raises the concern that GMPPNP may be not a bona fide functional analog of GTP for FtsZ.

      The authors should moderate the first and third claims, keeping speculations for discussion. Additional experiments required to assess the activity of GMPPNP inducing FtsZ assembly should be reported, even if the result was negative. Authors may consider partially refocusing the manuscript, including the title, towards the mechanism of the R to T transition, with Phe224Met modulating the opening of the cleft between NTD and CTD. Careful discussion of the structural basis of the kinetic polarity of FtsZ filaments in the light of previous and current results should be fine. In addition, SmFtsZ is now the third FtsZ that has been crystallized as a domain swapped dimer, suggesting a tendency for intramolecular dissociation of NTD and CTD with potential mechanistic implications, as the authors point out by the attractive end of the discussion.

      Specific comments (major and minor)

      Line 37 should read "...connected via H7 helix (15), with divergent C-terminal extensions".

      Line 55 Please note that the kinetic polarity of FtsZ has been deduced from mutational analysis (rather than observed as in the case of microtubules)

      Line 75 ""..transition of the NTD to the T-state conformation driven by GTP binding is sufficient..." This sentence appears conceptually wrong, because the R of T conformation of FtsZ is deemed independent of GTP or GDP binding in the literature (for example, Ref 23)

      Line 89 should read " in the presence of GTP and Mg2+ and not with GDP and Mg2+"

      Line 90-Figure 1A. The greyish gel electrophoresis image and those in SI require improving staining or photos. Standard Coomassie staining typically gives less background and better contrast.

      Line 90. Were the SmFtsZ filaments single or multiple in EM?

      Line 92. "...other characterized bacterial FtsZs" Some references should be cited

      Line 107 suggesting -> indicating

      Lines 120-122 " a truncated construct....SmFtsZdeltaCt showed similar GTPase activity as the wild type" is repeated from lines 110-111 above

      Line 124. Why the choice of GMPPNP, rather than GTP or GMPCPP?. Have SmFtsZ structures with GTP or GMPCPP been attempted?

      Line 124. It would be helpful to the reader to explain here that structure 7YSZ has two GDP-bound chains whereas structure 7YOP has GDP in chain A and GMPPNP in chain B.

      Lines 131 and 133. The names of chain A and chain B are swapped in the text Figure 2A-D. Consider enhancing the nucleotide tracing for easier visualization

      Line 141 and Figure 2F. Why change from the refraction detector in panel E to the absorbance detector in panel F? Importantly, how to know whether the shoulder corresponds to a dimer or to an extended monomer, and was the column calibrated?. In any case, do extended monomers and domain swapped dimers exist in solution? Additional crosslinking experiments, and analytical ultracentrifugation if available, could provide interesting results, although this is not a strict requirement for this manuscript.

      Lines 155-157 and Figure 3. The "GTP-bound T-state" 3WGN structure is not GTP but GTP-gamma S-bound, which makes a difference. 3WGM is GTP-bound SaFtsZ, although with a truncated loop T7. There is an unnecessary mix of FtsZs from different species in structures 2RHL and 3VOA; using instead 5H5G-molecule A (T-state, GDP) and 5H5G-molecule B (R-state, GDP) would simplify the structures employed for comparison in Figure 3 to a single species, SaFtsZ. In fact, 5H5G is employed as a reference in Figure 3C, although the distinction between 5H5G molecules A and B is not mentioned.

      Lines 157-160. The guanine ring angle depends on a stacking interaction with Phe183 form helix H7, which shifts in known FtsZ R and T structures. But this part of the structure is actually missing from the so called "T-state GDP" and "T-state GTP" SmFtsZ swapped domain structures. Instead, the guanine ring interacts with the main chain carbonyl of Phe137, an interaction which is not observed in the standard R or T FtsZ structures employed for comparison. This makes using the guanine ring angle alone misleading for conformational classification of SmFtsZ. In addition, both SmFtsZ "T-state" structures show a R-like Arg29 disengaged from interacting with the guanine (Figure 3), contrary to the interaction observed in the FtsZ T conformation. The overall conformation of the SmFtsZ structures does correspond to R-FtsZ. However, the swapped domain context of the SmFtsZ structure hampers meaningful comparisons with other FtsZ structures at a detailed local level around the guanine ring.

      Lines 175-177. "We concluded that in B chain the nucleotide-bound NTD is in T-state...". Importantly, the structure of the NTD of FtsZ, not including helix H7, is known to be very similar in the R and T conformations; differences are the position of helix H7, the position of the CTD relative to the NTD and the opening of the interdomain cleft (refs 23 and 24). The guanine ring angle is clearly related to the H7-Phe183 shift. Therefore, distinguishing R and T-conformations of the NTD in FtsZs and in SmFtsZ in particular seems unsupported by experimental data.

      Lines 197-199. Checking known FtsZ structures shows that Gly71 in loop T3 can be flipped out or in with GDP in both R and T conformation, whereas it is out with GTP or its analogs, making room for the gamma phosphate. It is interesting that the authors now observe this change with SmFtsZ, comparing the structures of GDP-bound and GMPPNP-bound protein. However, they should analyze and mention the precedents in the PDB, not only the GTP-gamma-S-bound 3WGN, and draw their conclusion very carefully due to the swapped domain context. There are known interactions made by the nucleotide gamma phosphate (PDB 3WGM) and one analog (PDB 7OHK) across the association interface in FtsZ filaments that explain FtsZ polymerization. In addition, is loop T3 really stabilized by the gamma phosphate of by filament formation?

      Lines 202-210. Tyr145 is not part of loop T5 but of helix H5. The observed interplay between loop T3 Pro73 and H5 Tyr145 is an attractive feature (apparently reminiscent of the tubulin T3-T5 story, but see Discussion). Please indicate if this has not been pointed out before in other FtsZs with the residue corresponding toTyr145, and consider analyzing existing FtsZ structures for T3-H5/T5 cross talk in different nucleotide states.

      Lines 212-324. The last three sections of Results convincingly demonstrate how residue 224 Phe/Met in the cleft between CTD and NTD modulates SmFtsZ assembly, EcFtsZ assembly, and E. coli cell division. In addition to this study, is it known whether SmFtsZ can replace EcFtsZ for E. coli cell division?

      Line 220 and Figure S3A. Please explain the color code in this Figure.

      Line 243. How can it be proposed that SmFtsZF224M could not be crystallized with GMPPNP probably due to efficient filament formation, if the activity of GMPPNP inducing filament formation has not been documented?

      Figure 6 panel F. The NeonGreen Z-ring microscopy images need enlargement to be properly appreciated.

      Discussion Line 342. Please notice that loop T3 is not always disordered with GDP. The proposal lacks an analysis of other FtsZ structures, in addition to 3WGN, and ignores intermolecular interactions of the nucleotide gamma phosphate and the coordinated Mg2+ ion (Matsui et al, 2014 J Biol Chem; Ruiz et al, 2022 PLoS Biol).

      Discussion Lines 356-370. The similarities to the classical GTP/GDP-dependent T3-T5 cross talk in the tubulin-RB3 complex (reviewed in Ref 27) is appealing, but notice that this was curved R-state tubulin with an accessory protein. But maybe the nucleotide dependent T3-T5 cross talk does not take place in T-tubulin from cryoEM microtubule structures with GDP and GTP (LaFrance et al and Nogales 2022 PNAS)?. And the authors should carefully check the tubulin T3 and T5 loop GDP/GTP-dependent conformations in the recently available cryoEM structures of free tubulin heterodimers (R-state) bound to GDP (PDB 7QUC) and GTP (PDB 7QUD) without any accessory proteins, which differ from the classical view.

      Discussion Lines 371-379. It should be noticed that a simpler interpretation of the results is that SmFtsZ is in the R-state, with R-CTD and R-H7, whereas the NTD is practically the same in both R and T states, as for other FtsZs (Ref 23). The T-like guanine angle may result from anomalous interactions of the swapped domains in SmFtsZ.

      Discussion Lines 381-384. There is really no need to postulate a NTD transition from R- to T-state in order to propose a kinetic polarity for the FtsZ filament from structure. In fact, having the NTD conformation constant results in a monomer top interface that is pre-formed for association and with the help of GTP should bind to the filament bottom subunit, as already proposed in Ref 35.

      Referees cross-commenting

      In addition to the concerns shared by the reviewers, especially those related to the existance or the role of distinct R and T conformations of the NTD of FtsZ, as welll as the individual reviewer concerns, we would like to highlight the relevance of:

      The comment of reviewer 1, requiring more information on the biological role of FtsZ in cell division of Spiroplasma and whether it forms a ring.

      Comment 2 of reviewer 3, requiring time-dependance of SmFtsZ polymerization and GTPase data, which are essential for properly analyzing the GTPase activity.

      Significance

      This interesting work, if successfully revised, will provide valuable insight into how the FtsZ polymerization switch and the nucleotide binding loops work for assembly of polar filaments, employing FtsZ from a wall-less bacterium. Please see the comments above for the existing literature context of the manuscript. This paper will be possibly suitable for a general biological audience, in addition to microbiologists and cytoskeletal researchers.

      This review has been prepared by biochemistry and structural biology experts familiar with FtsZ, hoping that it may be useful to the authors.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary: This paper reports the biochemical and structural characterization of FtsZ from a wall-less bacterial organism, Spiroplasma melliferum. From the analysis of the crystal structures (and comparison to known structures) the authors propose a model to explain the kinetic polarity of FtsZ treadmilling that was derived from a mostly genetic analysis (ref 26). In that model FtsZ with GTP bound adds to the bottom end of a filament with the C-terminal domain then shifting to the T-state. The status of the N-ter (whether in the T or R-state was not considered). Here the authors have proposed that they have captured an intermediate with the N-ter in the T-state and the C-ter in the R-state. It is proposed that this form adds to the bottom of a filament with the C-ter now adopting the T-state. I am not convinced this model is supported by the data as it is not clear when the N-ter domain switches to the T-state (before or after addition the end of a filament).

      Significance

      Note: the authors define the N-ter being in the T-state vs R-state based on the orientation of the guanine ring. The C-ter domain is in the T vs R-state based on whether the cleft is open or closed respectively.

      The basis of the authors' model comes mostly from the analysis of the crystal structure of FtsZ from the wall-less bacterial that was obtained in this study. The crystal structure revealed an unusual swapped dimer. Although in solution this FtsZ is a monomer, it crystallized as a swapped dimer indicating that during crystallization FtsZ domains came apart and reassociated with the opposite domains from another monomer. Careful analysis reveals that in one monomer the N-ter domain is in the T-state whereas in the other the N-ter domain is in the R-state (independent of nucleotide; GDP or GMPCPP). Although the N-ter domain is in the T-state in both monomers there are some differences - with GMPCPP the T3 loop is ordered whereas it is disordered with GDP. Also, they propose that the orientation of Gly71 is such in the GTP state that it favors interaction with the bottom end of a filament.

      Is it known whether FtsZ assembles into a Z ring and is required for cell division in this organism?

      In the dimer both C-terminal domains are in the R-state. From this the authors propose that the one monomer in the dimer is in the R-state whereas the other is in transition state (T-for N-ter and R-for C-terminal domain).

      The authors analyze sequences of FtsZ from different bacteria and notice that position 242 is a Phe in their organism whereas it is a Met in other bacteria. They wonder whether this residue influences the C-ter transitioning to the T-state so they swap residues - putting a Met in Sm and a Phe in Ecoli at this position. Interestingly, they notice that Sm-FtsN-met results in increased GTPase activity but longer filaments - this seems contradictory as higher GTPase is usually associated with shorter filaments - e.g. increased Mg slows GTPase activity and increased filament length and bundling. The Ec-FtsZ-Phe mutant displays increased cell length but not sure this can be ascribed to an effect on the GTPase activity.

      Overall, the work is well done and it comes to interpretation and whether the data support the model. The emphasis is on the N-ter getting to the T-state, but I am not sure that is the important step. It seems to me that rate-limiting step in FtsZ assembly is the C-ter getting into the T-state, which happens when a subunit is added to the end of a filament. Obviously the N-ter has to get to the T-state as well but how that happens is not clear. Presumably, it happens as a GTP-bound monomer in the R-state adds to the end of a filament resulting in the N-ter adopting the T-state followed by the C-ter adopting the T-state. In other words the T-state is only achieved by addition of a subunit to the end of the filament.

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      Reply to the reviewers

      __Reviewer 1____: __

      1-Localization of ESYT1 and SYNJ2BP

      The claim of a localization at ER-mitochondria contacts relies on two type of assays. Light microscopy and subcellular fractionation. Concerning microscopy, while the staining pattern is obviously colocalizing with the ER (a control of specificity of staining using KO cells would nevertheless be desirable)

      the idea that ESYT1 foci "partially colocalized with mitochondria" is either trivial or unfounded

      Every cellular structure is "partially colocalized with mitochondria" simply by chance at the resolution of light microscopy

      If the meaning of the experiment is to show that ESYT1 'specifically' colocalizes with mitochondria, then this isn't shown by the data

      There is no quantification that the level of colocalization is more than expected by chance

      nor that it is higher than that of any other ER protein

      Moreover, the author's model implies that ESYT1 partial colocalization with mitochondria is, at least partially, due to its interaction with SYNJ2BP. This is not tested.

      • To analyze and measure MERCs parameters and functions, we used a set of validated methods described in the following specialized review articles (Eisenberg-Bord, Shai et al. 2016, Scorrano, De Matteis et al. 2019).
      • To support and confirm the localization of ESYT1-SYNJ2BP complex at MERCs, we performed supplementary BioID analysis using ER target BirA*, OMM targeted BirA* and ER-mitochondria tether BirA* (Table S1, Figure S1 and Figure 1 A and B). These results confirmed the specificity of the interaction of the 2 partners. ESYT1 is not identified as a prey in OMM BioID and SYNJ2BP is not identified in ER BioID, on the other hand both partners are identified in the ER-mitochondria tether BioID.
      • To improve our description of the partial localization of ESYT1 at mitochondria, we performed a quantitative analysis using confocal microscopy on control human fibroblasts stably overexpressing SEC61B-mCherry as an ER marker which were labelled with ESYT1 and TOMM40 for mitochondria. We measured the % of ESYT1 signal colocalizing with mitochondria and the % of mitochondria positive for ESYT1 (Figure 1E).
      • To demonstrate than ESYT1 partial colocalization with mitochondria is, at least partially, due to its interaction with SYNJ2BP, we performed a quantitative analysis using confocal microscopy. Human control fibroblasts, KO SYNJ2BP fibroblasts and SYNJ2BP overexpressing fibroblasts were labelled with ESYT1, TOMM40 for mitochondria and CANX for ER. We measured the % of ESYT1 signal colocalizing with mitochondria in each condition (Figure 3C). Membranes (MAM) can be purified and are enriched for proteins that localize at ER-mitochondria contacts. This idea originated in the early 90's and since then, myriad of papers has been using MAM purification, and whole MAM proteomes have been determined. Yet the evidence that MAM-enriched proteins represent bona fide ER-mitochondria-contact-enriched proteins (as can nowadays be determined by microscopy techniques) remain scarce. Here, anyway, ESYT1 fractionation pattern is identical to that of PDI, a marker of general ER, with no indication of specific MAM accumulation.

      • To highlight the enrichment of ESYT1 in the MAM fraction, we quantified the ESYT1 signal in each fraction. Those results show a similar fractionation pattern than the MAM resident protein SIGMAR1 (Figure 1F). For SYNJ2BP, it is different as it is more enriched in the MAM than the general mitochondrial marker PRDX3. However, PRDX3 is a matrix protein, making it a poor comparison point, since SYNJ2BP is an OMM protein.

      • To confirm the partial enrichment of SYNJ2BP in the MAM fraction compared to another outer mitochondrial membrane protein, we added the signal of the well characterized OMM protein CARD19 (Rios, Zhou et al. 2022). Again, the model implies that ESYT1 and SYNJ2BP accumulation in the MAM should be dependent on each other. This is not tested.

      • As describe above, we demonstrated in Figure 3C than the accumulation of ESYT1 at mitochondria is, at least partially, dependent on the quantity of SYNJ2BP.

      • We moreover showed a reciprocal effect in Figure 3E. A quantitative analysis using confocal microscopy demonstrated that the effect of SYNJ2BP overexpression on MERCs formation is partially dependent of the presence of ESYT1. 2-ESYT1-SYNJ2BP interaction.

      The starting point of the paper is a BioID signal for SYNJ2BP when BioID is fused to ESYT1. One confirmation of the interaction comes in figure 4, using blue native gel electrophoresis and assessing comigration. Because BioID is promiscuous and comigration can be spurious, better evidence is needed to make this claim. This is exemplified by the fact that, although SYNJ2BP is found in a complex comigrating with RRBP1, according to the BN gel, this slow migrating complex isn't disturbed by RRBP1 knockdown, but is somewhat disturbed by ESYT1 knockdown. More than a change in abundance, a change in migration velocity when either protein is absent would be evidence that these comigrating bands represent the same complex.

      • We showed in Figure 4C that the presence of SYNJ2BP in a complex of a similar molecular weight that ESYT1 (410KDa) is totally dependent of the presence of ESYT1, suggesting an interaction of the 2 proteins.
      • To confirm this interaction, in figure 4A we analyzed on BN cells overexpressing SYNJ2BP together with a 3xFlag tagged version of ESYT1. As a result of the addition of the Flag tag, the complex positive for ESYT1 shifted to a higher molecular weight. The complex positive for SYNJ2BP shifted to a similar the molecular weight, demonstrating the interaction and dependence of the 2 partners. ESYT1-SYNJ2BP interaction needs to be tested by coimmunoprecipitation of endogenous proteins, yeast-2-hybrid, in vitro reconstitution or any other confirmatory methods.

      • To confirm the interaction of the 2 partners, we performed co-immunoprecipitation of the ESYT1-3xFlag protein that we showed in Figure 1H to form complexes similar to the endogenous protein. SYNJ2BP is found as the strongest prey, followed by ESYT2 and SEC22B two described interactors of ESYT1, confirming the quality of the analysis (Table S2) (Giordano, Saheki et al. 2013, Gallo, Danglot et al. 2020). 3-Tethering by ESYT1- SYNJ2BP.

      This is assessed by light and electron microscopy. Absence of ESYT1 decreases several metrics for ER-mitochondria contacts (whether absence of SYNJ2BP has the same effect isn't tested).

      • Using PLA (proximity ligation assay) we demonstrated that the loss of SYNJ2BP leads to a decrease in MERCs (Figure 7 H and I), confirming previous studies (Ilacqua, Anastasia et al. 2022, Pourshafie, Masati et al. 2022). This interesting phenomenon could be due to many things, including but not limited to the possibility that "ESYT1 tethers ER to mitochondria".

      This statement and the respective subheading title are therefore clearly overreaching and should be either supported by evidence or removed.

      Indeed, absence of ESYT1 ER-PM tethering and lipid exchange could have knock-on effects on ER-mito contacts, therefore strong statements aren't supported.

      Moreover, the effect on ER-mitochondria contact metrics could be due to changes in ER-mitochondria contact indeed but may also reflect changes in ER and/or mitochondria abundance and/or distribution, which favour or disfavour their encounter. Abundance and distribution of both organelles are not controlled for.

      • The mitochondrial phenotypes caused by the loss of ESYT1 are all rescued by the introduction of an artificial mitochondrial-ER tether, demonstrating that they are due to loss of the tethering function of ESYT1. Finally, the authors repeat a finding that SYNJ2BP overexpression induces artificial ER-mitochondria tethering. Again, according to the model, this should be, at least in part, due to interaction with ESYT1. Whether ESYT1 is required for this tethering enhancement isn't tested.

      • As described above, we demonstrated in Figure 3C that the accumulation of ESYT1 at mitochondria is, at least partially, dependent on the quantity of SYNJ2BP.

      • We moreover showed a reciprocal effect in Figure 3F. A quantitative analysis using confocal microscopy demonstrated that the effect of SYNJ2BP overexpression on MERC formation is partially dependent of the presence of ESYT1. 4-Phenotypes of ESYT1/SYNJ2BP KD or KO.

      The study goes in details to show that downregulation of either protein yields physiological phenotypes consistent with decreased ER-mitochondria tethering. These phenotypes include calcium import into mitochondria and mitochondrial lipid composition.

      Figure 5 shows that histamine-evoked ER-calcium release cause an increase in mitochondrial calcium, and this increase is reduced in absence of ESYT1, without detectable change in the abundance of the main known players of this calcium import. This is rescued by an artificial ER-mitochondria tether. However, Figure 5D shows that the increase in calcium concentration in the cytosol upon histamine-evoked ER calcium release is equally impaired by ESYT1 deletion, contrary to expectation. Indeed, if the impairment of mitochondrial calcium import was due to improper ER-mitochondria tethering in ESYT1 mutant cells, one would expect more calcium to leak into the cytosol, not less.

      The remaining explanation is that ESYT1 knockout desensitizes the cells to histamine, by affecting GPCR signalling at the PM, something unexplored here.

      In any case, a decreased calcium discharge by the ER upon histamine treatment, explains the decreased uptake by mitochondria.

      The authors argue that ER calcium release is unaffected by ESYT1 KO, but crucially use thapsigargin instead of histamine to show it. Thus, the most likely interpretation of the data is that ESYT1 KO affects histamine signalling and histamine-evoked calcium release upstream of ER-mitochondria contacts.

      • Silencing ESYT1 impairs SOCE efficiency in Jurkat cells (Woo, Sun et al. 2020), but not in HeLa cells (Giordano, Saheki et al. 2013, Woo, Sun et al. 2020). Analysis of the role of ESYT1 in HeLa cells prevents confounding effects due to the loss of ESYT1 at ER-PM. In this model, knock-down of ESYT1 led to a decrease of mitochondrial Ca2+ uptake from the ER upon histamine stimulation, as monitored by genetically encoded Ca2+ indicator targeted to mitochondrial matrix (Figure 5A and B). ESYT1 silencing in HeLa cells did not impact ER Ca2+ store measured by the ER-targeted R-GECO Ca2+ probe (Figure 5C and D). The expression of the artificial mitochondria-ER tether was able to rescue mitochondrial Ca2+ defects observed in ESYT1 silenced cells (Figure 5B), confirming that the observed anomalies are specifically due to MERC defects.
      • In contrast loss of ESYT1 impaired SOCE efficiency in fibroblasts (Figure 6 A and B). This phenotype was fully rescued by re-expression of ESYT1-Myc but not the artificial tether. We therefore investigated the influence of ESYT1 loss on cytosolic Ca2+ concentration following ATP (Figure 6F to H) or histamine stimulation (Figure S3 D to F), both of which showed a reduced cytosolic Ca2+ concentration and uptake in ESYT1 KO cells. This phenotype was fully rescued by the re-expression of ESYT1-Myc but not the artificial tether. Measurment of cytosolic Ca2+ after tharpsigargin treatment in Ca2+-fee media, an inhibitor of the sarco/endoplasmic reticulum Ca2+ ATPase SERCA that blocks Ca2+ pumping into the ER, showed that ESYT1 KO does not influence the total ER Ca2+ pool (Figure 6K and L). However, ER-Ca2+ release capacity upon histamine stimulation (Figure 6I and J) is decreased in ESYT1 KO cells. This phenotype was fully rescued by the re-expression of ESYT1-Myc but not the artificial tether. Loss of ESYT1 decreased the Ca2+ uptake capacities of mitochondria after activation with histamine (Figure S3 A to C) or ATP (Figure 6 C to E). This phenotype was rescued by re-expression of ESYT1-Myc and also the engineered ER-mitochondria tether. Thus, despite the ER-Ca2+ release defect observed after ESYT1 loss, the artificial tether fully rescued the mitochondrial phenotype.
      • These results highlight the distinct and dual roles of ESYT1 in Ca2+ regulation at the ER-PM and at MERCs. The data with SYNJ2BP deletion are more compatible with decreased ER-mito contacts, as no decreased in cytosolic calcium is observed. This is compatible with the previously proposed role of SYNJ2BP in ER-mitochondria tethering, but the difference with ESYT1 rather argue that both proteins affect calcium signaling by different means, meaning they act in different pathways.

      • We explain the different results concerning cytosolic calcium by the fact that ESYT1 is a bi-localized protein with dual functions on cellular calcium. Implicated both in SOCE at ER-PM and in mitochondrial calcium uptake at MERCs. On the other hand, SYNJ2BP is only present at MERCs and its loss do not influence PM-ER signaling or ER-Ca2+ release. Finally, the study delves into mitochondrial lipids to "investigated the role of the SMP-domain containing protein ESYT1 in lipid transfer from ER to mitochondria". In reality, it is not ER-mitochondria lipid transport that is under scrutiny, but general lipid homeostasis, and changes in ER-PM lipids could have knock-on effects on mitochondrial lipids without the need to invoke disruptions in ER-mitochondria transfer activity.

      • The fact that the artificial tether, which specifically rescue MERCs, fully rescue the lipid phenotype argue for a direct loss of MERCs tethering function when ESYT1 is missing. The changes observed are interesting but could be due to anything. Surprisingly, PCA analysis shows that the rescue of the knockout by the ESYT1 gene clusters with the rescue by the artificial tether, and not with the wildtype. This indicates that overexpressing either ESYT1 or a tether cause similar lipidomic changes. These could be due, for instance, to ER stress caused by protein overexpression, and not to a rescue.

      • In order to verify if the overexpression of ESYT1 or the artificial tether induces ER stress, we performed a WB analysis to compare markers of ER stress in control fibroblasts, KO ESYT1 fibroblasts, KO ESYT1 fibroblasts overexpressing ESYT1-Myc or the tether (Figure S4C). This showed no changes in the levels of several different markers of ER stress or cell death. __Reviewer 2____: __

      1) the interaction between those proteins is direct,

      2) if SYNJ2BP is necessary and sufficient to localize E-Syt1 at MERC, and

      3) if MERCs extension induced by SYNJ2BP is dependent on E-Syt1.

      Those points are important to investigate because SYNJ2BP has already been shown to induce MERCs by interacting with the ER protein RRBP1. In addition, some experiments need to be better quantified.

      Major comments: E-syt1/SYNJ2BP in MERCs formation: the authors provide several convincing lines of evidence that both proteins are in the same complex (proximity labelling, localization in the same complex in BN-PAGE, localization in MAM) but it is not clear in which extent the direct interaction between both proteins regulates ER-mitochondria tethering. 1- Pull down experiments or BiFC strategy could be performed to show the direct interaction between both proteins.

      • We showed in Figure 4C that the presence of SYNJ2BP in a complex of a similar molecular weight to that ESYT1 (410KDa) is totally dependent of the presence of ESYT1, suggesting an interaction of the 2 proteins.
      • To confirm this interaction, in figure 4A we analyzed on BN cells overexpressing SYNJ2BP together with a 3xFlag tagged version of ESYT1. As a result of the addition of the Flag tag, the complex positive for ESYT1 shifted to a higher molecular weight. Significantly, the complex positive for SYNJ2BP shifted to a similar the molecular weight, demonstrating the interaction and dependence of the 2 protein partners.
      • To confirm the interaction of the 2 partners, we performed co-immunoprecipitation of the ESYT1-3xFlag protein (Table S2). SYNJ2BP was found as the strongest prey, followed by ESYT2 and SEC22B two described interactors of ESYT1, confirming the quality of the analysis (Giordano, Saheki et al. 2013, Gallo, Danglot et al. 2020). 2- SYNJ2BP OE has already been demonstrated to increase MERCs and this being dependent on the ER binding partners RRBP1 (10.7554/eLife.24463). Therefore, it would be of interest to perform OE of SYNJ2BP in KO Esyt1 to address the question of whether ESyt1 is also required to increase MERCs.

      • A quantitative analysis using confocal microscopy demonstrated that the effect of SYNJ2BP overexpression on MERCs formation is partially dependent of the presence of ESYT1 (Figure 3F). 3- The authors show that Esyt1 punctate size increases when SYNJ2BP is OE (Fig3C), but this can be indirectly linked to the increase of MERCs in the OE line. Thus, it could be interesting to test if the number/shape of E-syt1 punctate located close to mitochondria decreases in KO SYNJ2B. This could really show the dependence of SYNJ2BP for E-syt1 function at MERCs.

      • To improve our description of the partial localization of ESYT1 at mitochondria, we performed a quantitative analysis using confocal microscopy on control human fibroblasts stably overexpressing SEC61B-mCherry as an ER marker which were labelled with ESYT1 and TOMM40 for mitochondria. We measured the % of ESYT1 signal colocalizing with mitochondria and the % of mitochondria colocalizing with ESYT1 (Figure 1E).

      • To demonstrate than ESYT1 partial colocalization with mitochondria is, at least partially, due to its interaction with SYNJ2BP, we performed a quantitative analysis using confocal microscopy. Human control fibroblasts, KO SYNJ2BP fibroblasts and SYNJ2BP overexpressing fibroblasts were labelled with ESYT1, TOMM40 for mitochondria and CANX for ER. We measured the % of ESYT1 signal colocalizing with mitochondria in each condition (Figure 3C). Lipid analyses: the results of MS on isolated mitochondria clearly show that mitochondrial lipid homeostasis is affected on KO-Syt1 and rescued by expression of Syt1-Myc and artificial mitochondria-ER tether. However, p.15, the authors wrote "The loss of ESYT1 resulted in a decrease of the three main mitochondrial lipid categories CL, PE and PI, which was accompanied by an increase in PC ». As the results are expressed in mol%, this interpretation can be distorted by the fact that mathematically, if the content of one lipid decreases, the content of others will increase. I would suggest to express the results in lipid quantity (nmol)/mg of mitochondria proteins instead of mol%. This will clarify the role of E-Syt1 on mitochondrial lipid homeostasis and which lipid increase and decrease.

      • We changed the sentence in the text as suggested. Also it could be of high interest to have the lipid composition of the whole cells to reinforce the direct involvement of E-Syt1 in mitochondrial lipid homeostasis and verify that the disruption of mitochondrial lipid homeostasis is not linked to a general perturbation of lipid metabolism as this protein acts at different MCSs.

      • This is beyond the scope of the project and we would argue that the results of such an experiment would be difficult to interpret. To better understand the impact of Esyt1 of mitochondria morphology, the author could analyze the mitochondria morphology (size, shape, cristae) on their EM images of crt, KO and OE lines. Indeed, on OE (Fig3A), the mitochondria look bigger and with a different shape compared to crt.

      • As we do not observe obvious differences in mitochondrial morphology between control, KO and OE fibroblasts we do not think that quantitative analysis would add to the understanding of the effect of ESYT1 on mitochondrial function. Also, they performed a lot of BN-PAGE. Is it possible to check whether the mitochondrial respiratory chain super-complexes are affected on Esyt1 KO line compared to crt?

      • We decided to remove the data on the metabolic consequences of ESYT1 loss since it was too preliminary and required deeper investigations, focusing instead on the effect of ESYT1 loss on calcium homeostasis. Quantifications: some western blots needs to be quantified (Fig 5K, 6J, S3E);

      • We did not observe obvious differences in the protein levels so we think that quantitation would not add significantly to the understanding of the differences in calcium dynamics that we report. Fig1A: Can the author provide a higher magnification of the triple labeling and perform quantification about the proportion of E-Syt1 punctate located close to mitochondria?

      • We added higher magnification of the same area in all channels and arrows that point to the foci of ESYT1 colocalizing with both ER and mitochondria (Figure 1D).

      • To improve our description of the partial localization of ESYT1 at mitochondria, we performed a quantitative analysis using confocal microscopy on control human fibroblasts stably overexpressing SEC61B-mCherry as an ER marker which were labelled with ESYT1 and TOMM40 for mitochondria. We measured the % of ESYT1 signal colocalizing with mitochondria and the % of mitochondria colocalizing with ESYT1 (Figure 1E). Minor comments:

      • Fig1E + text: according to the legend, the BN-PAGE has been performed on Heavy membrane fraction. Why the authors speak about complexes at MAM in the text of the corresponding figure? Is-it the MAM or the heavy fraction (MAM + mito + ER...)? If BN have been performed from heavy membranes, it is not a real proof that E-syt1 is in MAMs.

      • Heavy membranes have been used in this experiment. The text and conclusions have been changed accordingly.

      • On fig3C (panel crt): it seems like SYNJ2BP dots are not co-localizaed with mito. Is this protein targeted to another organelle beside mitochondria?

      • It is not described that SYNJ2BP would be targeted to another organelle beside mitochondria. It is possible that those dots outside of mitochondria could be non-specific signals from the antibody we used.

      • Fig4A: can the author provide a control of protein loading (membrane staining as example) to confirm the decrease of E-Syt1 in siSYNJ2BP?

      • As we performed this experiment only once we have removed the statement suggesting a decrease in ESYT1 protein in response to the siSYNJ2BP.

      • Fig5E/F: it is not clear to me why the expression of E-Syt1 in the KO is not able to complement the KO phenotype for cytosolic Ca++. Can the authors comment this?

      • We performed further analysis using ATP to trigger calcium release from the ER (figure 6 F to H). In those conditions, expression of ESYT1 in the KO is able to complement the KO phenotype for cytosolic Ca2+. __Reviewer 3____: __

      Main points 1. Confirming the MERC localization of ESYT1 should include some more of tethering factors as demonstrated interactors (some are mentioned above) and should not be limited to lipid homeostasis.

      • As shown in Figure 1B, VAPB, PDZD8 and BCAP31 are found as preys in the ESYT1 bioID analysis. Those proteins have been described as MERC tethers, their loss leading to mitochondrial calcium defects. To support and confirm the specificity of ESYT1-SYNJ2BP complex at MERCs, we performed a supplementary BioID analysis using ER targeted BirA* and OMM targeted BirA* (Table S1, Figure S1 and Figure 1 A and B). These results confirmed the specificity of the interaction of the 2 partners. ESYT1 is not identified as a prey in OMM BioID and SYNJ2BP is not identified in ER BioID. Additional ER-mitochondria tether BirA* analyses showed that tether-BirA* identified both ESYT1 and SYNJ2BP as a prey at MERCs, confirming the localisation of this interaction. Interestingly, a large majority of the known MERCs tethers VAPB-PTPIP51, MFN2, ITPRs, BCAP31 are also found as preys in the tether-BirA* (Figure 1B), confirming the quality of these data.
      • To confirm the interaction of the 2 partners, we performed co-immunoprecipitation of the ESYT1-3xFlag protein. SYNJ2BP is found as the strongest prey, followed by ESYT2 and SEC22B two described interactors of ESYT1, confirming the quality of the analysis (Table S2) (Giordano, Saheki et al. 2013, Gallo, Danglot et al. 2020).

      The fact that in ESYT1 KO cells both mitochondrial calcium transfer and cytosolic calcium accumulation are accompanied by decreased ER-cepia1ER signal decay upon histamine addition suggest that the main reason for ER-mitochondria calcium transfer defects are due to impaired SOCE. Calcium-free medium and histamine are used to show that ESYT1 does not affect ER calcium content. However, if it affects SOCE, then the absence of extracellular calcium would abolish such an effect; moreover, histamine does not test for leak effects. As additional information, the authors should investigate whether ER calcium content is affected by the presence of extracellular calcium in the ko scenario using thapsigargin. The authors should inhibit SOCE to test whether this mechanism is affected in ESYT1 KO and could account for observed signal differences. Excluding SOCE is critical, since any change in calcium entry from the outside would potentially negate a role of ESYT1 in mitochondrial calcium uptake.

      • Silencing ESYT1 impairs SOCE efficiency in Jurkat cells (Woo, Sun et al. 2020), but not in HeLa cells (Giordano, Saheki et al. 2013, Woo, Sun et al. 2020). Analysis of the role of ESYT1 in HeLa cells prevents confounding effects due to the loss of ESYT1 at ER-PM. In this model, knock-down of ESYT1 led to a decrease of mitochondrial Ca2+ uptake from the ER upon histamine stimulation, as monitored by genetically encoded Ca2+ indicator targeted to mitochondrial matrix (Figure 5A and B). ESYT1 silencing in HeLa cells did not impact ER Ca2+ store measured by the ER-targeted R-GECO Ca2+ probe (Figure 5C and D). The expression of the artificial mitochondria-ER tether was able to rescue mitochondrial Ca2+ defects observed in ESYT1 silenced cells (Figure 5B), confirming that the observed anomalies are specifically due to MERC defects.
      • In contrast loss of ESYT1 impaired SOCE efficiency in fibroblasts (Figure 6 A and B). This phenotype was fully rescued by re-expression of ESYT1-Myc but not the artificial tether. We therefore investigated the influence of ESYT1 loss on cytosolic Ca2+ concentration following ATP (Figure 6F to H) or histamine stimulation (Figure S3 D to F), both of which showed a reduced cytosolic Ca2+ concentration and uptake in ESYT1 KO cells. This phenotype was fully rescued by the re-expression of ESYT1-Myc but not the artificial tether. Measurment of cytosolic Ca2+ after tharpsigargin treatment in Ca2+-fee media, an inhibitor of the sarco/endoplasmic reticulum Ca2+ ATPase SERCA that blocks Ca2+ pumping into the ER, showed that ESYT1 KO does not influence the total ER Ca2+ pool (Figure 6K and L). However, ER-Ca2+ release capacity upon histamine stimulation (Figure 6I and J) is decreased in ESYT1 KO cells. This phenotype was fully rescued by the re-expression of ESYT1-Myc but not the artificial tether. Loss of ESYT1 decreased the Ca2+ uptake capacities of mitochondria after activation with histamine (Figure S3 A to C) or ATP (Figure 6 C to E). This phenotype was rescued by re-expression of ESYT1-Myc and also the engineered ER-mitochondria tether. Thus, despite the ER-Ca2+ release defect observed after ESYT1 loss, the artificial tether fully rescued the mitochondrial phenotype.
      • These results highlight the distinct and dual roles of ESYT1 in Ca2+ regulation at the ER-PM and at MERCs.

      The authors claim that ER-Geco measurements show that no change of ER calcium was observed. However, they use thapsigargin treatment and then get a peak, when the signal should show a decrease due to leak. This suggests they did not use ER-Geco in Figure S3C. What was measured and what does it mean?

      • We used R-GECO (not ER-GECO) which measures the cytosolic calcium.
      • We measured total ER Ca2+ store using the cytosolic-targeted R-GECO Ca2+ probe upon thapsigarin treatment, an inhibitor of the sarco/endoplasmic reticulum Ca2+ ATPase SERCA that blocks Ca2+ pumping into the ER (Figure 5C and D) and observed no difference in our different conditions.

      The findings on growth in galactose medium are intriguing but are not accompanied by respirometry to confirm mitochondria are compromised upon ESYT1 KO.

      • We decided to remove the data on the metabolic consequences of ESYT1 loss since it was to preliminary and required deeper investigations, focusing instead on the effect of ESYT1 loss on calcium homeostasis

      Minor points: 1. The authors mention they measure mitochondrial uptake of "exogenous" calcium by applying histamine. They should specify that these measures transferred calcium from the ER rather than uptake of calcium from the exterior (directly at the plasma membrane).

      • The text was clarified as suggested.

      • Expression levels of IP3Rs are not very indicative of any change of their activity. The authors should discuss how ESYT1 could affect their PTMs.

      • A large numer of post translational modifications are known to regulate IP3R activity (Hamada and Mikoshiba 2020), and it is possible that the loss of ESYT1 could interfere with these modifications, but an exploration of this issue is beyond the scope of this study. The text was clarified as suggested. Eisenberg-Bord, M., N. Shai, M. Schuldiner and M. Bohnert (2016). "A Tether Is a Tether Is a Tether: Tethering at Membrane Contact Sites." Dev Cell 39(4): 395-409.

      Gallo, A., L. Danglot, F. Giordano, B. Hewlett, T. Binz, C. Vannier and T. Galli (2020). "Role of the Sec22b-E-Syt complex in neurite growth and ramification." J Cell Sci 133(18).

      Giordano, F., Y. Saheki, O. Idevall-Hagren, S. F. Colombo, M. Pirruccello, I. Milosevic, E. O. Gracheva, S. N. Bagriantsev, N. Borgese and P. De Camilli (2013). "PI(4,5)P(2)-dependent and Ca(2+)-regulated ER-PM interactions mediated by the extended synaptotagmins." Cell 153(7): 1494-1509.

      Hamada, K. and K. Mikoshiba (2020). "IP(3) Receptor Plasticity Underlying Diverse Functions." Annu Rev Physiol 82: 151-176.

      Ilacqua, N., I. Anastasia, D. Aloshyn, R. Ghandehari-Alavijeh, E. A. Peluso, M. C. Brearley-Sholto, L. V. Pellegrini, A. Raimondi, T. Q. de Aguiar Vallim and L. Pellegrini (2022). "Expression of Synj2bp in mouse liver regulates the extent of wrappER-mitochondria contact to maintain hepatic lipid homeostasis." Biol Direct 17(1): 37.

      Pourshafie, N., E. Masati, A. Lopez, E. Bunker, A. Snyder, N. A. Edwards, A. M. Winkelsas, K. H. Fischbeck and C. Grunseich (2022). "Altered SYNJ2BP-mediated mitochondrial-ER contacts in motor neuron disease." Neurobiol Dis: 105832.

      Rios, K. E., M. Zhou, N. M. Lott, C. R. Beauregard, D. P. McDaniel, T. P. Conrads and B. C. Schaefer (2022). "CARD19 Interacts with Mitochondrial Contact Site and Cristae Organizing System Constituent Proteins and Regulates Cristae Morphology." Cells 11(7).

      Scorrano, L., M. A. De Matteis, S. Emr, F. Giordano, G. Hajnoczky, B. Kornmann, L. L. Lackner, T. P. Levine, L. Pellegrini, K. Reinisch, R. Rizzuto, T. Simmen, H. Stenmark, C. Ungermann and M. Schuldiner (2019). "Coming together to define membrane contact sites." Nat Commun 10(1): 1287.

      Woo, J. S., Z. Sun, S. Srikanth and Y. Gwack (2020). "The short isoform of extended synaptotagmin-2 controls Ca(2+) dynamics in T cells via interaction with STIM1." Sci Rep 10(1): 14433.

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      Referee #3

      Evidence, reproducibility and clarity

      Janer et al. have identified ESYT1 as a novel tether between the ER and mitochondria (MERCs) with roles in lipid and calcium homeostasis. They discovered extended synaptotagmin (ESYT1) in a BioID screen, where it interacts with SYNJ2BP and forms a high molecular weight complex. The study addressed a lack of information at the level of mammalian cell system, where a key protein complex known from yeast (ERMES) is absent, suggesting other proteins take over this critical role. These proteins then control the production of cardiolipin and PE, two lipid types essential for the functioning of mitochondria. They contain SMP motifs as a signature domain required for lipid transport. ESYT1 had previously been found to mediate lipid transfer at the plasma membrane and at peroxisomes, but the authors found it also localizes to MERCs. In a BioID screen, they have found numerous ER proteins with known roles in MERC tethering (e.g., EMC complex, BAP31, VAPB or TMX1). They have decided to focus on the aforementioned pair, which they demonstrate is enriched on MERCs (ESYT1) and mitochondria (SYNJ2BP), respectively, forming high molecular weight complexes, as detected by BN gels. Unlike RRBP1-SYNJ2BP, this complex is not dependent on ongoing protein synthesis. Upon generation of ESYT1 KO fibroblasts, they show that this SMP protein compromises MERC formation through electron microscopy. SYNJ2BP overexpression specifically increases contacts, as again shown by EM, independent of mitochondrial dynamics.

      In its present form, the manuscript accurately describes the role of the ESYT1-SYNJ2BP complex for MERCs. The study contains nice lipidomics that reinforce this point and suggest a metabolic consequence. This latter observation is, however, very basic and requires some extension by assaying respirometry. The calcium phenotype is currently not fully characterized either. Interference with SOCE remains a possibility and if true, this would compromise the statement that the complex also controls calcium signaling. Both would need to be investigated better to either confirm or reject these roles, in my opinion, an important question. Overall, the manuscript contains interesting characterization of a tether that could have important consequences for calcium signaling, which would be an exciting finding.

      Main points

      1. Confirming the MERC localization of ESYT1 should include some more of tethering factors as demonstrated interactors (some are mentioned above) and should not be limited to lipid homeostasis.
      2. The fact that in ESYT1 KO cells both mitochondrial calcium transfer and cytosolic calcium accumulation are accompanied by decreased ER-cepia1ER signal decay upon histamine addition suggest that the main reason for ER-mitochondria calcium transfer defects are due to impaired SOCE. Calcium-free medium and histamine are used to show that ESYT1 does not affect ER calcium content. However, if it affects SOCE, then the absence of extracellular calcium would abolish such an effect; moreover, histamine does not test for leak effects. As additional information, the authors should investigate whether ER calcium content is affected by the presence of extracellular calcium in the ko scenario using thapsigargin.
      3. The authors should inhibit SOCE to test whether this mechanism is affected in ESYT1 KO and could account for observed signal differences. Excluding SOCE is critical, since any change in calcium entry from the outside would potentially negate a role of ESYT1 in mitochondrial calcium uptake.
      4. The authors claim that ER-Geco measurements show that no change of ER calcium was observed. However, they use thapsigargin treatment and then get a peak, when the signal should show a decrease due to leak. This suggests they did not use ER-Geco in Figure S3C. What was measured and what does it mean?
      5. The findings on growth in galactose medium are intriguing but are not accompanied by respirometry to confirm mitochondria are compromised upon ESYT1 KO.

      Minor points:

      1. The authors mention they measure mitochondrial uptake of "exogenous" calcium by applying histamine. They should specify that this measures transferred calcium from the ER rather than uptake of calcium from the exterior (directly at the plasma membrane).
      2. Expression levels of IP3Rs are not very indicative of any change of their activity. The authors should discuss how ESYT1 could affect their PTMs.

      Significance

      The study is certainly of high interest due to its implications for cell metabolism and calcium signaling. It contains very strong data on MERC formation and lipidomics. However, the calcium and metabolic aspects are currently not well developed and require improvements.

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      Referee #2

      Evidence, reproducibility and clarity

      The work of Janer and al. investigates the role of E-Syt1, a well known lipid transfer protein tethering ER and PM and ER and peroxisome, at ER-mitochondria contact sites (MERCs). E-Syt1 was identified has a putative MERCs component by proximity labeling performed from four SMP domain containing proteins. They identified the mitochondrial SYNJ2BP as a binding partner of E-Syt1 only. By different biochemical and microscopy approaches, they show that 1) E-Syt1 is located at MERCs and is involved in MERCs formation, 2) SYNJ2BP is located at MERCs and regulate the extent of MERCs in cells, 3) E-Syt1 and SYNJ2BP are located in MAM and in the same high molecular weight complex. Then, they show that both proteins impaired ER-mitochondria Ca++ exchange and that E-Syt1 influences mitochondrial lipid homeostasis, both phenotypes being rescued by artificial tether showing that only the tethering function of E-Syt1 is required. The proximity labelling experiments suggests SYNJ2BP as the mitochondrial partners of E-Syt1, however, from the data, it is not clear whether 1) the interaction between those proteins is direct,2) if SYNJ2BP is necessary and sufficient to localize E-Syt1 at MERC, and 3) if MERCs extension induced by SYNJ2BP is dependent on E-Syt1. Those points are important to investigate because SYNJ2BP has already been shown to induce MERCs by interacting with the ER protein RRBP1. In addition, some experiments need to be better quantified.

      Major comments:

      E-syt1/SYNJ2BP in MERCs formation: the authors provide several convincing lines of evidence that both proteins are in the same complex (proximity labelling, localization in the same complex in BN-PAGE, localization in MAM) but it is not clear in which extent the direct interaction between both proteins regulates ER-mitochondria tethering.

      1. Pull down experiments or BiFC strategy could be performed to show the direct interaction between both proteins;
      2. SYNJ2BP OE has already been demonstrated to increase MERCs and this being dependent on the ER binding partners RRBP1 (10.7554/eLife.24463). Therefore, it would be of interest to perform OE of SYNJ2BP in KO syt1 to address the question of whether Syt1 is also required to increase MERCs.
      3. The authors show that Syt1 punctate size increases when SYNJ2BP is OE (Fig3C), but this can be indirectly linked to the increase of MERCs in the OE line. Thus, it could be interesting to test if the number/shape of E-syt1 punctate located close to mitochondria decreases in KO SYNJ2B. This could really show the dependence of SYNJ2BP for E-syt1 function at MERCs. Lipid analyses: the results of MS on isolated mitochondria clearly show that mitochondrial lipid homeostasis is affected on KO-Syt1 and rescued by expression of Syt1-Myc and artificial mitochondria-ER tether. However, p.15, the authors wrote "The loss of ESYT1 resulted in a decrease of the three main mitochondrial lipid categories CL, PE and PI, which was accompanied by an increase in PC ». As the results are expressed in mol%, this interpretation can be distort by the fact that mathematically, if the content of one lipid decreases, the content of others will increase. I would suggest to express the results in lipid quantity (nmol)/mg of mitochondria proteins instead of mol%. This will clarify the role of E-Syt1 on mitochondrial lipid homeostasis and which lipid increase and decrease. Also it could be of high interest to have the lipid composition of the whole cells to reinforce the direct involvement of E-Syt1 in mitochondrial lipid homeostasis and verify that the disruption of mitochondrial lipid homeostasis is not linked to a general perturbation of lipid metabolism as this protein acts at different MCSs.

      Role of Syt1 in mitochondria: the authors show a perturbation of ER-mito Ca exchange and mitochondrial lipid homeostasis in KO-Syt1 as well as a growth defect of cells grown on galactose media. Modification of lipid mitochondrial lipid homeostasis often leads to defect in mitochondria morphology and mitochondria respiration, usually because of defects in supercomplexes assembly. To better understand the impact of Syt1 of mitochondria morphology, the author could analyze the mitochondria morphology (size, shape, cristae) on their EM images of crt, KO and OE lines. Indeed, on OE (Fig3A), the mitochondria look bigger and with a different shape compared to crt. Also, they performed a lot of BN-PAGE. Is it possible to check whether the mitochondrial respiratory chain super-complexes are affected on Syt1 KO line compared to crt? <br /> Quantifications: some western blots needs to be quantified (Fig 5K, 6J, S3E); Fig1A: Can the author provide a higher magnification of the triple labeling and perform quantification about the proportion of E-Syt1 punctate located close to mitochondria?

      Minor comments:

      • Fig1E + text: according to the legend, the BN-PAGE has been performed on Heavy membrane fraction. Why the authors speak about complexes at MAM in the text of the corresponding figure? Is-it the MAM or the heavy fraction (MAM + mito + ER...)? If BN have been performed from heavy membranes, it is not a real proof that E-syt1 is in MAMs.
      • On fig3C (panel crt): it seems like SYNJ2BP dots are not co-localizaed with mito. Is this protein targeted to another organelle beside mitochondria?
      • Fig3C: can the author show each channel alone and not only the merge to better appreciate mito and ER shape in control vs OE lines (as in fig S2)
      • Fig4A: can the author provide a control of protein loading (membrane staining as example) to confirm the decrease of E-Syt1 in siSYNJ2BP?
      • Fig5E/F: it is not clear to me why the expression of E-Syt1 in the KO is not able to complement the KO phenotype for cytosolic Ca++. Can the authors comment this.

      Significance

      Sevral mitochondrial-ER tethers as well as some proteins involved in Ca and/or lipid exchanges have been identified in mammals. E-Syt1 is well known to be located at ER-PM contact sites as well as ER-peroxisomes, and the presence of E-Syt1 at MERCs and its role in Ca++ and lipid exchange are new exciting results further showing the versatility of this protein. The results concerning E-Syt1 in Ca++ and lipid exchange are very convincing. In addition, the proximity labeling performed from four different SMP domain containing proteins is a highly valuable source of information for future work about interaction networks of those proteins. What is less in the study is the involvement of E-Syt1 interaction with SYNJ2BP for localization and function at MERCs and vice versa. Indeed, SYNJ2BP has already been shown to promote MERCs extension and to interact with the ER protein RRBP1. Thus, it will be of interest to further investigate E-Syt1/SYNJ2BP interaction at MERCs.

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      Referee #1

      Evidence, reproducibility and clarity

      This manuscript reports the results of a study of the potential involvement of the SMP-domain-containing protein ESYT1 in ER-mitochondria tethering, and Ca+ and lipid exchange between the two organelles. SMP-domain proteins have been shown to localize to membrane contact site and have lipid transport activity. Esyt proteins have thus far been found at ER-plasma-membrane (PM) contacts. Here, starting from a BioID screen for partners of various SMP-domain proteins, the study focuses on a potential new interaction between ER-resident ESYT-1 and the mitochondrial outer-membrane protein SYNJ2BP. Then using a host of different approaches, the study concludes with a model in which ESYT-1-SYNJ2BP interaction tethers ER and mitochondria to regulate ion and lipid exchange between the two organelles.

      This model would be very novel and interesting, as ESYT proteins have thus far only been detected at ER-PM contacts. However, the data supporting it are not unambiguous, are subject to alternative interpretation, and are sometimes contrary to the interpretation that the authors make of them. A lot of the reasoning behind the interpretation seems to be based on the fact that the authors have a hypothesis of what the effect of impacting ER-mitochondria should be, a priori, and when they observe such effects, they take it as evidence that they have indeed impacted tethering, disregarding alternative hypotheses and the possibility that the same effects can be wrought by entirely different mechanisms. Thus, the manuscript takes a few steps to involve ESYT1 in ER-mitochondria contacts but fails to make a decisive point.

      Here are major points:

      1. Localization of ESYT-1 and SYNJ2BP. The claim of a localization at ER-mitochondria contacts relies on two type of assays. Light microscopy and subcellular fractionation. Concerning microscopy, while the staining pattern is obviously colocalizing with the ER (a control of specificity of staining using KO cells would nevertheless be desirable), the idea that ESYT1 foci "partially colocalized with mitochondria" is either trivial or unfounded. Every cellular structure is "partially colocalized with mitochondria" simply by chance at the resolution of light microscopy. If the meaning of the experiment is to show that ESYT1 'specifically' colocalizes with mitochondria, then this isn't shown by the data. There is no quantification that the level of colocalization is more than expected by chance, nor that it is higher than that of any other ER protein. Moreover, the author's model implies that ESYT1 partial colocalization with mitochondria is, at least partially, due to its interaction with SYNJ2BP. This is not tested.

      The subcellular fractionation assays are grounded on the idea that Mitochondria-Associated (ER) Membranes (MAM) can be purified, and are enriched for proteins that localize at ER-mitochondria contacts. This idea originated in the early 90's and since then, myriad of papers has been using MAM purification, and whole MAM proteomes have been determined. Yet the evidence that MAM-enriched proteins represent bona fide ER-mitochondria-contact-enriched proteins (as can nowadays be determined by microscopy techniques) remain scarce. Here, anyway, ESYT1 fractionation pattern is identical to that of PDI, a marker of general ER, with no indication of specific MAM accumulation. For SYNJ2BP, it is different as it is more enriched in the MAM than the general mitochondrial marker PRDX3. However, PRDX3 is a matrix protein, making it a poor comparison point, since SYNJ2BP is an OMM protein.

      Again, the model implies that ESYT1 and SYNJ2BP accumulation in the MAM should be dependent on each other. This is not tested. 2. ESYT1-SYNJ2BP interaction. The starting point of the paper is a BioID signal for SYNJ2BP when BioID is fused to ESYT1. One confirmation of the interaction comes in figure 4, using blue native gel electrophoresis and assessing comigration. Because BioID is promiscuous and comigration can be spurious, better evidence is needed to make this claim. This is exemplified by the fact that, although SYNJ2BP is found in a complex comigrating with RRBP1, according to the BN gel, this slow migrating complex isn't disturbed by RRBP1 knockdown, but is somewhat disturbed by ESYT1 knockdown. More than a change in abundance, a change in migration velocity when either protein is absent would be evidence that these comigrating bands represent the same complex.

      ESYT1-SYNJ2BP interaction needs to be tested by coimmunoprecipitation of endogenous proteins, yeast-2-hybrid, in vitro reconstitution or any other confirmatory methods. 3. Tethering by ESYT1- SYNJ2BP. This is assessed by light and electron microscopy. Absence of ESYT1 decreases several metrics for ER-mitochondria contacts (whether absence of SYNJ2BP has the same effect isn't tested). This interesting phenomenon could be due to many things, including but not limited to the possibility that "ESYT1 tethers ER to mitochondria".This statement and the respective subheading title are therefore clearly overreaching and should be either supported by evidence or removed. Indeed, absence of ESYT1 ER-PM tethering and lipid exchange could have knock-on effects on ER-mito contacts, therefore strong statements aren't supported. Moreover, the effect on ER-mitochondria contact metrics could be due to changes in ER-mitochondria contact indeed, but may also reflect changes in ER and/or mitochondria abundance and/or distribution, which favour or disfavour their encounter. Abundance and distribution of both organelles are not controlled for.

      Finally, the authors repeat a finding that SYNJ2BP overexpression induces artificial ER-mitochondria tethering. Again, according to the model, this should be, at least in part, due to interaction with ESYT1. Whether ESYT1 is required for this tethering enhancement isn't tested. 4. Phenotypes of ESYT1/SYNJ2BP KD or KO. The study goes in details to show that downregulation of either protein yields physiological phenotypes consistent with decreased ER-mitochondria tethering. These phenotypes include calcium import into mitochondria and mitochondrial lipid composition.

      Figure 5 shows that histamine-evoked ER-calcium release cause an increase in mitochondrial calcium, and this increase is reduced in absence of ESYT1, without detectable change in the abundance of the main known players of this calcium import. This is rescued by an artificial ER-mitochondria tether.

      However, Figure 5D shows that the increase in calcium concentration in the cytosol upon histamine-evoked ER calcium release is equally impaired by ESYT1 deletion, contrary to expectation. Indeed, if the impairment of mitochondrial calcium import was due to improper ER-mitochondria tethering in ESYT1 mutant cells, one would expect more calcium to leak into the cytosol, not less. The remaining explanation is that ESYT1 knockout desensitizes the cells to histamine, by affecting GPCR signalling at the PM, something unexplored here. In any case, a decreased calcium discharge by the ER upon histamine treatment, explains the decreased uptake by mitochondria. The authors argue that ER calcium release is unaffected by ESYT1 KO, but crucially use thapsigargin instead of histamine to show it. Thus, the most likely interpretation of the data is that ESYT1 KO affects histamine signalling and histamine-evoked calcium release upstream of ER-mitochondria contacts.

      The data with SYNJ2BP deletion are more compatible with decreased ER-mito contacts, as no decreased in cytosolic calcium is observed. This is compatible with the previously proposed role of SYNJ2BP in ER-mitochondria tethering, but the difference with ESYT1 rather argue that both proteins affect calcium signalling by different means, meaning they act in different pathways.

      Finally, the study delves into mitochondrial lipids to "investigated the role of the SMP-domain containing protein ESYT1 in lipid transfer from ER to mitochondria". In reality, it is not ER-mitochondria lipid transport that is under scrutiny, but general lipid homeostasis, and changes in ER-PM lipids could have knock-on effects on mitochondrial lipids without the need to invoke disruptions in ER-mitochondria transfer activity. The changes observed are interesting but could be due to anything. Surprisingly, PCA analysis shows that the rescue of the knockout by the ESYT1 gene clusters with the rescue by the artificial tether, and not with the wildtype. This indicates that overexpressing either ESYT1 or a tether cause similar lipidomic changes. These could be due, for instance, to ER stress caused by protein overexpression, and not to a rescue.

      In any case the data here do not support the strong statement "Together these results demonstrate that ESYT1 is required for lipid transfer from ER to mitochondria [...]".

      Significance

      This model would be very novel and interesting, as ESYT proteins have thus far only been detected at ER-PM contacts. However, the data supporting it are not unambiguous, are subject to alternative interpretation, and are sometimes contrary to the interpretation that the authors make of them. A lot of the reasoning behind the interpretation seems to be based on the fact that the authors have a hypothesis of what the effect of impacting ER-mitochondria should be, a priori, and when they observe such effects, they take it as evidence that they have indeed impacted tethering, disregarding alternative hypotheses and the possibility that the same effects can be wrought by entirely different mechanisms. Thus, the manuscript takes a few steps to involve ESYT1 in ER-mitochondria contacts but fails to make a decisive point.

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      Reply to the reviewers

      We are grateful to both reviewers for reviewing our manuscript, and for providing very helpful feedback as to how we can improve this work. We have now implemented nearly all of the changes as recommended, and provide responses to these points below.

      In terms of novelty, while recent pre-prints and publications have suggested that the application of multi-omics analysis improves GRN inference, there has yet to be a systematic comparison of linear and non-linear machine learning methods for GRN prediction from single cell multi-omic data. here are many computational and statistical challenges to such a study, and we therefore believe that others in the field will be especially interested in our systematic comparison of network inference methods, especially given the increased interest and utility of multi-omic data.

      In addition, we report the first comprehensive inference of GRNs in early human embryo development. This is a particularly challenging to study developmental context given genetic variation, limitations of sample size due to the precious nature of the material and regulatory constraints. We anticipate that the methodology we developed and datasets we generated will be informative for computational, developmental and stem cell biologists.

      We have uploaded all the network predictions on FigShare and these can be accessed using the following link: https://doi.org/10.6084/m9.figshare.21968813. In addition, we anticipate that the computational and statistical codes and pipelines we developed (available on https://github.com/galanisl/early_hs_embryo_GRNs) will be applied to other cellular and developmental contexts, especially in challenging contexts such as human development, non-typical model organisms and in clinically relevant samples.

      Reviewer 1

      Major comments

      - The proposed strategy (i.e. combining gene expression-based regulatory inference with cis-*regulatory evidence) have been well developed (and implemented) by multiple published works like SCENIC and CellOracle, which is also properly acknowledged by the authors in the discussion section too. This leads to a serious concern on the major methodological contribution of this work. *

      We would like to note that our study is the first to comprehensively evaluate machine learning linear or non-linear gene regulatory network prediction strategies from single-cell transcriptional datasets combined with available multi-omic data. We also apply these methods to a challenging to study context of human early embryogenesis. There are specific methodological challenges arising in this context that other published work has not yet addressed. In particular, the precious nature of the source material means that sample sizes are limited, unlike the contexts where SCENIC and CellOracle were applied. Notably, the numbers of cells available for downstream analysis is typically several orders of magnitude fewer than when scRNA-seq data are collected from adult human tissue or from cell culture. This restriction on sample sizes places corresponding restrictions on statistical power, and is therefore likely to mean that different statistical network inference methodologies are optimal in specific contexts. Furthermore, the inclusion of multi-omic data from complementary platforms (such as ATAC-seq data) becomes even more important in this context to mitigate the effect of reduced sample sizes. These issues are very important for choice of gene regulatory network inference methodology in relation to studies of human embryo development, and ours is the first study to address these issues directly in any context. We have further clarified the novelty of our work in the manuscript in the abstract, introduction and discussion sections.

      - Most of the compared network reconstruction methods involve hyper-parameters setup (e.g., *sparsity regularization weights of the regression methods). The authors did not discuss how these hyper-parameters were chosen. *

      For sparse regression, the hyperparameter controlling sparsity was set by cross-validation (CV), using the internal CV function of the R package. All default settings for GENIE3 were used. This information has now been added to the manuscript (in the Methods section), along with a description of the implementation of the mutual information method we use.

      - For the real-world blastocyst data, the network prediction methods were compared in terms of their reproducibility across validation folds (Fig. 3, Fig. S4-6). However, reproducibility does not necessarily imply accuracy. In fact, statistical learning methods are generally subject to the bias-variance tradeoff, where lower variance (i.e., higher reproducibility) could imply higher bias in model prediction. While there is a lack of gold-standard ground truth to evaluate network accuracy in real biological systems, silver-standards like the ranking of known regulatory interactions in the predictions could be employed as an indirect estimate.

      We thank the reviewer for the opportunity to clarify this point. We would like to avoid any misunderstanding of the reproducibility statistic R, as follows. A higher value of R indicates that the fitted model would generalise well to new data; i.e., R=1 indicates that the model is robust (stable) to perturbations of the data-set. We note that this is not the same as analysing the residual variance of the data after model fitting and related over-fitting (i.e., bias-variance trade-off). The variance that is referred to when discussing bias-variance trade-off is the mean-squared error (of data compared to model), which is not the same as what is assessed by reproducibility statistic R . Specifically, R is a Bayesian estimate of the posterior probability of observing a gene regulation given the data. R is calculated by taking a random sample of the data, doing the network inference again, checking if each gene regulation still appears in the GRN, and then recording (as the R statistic) the average fraction of inclusions over many repetitions. So when we have R close to 1, this indicates that our model predictions generalise well to new data, which is the opposite of what is suggested in this comment. In summary, the accuracy quantified by the reproducibility statistic R relates to the stability of the model predictions to perturbation of the data. We thank the reviewer for the helpful comment to draw our attention to this point, and have now clarified this point in the manuscript on page 6 line 252.

      - The gene set enrichment results were reported only on EPI and TE cell types (Fig. 4C and Fig. *S12), due to the reason that CA data is only available for TE and ICM. However, many of the other results presented in Fig. 3-6 did include the PE cell type albeit using the same CA data. It is not particularly convincing why the cell type inclusion standard for gene set enrichment is different from the other results. *

      We thank the reviewer for noting this and would like to clarify that we restricted the analysis to the EPI and TE, because similar lists of gene-sets were not available for primitive endoderm, where it is currently unclear which pathways are most relevant to this cell type. This has now been clarified in the manuscript on page 8, line 337.

      - The authors cited TF binding in cis-regulatory regions as supporting evidence of several MICA-inferred regulatory interactions (e.g., NANOG -> ZNF343). However, the same cis-regulatory *evidence has already been used in the CA filtering step. All interactions passing CA filtering should in principle have TF-binding support. It would be more convincing if the authors provided other types of evidence as independent support, such as genetic associations like eQTL, experimental perturbations like gene knockdown/knockout, etc. *

      We appreciate the reviewer’s point. We address this by describing published ChIP-seq validation in human pluripotent stem cells which is widely used as a proxy for the study of the epiblast. We feel that the ChIP-seq validation in this context is an appropriate independent validation to support the MICA-inferred cis regulatory interactions predicted from the human embryo datasets we analysed. Our inferences from ATAC-seq data cannot identify TF-DNA binding directly. ChIP-seq data is a widely accepted independent methods to support the inferred interactions from ATAC-seq data.

      We agree that knockdown/knockout would provide further evidence suggesting gene regulation, and indeed these are experiments we would like to conduct systematically in the future, but such perturbations are difficult to achieve at genome-wide scale, especially with very restricted quantities of human embryo material. Notably, these studies would not be evidence of direct regulation and the gold-standard in our opinion is to perturb the cis regulatory region to demonstrate its functional importance in gene regulation. These are important experiments to conduct systematically in the future. We also note that assessing quantitative trait loci in the context of human pre-implantation embryos is extremely challenging due to the restricted sample sizes and genetic variance in the samples collected.

      *- Many of the MICA-inferred regulatory interactions do not exhibit Spearman correlation (Fig. 5, Fig. S17), which could probably be explained by the ability of mutual information to capture complex non-monotonic dependencies. It would be interesting to provide further investigation on these "uncorrelated" edges, which may help demonstrate the superiority of mutual information over Spearman correlation. *

      This has been added as a new Fig.S18.

      - The authors conducted immunostaining experiments to validate the MICA-inferred regulatory *interaction between TFAP2C and JUND. While the identified protein co-localization is a step further than RNA co-expression, it is still correlation rather than causality. Additional evidence like the effect of knockout/knockdown perturbations would be more convincing. *

      We agree with Reviewer 1 that experimental perturbations of TFAP2C and JUND to determine what consequence this has for interactions between these proteins would be informative. However due to the complexity of such an investigation in human embryos, we feel that this is beyond the scope of the current study. One option is to conduct the perturbations in human pluripotent stem cells, however it is unclear if the GRN in this context reflects the same interactions as human embryos and is a distinct question to address in the future. Moreover, while knockdown/knockout studies would be suggestive of up-stream regulation, it will not address the question of whether this is a direct or indirect effect without systematic further analysis including transcription factor-DNA binding (such as CUT&RUN, CUT&Tag or ChIP-seq) analysis as well as perturbations of the putative cis regulatory regions. These are all exciting future experiments and our study provides us and others with hypotheses to functionally test in the future. These are future directions and we have clarified this in the discussion section on page 16, line 576.

      __Minor comments __

      • *The γ symbols in AP-2γ are not correctly rendered. *

      We note that this applies only to the way AP-2γ appears on the Review Commons website, and we are trying to fix this issue. We hope this transformation after the manuscript upload will not apply to a subsequent transfer to a journal.

      • The UMAP figures (Fig. 4A, Fig. S7) are of low resolution compared to other figures.

      We thank the reviewer for noting this. These figures have now been added as vector graphics files to overcome this issue.

      • As the authors are focused on studying the blastocyst regulatory network, the inferred regulatory interactions should be provided as supplementary data.

      We have included all of the inferred gene regulatory interactions as a supplementary folder for the MICA predictions using FigShare: doi.org/10.6084/m9.figshare.21968813. We have included code to reproduce the inferred gene regulatory interactions for the other methods which we compared to MICA. Because this includes 100,000 regulatory interactions per method, we feel that it would be impractical to include the alternative inferred interaction as supplementary data.

      Reviewer 2

      Minor comments

      *- In the abstract, it would be adequate to already mention which normalisation method works the best. *

      This has now been added to the abstract and we appreciate this suggestion.

      *- In Fig. 1: *

      * Describe what are squares and circles

      This information has been included in the figure 1 legend.

      ** In the GRNs refined by keeping CA-predicted regulations only, mention that this are Cis interactions *

      We have modified the figure 1 legend and the text on page 5, line 224 to clarify that these are putative cis-regulatory interactions.

      * The ATAC seq shows KRT8, GATA3, RELB motifs, while the rest of the figure is very general. Maybe make the ATAC-seq peaks panel also as a sketch and relate it to the square/circles graphs on the right hand side to showcase how the filtering of the network is performed.

      We appreciate this suggestion and modified figure 1 accordingly.

      ** The caption says Five GRN inference approaches, while abstract and text say 4. If is clear after reading that the 5th is a random approach. However, it was a surprise at first. *

      We have modified the figure 1 legend to clarify that we also compared random prediction in addition to the 4 GRN inference approaches.

      *- How the Simulation study was performed is not understandable for non experts as it is described in the Methods section. This is an important approach in general, and I think the audience would benefit if the authors add a full section about it in their supplementary data. *

      Further details have now been added to the subsection ‘simulation study’ in the Methods section.

      *- Fig. 2: *

      ** As it is, it is hard to tell the difference between GRN inference methods for a given sample size and number of regulators. Could the authors add a comparative panel for this (maybe some scatter plots would be enough)? MI by itself looks worse here? *

      We thank the reviewer for this helpful suggestion. This comparative plot has now been included in figure 2 and indicates that MI is on par with the other GRN inference methods using simulation RNA-seq data.

      *- When mentioning "samples" (e.g. last paragraph of section 1 in results), do the authors refer to "cells"? *

      We appreciate the reviewer pointing this out and have amended the text throughout to state that these are cells.

      *- What about normalisation effects in the simulated data? *

      With regards to the simulated data, normalisation effects are not relevant as we are generating data that are idealised and therefore not subject to unwanted sources of variation such as read depth. However, in future work, this could be investigated with an expanded simulation study and we appreciate the reviewer’s suggestion.

      *- Figure S7 should be cited in the first paragraph of section 2 in results. *

      This has now been cited.

      *Could the authors add a panel to indicate whether the data is SMART-seq2 or 10X. *

      We thank the reviewer for the suggestion to clarify this, which we think is an important point. We have included a statement that all data used was generated using the SMART-seq2 sequencing technique in the figure legend. The choice of sequencing method/depth of sequencing will likely impact on the choice of GRN inference method and we have also clarified this in the discussion section on page 13, line 516.

      *- In the association of inferred GRNs to human blastocyst cell lineages, the authors find the GRN edges predicted that overlap between the 4 inference methods in each cell type. Do they, therefore, recommend to always use more than one GRN inference method? *

      Identifying overlapping inferences by comparing more than one GRN inference method may be a strategy to identify network edges with more confidence due to the agreement between several inference methodologies. However, this strategy may also miss some edges which can only be detected by one method and not another. We have included a statement in the discussion section to clarify this point on page 15, line 571.

      - If the CA data used was only generated for the TE and ICM only, how do the authors use it to perform MICA on PE?

      We appreciate that this is confusing and have since revised the manuscript on page 5, line 223 to state that the inner cell mass (ICM), comprises EPI (epiblast) and PE (primitive endoderm) cells. It may be that we miss putative cis-regulatory interactions if the ICM CA data does not reflect developmentally progressed PE and EPI cells and we have noted this caveat in the discussion section on page 15, line 561.

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      Referee #2

      Evidence, reproducibility and clarity

      In this work, Alanis-Lobato et al apply different GRN inference methods on scRNA-seq data from human blastocysts. By integrating the data with ATACseq, they manage to address the small sample size challenge and predict novel TF-gene interactions that they later validate with immunofluorescence. Main take-home-messages from this work are that proper GRN inference methods work better upon integration of different omic technologies (here RNA and ATAC seq) and proper data normalisation strategies (logTPM or logFPKM).

      Hereby I present some minor concerns and questions that I have after reading the manuscript, that I hope the authors can address.

      • In the abstract, it would be adequate to already mention which normalisation method works the best.
      • In Fig. 1:
        • Describe what are squares and circles
        • In the GRNs refined by keeping CA-predicted regulations only, mention that this are Cis interactions
        • The ATAC seq shows KRT8, GATA3, RELB motifs, while the rest of the figure is very general. Maybe make the ATAC-seq peaks panel also as a sketch and relate it to the square/circles graphs on the right hand side to showcase how the filtering of the network is performed.
        • The caption says Five GRN inference approaches, while abstract and text say 4. If is clear after reading that the 5th is a random approach. However, it was a surprise at first.
      • How the Simulation study was performed is not understandable for non experts as it is described in the Methods section. This is an important approach in general, and I think the audience would benefit if the authors add a full section about it in their supplementary data.
      • Fig. 2:
        • As it is, it is hard to tell the difference between GRN inference methods for a given sample size and number of regulators. Could the authors add a comparative panel for this (maybe some scatter plots would be enough)? MI by itself looks worse here?
      • When mentioning "samples" (e.g. last paragraph of section 1 in results), do the authors refer to "cells"?
      • What about normalisation effects in the simulated data?
      • Figure S7 should be cited in the first paragraph of section 2 in results. Could the authors add a panel to indicate whether the data is SMART-seq2 or 10X.
      • In the association of inferred GRNs to human blastocyst cell lineages, the authors find the GRN edges predicted that overlap between the 4 inference methods in each cell type. Do they, therefore, recommend to always use more than one GRN inference method?
      • If the CA data used was only generated for the TE and ICM only, how do the authors use it to perform MICA on PE?

      Significance

      In this paper, one main message is that to infer GRN one should combine different omic datasets. This does not come as a surprise and has been published before. What it is very well addressed in this study is the problem of the sample size: the authors decide to test GRN inference methods in the human blastocyst, for which currently we do not have a lot of sequencing data available. Interestingly, they find that 1k cells should be enough to infer relevant GRN. Maybe the manuscript would benefit if the authors emphasize this more in their text.

      Interestingly, and despite the fact that the sample size here is below 1k, the authors identify novel regulatory relationships between TFs for different cell types, that they also validate.

      This paper will be relevant to a wide audience of scientists interested in human developmental biology, or in the development of computational approaches to analyse single cell sequencing data.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      The authors proposed MICA strategy as an attempt to infer gene regulatory network at the blastocyst stage of early embryo development which features limited sample size. While the motivation seems reasonable to me and the results showed several interesting insights, the methodological novelty and significance of this study need further elaboration, and the evaluation/benchmark part is largely insufficient.

      Major comments

      • The proposed strategy (i.e. combining gene expression-based regulatory inference with cis-regulatory evidence) have been well developed (and implemented) by multiple published works like SCENIC and CellOracle, which is also properly acknowledged by the authors in the discussion section too. This leads to a serious concern on the major methodological contribution of this work.
      • Most of the compared network reconstruction methods involve hyper-parameters setup (e.g., sparsity regularization weights of the regression methods). The authors did not discuss how these hyper-parameters were chosen.
      • For the real-world blastocyst data, the network prediction methods were compared in terms of their reproducibility across validation folds (Fig. 3, Fig. S4-6). However, reproducibility does not necessarily imply accuracy. In fact, statistical learning methods are generally subject to the bias-variance tradeoff, where lower variance (i.e., higher reproducibility) could imply higher bias in model prediction. While there is a lack of gold-standard ground truth to evaluate network accuracy in real biological systems, silver-standards like the ranking of known regulatory interactions in the predictions could be employed as an indirect estimate.
      • The gene set enrichment results were reported only on EPI and TE cell types (Fig. 4C and Fig. S12), due to the reason that CA data is only available for TE and ICM. However, many of the other results presented in Fig. 3-6 did include the PE cell type albeit using the same CA data. It is not particularly convincing why the cell type inclusion standard for gene set enrichment is different from the other results.
      • The authors cited TF binding in cis-regulatory regions as supporting evidence of several MICA-inferred regulatory interactions (e.g., NANOG -> ZNF343). However, the same cis-regulatory evidence has already been used in the CA filtering step. All interactions passing CA filtering should in principle have TF-binding support. It would be more convincing if the authors provided other types of evidence as independent support, such as genetic associations like eQTL, experimental perturbations like gene knockdown/knockout, etc.
      • Many of the MICA-inferred regulatory interactions do not exhibit Spearman correlation (Fig. 5, Fig. S17), which could probably be explained by the ability of mutual information to capture complex non-monotonic dependencies. It would be interesting to provide further investigation on these "uncorrelated" edges, which may help demonstrate the superiority of mutual information over Spearman correlation.
      • The authors conducted immunostaining experiments to validate the MICA-inferred regulatory interaction between TFAP2C and JUND. While the identified protein co-localization is a step further than RNA co-expression, it is still correlation rather than causality. Additional evidence like the effect of knockout/knockdown perturbations would be more convincing.

      Minor comments

      • The γ symbols in AP-2γ are not correctly rendered.
      • The UMAP figures (Fig. 4A, Fig. S7) are of low resolution compared to other figures.
      • As the authors are focused on studying the blastocyst regulatory network, the inferred regulatory interactions should be provided as supplementary data.

      Significance

      Given the concerns listed above, I still hold doubts on the significance of the manuscript in its current form. In particular, the major contribution of this work, in methodological senses, seems to be the specific choice of mutual information for regulatory inference in the low-data regime, which may have a limited audience and impact.

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      Reply to the reviewers

      1. Point-by-point description of the revisions

      Reviewer #1

      Evidence, reproducibility and clarity (Required):

      In this paper by Wideman et al, the authors seek to determine the role of cellular iron homeostasis in the pathogenesis of murine malaria.

      The authors to attempt to disentangle the effects of anemia from that of cellular iron deficiency. The authors elegantly make use of a murine model of a rare human mutation in the transferrin receptor. This mutation leads to decreased receptor internalization and decreased cellular iron, but otherwise healthy mice. Using this model, the authors use a P. chabaudi infection model and show an increase in pathogen burden and a decrease in pathology. They show in some detail that the immune response to P. chabaudi infection is blunted, both T and B-cell responses are attenuated in the TfRY20H/Y20H model, and the block in proliferation can be rescued by exogenous iron supplementation. They also show that decreased cellular iron attenuates liver pathology through potentially multiple mechanisms.

      Minor comments:

      • The peak of parasitemia is relatively low (approx..3%) compared to other published studies (e.g. PMID: 22100995, 16714546, 31110285) where the peak in C57BL/6 mice reached 25 - 40%. Can the authors account for this low parasitemia?

      Response: We thank the reviewer for their constructive comments and appreciate that they are highlighting this important point. It has previously been shown (PMID: 23217144, 23719378) that mosquito-transmission of P. chabaudi leads to significantly lower parasitaemia (“Recently mosquito-transmitted parasites were used to mimic a natural infection more closely, as vector transmission is known to regulate Plasmodium virulence and alter the host’s immune response (48-50). Consequently, parasitaemia is expected to be significantly lower upon infection with recently mosquito-transmitted parasites, compared to infection with serially blood-passaged parasites that are more virulent (48,49).”

      • Figure 1K - At homeostasis, serum iron is low in TfR mice however increases to significantly higher than the WT mice at 8 days post infection. Do the authors have an explanation on why these dramatic changes in serum iron are seen?

      Response: During malaria infection, RBC lysis releases haem and iron into circulation, which leads to an increase in serum iron levels. This effect is observed in both wild-type and TfrcY20H/Y20H mice infected with P. chabaudi (Supplementary Figure 1F & Figure 1K). However, the significantly higher serum iron levels observed in infected TfrcY20H/Y20H mice can likely be explained by their decreased capacity for transferrin receptor-1 mediated iron uptake, leading to relatively slower uptake and storage of circulating transferrin-bound iron into tissues. This has been clarified in the manuscript (line 142-143):

      “The elevated serum iron observed in infected TfrcY20H/Y20H mice was consistent with their restricted capacity to take up transferrin-bound circulating iron into tissues.”

      • Figure S3 - Is it surprising that no effects on splenic neutrophils are seen? Were neutrophils quantified at any other point? These would also be expected to have a role in both the control of malaria infection and on any pathology.

      Response: We thank the reviewer for raising this interesting question. It is known that neutrophils can be sensitive to cellular iron deficiency (PMID: 36197985) and that neutrophils can play an important part in malaria infection (PMID: 31628160). However, the magnitude and significance of the neutrophil response to recently mosquito-transmitted P. chabaudi parasites has not been thoroughly investigated. A recent study demonstrated that monocytes and macrophages may be more important than granulocytes in the early response to recently mosquito-transmitted P. chabaudi infection (PMID: 34532703).

      Moreover, we performed neutrophil quantifications in our initial experiments and found that the splenic neutrophil response was not altered in TfrcY20H/Y20H mice eight days after infection. Additionally, no neutrophil infiltration was observed in the liver of either genotype upon P. chabaudi infection. In light of these findings, we did not characterise the neutrophil response further, as it appeared unlikely that neutrophils were the principal causal agent of either the altered immunity or pathology, in this context. However, we agree with the reviewer that larger question of whether neutrophil iron plays a role in the pathology of malaria is an interesting open question which we hope future studies can elucidate.

      A section was added to the discussion to address the role of innate immune cells in our model (line 354-363):

      “The inhibited innate immune response to P. chabaudi in TfrcY20H/Y20H mice likely contributed to both the increased pathogen burden and the decreased liver pathology. Splenic MNPs are important for controlling parasitaemia (34,35,72), but MNPs are also vital for maintaining tissue homeostasis and preventing tissue damage in malaria (43,73). Although other innate cells, such as neutrophils, NK cells and γδT cells are an important part of the immune response to malaria, only the MNP response was distinctly impaired in TfrcY20H/Y20H mice. Notably, neutrophils are known to be sensitive to iron deficiency (16,74) and to affect both immunity and pathology in malaria (75,76). However, in the context of recently mosquito-transmitted P. chabaudi it appears that monocytes and macrophages, rather than granulocytes, may be particularly important for parasite control and tissue homeostasis (43,72).”

      Changes to the text:

      • Fig S1EandF - Please add to the figure legend that these were measured at homeostasis.

      Response: This clarification has been added to the legend of Supplementary Figure 1 (line 954-957).

      • Figure 3 - In the legend, H and I are the wrong way around.

      Response: The legend of Figure 3 has been corrected accordingly (line 888-890).

      • Figure 4 - please add the units of concentration of FeSO4 to all panels

      Response: The units of concentration for FeSO4 and AFeC have been added to all panels of Figure 4 and 6, respectively.

      • Line 246 - The authors state: "there was some evidence of decreased malaria-induced hepatomegaly" however there is no significant difference between WT and TfR mice and both show significant hepatomegaly. I feel that this line should be reworded.

      Response: The sentence (line 252-254) has been reworded as follows:

      Furthermore, while both genotypes developed malaria-induced hepatomegaly, there was a trend toward less severe hepatomegaly in TfrcY20H/Y20H mice (Figure S5C).”

      Significance (Required):

      This work is one of the first to attempt to define the requirements for cellular iron in malaria infection. This is a difficult topic, as infection and associated inflammation and the red blood cell destruction caused by malaria all have complex effects on iron within the body. This study fits well with previous observations showing that anemia can be protective as it both prevents parasite growth and limit immunopathology. This work advances the field by demonstrating a cell intrinsic role for iron in malaria infection. There is a broad possible audience for this work, including malaria researchers, immunologists and people interested in the role or iron, both at a cellular level and systemically.

      Reviewer #2

      Evidence, reproducibility and clarity (Required):

      In this manuscript, the authors have studied the role of iron deficiency in the host response to Plasmodium infection using a transgenic mouse model that carries a mutation in the transferrin receptor. They show that restricted cellular iron acquisition attenuated P. chabaudi infection- induced splenic and hepatic immune responses which in turn mitigated the immunopathology, even though the peak parasitemia was significantly high in the mutant mice. Interestingly, the course of parasite infection doesn't seem to be affected in the mutant mice compared to the wildtype mice despite the induction of poor immune responses. The authors show that the decreased cellular iron uptake broadly impact both innate and adaptive components of the immune system. Conversely, free iron supplementation restored the immune cell functions.

      • The study is well performed, and the manuscript is well written. However, the authors should show how conserved the role of cellular iron is across other rodent malaria parasite species at least with * yoelii or P. berghei* blood stage infection models. This question becomes critical to address in order to understand broad relevance to human malaria infections where both the host and parasites are genetically diverse.

      Response: We thank the reviewer for appreciating our study and for the thoughtful comments. We agree with the reviewer that the diverse genetic background of both parasites and hosts makes it difficult to draw broad conclusions about human malaria infection from animal studies performed in a laboratory setting. The recently mosquito-transmitted P. chabaudi chabaudi AS blood-stage infection model replicates many key features of mild to moderate malaria infection in humans, such as low parasitaemia, anaemia, cyto-adhesive sequestration in microvasculature, and self-resolving immunopathology. Importantly, the immune response elicited by recently mosquito-transmitted parasites also more closely mimics the immune response to a natural infection (PMID: 23719378). Therefore, we consider the recently mosquito-transmitted P. chabaudi chabaudi AS model as the most relevant to answer our particular research questions.

      Furthermore, specific pathogen-free parasitised erythrocyte stabilates made from recently mosquito-transmitted P. berghei or P. yoelii parasites are unfortunately not readily accessible (e.g. through the European Malaria Reagent Repository), in contrast to P. chabaudi. Consequently, preparing and characterising recently mosquito-transmitted strains to perform the experiments suggested by the reviewer would require a substantial amount of additional time and labour, which we deem out of scope for this study.

      In the design of our model we have also taken care to minimise the effects of anaemia, something which would be difficult or impossible to achieve using serially blood passaged P. yollii or P. berghei parasites. Both P. yoelii and P. berghei merozoites preferentially invade immature RBCs (PMID: 34322397) making readouts such as parasitaemia far more sensitive to small variations in erythropoietic output. In addition, the extensive RBC destruction caused by most serially blood-passaged murine Plasmodium strains would likely exaggerate any erythropoietic impairment caused by the TfrcY20H/Y20H mutation.

      Although we strongly believe that the chosen mouse model of malaria is the most appropriate for our study, ultimately, no mouse model can replicate all features of human malaria infection. Inevitably, the direct relevance of animal studies for human infection will always be somewhat opaque. Hence, we respectfully disagree with the reviewer that repeating the experiments with additional murine malaria parasite species would allow us to extrapolate conclusions about human malaria infection. Such experiments would also conflict with the 3Rs principles that govern work with animals in the UK (https://nc3rs.org.uk/). Especially, because most strains of P. yoelii and P. berghei cause severe or non-resolving infections and have a significant negative impact on animal welfare.

      In our opinion, the logical continuation of this study must be to utilise the insights from our research to inform future human studies on the relationships between iron deficiency and malaria-related immunopathology. However, we agree that this is an important topic and have added a section addressing the broad relevance of our findings to the discussion (line 393-396):

      “It remains to be seen what the broader importance of cellular iron is in human malaria infection, in particular within the diverse genetic context of both humans and parasites found in malaria endemic regions. Murine models of malaria are useful in providing hypothesis-generating results, but such findings ultimately ought to be confirmed and developed further through studies in human populations.”

      • Since, restricted cellular iron uptake mitigates the immunopathology, the authors should explore whether this could also relieve the cerebral malaria condition that is caused by the hyper inflammation in the brain. They should use the * berghei* ANKA parasite strain which causes t cerebral malaria in mice. I think would increase impact of the paper.

      Response: Although we agree that this would be an interesting line of inquiry, we think that it is outside of the scope of this study, which predominantly aims to characterise and study the effects of cellular iron deficiency in host cells, particularly immune cells, during mild to moderate malaria infection. The severe pathology underlying cerebral malaria differs greatly from that of a self-resolving blood-stage infection. Furthermore, the relevance to human cerebral malaria of the P. berghei ANKA model is controversial within the field (PMID: 21288352) and as a severe infection its use would again conflict with the 3Rs principles.

      Minor comments:

      • Line 222: repeating word, "iron iron-supplemented...."

      Response: The sentence has been corrected (line 228).

      • Figure 3C, S4C & S5F: Why Mann-Whitney test is performed in these particular graphs, whereas rest of the two groups comparison were done using Welch's test? The authors should clearly mention this in the methods section.

      Response: We apologise if this was unclear in the manuscript. We routinely tested all our datasets for normality to identify the appropriate tests for each dataset. In case of the graphs shown in figure 3C, S4C and S5F, the dataset did not pass the D’Agostino-Pearson normality test and we therefore applied a non-parametric test (i.e. Mann-Whitney), in contrast to the other datasets that passed the test for normal or lognormal distribution. This has been further clarified in the method section (line 581-586):

      The D’Agostino-Pearson omnibus normality test was used to determine normality/lognormality. Parametric statistical tests (e.g. Welch’s t-test) were used for normally distributed data. For lognormal distributions, the data was log-transformed prior to statistical analysis. Where data did not have a normal or lognormal distribution, or too few data points were available for normality testing, a nonparametric test (e.g. Mann-Whitney test) was applied.“

      • Have authors explored whether gamma-delta T cell responses are affected in the mutant mouse strain compared to wildtype mice as they are one of the early responders and the key cytokine producing cells against the Plasmodium blood stage infection.

      Response: __We thank the reviewer for this valuable comment. We briefly explored the role of γδT cells, but did not observe a significant difference in splenic γδT cell numbers between wild-type and TfrcY20H/Y20H mice, eight days post-infection (__Reviewer Figure 1). It is of course possible that γδT cell numbers were affected at an earlier stage, or that γδT cell function (e.g. cytokine production) was affected by cellular iron deficiency during P. chabaudi infection. However, γδT cells may also be less sensitive to cellular iron deficiency than conventional T cells, as has been previously demonstrated for developing T cells (PMID: 7957580).

      A section was added to the discussion to address the role of innate immune cells in our model (line 354-363):

      “The inhibited innate immune response to P. chabaudi in TfrcY20H/Y20H mice likely contributed to both the increased pathogen burden and the decreased liver pathology. Splenic MNPs are important for controlling parasitaemia (34,35,72), but MNPs are also vital for maintaining tissue homeostasis and preventing tissue damage in malaria (43,73). Although other innate cells, such as neutrophils, NK cells and γδT cells are an important part of the immune response to malaria, only the MNP response was distinctly impaired in TfrcY20H/Y20H mice. Notably, neutrophils are known to be sensitive to iron deficiency (16,74) and to affect both immunity and pathology in malaria (75,76). However, in the context of recently mosquito-transmitted P. chabaudi it appears that monocytes and macrophages, rather than granulocytes, may be particularly important for parasite control and tissue homeostasis (43,72).”

      Significance (Required):

      Overall, the study provides novel insights into the role of iron in the immune response to Plasmodium blood stage infection using a rodent malaria model and the interplay of infection, immunity and the development of pathology. As such it is an important study.

      Reviewer #3

      Evidence, reproducibility and clarity (Required):

      Herein Wideman provide novel and important evidence on the role of iron availability for mounting an efficient immune response in a malaria infection model. They employed TfRC Y201H/Y201H mice which develop iron deficiency due to impaired cellular ingestion of transferrin bound iron. They found that those mice develop higher peak parasitemia after vector borne exposure to Pl. chabaudi chabaudi which was paralleled by an impaired immune response as reflected by altered CD4 cell activation, reduced IFN-g formation or reduced B-cell responsiveness. Those deficiencies could be re-covered upon ex vivo iron supplementation pointing to the importance of iron availability for mounting-CD4+ and B-cell specific anti-plasmodial immune responses at the initial phase of infection. However, TFRC mutated mice were able to clear infection over time in a comparable fashion to wt mice.

      This excellent study is important in convincingly showing (by employing high quality immunological analyses) the importance of cellular iron deficiency on immune responses in an infection model of general interest. It also indicates that overwhelming immune response as seen in wt mice is associated with organ damage over time.

      Minor comments:

      • The authors should discuss why and how TFRC mutated mice were able to control infection over time in a comparable fashion as wt mice although peak parasitemia was significantly higher?

      __Response: __We thank the reviewer for the helpful feedback on our study and for posing this interesting question. It does indeed appear as if the immune response, while significantly inhibited in the TfrcY20H/Y20H mice, is still sufficient to clear the infection. It is plausible that the early cell-mediated immune response is inhibited to the degree that parasite control is impaired, resulting in higher peak parasitaemia in TfrcY20H/Y20H mice. In contrast, parasite clearance is comparable and contemporary in both genotypes. Based on the fact that parasite clearance occurs at a time when a substantial adaptive immune response is expected to emerge, we hypothesize that this significantly contributes to pathogen clearance. Thus, it seems likely that the humoral response in TfrcY20H/Y20H mice, even if inhibited, may still be effective enough to clear the parasites and prevent recrudescence.

      As malaria infection progresses, RBC loss and increasing anaemia also contributes to limiting exponential parasite growth. This occurs more or less equally in both genotypes, but it could be particularly important for parasite control in the TfrcY20H/Y20H mice that have an inhibited immune response.

      We have added a section to the discussion to address this (line 380-386):

      “Despite the higher peak parasitaemia in TfrcY20H/Y20H mice, both genotypes were able to clear P. chabaudi parasites at a comparable rate and prevent recrudescence. It follows that even a weakened humoral immune response appears to be sufficient to control P. chabaudi infection. However, our study did not investigate the effects of immune cell iron deficiency on the formation of long-term immunity, which may have been more severely affected. The impaired GC response, in particular, suggests that iron deficiency could counteract the formation of efficient immune memory to subsequent malaria infections.”

      • The authors and others have previously shown (Frost J et al. Sci Adv 2022, Hoffmann et al. EBioMedicine 2021) that iron deficiency results in reduced neutrophil numbers in different infection models. This could also have contributed to the observed effect in initial infection control but may have also been linked altered histopathology seen in Figure 7. However, no mention of neutrophil numbers in this model is made. It would be important if the authors could provide information on neutrophil numbers (only if this analysis has been already performed) and discuss this issue in association with their observation.

      Response: We appreciate that the reviewer has brought attention to this important topic. As they mention, iron deficiency can have a negative impact on the neutrophil response (PMID: 36197985, 34488018) but it can also cause a maladaptive excessive neutrophil response due to failed adaptive immunity (PMID: 33665641). In this study, we show that there is no difference in splenic neutrophil numbers between wild-type and TfrcY20H/Y20H mice, eight days after P. chabaudi infection (Figure S3B). Moreover, the histopathologists detected no liver neutrophil infiltration in either genotype, but rather observed infiltration of mononuclear leukocytes upon P. chabaudi infection. Hence, it appears unlikely that neutrophils were a major contributor to differences in either immunity or pathology in this specific context. However, we cannot definitively rule out that neutrophil numbers were affected earlier in the infection or that neutrophil function was impaired due to cellular iron deficiency.

      A section was added to the discussion to address the role of innate immune cells in our model (line 354-363):

      “The inhibited innate immune response to P. chabaudi in TfrcY20H/Y20H mice likely contributed to both the increased pathogen burden and the decreased liver pathology. Splenic MNPs are important for controlling parasitaemia (34,35,72), but MNPs are also vital for maintaining tissue homeostasis and preventing tissue damage in malaria (43,73). Although other innate cells, such as neutrophils, NK cells and γδT cells are an important part of the immune response to malaria, only the MNP response was distinctly impaired in TfrcY20H/Y20H mice. Notably, neutrophils are known to be sensitive to iron deficiency (16,74) and to affect both immunity and pathology in malaria (75,76). However, in the context of recently mosquito-transmitted P. chabaudi it appears that monocytes and macrophages, rather than granulocytes, may be particularly important for parasite control and tissue homeostasis (43,72).”

      • In addition, alternative mechanism leading to immune tolerance and reduced tissue damage such as induction of heme oxygenase-1, which is also affected by systemic iron availability, should be discussed.

      Response: __An addition was made to the results section and to Figure S5 to address this reviewer comment (line __269-274):

      “In addition, we measured the expression of two genes that are known to have a hepatoprotective effect in the context of iron loading in malaria: Hmox1 (encodes haemoxygenase-1) and Fth1 (encodes ferritin heavy chain). Liver gene expression of Hmox1 was higher in TfrcY20H/Y20H mice, while the expression of Fth1 did not differ between genotypes, eight days after infection (Figure S5H-I). Thus, the higher expression of Hmox1 may have contributed to the hepatoprotective effect in TfrcY20H/Y20H mice.”

      A relevant sentence was also added to the discussion (line 313-318):

      “For example, HO-1 plays an important role in detoxifying free haem that occurs as a result of haemolysis during malaria infection, thus preventing liver damage due to tissue iron overload, ROS and inflammation (62). Interestingly, infected TfrcY20H/Y20H mice had higher expression of Hmox1, but levels of liver iron and ROS comparable to that of wild-type mice. Consequently, this may be indicative of increased haem processing that could have a tissue protective effect”

      Significance (Required):

      Important and intersting study highlighting the central role of iron homeostasis for immune repsonse to infection. General interest because iron deficiency has high prevalence in areas with high enedemic burden of infection

      Reviewer's expertise: infectious disease, immunity, iron homeostasis-- both basic science and clincal expertise (more than 300 peer reviewed publications on these topcis)

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      Referee #3

      Evidence, reproducibility and clarity

      Herein Wideman provide novel and important evidence on the role of iron availability for mounting an efficient immune response in a malaria infection model. They employed TfRC Y201H/Y201H mice which develop iron deficiency due to impaired cellular ingestion of transferrin bound iron. They found that those mice develop higher peak parasitemia after vector borne exposure to Pl. chabaudi chabaudi which was paralleled by an impaired immune response as reflected by altered CD4 cell activation, reduced IFN-g formation or reduced B-cell responsiveness. Those deficiencies could be re-covered upon ex vivo iron supplementation pointing to the importance of iron availability for mounting-CD4+ and B-cell specific anti-plasmodial immune responses at the initial phase of infection. However, TFRC mutated mice were able to clear infection over time in a comparable fashion to wt mice. This excellent study is important in convincingly showing (by employing high quality immunological analyses) the importance of cellular iron deficiency on immune responses in an infection model of general interest. It also indicates that overwhelming immune response as seen in wt mice is associated with organ damage over time.

      Minor points:

      The authors should discuss why and how TFRC mutated mice were able to control infection over time in a comparable fashion as wt mice although peak parasitemia was significantly higher? The authors and others have previously shown (Frost J et al. Sci Adv 2022, Hoffmann et al. EBioMedicine 2021) that iron deficiency results in reduced neutrophil numbers in different infection models. This could also have contributed to the observed effect in initial infection control but may have also been linked altered histopathology seen in Figure 7. However, no mention of neutrophil numbers in this model is made. It would be important if the authors could provide information on neutrophil numbers (only if this analysis has been already performed) and discuss this issue in association with their observation. In addition, alternative mechanism leading to immune tolerance and reduced tissue damage such as induction of heme oxygenase-1, which is also affected by systemic iron availability, should be discussed.

      Significance

      Important and intersting study highlighting the central role of iron homeostasis for immune response to infection General interest because iron deficiency has high prevalence in areas with high enedemic burden of infection

      Reviewer's expertise: infectious disease, immunity, iron homeostasis-- both basic science and clincal expertise (more than 300 peer reviewed publications on these topics)

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

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      Referee #2

      Evidence, reproducibility and clarity

      In this manuscript, the authors have studied the role of iron deficiency in the host response to Plasmodium infection using a transgenic mouse model that carries a mutation in the transferrin receptor. They show that restricted cellular iron acquisition attenuated P. chabaudi infection- induced splenic and hepatic immune responses which in turn mitigated the immunopathology, even though the peak parasitemia was significantly high in the mutant mice. Interestingly, the course of parasite infection doesn't seem to be affected in the mutant mice compared to the wildtype mice despite the induction of poor immune responses. The authors show that the decreased cellular iron uptake broadly impact both innate and adaptive components of the immune system. Conversely, free iron supplementation restored the immune cell functions.

      • The study is well performed, and the manuscript is well written. However, the authors should show how conserved the role of cellular iron is across other rodent malaria parasite species at least with P. yoelii or P. berghei blood stage infection models. This question becomes critical to address in order to understand broad relevance to human malaria infections where both the host and parasites are genetically diverse.
      • Since, restricted cellular iron uptake mitigates the immunopathology, the authors should explore whether this could also relieve the cerebral malaria condition that is caused by the hyper inflammation in the brain. They should use the P. berghei ANKA parasite strain which causes t cerebral malaria in mice. I think would increase impact of the paper.

      Minor comments:

      • Line 222: repeating word, "iron iron-supplemented...."
      • Figure 3C, S4C & S5F: Why Mann-Whitney test is performed in these particular graphs, whereas rest of the two groups comparison were done using Welch's test? The authors should clearly mention this in the methods section.
      • Have authors explored whether gamma-delta T cell responses are affected in the mutant mouse strain compared to wildtype mice as they are one of the early responders and the key cytokine producing cells against the Plasmodium blood stage infection.

      Significance

      Overall, the study provides novel insights into the role of iron in the immune response to Plasmodium blood stage infection using a rodent malaria model and the interplay of infection, immunity and the development of pathology. As such it is an important study.

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      Referee #1

      Evidence, reproducibility and clarity

      In this paper by Wideman et al, the authors seek to determine the role of cellular iron homeostasis in the pathogenesis of murine malaria.

      The authors to attempt to disentangle the effects of anemia from that of cellular iron deficiency. The authors elegantly make use of a murine model of a rare human mutation in the transferrin receptor. This mutation leads to decreased receptor internalization and decreased cellular iron, but otherwise healthy mice. Using this model, the authors use a P. chabaudi infection model and show an increase in pathogen burden and a decrease in pathology. They show in some detail that the immune response to P. chabaudi infection is blunted, both T and B-cell responses are attenuated in the TfRY20H/Y20H model, and the block in proliferation can be rescued by exogenous iron supplementation. They also show that decreased cellular iron attenuates liver pathology through potentially multiple mechanisms.

      Minor comments:

      • The peak of parasitemia is relatively low (approx..3%) compared to other published studies (e.g. PMID: 22100995, 16714546, 31110285) where the peak in C57BL/6 mice reached 25 - 40%. Can the authors account for this low parasitemia?
      • Figure 1K - At homeostasis, serum iron is low in TfR mice however increases to significantly higher than the WT mice at 8 days post infection. Do the authors have an explanation on why these dramatic changes in serum iron are seen?
      • Figure S3 - Is it surprising that no effects on splenic neutrophils are seen? Were neutrophils quantified at any other point? These would also be expected to have a role in both the control of malaria infection and on any pathology

      Changes to the text

      • Fig S1EandF - Please add to the figure legend that these were measured at homeostasis
      • Figure 3 - In the legend, H and I are the wrong way around.
      • Figure 4 - please add the units of concentration of FeSO4 to all panels
      • Line 246 - The authors state: "there was some evidence of decreased malaria-induced hepatomegaly" however there is no significant difference between WT and TfR mice and both show significant hepatomegaly. I feel that this line should be reworded.

      Significance

      This work is one of the first to attempt to define the requirements for cellular iron in malaria infection. This is a difficult topic, as infection and associated inflammation and the red blood cell destruction caused by malaria all have complex effects on iron within the body. This study fits well with previous observations showing that anemia can be protective as it both prevents parasite growth and limit immunopathology. This work advances the field by demonstrating a cell intrinsic role for iron in malaria infection. There is a broad possible audience for this work, including malaria researchers, immunologists and people interested in the role or iron, both at a cellular level and systemically.

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      Reply to the reviewers

      We thank the reviewers for their comments and insights, we feel the manuscript is now greatly improved. Please find below our answers to the reviewer’s queries

      Reviewer #1 (Evidence, reproducibility and clarity):

      The manuscript by Niccoli et al. describes the identification of a novel modifier of C9orf72-derived toxicity based on the manipulation of the brain metabolic pathways. The premise for this work is supported by strong literature describing the aberrant glucose metabolism in FTD, AD and other degenerative disorders. The idea tested here is whether increasing the import of pyruvate produced in glia into neurons. They test three different types of importers and find that one of them, Bumpel, the orthologue of human SLC5A12, suppresses toxicity and reduces the accumulation of arginine-containing repeats, GP and PR. The authors investigate several potential mechanisms mediating this reduction of toxic DPRs, but do not find strong evidence linking pyruvate import and increase autophagy or mitochondria metabolism.

      Overall, this is an interesting discovery based on a candidate approach that shows the power of Drosophila to efficiently identify novel mediators of neurodegeneration. The article is well written, although more detailed explanations of some experiments would be helpful. The weaknesses of the manuscript are the lack of a clear mechanism mediating the protective activity of pyruvate, the incomplete experiments lacking relevant controls, and the presentation of western blots.

      Specific comments:

      1. The reduced levels of DPRs require that the expression of C9 mRNA or the GR and PR constructs is examined by qPCR. In figure 3E, GP is not even detectable_

      We agree with the reviewer, ideally we would have measured the RNA by qPCR. However, the C9 repeats and the DPR constructs are highly repetitive, it is therefore impossible to do a qPCR for them. The upstream and downstream sequence is identical for the C9 and the bumpel constructs, there isn’t, to our knowledge any unique sequence we can use to measure levels of expression in the presence of bumpel.

      We did run a GFP control (Fig 2D) and did not see any difference and we have now carried out a qPCR for Gal4-GeneSwitch (Fig S3) to show that the levels of the driver do not change.

      1. I wonder if there are constructs available to silence Bumpel or overexpress the human orthologues of bumpel. These would be nice controls for the effects observed with the Bumpel overexpression

      This would be an extremely interesting experiment, however bumpel is normally only expressed in glia, therefore we can’t down-regulated it in glia whilst upregulating 36R in neurons, as we are limited to one driver (since everything is driven by the Gal4/UAS system). Expression of C9 in glia does not have a clear phenotype (our observation), so we can’t drive both in glia. We tried over-expressing the human homologue SLC5A12 , but it did not rescue the C9 phenotype (data not shown), possibly because it requires (like other human SLC5A type transporters) PDZK1 as extra co-factor (Srivastava S. et al, 2019), and this is not present in flies.

      1. The argument about bumpel modulating autophagy downstream of Atg1 is not supported by the experimental data

      We now have imaging data showing that bumpel modulates the formation of lysosomes, downstream of Atg1 (Fig 5). We also show that bumpel and Atg1 can act synergistically, leading to a much stronger rescue of C9 expression (See Fig 5I.), which also suggests that the two are acting at different points in the same pathway. We also show that bumpel rescues the downregulation of TFEB targets (Fig 5J)

      1. Western blots throughout show no control lanes and in several occasions are created with cutout bands. The standard for this type of experiments should be more stringent, with entire gels showing all experimental conditions, which requires consistent methods and results vs selecting the best bands from different gels.

      We apologise if this was mis-understood, the lanes shows are all from the same blot, where other samples were run too, and it would be confusing for the reader to include them. We have re-run samples where we had remaining sample from our quantifications, so that the lanes are now contiguous and we provide original blot images in the supplemental information for those we could not re-run. The control for all experiments are the C9 expressing line without bumpel, and this is always present, if the reviewer means we are missing -RU controls, these do not produce any DPRs so are not included in western blot or ELISA quantifications as the signal is not above back-ground.

      1. For figures 2B and 5C, please, show representative WBs

      These are ELISA quantifications, not western blots, we choose to run these when possible, as they are more quantitative.

      1. Figure 5D describes the survival curve as significantly rescued. Statistical tests can indicate differences, but that is in no way convincing. The test may show the curves are different, but the abeta Atg1 flies also seem to start falling early, so an argument could be made in both directions, as a suppressor or an enhancer.

      We agree the rescue is not strong enough, we have now removed this lifespan.

      1. It is unclear why several results are placed in the supplemental materials. In general, all this material seems highly relevant and related to what is shown in the main figures

      We are happy to include them in the main manuscript if this would help the reader, and we have now placed all mitochondrial data in Fig 4.

      Minor comments:

      Please, define several abbreviations throughout

      We apologise for this over-sight, we have now does this.

      A couple of sections could be improved by carefully sequencing human vs Drosophila background to advance the argument rather than going in circles. There is also a section on mitophagy in between two sections related to autophagy that could be sequenced better.

      We have re-structured the sections, we think this has improved the flow.

      There is a sentence at the end of page 6 that seems misplaced

      We apologise for the over-sight, and we have removed this

      Reviewer #1 (Significance):

      Overall, this is an interesting discovery based on a candidate approach that shows the power of Drosophila to efficiently identify novel mediators of neurodegeneration. The article is well written, although more detailed explanations of some experiments would be helpful. The weaknesses of the manuscript are the lack of a clear mechanism mediating the protective activity of pyruvate, the incomplete experiments lacking relevant controls, and the presentation of western blots.

      We thank the reviewer for the helpful comments, we have added some details in the methods section, we apologise for not having made it clear that the westerns were all derived from the same blot (we have now placed the originals in the supplemental materials). Regarding mechanism, we now show that bumpel over-expression increases clearance of late stage autolysosomes, possibly by increasing transcription of TFEB target lysosomal genes.

      Reviewer #2 (Evidence, reproducibility and clarity):

      Summary:<br /> Project investigates the role in dementias of glial glucose uptake, conversion to lactate and shuttling via transporters to neurons to produce pyruvate to fuel TCA cycle production of ATG. The experiments are conducted in Drosophila melanogaster, which have become a powerful model system for understanding neurodegeneration mechanisms associated with ALS/FTD associated C9orf72 pathology. Bumple misexpression is shown to rescue early death phenotype in flies expressing a C9orf72 expansion and flies expressing arginine containing di-peptide repeat proteins. The report describes novel insight into the function of bumpel, demonstrating that this conserved orthologue of human SLC14A functions as a sodium exchange transporter for monocarboxylates pyruvate and lactate. These findings conclude that increased neuronal pyruvate, but not its metabolites, rescues C9orf72 associated pathology.<br /> The authors next set out to describe the mechanism by which increase pyruvate rescues survival in C9orf72 expressing flies. Levels of autolysosomes were increased in C9orf72 expressing flies, and stimulation of autophagy by overexpression of atg1 shown to decrease levels of DPRs (though not to same extent as bumple expression). Expression of bumple in C9orf72 flies led to a modest increase in LC3-II, indicating increased autophagy. Co-overexpression of bumple and atg1 did not have an additive effect, suggesting bumple activates autophagy downstream or independent of atg1 activity. Finally the author extend their findings to amyloid models, suggest a common protective mechanism for elevating neuronal pyruvate levels in neurodegenerative disease.

      Major comments

      Prior data suggests that bumpel is expressed in glia (for example Yildirim et al 2022). In their study the authors do not present any data to demonstrate that the transporter is normally expressed in neurons in flies. This calls into questions the physiological relevance of their findings, that neuronal upregulation of bumpel is protective against C9orf72 associated pathology in neurons, from which it is reasonable for a reader to conclude that bumpel may be a neuronal target for therapeutic intervention. However, the report well demonstrates that regardless of whether the transporter in native to neurons, the increase in monocarboxylates it facilitates is projective against C9orf72 pathology and thus the overall conclusion of the project is supported by experimental evidence. The point of upregulation of a natively expressed gene versus misexpression of a glial enriched transporter should be considered in a bit more detail in the discussion text. The authors may consider speculating the identify of members of the sodium coupled monocarboxylate transporters that are enriched in neurons. Are any of the bumple human orthologues expressed in neurons?_

      We thank the reviewer for this comment and suggestion. The reviewer correctly points out that we do not show whether there is a defect in pyruvate import in C9 expressing flies. We could not identify a validated sodium coupled pyruvate transporter in flies with a strong neuronal expression, we have added a comment in the discussion about this. There are a number of human homologues, some, such as SLC5A8, are expressed in neurons, thus providing a possible therapeutic target. We have added a sentence to this regard in the discussion.

      [_OPTIONAL] cDNA overexpression of neuron specific sodium coupled monocarboxylate transporters in C9orf72 fly models would strengthen the conclusion their physiological relevance for ALS/FTD. Fly lines for these are not available in repositories, but could be generated and tested at reasonable cost (<£700, ~3 month duration).

      This would be an ideal experiment, however, we could not find a neuronal sodium coupled transporter which is known to import monocarboxylates. There are a number of sodium coupled neuronal transporters, but they are mostly homologous to SLC5A6, which is a glucose coupled transporter. Going forward, we will screen a number of transporters to identify if there are any which import pyruvate.

      The role of bumple expression in survival (Figure 1) could be a technical artifact due to dilution of Gal4 between C9orf72 and bumple-ORF transgenes. No expression control is shown (for example GFP, LacZ etc). This theory is unlikely as no improvement in survival was seen for the SLC14A class of transporters which have a matching site directed transgene insertion. For clarity this point relating to controls should be commented on in the text.

      The reviewer is correct, there could be a dilution of the Gal4. We don’t like using GFP as a control as we have often seen a worsening when expressing other highly stable proteins at high levels. We have generated an “empty” flyORF line (generated by injecting the empty plasmid into the identical attP site), and used it as a control to check for dilution effects, bumpel still rescued relative to this control, we now include this is the supplementary (Fig S1B).

      Reduced Mito-GFP levels are used to support a role for bumple in increasing mitophagy. As mito-GFP is a marker for mitochondria but not specifically mitophagy, an alternative explanation for decreased levels could be reduced mitochondria biogenesis. The text should be amended to clarify this point.<br /> The role of Pink1 RNAi in modifying mitophagy is a bit overstated. Whilst Pink1 is involved in stress associated mitophagy, its role in basal mitochondria turnover is less well defined. Text should be adapted.

      We have added qualifying statements regarding the possibility of reduced mitochondrial biogenesis, and the fact that Pink1’s role in basal mitophagy is not very clear. The use of the mitophagy inducer drug, Kaempferol, however, suggests that mitophagy is unlikely to be a cause of the DRP reduction.

      Minor comments

      Introduction well describes current state of C9orf72 fly models. Introduction would benefit from a few comparable lines for AD models. The first paragraph of reports may also be better placed in the introduction._

      We thank the reviewer for the suggestion, and have added a more in depth introduction to Aß and have moved the first paragraph of the results section to the introduction

      Figure 1 presents survival for three SLC16A transporters and bumple. The C9 control curve appears to be consistent between charts, likely indicating the same control used across experiments, rather than independent controls for each chart. The authors should considered showing either all SLC16A and bumple data on a single chart, or clarify in the figure legend that a common control dataset is used. GFP control is used in later experiments (Figure 2).

      We have now indicated that the SLC16A transporters were run together in the figure legend.

      Choice of amyloid model needs a line of explanation, particularly with regard to extra/intracellular deposition of amyloid in this model.

      We have now added a few sentences describing this when the model is introduced

      Fruit Fly Injection method section needs a bit more detail to describe site of injection (head, body etc). This is not clear in the result section either.

      We have now added this, the injection was done in the abdomen.

      How were bumple orthologues identified? What degree of conservation (sequence homology etc?)

      The bumpel orthologues are those identified as most similar by flybase. We have now added the degree of conservation in the text

      The speculative mechanism for C9 pathology modification involves interaction of neurons and glia, monocarboxylate transporters and changes in autophagy activity. For clarity a diagram showing the model may be a helpful addition.

      We have now added a diagram explaining how we think the rescue is achieved

      Typos:<br /> Figure 1 Legend - "p values of ona way ANOVA "

      We apologise for the error, and have now corrected it

      Figure S2 Legend - Atg1 RNAi genotypes from S2 legend are mentioned erroneously

      We apologise for the error, and have now corrected it

      Repetition of text in results: "Bumpel, together with its paralogues kumpel and rumpel, is expressed in glia in flies, where it is thought to promote transport of substrates across the brain (31)."

      We apologise and have rectified this

      "Modulation of Atg1 when bumpel was co-overexpressed, however, did not affect GP<br /> levels (Fig 4E, F)" - Should be refering to Fig 4D, E)

      We apologise and have rectified this

      Reviewer #2 (Significance):

      The study will be of broadly of interest to researcher working in the fields of neurodegeneration and metabolism, providing evidence for a protective role of elevated pyruvate in neuron that provide new understand relating to pathology in C9orf72 associated motor neuron disease and frontotemporal dementia.

      Strengths:<br /> The study presents novel data to demonstrate that overexpression of fly monocarboxylate transporter bumple rescues an early death phenotype associate with ALS/FTD gene C9orf72. Any novel therapeutic strategies of ALS are of interest to the field, and the strategy demonstrated here may be readily translated to human cell culture systems for proof of principle translational studies to a more physiologically relevant system. This study further demonstrates the utility of invertebrate models to generate novel understanding of C9orf72 pathology.

      Limitations:<br /> The study speculates that there is a link between pyruvate levels and increased autophagy, however the mechanisms by which this occurs is not defined in present study. This is a limitation of the experiment, though opens up an interesting question for future studies._

      We thank the reviewer for their comments, and we have now added experiments characterising the role of bumpel in autophagy, particularly showing its rescue of a late autolysosomal block.

      Reviewer expertise: The reviewer researches ALS and dementia associated neurodegeneration, utilising Drosophila, rodent and stem cell derived model systems.

      Reviewer #3 (Evidence, reproducibility and clarity):

      This is an interesting manuscript in which the authors provide evidence that elevated neuronal expression of the pyruvate transporter bumpel can partially rescue shortened lifespan in fly models of frontotemporal dementia and Alzheimer's disease. In addition, elevated neuronal bumpel expression can reduce accumulation of arginine containing FTD-linked dipeptide repeat proteins. Some evidence is presented that elevated neuronal bumpel expression may activate autophagy. These findings are novel and may have implications for therapeutic interventions based on pyruvate import/metabolism to treat neurodegenerative disorders. However, I have several concerns as follows:

      Major Comments:

      1. The authors provide no explanation as to why they targeted bumpel overexpression in neurons. Endogenous bumpel appears to be predominately expressed in glia cells so why not target these cells instead?

      We wanted to increase pyruvate import in neurons, so we over-expressed a number of pyruvate transporter that were available in the fly ORF stock centre (so that they would all be inserted into the same site and therefore directly comparable), we were mainly interested in cell autonomous effects of importing glycolytic metabolites. Over-expressing bumpel in glia would be indeed an extremely interesting experiment, unfortunately we do not have the ability to express C9 in neurons while over-expressing bumpel in glia as we only have one over-expression system that works. We are working towards generating a new C9 model so we can then use the Gal 4 system to over-express bumpel in glia, but this is currently not available yet. Over-expression of C9 in glia is not toxic and not a good model of disease.

      1. Data is shown that overexpressed bumpel can suppress GR and PR dipeptide repeat toxicity when these peptides are translated using an ATG start codon (Fig 2D,E). Does bumpel mediated neuroprotection also correlate with a reduction in DPR levels driven with an ATG start codon?

      This would be a very interesting question, unfortunately, whist the Isaacs lab kindly made available the GR antibody for the initial ELISA experiment, we no longer have that antibody available and we do not have a working PR antibody. GR and PR westerns are not possible to carry out as the proteins are too positively charged to run. We do show that bumpel can down-regulate Aß from a UAS promoter, so its effect is not specific to RAN translation.

      1. The authors provide some evidence suggesting that overexpression of bumpel increases autophagy in the fly brain. However, knockdown of Atg1 while co-expressing bumpel (Fig 4E) did not result in increased GP protein levels. In addition, Atg1 knockdown did not attenuate the protective effects of bumpel overexpression (Fig 4I), suggesting that bumpel is working through a pathway independent of autophagy to promote DPR clearance and protection against toxic peptide accumulation. The authors need to modify the interpretation of their data and temper their claim that autophagy contributes to bumpel-mediated protective effects in the CNS.

      We apologise the data was not strong enough. We have now added evidence that bumpel acts downstream of Atg1, on late stage autolysosomal clearance. We also show that bumpel and Atg1 can act synergistically to improve the C9 phenotype when over-expressed, this is now described in Fig 5.

      1. Although the authors present evidence that increased bumpel expression can activate autophagy, the data is not convincing that the neuroprotective effects associated with bumpel are mediated through autophagy. Pyruvate, in some circumstances, can non-enzymatically scavenge hydrogen peroxide or in other cases trigger oxidative stress resistance through hormetic ROS signaling. The authors should consider these alternative possibilities.

      These are indeed possibilities, we have added a sentence to that effect in the discussion, we have now also showed that bumpel is affecting late clearance of autolysosomes, and is leading to an increase in TFEB targets.

      1. The authors rely on overexpressing bumpel to attenuate C9 toxicity in flies. They should perform the opposite experiment and knockdown bumpel to demonstrate that reduced bumpel expression results in potentiation of C9 and amyloid beta neurotoxicity. In addition, then should show that knockdown of bumpel expression has some effect on autophagy.

      This would be a very interesting experiment, unfortunately bumpel is expressed only in a few glia subtypes in a wild type fly, and we can’t downregulate it in glia while over-expressing toxic proteins in neurons, because of limitations of our expression system, both genes need to be over-expressed in the same cell type. We have tried downregulating bumpel in neurons, and don’t get an effect on phenotype, and no effect on DPR levels, but bumpel expression in neurons is extremely low. Moreover, bumpel has 2 paralogs, rumpel and kumpel,(also only present in glia) and all three need to be knocked out for phenotypes to become visible in glia (Yildirim et al, 2022). These experiments would be interesting but outside out scope.

      We are in the process of generating new C9 models to be able to do these experiments, but these are currently outside the scope of this work.

      Minor Comments:

      1. Neuronal overexpression of bumpel appears to shorten lifespan of wild type flies (Fig 2A). It is possible that neuronal import of pyruvate may drive mitochondrial oxidative phosphorylation and ROS formation. The authors should comment on this possibility in the discussion._

      This is a very good point, we have added a point to that effect.

      1. In Fig 3 the authors used a mixture of sodium pyruvate and ethyl pyruvate to demonstrate the import properties of bumpel. The rationale for using ethyl pyruvate is unclear as this membrane-permeable metabolite can by-pass any transporters.

      The ethyl pyruvate was only used in the injection of flies, not for the FRET experiments looking at the import properties of bumpel. Since we were not over-expressing bumpel, we needed the pyruvate to by-pass the requirement for a transporter. We were showing that delivery of pyruvate by another methods (other than by a transporter) was able to phenocopy the over-expression of bumpel, thus showing the effect is mediated by pyruvate entrance into the cell.

      1. In the introduction several acronyms are used (i.e. GRN, MAPT, TREM2) that are not defined.

      We apologise and have now rectified this.

      Reviewer #3 (Significance):

      To my knowledge, this is the first study to identify that bumpel can permit the import of pyruvate and lactate into neurons when ectopically expressed in the fly brain. The fact that increased neuronal pyruvate import can partially protect against toxic peptide accumulation is unexpected and quite novel. Although some evidence is presented that bumpel can trigger autophagy, it is not clear if autophagy is mediating bumpel neuroprotective effects. Alternative mechanisms related to pyruvate effects on ROS and oxidative stress resistance should be considered.

      We thank the reviewer for their comments, and have added clarifying statements regarding the potential role of ROS.

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      Referee #3

      Evidence, reproducibility and clarity

      This is an interesting manuscript in which the authors provide evidence that elevated neuronal expression of the pyruvate transporter bumpel can partially rescue shortened lifespan in fly models of frontotemporal dementia and Alzheimer's disease. In addition, elevated neuronal bumpel expression can reduce accumulation of arginine containing FTD-linked dipeptide repeat proteins. Some evidence is presented that elevated neuronal bumpel expression may activate autophagy. These findings are novel and may have implications for therapeutic interventions based on pyruvate import/metabolism to treat neurodegenerative disorders. However, I have several concerns as follows:

      Major Comments:

      1. The authors provide no explanation as to why they targeted bumpel overexpression in neurons. Endogenous bumpel appears to be predominately expressed in glia cells so why not target these cells instead?
      2. Data is shown that overexpressed bumpel can suppress GR and PR dipeptide repeat toxicity when these peptides are translated using an ATG start codon (Fig 2D,E). Does bumpel mediated neuroprotection also correlate with a reduction in DPR levels driven with an ATG start codon?
      3. The authors provide some evidence suggesting that overexpression of bumpel increases autophagy in the fly brain. However, knockdown of Atg1 while co-expressing bumpel (Fig 4E) did not result in increased GP protein levels. In addition, Atg1 knockdown did not attenuate the protective effects of bumpel overexpression (Fig 4I), suggesting that bumpel is working through a pathway independent of autophagy to promote DPR clearance and protection against toxic peptide accumulation. The authors need to modify the interpretation of their data and temper their claim that autophagy contributes to bumpel-mediated protective effects in the CNS.
      4. Although the authors present evidence that increased bumpel expression can activate autophagy, the data is not convincing that the neuroprotective effects associated with bumpel are mediated through autophagy. Pyruvate, in some circumstances, can non-enzymatically scavenge hydrogen peroxide or in other cases trigger oxidative stress resistance through hormetic ROS signaling. The authors should consider these alternative possibilities.
      5. The authors rely on overexpressing bumpel to attenuate C9 toxicity in flies. They should perform the opposite experiment and knockdown bumpel to demonstrate that reduced bumpel expression results in potentiation of C9 and amyloid beta neurotoxicity. In addition, then should show that knockdown of bumpel expression has some effect on autophagy.

      Minor Comments:

      1. Neuronal overexpression of bumpel appears to shorten lifespan of wild type flies (Fig 2A). It is possible that neuronal import of pyruvate may drive mitochondrial oxidative phosphorylation and ROS formation. The authors should comment on this possibility in the discussion.
      2. In Fig 3 the authors used a mixture of sodium pyruvate and ethyl pyruvate to demonstrate the import properties of bumpel. The rationale for using ethyl pyruvate is unclear as this membrane-permeable metabolite can by-pass any transporters.
      3. In the introduction several acronyms are used (i.e. GRN, MAPT, TREM2) that are not defined.

      Significance

      To my knowledge, this is the first study to identify that bumpel can permit the import of pyruvate and lactate into neurons when ectopically expressed in the fly brain. The fact that increased neuronal pyruvate import can partially protect against toxic peptide accumulation is unexpected and quite novel. Although some evidence is presented that bumpel can trigger autophagy, it is not clear if autophagy is mediating bumpel neuroprotective effects. Alternative mechanisms related to pyruvate effects on ROS and oxidative stress resistance should be considered.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      Project investigates the role in dementias of glial glucose uptake, conversion to lactate and shuttling via transporters to neurons to produce pyruvate to fuel TCA cycle production of ATG. The experiments are conducted in Drosophila melanogaster, which have become a powerful model system for understanding neurodegeneration mechanisms associated with ALS/FTD associated C9orf72 pathology. Bumple misexpression is shown to rescue early death phenotype in flies expressing a C9orf72 expansion and flies expressing arginine containing di-peptide repeat proteins. The report describes novel insight into the function of bumpel, demonstrating that this conserved orthologue of human SLC14A functions as a sodium exchange transporter for monocarboxylates pyruvate and lactate. These findings conclude that increased neuronal pyruvate, but not its metabolites, rescues C9orf72 associated pathology.

      The authors next set out to describe the mechanism by which increase pyruvate rescues survival in C9orf72 expressing flies. Levels of autolysosomes were increased in C9orf72 expressing flies, and stimulation of autophagy by overexpression of atg1 shown to decrease levels of DPRs (though not to same extent as bumple expression). Expression of bumple in C9orf72 flies led to a modest increase in LC3-II, indicating increased autophagy. Co-overexpression of bumple and atg1 did not have an additive effect, suggesting bumple activates autophagy downstream or independent of atg1 activity. Finally the author extend their findings to amyloid models, suggest a common protective mechanism for elevating neuronal pyruvate levels in neurodegenerative disease.

      Major comments

      Prior data suggests that bumpel is expressed in glia (for example Yildirim et al 2022). In their study the authors do not present any data to demonstrate that the transporter is normally expressed in neurons in flies. This calls into questions the physiological relevance of their findings, that neuronal upregulation of bumpel is protective against C9orf72 associated pathology in neurons, from which it is reasonable for a reader to conclude that bumpel may be a neuronal target for therapeutic intervention. However, the report well demonstrates that regardless of whether the transporter in native to neurons, the increase in monocarboxylates it facilitates is projective against C9orf72 pathology and thus the overall conclusion of the project is supported by experimental evidence. The point of upregulation of a natively expressed gene versus misexpression of a glial enriched transporter should be considered in a bit more detail in the discussion text. The authors may consider speculating the identify of members of the sodium coupled monocarboxylate transporters that are enriched in neurons. Are any of the bumple human orthologues expressed in neurons?<br /> [OPTIONAL] cDNA overexpression of neuron specific sodium coupled monocarboxylate transporters in C9orf72 fly models would strengthen the conclusion their physiological relevance for ALS/FTD. Fly lines for these are not available in repositories, but could be generated and tested at reasonable cost (<£700, ~3 month duration).<br /> The role of bumple expression in survival (Figure 1) could be a technical artifact due to dilution of Gal4 between C9orf72 and bumple-ORF transgenes. No expression control is shown (for example GFP, LacZ etc). This theory is unlikely as no improvement in survival was seen for the SLC14A class of transporters which have a matching site directed transgene insertion. For clarity this point relating to controls should be commented on in the text.<br /> Reduced Mito-GFP levels are used to support a role for bumple in increasing mitophagy. As mito-GFP is a marker for mitochondria but not specifically mitophagy, an alternative explanation for decreased levels could be reduced mitochondria biogenesis. The text should be amended to clarify this point.<br /> The role of Pink1 RNAi in modifying mitophagy is a bit overstated. Whilst Pink1 is involved in stress associated mitophagy, its role in basal mitochondria turnover is less well defined. Text should be adapted.

      Minor comments

      Introduction well describes current state of C9orf72 fly models. Introduction would benefit from a few comparable lines for AD models. The first paragraph of reports may also be better placed in the introduction.

      Figure 1 presents survival for three SLC16A transporters and bumple. The C9 control curve appears to be consistent between charts, likely indicating the same control used across experiments, rather than independent controls for each chart. The authors should considered showing either all SLC16A and bumple data on a single chart, or clarify in the figure legend that a common control dataset is used. GFP control is used in later experiments (Figure 2).

      Choice of amyloid model needs a line of explanation, particularly with regard to extra/intracellular deposition of amyloid in this model.

      Fruit Fly Injection method section needs a bit more detail to describe site of injection (head, body etc). This is not clear in the result section either.

      How were bumple orthologues identified? What degree of conservation (sequence homology etc?)

      The speculative mechanism for C9 pathology modification involves interaction of neurons and glia, monocarboxylate transporters and changes in autophagy activity. For clarity a diagram showing the model may be a helpful addition.

      Typos:

      Figure 1 Legend - "p values of ona way ANOVA "

      Figure S2 Legend - Atg1 RNAi genotypes from S2 legend are mentioned erroneously

      Repetition of text in results: "Bumpel, together with its paralogues kumpel and rumpel, is expressed in glia in flies, where it is thought to promote transport of substrates across the brain (31)."

      "Modulation of Atg1 when bumpel was co-overexpressed, however, did not affect GP<br /> levels (Fig 4E, F)" - Should be refering to Fig 4D, E)

      Significance

      The study will be of broadly of interest to researcher working in the fields of neurodegeneration and metabolism, providing evidence for a protective role of elevated pyruvate in neuron that provide new understand relating to pathology in C9orf72 associated motor neuron disease and frontotemporal dementia.

      Strengths:

      The study presents novel data to demonstrate that overexpression of fly monocarboxylate transporter bumple rescues an early death phenotype associate with ALS/FTD gene C9orf72. Any novel therapeutic strategies of ALS are of interest to the field, and the strategy demonstrated here may be readily translated to human cell culture systems for proof of principle translational studies to a more physiologically relevant system. This study further demonstrates the utility of invertebrate models to generate novel understanding of C9orf72 pathology.

      Limitations:

      The study speculates that there is a link between pyruvate levels and increased autophagy, however the mechanisms by which this occurs is not defined in present study. This is a limitation of the experiment, though opens up an interesting question for future studies.

      Reviewer expertise: The reviewer researches ALS and dementia associated neurodegeneration, utilising Drosophila, rodent and stem cell derived model systems.

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      Referee #1

      Evidence, reproducibility and clarity

      The manuscript by Niccoli et al. describes the identification of a novel modifier of C9orf72-derived toxicity based on the manipulation of the brain metabolic pathways. The premise for this work is supported by strong literature describing the aberrant glucose metabolism in FTD, AD and other degenerative disorders. The idea tested here is whether increasing the import of pyruvate produced in glia into neurons. They test three different types of importers and find that one of them, Bumpel, the orthologue of human SLC5A12, suppresses toxicity and reduces the accumulation of arginine-containing repeats, GP and PR. The authors investigate several potential mechanisms mediating this reduction of toxic DPRs, but do not find strong evidence linking pyruvate import and increase autophagy or mitochondria metabolism.

      Overall, this is an interesting discovery based on a candidate approach that shows the power of Drosophila to efficiently identify novel mediators of neurodegeneration. The article is well written, although more detailed explanations of some experiments would be helpful. The weaknesses of the manuscript are the lack of a clear mechanism mediating the protective activity of pyruvate, the incomplete experiments lacking relevant controls, and the presentation of western blots.

      Specific comments:

      1. The reduced levels of DPRs require that the expression of C9 mRNA or the GR and PR constructs is examined by qPCR. In figure 3E, GP is not even detectable
      2. I wonder if there are constructs available to silence Bumpel or overexpress the human orthologues of bumpel. These would be nice controls for the effects observed with the Bumpel overexpression
      3. The argument about bumpel modulating autophagy downstream of Atg1 is not supported by the experimental data
      4. Western blots throughout show no control lanes and in several occasions are created with cutout bands. The standard for this type of experiments should be more stringent, with entire gels showing all experimental conditions, which requires consistent methods and results vs selecting the best bands from different gels.
      5. For figures 2B and 5C, please, show representative WBs
      6. Figure 5D describes the survival curve as significantly rescued. Statistical tests can indicate differences, but that is in no way convincing. The test may show the curves are different, but the abeta Atg1 flies also seem to start falling early, so an argument could be made in both directions, as a suppressor or an enhancer.
      7. It is unclear why several results are placed in the supplemental materials. In general, all this material seems highly relevant and related to what is shown in the main figures

      Minor comments:

      Please, define several abbreviations throughout

      A couple of sections could be improved by carefully sequencing human vs Drosophila background to advance the argument rather than going in circles. There is also a section on mitophagy in between two sections related to autophagy that could be sequenced better.

      There is a sentence at the end of page 6 that seems misplaced

      Significance

      Overall, this is an interesting discovery based on a candidate approach that shows the power of Drosophila to efficiently identify novel mediators of neurodegeneration. The article is well written, although more detailed explanations of some experiments would be helpful. The weaknesses of the manuscript are the lack of a clear mechanism mediating the protective activity of pyruvate, the incomplete experiments lacking relevant controls, and the presentation of western blots.

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      Reply to the reviewers

      We would like to thank the reviewers for their insightful comments. We believe that the changes that have been suggested will add greatly to this paper, and we will endeavor to incorporate as many of these suggestions as we can.

      Reviewer #1

      This is an interesting study, which presents yet another mechanism involved in the regulation of tumour associated paraneoplastic syndromes, such as muscle wasting. It suggest the intriguing possibility of using a hight fat diet and modulating mitochondrial metabolism as a means of alleviating cachectic muscle wasting. However, as it stands, these aspects of the study remains rather preliminary. This is particularly the case regarding the role of dietary interventions in the model and understanding of the type of metabolic reprogramming in wasting muscles, which lack direct experimental evidence. If the authors were able to further develop this aspects of the study with robust experimental work, it will make it a very valuable and impactful report.

      1- All the mitochondrial phenotypes presented should be compared in the two different tumour models (Gal4/UAS and the QF/QUAS driven), which are indistinctively used throughout the study.

      We will ensure that mitochondrial size and TMRE staining are performed in the two different tumour models so that they can be compared.

      2- The mitochondrial phenotype of wasting muscles is only evident towards the late stages of tumourigenesis (7 day old larvae). Mitochondria of 5 day old tumour bearing animals is indistinct from the control ones. Given that 5 days is the oldest wild type larvae available, the authors need to assess the mitochondrial size and function in muscles form developmentally delayed, no-tumour bearing larvae to discard a trivial contribution of failed metamorphosis in such phenotype.

      We will examine mitochondrial size and TMRE in pmhGal4 > torsoRNAi animals (which undergo delayed metamorphosis) compared with control animals.

      4- TMRE staining presented in Figure 1 is not convincing. If available, a biochemical and/or more quantitative method to address mitochondrial function should be used.

      We will perform ATP synthesis and O2 consumption assays to provide a biochemical method to accompany the TMRE assays.

      5- Related to the point above. The extent of the mitochondrial phenotype following genetic manipulations in the tumour or muscle is not consistently analysed. In some cases, mitochondrial size and activity is assessed but in multiple cases, only mitochondrial size is measured. Mitochondrial activity should be assessed in all cases also.

      We will assess mitochondrial activity in a time course of RasV12DlgRNAi vs w1118, as well as tumor-bearing animals treated with nicotinamide, QF-QUAS RasV12scribRNAi, MHC> foxoRNAi, and RasV12DlgRNAi > Impl2RNAi.

      6- Are mitochondrial fusion proteins such as Marf upregulated in muscles undergoing wasting in Rasv12dlg RNAi animals?

      Regulation of neither Opa1 nor Marf are altered in our proteomics study.

      7- Is overexpression of mitochondrial fusion proteins alone sufficient to induce muscle wasting?

      No, overexpression of Marf was not sufficient to induce muscle wasting, however overexpression of Marf caused worsened muscle wasting in tumour-bearing animals. We will include this data in our revised manuscript.

      8- Is there a change in the expression of ATP5A in the muscles of bearing animals RasV12dlgRNAi, which has dysfunctional mitochondria compared to the control?

      There is no change in ATP5A expression in our proteomics study.

      9- Regarding measures of insulin signaling activity in muscle (Figure 2): the data provide on FOXO staining is not very convincing. Improved staining and robust and more quantitative measure of insulin signaling activity, such as western blot analysis of pAkt should be provided. Apart from the nucleus, there is an overall increase in FOXO expression in the muscle cells of RasV12dlgRNAi compared to the control. In control animals, there is no signal of FOXO. How do you explain this?

      We have attempted western blots of pAkt in tumour-bearing muscle previously and found that tumour metastases caused unreliable results, making immuno-staining a more reliable option. However, pAkt antibody staining also does not work well in the muscles. The control image we displayed was an extreme example, so we will choose more representative images that show more consistent FOXO staining.

      12- In S3 J-L, Since MHC expression is also dependent upon muscle health and integrity, it would be better to use another, and more universal, readout for protein translation/synthesis. For example, labelling the tissue with Puromycin or staining for translation initiation factors.

      We will perform O-propargyl-puromycin (OPP) staining for a w1118 vs RasV12DlgRNAi time course to provide another translation readout to accompany the MHC staining.

      13- How does lipid/high fat diet restore muscle wasting? What happens to the tumours of high fat and Nicotinamide feed animals? In all cases, the impact on tumour size upon genetic manipulations of the muscle should be shown.

      We will measure tumour size in tumour-bearing animals on both nicotinamide and high-fat diets, as well as QF-QUAS RasV12scribRNAi MHC> foxoRNAi, marfRNAi and whdRNAi animals. Impl2RNAi in tumour-bearing animals has been shown already (Lodge et al., 2021).

      14- Does NAM feeding or High-fat diet restore whd transcript levels??

      We will perform qPCR to examine whd transcript levels in tumour-bearing animals on nicotinamide diets as well as high-fat diets.

      15- Do these feeding regimes restore insulin signaling in RasV12dlgRNAi animals?

      We have demonstrated that for RasV12dlgRNAi animals fed a nicotinamide diet, FOXO levels are decreased (Fig 5D). We will do the same experiment for tumour-bearing animals fed a high fat diet.

      17- Related to the point above, DAPI and phalloidin should be included when showing lipid staining to understand better the cellular structures present in the field of view along with the lipid droplets.

      DAPI and phalloidin staining is not compatible with lipid staining, as they require the use of PBST (detergent) which breaks down extracellular lipids. We will include more representative, raw images in which the details of the muscle can be seen.

      Minor comments<br /> 1. The order of panels in the figures and the main text should be the same for better readability.

      We will revisit the figures to ensure readability is improved.

      1. Figure S3 G-H: The image looks out of focus. Is Atg8 expression high near to the nucleus?

      Atg8a expression is highest near the nucleus, and is decreased in RasV12dlgRNAi > Impl2RNAi animals. We will provide more representative images to make this clearer.

      Reviewer #2

      This manuscript proposes and interesting new mechanism how tumours non-autonomously induce muscle mass loss (cachexia) in a genetic Drosophila model. These effects can be modified by diet. Hence results are interesting for both basic and more clinically interested audience.<br /> The weak point of the paper is the limited quantification of mitochondria sizes/morphologies, which is an important point that asks for significant improvement of either the imaging conditions or the image analysis.

      1. The authors provide evidence that eye or imaginal disc tumours induce larger mitochondria in muscles. The authors try to quantify mitochondrial sizes using an automated analysis. This is a tricky task from their light microscopy images that appear to be limited in resolution. By looking at the Suppl. Figure 1, I wonder how relevant an increase of a "large" mitochondria fraction from 7 to 12 % is in the tumour larvae, considering that a significant fraction of the mitochondria are currently not counted, as they are too large to be investigated (white colours in S1F, G). Can the authors increase resolution to resolve these large clumps that likely consist of individual mitochondria to reliably segment all of them, and not only a sub fraction. It would be useful to display the size profiles of all mitochondria in various conditions and not only of a very selected subset of "large" mitochondria. This comment applies to all figures in which mitochondria size was quantified and hence is critical for the entire manuscript.

      We will utilise a newly developed segmentation and centroid tracking-based analysis pipeline based in MATLAB, that may be able to separate the large clumps of mitochondria, to ensure that as many mitochondria can be quantified as possible. We will also provide size profiles of all mitochondria sizes from all conditions in which we performed mitochondria size analysis.

      1. Comparing MitoTracker to TMRE is a valid approach to estimate mitochondria activity/health. The images shown in 1H,I are overview images that seem to show large regional differences in the muscles of unclear origin. High resolution images of representative regions as shown for the ATP5A stains would be more convincing as these can resolve individual mitochondria to hopefully see damaged ones next to normal ones. Would "active" mitochondria not be expected to be the ones that oxidise a lot of fatty acid break down products?

      We will take representative zoomed in images for 1H & I to better demonstrate mitochondria morphology.

      1. The authors find that co-overexpressing FOXO in muscles results in a more severe muscle degeneration phenotype in tumour bearing animals than tumour alone. However, it seems the important control of FOXO overexpression in an otherwise wildtype animal is missing. In order to judge if the muscles really detach in these genotypes, instead of shrink and finally rupture, high resolution images of muscle attachment sites would be needed.

      We will assess if MHCGal4 > UAS dFOXO causes loss of muscle integrity. In addition, in both wildtype and tumour-bearing animals, we will overexpress FOXO in the muscles and stain for muscle attachment proteins such as tiggrin to determine if the phenotype seen is caused by a mislocalisation of proteins at attachment sites.

      1. The strongly reduced lipid droplets in the tumour bearing animals is interesting. To better normalise for the reduced size of the muscles, a counter staining for muscle and a following normalisation would make the statement stronger and thus better support the conclusion.

      As mentioned above we will provide more representative images to help visualize muscle structures in LipidTOX experiments. In addition, we will normalize the amount of lipid droplets detected to a set area, as opposed to just measuring total lipid droplets.

      Reviewer #3

      The strength of the study is the use of suitable in vivo model systems, combined with genetic manipulations to study the mechanisms behind cancer cachexia. The weak points of the study is the lack of functional assays such as quantitative measurements of oxidative phosphorylation and metabolites.

      1, Throughout the manuscript the authors use TMRE staining to evaluate mitochondrial function. To me it is not clear what function they are actually referring to. I assume they mean respiration/respiratory chain function, as this generates the proton motif force measured, but neither oxygen consumption nor aerobic ATP synthesis is ever mentioned or measured. Especially considering that the authors suggest that an increased flux through beta oxidation, which is a mitochondrial function, results in muscle wasting, the authors might want to consider measuring respiration with different substrates, using either a seahorse or Oroboros or equivalent.

      We do not have the necessary equipment or resources to perform Seahorse or Oroboros experiments. Therefore, we will perform O2 consumption and ATP synthesis assays for RasV12dlgRNAi and QF-QUAS RasV12scribRNAi vs w1118, RasV12dlgRNAi > Impl2RNAi, QF-QUAS RasV12scribRNAi > marfRNAi, whdRNAi, and tumour-bearing animals fed high fat diets to provide more insights into mitochondria function.

      3, It is difficult to understand that it is even possible for beta oxidation to exceed the capacity of the OXPHOS system. In that case one would have excess of acetyl CoA and NADH, inevitably inhibiting further beta oxidation and the TCA cycle due to lack of NAD, as well as numerous regulatory mechanisms. Additionally, one would expect increased ketone body production. The authors might want to clarify how the excess redox potential, due to increased beta oxidation is utilised.

      We will perform acetyl-CoA and NAD/NADH assays in RasV12dlgRNAi and QF-QUAS RasV12scribRNAi vs w1118 to determine if beta-oxidation is occurring in excess. In addition, we will clarify in the text that we hypothesize that increased beta-oxidation is utilizing the muscle’s resources to the point that there is none left to continue energy production.

      Minor:

      Line 223 "Together, this data suggests that FOXO lies upstream of beta-oxidation, and mitochondria function lies downstream of beta-oxidation".<br /> I would suggest to rephrase. Of course beta-oxidation and the TCA takes place inside mitochondria, so what mitochondrial functions do the authors refer to?

      As mentioned earlier, we will perform O2 consumption and ATP synthesis assays to strengthen this claim. In addition, we will rephrase this sentence to avoid confusion.

      Line 238 "Overall, this data suggests that the depletion of muscle lipid stores via beta oxidation affects mitochondrial function and is negatively correlated with muscle health in cachectic flies, mice and patients" - The mechanism is not fully clear to me as other energy sources are still available to the fly. The authors might want to expand here.

      We will clarify that there may be other energy sources available that were not investigated in this paper.

      Line 93 : "To test whether this increase in mitochondrial size could lead to compromised mitochondrial function, we performed live staining with tetramethylrhodamine ethyl ester (TMRE), a compound used to measure the membrane potential of mitochondria." - I am not sure that size on its own correlates with mitochondrial function, but rather the energetic and metabolic state of the cell. Increased biogenesis is a common response to dysfunction, but often reflected in increased mass.

      We will clarify the that the increase in size may be a reflection of increased metabolic need of the muscle.

      1. 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:

      3- In all cases, the age of experimental animals must be clearly indicated in figures and/or figure legends.

      We have already put the ages of the experimental animals in the bottom of the figure legends.

      11- Does insulin signaling influence Lipid metabolism in muscle?

      We demonstrate in the manuscript that FoxoRNAi in the muscle of tumour-bearing animals reduces whd transcript levels (Fig 4C), and Impl2RNAi in the tumour restores muscle lipid droplet levels (Fig 3G-I).

      2. 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:

      10- The phenotype of increased fatty acid oxidation in wasting muscles is inferred as per the proteomic signature but not directly demonstrated. TCA metabolite tracing using 13C-Palmitate should be used to demonstrate this, which is a central point of the manuscript.

      The examination of 13C-palmitate would require metabolomic approaches, for which we do not have the necessary equipment and is beyond our timeframe. Thus, we will aim to examine changes in mitochondria metabolism through other measures mentioned above.

      16- The lipid phenotype in cachectic fly muscles is not consistent with that reported in humans and shown by the authors in their xenograft model. While loss of lipid droplets is observed in the fly muscle cells, there is increase in the lipid content within the mouse muscle and only extramyocellular lipid is decreased. The relevance of the extracellular lipid is unclear.

      We hypothesize that this is due to a transport of lipids from extracellular lipid droplets to mitochondria for utilization, as has been suggested previously (Rambold et al., 2015). Examining in detail if this is the case in our models is beyond the scope of this paper.

      Reviewer 3:

      2, The authors suggest that an increase in beta oxidation exceeds mitochondrial function (?), which in turn induces a change in mitochondrial morphology, further contributing to the muscle wasting. The authors may want to demonstrate that there is indeed excess beta oxidation, by measuring a toxic accumulation of different lengths of acylcarnitines. For instance, it is well known that patients with beta oxidation defects accumulate toxic intermediates of beta oxidation that can ultimately affect mitochondrial function.<br /> The manuscript would be much improved if oxygen consumption is measured and combined with analysis of acylcarnitines.

      The examination of acylcarnitines would require lipidomic approaches, and is beyond our timeframe for these revisions. To try to address the need for investigations if beta-oxidation is in excess, we will perform oxygen consumption assays as mentioned and alter the manuscript to de-emphasize excess beta-oxidation.

      4, Unfortunately the supplementary information is in a format I can't open, thus I can't evaluate the method for identifying large mitochondria and other results in these files. This makes part of the reviewing process difficult.

      N/A

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      Referee #3

      Evidence, reproducibility and clarity

      In this manuscript the authors study the mechanisms behind cancer cachexia, using drosophila cancer models. They find that muscle wasting in cachexia is mediated via two different mechanisms: either via insulin signalling and FOXO activation or beta oxidation via mitochondrial fusion.<br /> It is well known that many cancers can induce a catabolic state, compatible with a decrease in insulin signalling and one of the mechanisms proposed. Additionally, the authors suggest that an imbalance between mitochondrial capacity and beta oxidation flux leads to muscle wasting.

      Major comments:

      1. Throughout the manuscript the authors use TMRE staining to evaluate mitochondrial function. To me it is not clear what function they are actually referring to. I assume they mean respiration/respiratory chain function, as this generates the proton motif force measured, but neither oxygen consumption nor aerobic ATP synthesis is ever mentioned or measured. Especially considering that the authors suggest that an increased flux through beta oxidation, which is a mitochondrial function, results in muscle wasting, the authors might want to consider measuring respiration with different substrates, using either a seahorse or Oroboros or equivalent.
      2. The authors suggest that an increase in beta oxidation exceeds mitochondrial function (?), which in turn induces a change in mitochondrial morphology, further contributing to the muscle wasting. The authors may want to demonstrate that there is indeed excess beta oxidation, by measuring a toxic accumulation of different lengths of acylcarnitines. For instance, it is well known that patients with beta oxidation defects accumulate toxic intermediates of beta oxidation that can ultimately affect mitochondrial function.<br /> The manuscript would be much improved if oxygen consumption is measured and combined with analysis of acylcarnitines.
      3. It is difficult to understand that it is even possible for beta oxidation to exceed the capacity of the OXPHOS system. In that case one would have excess of acetyl CoA and NADH, inevitably inhibiting further beta oxidation and the TCA cycle due to lack of NAD, as well as numerous regulatory mechanisms. Additionally, one would expect increased ketone body production. The authors might want to clarify how the excess redox potential, due to increased beta oxidation is utilised.
      4. Unfortunately the supplementary information is in a format I can't open, thus I can't evaluate the method for identifying large mitochondria and other results in these files. This makes part of the reviewing process difficult.

      Minor:

      Line 223 "Together, this data suggests that FOXO lies upstream of beta-oxidation, and mitochondria function lies downstream of beta-oxidation".<br /> I would suggest to rephrase. Of course beta-oxidation and the TCA takes place inside mitochondria, so what mitochondrial functions do the authors refer to?

      Line 238 "Overall, this data suggests that the depletion of muscle lipid stores via beta oxidation affects mitochondrial function and is negatively correlated with muscle health in cachectic flies, mice and patients" - The mechanism is not fully clear to me as other energy sources are still available to the fly. The authors might want to expand here.

      Line 93 : "To test whether this increase in mitochondrial size could lead to compromised mitochondrial function, we performed live staining with tetramethylrhodamine ethyl ester (TMRE), a compound used to measure the membrane potential of mitochondria." - I am not sure that size on its own correlates with mitochondrial function, but rather the energetic and metabolic state of the cell. Increased biogenesis is a common response to dysfunction, but often reflected in increased mass.

      Significance

      General assessment: The strength of the study is the use of suitable in vivo modelsystems, combined with genetic manipulations to study the mechanisms behind cancer cachexia. The weak points of the study is the lack of functional assays such as quantitative measurements of oxidative phosphorylation and metabolites.

      Advance: The main advance of this study is attributed to mechanistic insights behind cancer cachexia and the role of mitochondria in more conditions as opposed to the its involvement in inherited mitochondria disease.

      Audience: This report should be of interest to a broad audience since it's studying a condition connected to cancer and cancer metabolism.

      Reviewers field of expertise: Mitochondrial disease/dysfunction, in vivo modelling, molecular biology, bioenergetics and metabolism

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      Referee #2

      Evidence, reproducibility and clarity

      Chen and colleagues are using the Drosophila larval muscles model to investigate how a tumour can non-autonomously induce muscle mass loss, a known phenomenon called cancer cachexia. They report that tumours change muscle mitochondria morphologies, specifically their size and their chemistry. These changes correlate with increase in fat metabolism and a depletion of fat and glycogen reserves. Regarding the molecular mechanism, the authors propose that tumour cells secrete IGF binding protein that reduces the level of insulin and thus insulin signalling in muscle. They test this hypothesis by reducing FOXO activity, a negative regulator of insulin signalling, or mitochondrial fusion in muscles of tumour carrying larvae, which indeed appears to result in muscle improvements. These insights from Drosophila muscles suggest that tumour-caused reduced insulin signalling in muscles can be responsible for tumour induced muscle loss. A similar mechanism may apply to mammals and hence these findings are of clinical interest.

      Major comments

      1. The authors provide evidence that eye or imaginal disc tumours induce larger mitochondria in muscles. The authors try to quantify mitochondrial sizes using an automated analysis. This is a tricky task from their light microscopy images that appear to be limited in resolution. By looking at the Suppl. Figure 1, I wonder how relevant an increase of a "large" mitochondria fraction from 7 to 12 % is in the tumour larvae, considering that a significant fraction of the mitochondria are currently not counted, as they are too large to be investigated (white colours in S1F, G). Can the authors increase resolution to resolve these large clumps that likely consist of individual mitochondria to reliably segment all of them, and not only a sub fraction. It would be useful to display the size profiles of all mitochondria in various conditions and not only of a very selected subset of "large" mitochondria.<br /> This comment applies to all figures in which mitochondria size was quantified and hence is critical for the entire manuscript.
      2. Comparing MitoTracker to TMRE is a valid approach to estimate mitochondria activity/health. The images shown in 1H,I are overview images that seem to show large regional differences in the muscles of unclear origin. High resolution images of representative regions as shown for the ATP5A stains would be more convincing as these can resolve individual mitochondria to hopefully see damaged ones next to normal ones. Would "active" mitochondria not be expected to be the ones that oxidise a lot of fatty acid break down products?
      3. The authors find that co-overexpressing FOXO in muscles results in a more severe muscle degeneration phenotype in tumour bearing animals than tumour alone. However, it seems the important control of FOXO overexpression in an otherwise wildtype animal is missing. In order to judge if the muscles really detach in these genotypes, instead of shrink and finally rupture, high resolution images of muscle attachment sites would be needed.
      4. The strongly reduced lipid droplets in the tumour bearing animals is interesting. To better normalise for the reduced size of the muscles, a counter staining for muscle and a following normalisation would make the statement stronger and thus better support the conclusion.

      Significance

      This manuscript proposes and interesting new mechanism how tumours non-autonomously induce muscle mass loss (cachexia) in a genetic Drosophila model. These effects can be modified by diet. Hence results are interesting for both basic and more clinically interested audience.<br /> The weak point of the paper is the limited quantification of mitochondria sizes/morphologies, which is an important point that asks for significant improvement of either the imaging conditions or the image analysis.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      Larvae bearing RasV12; dlgRNAi eye tumours recapitulate aspects of cachexia, such as muscle wasting. In this manuscript, the authors use their previously characterized RasV12; dlgRNAi larval model of cancer cachexia to show that tumour induced cachectic muscle wasting is associated with excessive mitochondrial fusion, resulting in the formation of enlarged dysfunctional mitochondria in wasted muscle cells. Muscle specific blockade of mitochondrial fusion prevents muscle wasting and restores mitochondrial potential in tumour bearing animals. The authors also link increased mitochondrial size to decreased insulin signaling (increased foxo) caused by the tumour induced pro-cachexia factor and insulin inhibitor Impl2. Consistently, downregulation of ImpL2 from the tumour decreases foxo levels in muscle and reduces mitochondrial size. Finally, the authors show that wasting muscles in flies show decrease lipid droplets and a molecular and proteomic signature indicative of increased fatty acid oxidation. Muscle wasting, loss of lipids and mitochondrial integrity can be restored upon inhibition of Impl2 in the tumour, downregulation of the mitochondrial lipid transporter CPT1 or feeding animals with a high fat diet.

      Major comments

      1. All the mitochondrial phenotypes presented should be compared in the two different tumour models (Gal4/UAS and the QF/QUAS driven), which are indistinctively used throughout the study.
      2. The mitochondrial phenotype of wasting muscles is only evident towards the late stages of tumourigenesis (7 day old larvae). Mitochondria of 5 day old tumour bearing animals is indistinct from the control ones. Given that 5 days is the oldest wild type larvae available, the authors need to assess the mitochondrial size and function in muscles form developmentally delayed, no-tumour bearing larvae to discard a trivial contribution of failed metamorphosis in such phenotype.
      3. In all cases, the age of experimental animals must be clearly indicated in figures and/or figure legends.
      4. TMRE staining presented in Figure 1 is not convincing. If available, a biochemical and/or more quantitative method to address mitochondrial function should be used.
      5. Related to the point above. The extent of the mitochondrial phenotype following genetic manipulations in the tumour or muscle is not consistently analysed. In some cases, mitochondrial size and activity is assessed but in multiple cases, only mitochondrial size is measured. Mitochondrial activity should be assessed in all cases also.
      6. Are mitochondrial fusion proteins such as Marf upregulated in muscles undergoing wasting in Rasv12dlg RNAi animals?
      7. Is overexpression of mitochondrial fusion proteins alone sufficient to induce muscle wasting?
      8. Is there a change in the expression of ATP5A in the muscles of bearing animals RasV12dlgRNAi, which has dysfunctional mitochondria compared to the control?
      9. Regarding measures of insulin signaling activity in muscle (Figure 2): the data provide on FOXO staining is not very convincing. Improved staining and robust and more quantitative measure of insulin signaling activity, such as western blot analysis of pAkt should be provided. Apart from the nucleus, there is an overall increase in FOXO expression in the muscle cells of RasV12dlgRNAi compared to the control. In control animals, there is no signal of FOXO. How do you explain this?
      10. The phenotype of increased fatty acid oxidation in wasting muscles is inferred as per the proteomic signature but not directly demonstrated. TCA metabolite tracing using 13C-Palmitate should be used to demonstrate this, which is a central point of the manuscript.
      11. Does insulin signaling influence Lipid metabolism in muscle?
      12. In S3 J-L, Since MHC expression is also dependent upon muscle health and integrity, it would be better to use another, and more universal, readout for protein translation/synthesis. For example, labelling the tissue with Puromycin or staining for translation initiation factors.
      13. How does lipid/high fat diet restore muscle wasting? What happens to the tumours of high fat and Nicotinamide feed animals? In all cases, the impact on tumour size upon genetic manipulations of the muscle should be shown.
      14. Does NAM feeding or High-fat diet restore whd transcript levels??
      15. Do these feeding regimes restore insulin signaling in RasV12dlgRNAi animals?
      16. The lipid phenotype in cachectic fly muscles is not consistent with that reported in humans and shown by the authors in their xenograft model. While loss of lipid droplets is observed in the fly muscle cells, there is increase in the lipid content within the mouse muscle and only extramyocellular lipid is decreased. The relevance of the extracellular lipid is unclear.
      17. Related to the point above, DAPI and phalloidin should be included when showing lipid staining to understand better the cellular structures present in the field of view along with the lipid droplets.

      Minor comments

      1. The order of panels in the figures and the main text should be the same for better readability.
      2. Figure S3 G-H: The image looks out of focus. Is Atg8 expression high near to the nucleus?

      Significance

      This is an interesting study, which presents yet another mechanism involved in the regulation of tumour associated paraneoplastic syndromes, such as muscle wasting. It suggest the intriguing possibility of using a hight fat diet and modulating mitochondrial metabolism as a means of alleviating cachectic muscle wasting. However, as it stands, these aspects of the study remains rather preliminary. This is particularly the case regarding the role of dietary interventions in the model and understanding of the type of metabolic reprogramming in wasting muscles, which lack direct experimental evidence. If the authors were able to further develop this aspects of the study with robust experimental work, it will make it a very valuable and impactful report.

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      Reply to the reviewers

      We would like to thank the reviewers for their comments and suggestions, which were very helpful to improve our manuscript. The revised manuscript notably includes the following improvements:

      • To evaluate the relevance of identified candidate targets genes, we integrated an additional screening step in our method, corresponding to the analysis of RNAseq datasets specific of blood or brain cells. RNAseq data from irradiated hematopoietic stem cells or splenic cells were analyzed and included in the new Table S19, and RNAseq data from zika virus-infected neural progenitors were analyzed and included in the new Table S28. In addition, we also verified that the expression of a subset of blood related genes was decreased in the bone marrow cells of p53Δ31/Δ31 mice, known to exhibit increased p53 activity and to phenocopy dyskeratosis congenita (new Figure S8).
      • Luciferase data were expanded to show that, for promoters exhibiting a significant p53-mediated repression in luciferase assays, the p53-dependent regulation was abrogated after mutation of the putative DREAM binding site (new Figures 2e and 2i).
      • We found putative DREAM binding sites for 151 targets, and the predicted binding sites were precisely mapped relative to the position of ChIP peaks of DREAM subunits (E2F4 and LIN9) and to transcription start sites of target genes. These additional analyses, shown in the new Figures 3a and 3b, further suggest the reliability of our predicted binding sites. Notably, hypergeometric tests of the distribution of DREAM binding sites relative to E2F4/LIN9 ChIP peaks reveal a significant >1300-fold enrichment of these sites at ChIP peaks.
      • We now present a detailed comparison of our results with those reported in other studies, notably the predicted E2F and CHR sites from the Target gene regulation database (new Figure S11), or the list of candidate DREAM targets suggested from Lin37 KO cells (new Figure S10 and new Table S35). This also leads us to discuss the different types of DREAM binding sites (bipartite sites (e.g. CDE/CHR or E2F/CLE) vs sites composed of a single E2F or a single CHR motif).
      • We integrated updates of the Human phenotype ontology website to include the latest lists of genes related to blood or brain ontology terms in our analysis. In the previous version of the manuscript we had analyzed a total of 811 genes downregulated ≥ 1.5 fold upon bone marrow cell differentiation. Our revised manuscript now includes the analysis of 883 genes.
      • Several improvements were made to present our results more clearly and with more details : 1) additional evidence that the differentiation of Hoxa9ER cells correlates with p53 activation is now provided in the new Figure S1; 2) the precise values for gene expression after bone marrow cell differentiation, as well as p53 regulation scores from the Target gene regulation databases are included in the new Tables S1, S5, S8, S11, S14, S20 and S23; 3) A Venn-like diagram was included to summarize the different steps of our approach in the new Figure 3c, with detailed lists of genes selected at each step in new Tables S17 and S26; 4) for genes associated with blood or brain genetic disorders, bibliographic references describing gene mutations and clinical traits were included in a new Table S36; 5) Figure 4a and Table S37 were improved to include evidence that increased BRD8 in glioblastoma cells leads to a decreased expression of several genes transactivated by p53.

      Reviewer #1 (Evidence, reproducibility and clarity):

      Summary<br /> In this paper the authors describe a data driven approach to identify and prioritise p53-DREAM targets whose repression might contribute to abnormal haematopoiesis and brain abnormalities observed in p53-CTD deleted mice. The premise is that in these mice, (where they have previously demonstrated p53 to be hyperactive in at least a subset of tissues), that the p53-p21-E2F/DREAM axis is at least in part responsible for observed phenotypes due to the repression of E2F and CDE/CHE element containing genes. Their approach to home in on relevant genes is based on transcriptomic gene ontology analysis of genes repressed in these disease settings where they primarily use publicly available data from HOXA9-ER regulated model of HSC expansion wherein they observe increases on p53-p21 expression upon differentiation where they demonstrate that p53-p21 DREAM target genes are suppressed as we would expect in this scenario where p53-p21 is activating withdrawal from cell cycle. They then spend a lot of effort analysing this datasets combining "gene-ontology", "disease phenotype" and "meta-ChIP-seq" analysis of public data to support the observation that mutations of genes suppressed in this manner are disproportionately linked to heritable haematopoetic and brain disorders. While these results are interesting in terms of framing a hypothesis about how mutations in p53-p21-DREAM regulated targets contribute to such conditions, they are to be expected given the now very well described impact of p53-p21 on both E2F4/DREAM targets.

      We agree with the referee that the impact of p53-p21 on both E2F4/DREAM targets is well described. However, discussions with many scientists or clinicians specialized in bone marrow failure syndromes or microcephaly diseases led us to realize that most were not familiarized with the p53-DREAM pathway, so that a study that would bridge the gap between DREAM experts and bone marrow or microcephaly specialists would be particularly useful. In addition, we thought that strategies that would rely on disease-based ontology terms were likely to identify new targets, compared to previous studies that considered cell cycle regulation instead of disease phenotypes. Consistent with this, many genes we identified as candidate DREAM targets were not reported in previous studies. In addition, as detailed below, our positional frequency matrices led to identify DREAM binding sites that had not been predicted by previous approaches.

      The natural progression of this work would be to go on to show this occurs in relevant cells or tissues derived from the p53-CTD mice as well as look at modulating target genes to understand underlying mechanisms and consequences.<br /> Rather than this, they focus on validating that a sub-set of these targets are indeed suppressed by specific p53 activation by MDM2 inhibitor Nutlin-3A in MEFs by qPCR and that mutation of predicted CDE CHR elements in luciferase constructs leads to increase luciferase activity. While these findings support their predictions, the results are entirely expected based on what is known about such targets and demonstrating that this occurs in MEFs does not closely relate to haematopoietic and brain cells they suggest this regulation is important. In fact, in the discussion, the authors comment on the importance of cell type context specificity in terms of discordance between predictions of TF binding sites and public datasets.

      We agree that additional data from relevant cells or tissues were required to strengthen our conclusions. In the revised manuscript, we evaluated the relevance of candidate target genes related to blood ontology terms by integrating an additional screening step in our method, corresponding to the analysis of RNAseq datasets specific of blood cells. We analyzed dataset GSE171697, with RNAseq data from hematopoietic stem cells of unirradiated p53 KO, or unirradiated or irradiated WT mice, as well as dataset GSE204924, with RNAseq data from splenic cells of irradiated p53Δ24/- or p53+/- mice. The latter dataset appeared interesting because p53Δ24 is a mouse model prone to bone marrow failure and the spleen is a hematopoietic organ in mice. The analysis of these datasets is included in the new Table S19. In the datasets,increased p53 activity correlated with the downregulation of most of the 269 candidate DREAM targets. However, 56 genes which appeared upregulated in cells with increased p53 activity were considered poor candidate p53-DREAM targets and removed from further analyses, leading to a list of 213 genes that appeared as better candidate p53-DREAM targets related to blood abnormalities. Furthermore, we also verified that the expression of a subset of blood-related candidate genes was decreased in the bone marrow cells of p53Δ31/Δ31 mice (prone to bone marrow failure) compared to bone marrow cells from WT mice. This result is presented in the new Figure S8.

      As for genes related to brain development, we discussed in the previous version of the manuscript that most genes mutated in syndromes of microcephaly or cerebellar hypoplasia are involved in ubiquitous cellular functions (chromosome condensation, mitotic spindle activity, tRNA splicing…), which suggested that our analysis of transcriptomic changes associated with bone marrow cell differentiation might also be used to identify brain specific targets. However, we agree with the referee that confirmation of these brain specific targets in a more relevant cellular context was preferable. In the revised manuscript, we included the analysis of datasets GSE78711 and GSE80434, containing RNAseq data from human cortical neural progenitors infected by the Zika virus (ZIKV) or mock-infected, because ZIKV was shown to cause p53 activation in cortical neural progenitors and microcephaly. This analysis is detailed in the new supplementary Table S28. In both datasets, increased p53 activity correlated with the downregulation of most of the 226 candidate DREAM targets. Sixty-four genes which appeared more expressed in ZIKV-infected cells were considered poor candidate p53-DREAM targets and removed from further analyses, leading to a list of 162 candidate p53-DREAM targets related to brain abnormalities. We think this significantly increases the relevance of our analysis of brain-specific targets.

      Finally, they try and contextualise effects in glioblastoma data by correlating target gene expression with levels of BRD8 since it has recently been shown to attenuate p53 function in glioblastoma and show that some of the brain disease associated genes are expressed at higher levels in BRD8 high patient samples. It seems strange here that they do not also look at expression of p21 or other p53 targets that would help ascertain if p53 activity is indeed suppressed. Moreover, much more elegant methods for predicting transcription factor activity could be applied to this data.

      We agree with the referee. Indeed, when we had performed the analysis of glioblastoma cells, we first verified that increased BRD8 levels correlated with decreased p21 levels in these cells. However, we had not included this verification in the previous version of the manuscript. In this revision, we improved the Figure 4 (and Table S37) reporting the analysis of glioblastoma cells to address this point. In Figure 4a, we now show the variations in mRNA levels between BRD8Low and BRD8High tumors, for BRD8 itself, as well as 5 genes well-known to be transactivated by p53 (p21, MDM2, BAX, GADD45A and PLK3) and the 77 p53-DREAM targets associated with microcephaly or cerebellar hypoplasia. The data clearly show that tumors with high BRD8 exhibit a decrease in the expression of p53 transactivated targets, and an increase in p53-DREAM repressed targets.

      Major Comments<br /> The major result of this paper as it stands is the prioritisation of candidate genes in the p53-DREAM pathway involved in these conditions, and their refined approach used to identify and prioritise these genes and is such more of a starting point for further investigation. They fall short of demonstrating the relevance of their predictions physiologically in tissues from the mice and do not demonstrate functional importance of regulation of targets they put forward. Given that these genes will be co-ordinately regulated, without a mechanistic experiment in physiologically relevant model it is impossible to infer causality. For example, depleting individual targets in the HOXA9 model and evaluating impact on survival, proliferation and differentiation may be a (relatively) simple way to explore this, perhaps comparing to effects of p53 activating agents such as Nutlin-3A. Of note the authors (Jaber 2016 PMID: 27033104) and several other groups had (Fischer 2014 PMID: 25486564 McDade 2014 PMID: 24823795) previously demonstrated the link between p53-p21 and suppression of DNA-repair/Damage related genes (as is also observed here in particular FA-related genes that they discuss briefly here. I would have thought that this would be an obvious starting point for some mechanistic experiments and in fact I note this has been demonstrated before (Li et al 2018 PMID: 29307578)

      The starting point of our study is not the prioritization of DREAM target genes, but rather the detailed phenotyping of p53Δ31/Δ31 mice that we performed in previous publications (Simeonova et al. Cell Rep 2013, Toufektchan et al. Nat. Commun. 2016), in which we mentioned phenotypical traits typical of dyskeratosis congenita and Fanconi anemia, including notably bone marrow failure and cerebellar hypoplasia.

      We understand that depleting individual targets in the Hoxa9 system and evaluating impact on survival, proliferation and differentiation might seem appropriate to explore their potential causality. However, our previous work on Fanc genes leads us to think that this might not be informative. Regarding this, we now clearly discuss in the revised version of the manuscript : “Finding a functionally relevant [DREAM binding site] for Fanca, mutated in 60% of patients with Fanconi anemia [59,60], may help to understand how a germline increase in p53 activity can cause defects in DNA repair. Importantly however, we previously showed that p53Δ31/Δ31 cells exhibited defects in DNA interstrand cross-link repair, a typical property of Fanconi anemia cells, that correlated with a subtle but significant decrease in expression for several genes of the Fanconi anemia DNA repair pathway, rather than the complete repression of a single gene in this pathway [25]. Thus, the Fanconi-like phenotype of p53Δ31/Δ31 cells most likely results from a decreased expression of not only Fanca, but also of additional p53-DREAM targets mutated in Fanconi anemia such as Fancb, Fancd2, Fanci, Brip1, Rad51, Palb2, Ube2t or Xrcc2, for which functional or putative [DREAM binding sites] were also found with our systematic approach.” We further discuss in the manuscript how this may also apply to telomere-, ribosome-, of microcephaly-related genes.

      The analysis of brain specific targets and the link to BRD8 sits largely as an aside and the analysis of patient data from glioblastomas is underdeveloped as noted above.

      As we previously mentioned, the revised manuscript includes the analysis of RNAseq datasets from human cortical neural progenitors infected by the Zika virus (ZIKV) or mock-infected, which significantly increases the relevance of our analysis of brain-specific targets. Furthermore, we improved Figure 4 to present more clearly the impact of BRD8 levels on the expression of genes transactivated by p53 or repressed by p53-DREAM.

      The computational methods applied are robust, albeit predominantly coorelative, in terms of identifying regulation of potential causative target genes, validated across human and mouse cell lines, and this indicates a role of these genes in the relevant conditions. However, further validation through application in a bulk or single cell RNAseq patient cohort, or at least an in vivo model would strengthen these conclusions and complement the work presented here which is based on in vitro mouse and human cells. This is pertinent as this study improves upon previously published approaches by focusing on "clinically relevant target genes". Additionally, this would exhibit the potential applications of the findings presented.

      We thank the referee for this comment. As mentioned above, in the revised manuscript we analyzed RNAseq data from hematopoietic stem cells of unirradiated WT or p53 KO mice, or irradiated WT mice, and from splenic cells of irradiated p53D24/- or p53+/- mice, and quantified the expression of a subset of blood-related candidate genes in the bone marrow cells of p53Δ31/Δ31 mice (prone to bone marrow failure) and WT mice (new Figure S8 and Table S19). For genes related to brain development, we included the analysis of RNAseq data from human cortical neural progenitors infected by the Zika virus (ZIKV) or mock-infected (Table S28). These RNAseq analyses were added as an additional screening criterion in our approach, which significantly increased the relevance of the target genes identified.

      In terms of statistical analysis, the hypergeometric test should be applied to assess significant enrichment of genes for example with CDE/CHR regions within the previously identified lists.

      In the revised manuscript, we precisely mapped the DREAM binding sites in 50 bp windows within regions bound by E2F4 and/or LIN9, an analysis included in new Figure 3a. We then compared the distribution of DREAM binding sites at the level of ChIP peaks compared to their distribution over the entire genome and found a > 1300-fold enrichment of these sites at ChIP peaks. This significant enrichment (f=3 10-239 in a hypergeometric test) is most likely underestimated because mouse-human DNA sequence conservations were not determined for putative DBS over the full genome. These new analyses clearly reinforce our previous conclusions.

      Minor Comments<br /> References are required for the genes listed which play a role in the diseases of interest.

      In the revised manuscript, references are provided for genes which play a role in the diseases of interest. Due to the large number of added references, these were included in a new supplementary table, Table S36.

      This paper would benefit from the inclusion of summary schematics and tables throughout (rather than relying only on somewhat unwieldy heatmaps which show little other than all these genes are co-ordinately regulated), this could include summaries of the methods applied, gene or CDE/CHR inclusion criteria, and Venn diagrams indicating the subsets of final genes identified through this approach.

      We thank the referee for this suggestion. In the revised manuscript we provide a Venn-like diagram of the different steps of our approach (new Figure 3c), as well as tables listing the genes retained after each step of the selection (new Tables S17 and S26) and these additions improve the clarity of our manuscript.

      Reviewer #1 (Significance):

      In its current form this is a very limited study that would require significant additional work to move conclusions beyond correlation and hypothesis generation.<br /> Overall, while limited largely to target prioritisation, this research nicely exemplifies how genes affected by the p53-DREAM pathway can be robustly identified, providing a potential resource for individuals working on this pathway or on abnormal haematopoiesis and brain abnormalities. These results are complementary to work previously published by Fischer et al, which has been referenced throughout the analysis (highlighting Target Gene Regulation Database p53 and DREAM target genes) and discussion.

      This paper will be of interest to researchers of blood/neurological diseases who can assess if these genes are dysregulated in their datasets, or those investigating the p53-DREAM pathway. This work represents a useful resource detailing genes affected by this pathway in these disease settings, however researchers of the p53-DREAM pathway may find this paper useful when planning an approach to identify and prioritise genes of interest.

      We thank the reviewer for considering that our study represents a useful resource for researchers working on the p53-DREAM pathway, abnormal haematopoiesis and brain abnormalities, because it was exactly the purpose of our work. As mentioned above, we think that a study bridging the gap between DREAM experts and bone marrow or microcephaly specialists should be particularly useful.

      We also agree with the referee that our approach could be used to identify DREAM targets relevant to other disease settings, and we now mentioned this clearly in the revised manuscript.

      While our results are complementary to work previously published by Fischer et al and included in the Target gene regulation database, in the revised manuscript we discuss the novelty of our results in more details, notably by performing additional analyses. For example, our method identified bipartite DREAM binding sites for 151 candidate DREAM targets (of which 56 genes were not previously mentioned by Fischer et al.) and we now provide a detailed mapping (using 50 bp windows) of the bipartite DREAM binding sites we identified relative to ChIP peaks for DREAM subunits, then performed a similar mapping of the E2F and CHR sites included in the Target gene regulation database. Our predicted DREAM binding sites coincided with ChIP peaks more frequently (Figure 3a) than the predicted E2F or CHR from the Target gene regulation database (Figure S11), which further indicates the usefulness of our study as a resource.

      Reviewer #2 (Evidence, reproducibility and clarity):

      The authors used various systems including Hoxa9-indubible BMCs, human and mouse cells, WT and p53 knockout MEF, glioblastoma cells to screen p53-DREAM targets and observed distinct finding for each system. Since different cell types have various p53 activation and p53 target genes expression, the authors might want to select proper cell type(s) to screen p53-DREAM target genes and design experiments to confirm that these genes are really p53-DREAM target genes.

      We agree that additional data from relevant cells or tissues were required to strengthen our conclusions. As mentioned in response to referee #1, in the revised manuscript we evaluated the relevance of candidate target genes related to blood ontology terms by integrating an additional screening step in our method, corresponding to the analysis of RNAseq dataset GSE171697, with data from hematopoietic stem cells of unirradiated or irradiated WT mice and unirradiated p53 KO mice , as well as RNAseq dataset GSE204924, with data from splenic cells of irradiated p53D24/- or p53+/- mice. As for genes related to brain development, we included the analysis of RNAseq datasets GSE78711 and GSE80434 for validation, two datasets from human cortical neural progenitors infected by the Zika virus or mock-infected. Together, the 4 datasets provide evidence for a p53-dependent downregulation in blood- and brain- relevant settings (new Tables S19 and S28).

      Importantly, in the revision we also compared our list of 151 genes appearing as the best p53-DREAM candidates with the results of Magès et al., who analyzed, in murine cells with a CRISPR-mediated KO of Lin37 (a subunit of DREAM), the transcriptomic changes that follow a reintroduction of Lin37. This comparison is detailed in the discussion section, with the new Figure S10 and Table S35. We mention: “Our list of 151 genes overlaps only partially with the list of candidate DREAM targets obtained with this approach, with 51/151 genes reported to be downregulated in Lin37-rescued cells [17]. To better evaluate the reasons for this partial overlap, we extracted the RNAseq data from Lin37 KO and Lin37-rescued cells and focused on the 151 genes in our list. For the 51 genes that Mages et al. reported as downregulated in Lin37-rescued cells, an average downregulation of 14.8-fold was observed (Figure S10, Table S35). Furthermore, when each gene was tested individually, a downregulation was observed in all cases, statistically significant for 47 genes, and with a P value between 0.05 and 0.08 for the remnant 4 genes (Table S35). By contrast, for the 100 genes not previously reported to be downregulated in Lin37-rescued cells, an average downregulation of 4.7-fold was observed (Figure S10, Table S35), and each gene appeared downregulated, but this downregulation was statistically significant for only 35/100 genes, and P values between 0.05 and 0.08 were found for 23/100 other genes (Table S35). These comparisons suggest that, for the additional 100 genes, a more subtle decrease in expression, together with experimental variations, might have prevented the report of their DREAM-mediated regulation in Lin37-rescued cells.”

      This comparison provides additional evidence that the 151 candidate target genes we identified are bona fide DREAM targets.

      Specific comments:<br /> The authors need to describe and define HSC and Diff in Figure 1.

      This has been corrected in the revised manuscript. “HSC” was replaced by “Hematopoietic Stem / Progenitor cells (+OHT)” and “Diff” was replaced by “Differentiated cells (5 days – OHT).

      Are Figure 1B and 1D list genes p53 targets in bone marrow cells?

      In the revised manuscript, we now analyzed RNAseq data to address this point. The question refers to lists of telomere-related genes (Figure 1b in both versions of the manuscript) and Fanconi-related genes (Figure 1d in the previous version, now Figure S2a), but could also apply to other lists of genes related to blood ontology terms (Figures S3-S5 in the revised manuscript). As mentioned in response to referee #1, in the revised manuscript we integrated an additional screening step in our method, corresponding to the analysis of RNAseq datasets specific of blood cells. We analyzed dataset GSE171697, with RNAseq data from hematopoietic stem cells of unirradiated WT or p53 KO mice, or irradiated WT mice, as well as dataset GSE204924, with RNAseq data from splenic cells of irradiated p53D24/- or p53+/- mice. The latter dataset appeared interesting because p53D24 is a mouse model prone to bone marrow failure and the spleen is a hematopoietic organ in mice. Furthermore, we also verified that the expression of a subset of blood-related candidate genes was decreased in the bone marrow cells of p53Δ31/Δ31 mice (prone to bone marrow failure) compared to bone marrow cells from WT mice, a result presented in the new Figure S8.

      Where is the detailed information for mouse and human cells in Figure 1 and Figure 2?

      In the first draft of the manuscript, supplementary tables provided precise values for ChIP binding. In the revised manuscript, we also provide the precise values for gene expression after bone marrow cell differentiation, as well as p53 regulation scores from the Target gene regulation databases. This additional information is included in the new Tables S1, S5, S8, S11, S14, S20 and S23.

      Are Figure 3B list genes also p53 target genes in other cell types such as bone marrow cells and glioblastoma?

      For genes in the Figure 3B of the previous version of the manuscript (now Figure 2B in the revised version), we now provide evidence that the blood-related genes are less expressed in the bone marrow cells of p53Δ31/Δ31 mice (mice with increased p53 activity and prone to bone marrow failure) compared to bone marrow cells from WT mice. This result is presented in the new Figure S8. For the brain-related genes of the same Figure, evidence of their p53-mediated regulation is provided by the RNAseq datasets GSE78711 and GSE80434, from human cortical neural progenitors infected by the Zika virus or mock-infected (analyzed in the new Table S28). Evidence of that a decreased p53 activity in glioblastomas correlates with increased expression of the brain-related genes of the same Figure is provided in supplementary Table S37.

      Does BRD8high has high p53 and p21?

      We now clearly show, in both Figure 4a and Table S37, that glioblastoma cells with high BRD8 exhibit a decreased expression of CDKN1A/p21 and other genes known to be transactivated by p53 (BAX, GADD45A, MDM2, PLK3), consistent with the fact that BRD8 attenuates p53 activity.

      Are genes listed in Figure 4B all p53 target genes? can some validation be done?

      For genes in Figure 4B, in the revision we focused on the genes that appeared more relevant, i.e. the 77 genes mutated in diseases with microcephaly or cerebellar hypoplasia. All the genes in Figure 4B are repressed in neural progenitors upon infection by the Zika virus, a virus known to cause p53 activation in those cells. This is reported in the new Table S28.

      Reviewer #2 (Significance):

      This is a potentially interesting study. The major limitation is the absence of validation from the screening. This study would definitely benefit the research community as long as some of the key findings are validated.

      We thank the referee for this comment. We hope the new evidence in this revision provide the validation requested by the referee.

      Reviewer #3 (Evidence, reproducibility and clarity):

      In their work submitted to Review Commons, Rakotopare et al. aim to identify p53-DREAM target genes associated with blood or brain abnormalities. To this end, they utilize published data generated with a cellular model that results in cell-cycle exit and differentiation of murine bone marrow progenitor cells upon inducible expression of Hoxa9. By analyzing this gene expression data set published by Muntean et al., they find that multiple of the 3631 genes which are downregulated more than 1.5-fold in differentiated BMCs are also mutated in several disorders connected to proliferation and differentiation defects during hematopoiesis and brain development. By screening ChIP-seq data sets available at ChIP-Atlas, they find that the promoters of many of these genes are bound by DREAM complex components, and most of them were identified as genes indirectly repressed by p53 before (Fischer et al. 2016, targetgenereg.org). They then use a computational approach to identify putative CDE/CHR DREAM-binding sites in the promoters of 372 genes associated with blood/brain abnormalities which are downregulated in differentiated BMCs and bound by DREAM components. Out of the 173 candidate genes, they select twelve to analyze whether mutation of the putative DREAM binding sites results in increased activity of the promoters in luciferase reporter assays. The authors conclude that their findings suggest a general role for the p53-DREAM pathway in regulating hematopoiesis and brain development.<br /> While the study supports a large body of publications proving that repression of cell cycle genes by the DREAM complex is crucial for cell cycle arrest and exit, it is noted that none of the main conclusions here are unexpected or particularly exciting. All the analyses are based on data sets that compare gene expression in highly proliferative cells with cells that underwent terminal cell cycle exit. Thus, a large portion of the genes that are downregulated in differentiated BMCs are cell cycle genes and well-established targets of DREAM and E2F:RB complexes. Furthermore, it is not surprising that some of these pro-proliferative genes are mutated in diseases connected to proliferation defects like anemias or microcephaly.

      We agree with the referee that the DREAM complex is well known to regulate cell cycle genes – in fact, this is what we mention in the first sentence of our introduction in both versions of our manuscript. However, as we already pointed out in response to Referee #1, many scientists or clinicians specialized in bone marrow failure syndromes or microcephaly diseases are not familiarized with the p53-DREAM pathway, and we think our study will be particularly useful to them. Furthermore, our strategy relying on disease-based ontology terms rather than cell cycle regulation led to identify many DREAM targets that were not reported in previous studies, and our positional frequency matrices led to identify DREAM binding sites not predicted by previous approaches. As discussed below, our revised manuscript provides a more detailed comparison of our findings with those from previous studies.

      Additionally, I am not very enthusiastic about this manuscript because of several major concerns:

      1. The authors draw conclusions about the p53-DREAM pathway based on data that was generated in a cellular differentiation model without convincingly showing that p53 plays a central role in gene repression in this experimental setup.<br /> (A) Rakotopare et al. define p53-DREAM target genes based on RNA expression data from proliferating precursor cells and non-proliferating, differentiated BMCs (Muntean et al., 2010). This paper has not studied whether p53 gets activated in the particular experimental setup during Hox9a-induced BMC differentiation. On page 4 of their manuscript, the authors state: "Consistent with the fact that BMC differentiation strongly correlates with p53 activation..." without citing any literature or explaining why this is supposed to be a fact. Furthermore, they imply that cell cycle gene repression in this model system depends on p53 because mRNA expression of the p53 targets p21 and Mdm2 was found to be increased in the differentiated cells (Fig. 1A, 5-fold and 2-fold, respectively). However, defining a large set of "p53-DREAM target genes" based on the moderate increase in mRNA levels of two genes that are known to be activated by p53 without showing any evidence that p53 is even involved in this effect during BMC differentiation is not appropriate.

      We agree that Muntean et al. did not study whether p53 gets activated when BMCs differentiate in the Hox9a-ER system. We previously mentioned: “We observed that p53 activation correlated with cell differentiation in this system, because genes known to be transactivated by p53 (e.g. Cdkn1a, Mdm2) were induced, whereas genes repressed by p53 (e.g. Rtel1, Fancd2) were downregulated after tamoxifen withdrawal (Figure 1a)”. We had provided examples for 2 genes transactivated and 2 genes repressed, but clearly mentioned that they were given as examples. In the revised manuscript, we provide additional evidence with a new supplementary Figure that includes changes in expression for 15 additional genes known to be transactivated by p53, and 5 additional genes known to be repressed by p53 (Figure S1). In total, we now correlate HSC differentiation with p53 activation based on the expression of 24 well-known p53-regulated genes, which we hope is more convincing.

      In addition, we changed our phrasing and mention “Consistent with the notion that BMC differentiation strongly correlates with p53 activation in this system, 72 of these 76 genes have negative score(s) in the Target gene regulation (TGR) database”.

      (B) Interestingly, p53 is among the genes that get repressed on mRNA level in differentiated BMCs (Fig. 1B; Trp53), and the authors also identify the DREAM components E2F4 and LIN9 as bound to the p53 promoter by screening ChIP-Atlas data (Fig. 1C). Given that p53 has never been described as a DREAM target, I find this rather surprising and it makes me wonder whether appropriate parameters were selected for analyzing the ChIP data, particularly since the authors do not provide binding data for sets of non-cell cycle genes as a negative control.

      We retrieved ChIP data from the ChIP Atlas database without any specific parameters, thus in a completely unbiased manner. Importantly however, for reasons detailed in the manuscript, we clearly mentioned that total ChIP scores <979/4000 were considered too low to reflect significant DREAM binding. The ChIP score for Trp53 was 630, which rapidly led us to eliminate this gene from our screen.

      This ChIP score criterion was already mentioned in the previous version of our manuscript, but we think the addition of a Venn-like diagram (Figure 3c) and summary tables (S17 and S26) in the revised manuscript will probably make it easier to understand.

      (C) Finally, the authors utilize the targetgenereg.org database to show that many of the genes they describe as p53-repressed were already identified as p53 targets. This database (Fischer et al. 2016) was created by performing a meta-analysis integrating a plethora of RNA-seq and ChIP-seq datasets with the aim to identify whether a particular gene gets up- or downregulated by p53, shows cell-cycle-dependent expression, is a DREAM/MuvB or E2F:RB target, etc. For example, 57 datasets analyzing p53-dependent RNA expression in human and 15 datasets generated with mouse cells were included, and a positive or negative score shows in how many of these experiments the gene was found to be up (positive score) or downregulated (negative score). Combining a large number of datasets in such a study is very helpful to get an idea if a gene is indeed generally regulated by a transcription factor, or if it just showed up in a few experiments - either as a false positive or because the regulation depends on a particular biological setting. The authors find most of the genes they identify as repressed in differentiated BMCs also as downregulated by p53 in targetgenereg.org, however, it remains unclear what parameters they used to define a gene as p53-repressed. For example, in the caption of Fig. 1C, they state: "According to the Target gene regulation database, 72/76 genes are downregulated upon mouse and/or human p53 activation." The four exemptions are SLX1B (human score: 0, mouse score : na), PML (+41, +9), RAD50 (0, na), and TNKS2 (+17, +4). However, there are several other genes that do not appear to be generally repressed by p53, e.g. HMBOX1 (+4, -2); UPF1 (+1, -2), SMG6 (+18, -2), CTC1 (-5, +11), etc. Thus, without providing details regarding the parameters they use to define p53-target genes, such statements are rather misleading. An easy way to solve this problem would be to show the p53 scores in the tables together with the E2F4/LIN9 ChIP data.

      All the genes mentioned as downregulated by p53 had a negative TGR score in human and/or mouse cells. In the revised manuscript, we mention clearly what a negative TGR score means, by stating: “Consistent with the notion that BMC differentiation strongly correlates with p53 activation in this system, 72 of these 76 genes have negative p53 expression score(s) in the Target gene regulation (TGR) database [23], which indicates that they were downregulated upon p53 activation in most experiments carried out in mouse and/or human cells (Figure 1b, Table S1).” We agree with the referee that adding precise TGR scores is informative. In the revised manuscript, we provide the TGR scores for all the genes analyzed, as part of the new supplementary Tables S1, S5, S8, S11, S14, S20 and S23, together with their expression levels in undifferentiated or differentiated cells (as requested by Referee #2). The ChIP data are provided in separate tables (Tables S2, S3, S6, S7, S9, S10, S12, S13, S15, S16, S21, S22, S24 and S25).

      1. The authors define a large set of genes containing "CDE-CHR" promoter elements and thereby ignore how these elements are defined and what properties they have.<br /> (A) At the beginning of the introduction, the authors state: "The DREAM complex typically represses the transcription of genes whose promoter contain a bipartite CDE/CHR binding site, with a cell cycle-dependent element (CDE) bound by E2F4 or E2F5, and a cell cycle gene homology region (CHR) bound by LIN54, the DNA binding subunit of MuvB (Zwicker et al., 1995; Müller and Engeland, 2010)."<br /> This statement is incorrect. The authors ignore that the CDE/CHR tandem site is just one of four promoter elements that have been shown to recruit DREAM for the transcriptional repression of several hundred genes. It has been studied in detail that DREAM can bind to the following promoter sites:<br /> (I) CHR elements - bound by DREAM via LIN54; also bound by the activator MuvB complexes B-MYB-MuvB and FOXM1-MuvB which results in maximum gene expression in G2/M<br /> (II) CDE-CHR tandem elements - like (I) but binding of DREAM can be stabilized via E2F4/DP interacting with a truncated E2F binding site. Since CDE elements do not represent functional E2F sites, E2F:RB complexes do not bind.<br /> (III) E2F binding sites - bound by DREAM via E2F4/DP; also bound by E2F:RB complexes and activator E2Fs which results in maximum gene expression in G1/S<br /> (IV) E2F-CLE tandem elements - like (III) but binding of DREAM can be stabilized via LIN54 interacting with a non-canonical CHR-like element. Since CLE elements do not represent functional CHR sites, B-MYB-MuvB and FOXM1-MuvB do not bind.<br /> Thus, these promoter sites have different functions and can be clearly distinguished from each other based on their properties - a fact that is completely ignored by the authors. Since the authors do not differentiate between G1/S and G2/M expressed genes and (CDE)-CHR and E2F-(CLE) sites, they identify CDE-CHR elements in G1/S genes that are functional E2F-(CLE) sites. A good example of this is the Rad51ap1 gene (and also the Rad51 gene that the Toledo lab described before as a CDE-CHR gene (Jaber et al. 2016)): these genes get expressed in G1/S and the promoters contain highly conserved E2F sites (parts of which the authors define as CDEs), and CLEs (which the authors define as CHRs). Furthermore, E2F:RB complexes bind to the promoters. Again: even though (CDE)-CHR and E2F-(CLE) sites both bind DREAM, they are otherwise functionally different in their ability to recruit non-DREAM complexes.

      We agree that in the previous version of our manuscript we should have presented in more details the different types of DREAM binding sites and have corrected this in the revised manuscript. We now mention in the introduction that “The DREAM complex was initially reported to repress the transcription of genes whose promoter sequences contain a bipartite binding motif called CDE/CHR [19,20] (or E2F/CHR [21]), with a GC-rich cell cycle dependent element (CDE) that may be bound by E2F4 or E2F5, and an AT-rich cell cycle gene homology region (CHR) that may be bound by LIN54, the DNA-binding subunit of MuvB [19,20]. Later studies indicated that DREAM may also bind promoters with a single E2F binding site, a single CHR element, or a bipartite E2F/CHR-like element (CLE), and concluded that E2F and CHR elements are required for the regulation of G1/S and G2/M cell cycle genes, respectively [14,22].”

      We hope that the referee will agree with this complete yet concise way of presenting DREAM binding sites. Importantly, we agree that CDE/CHR and E2F/CLE are sites bound by different non-DREAM complexes, but both sites are bound by DREAM, so it makes perfect sense to use them together to define positional frequency matrices for DREAM binding predictions. We would also like to point out that terms used to define DREAM binding sites may vary in the literature. For example, to our knowledge Müller et al. were the first to propose a clear distinction between “CDE/CHR” and “E2F/CLE” sites (Müller et al. (2017) Oncotarget 8, 97737-97748), yet Müller recently co-authored a review in which these two distinct terms were not used, but were replaced by a single, apparently more generic term of “E2F/CHR” (Fischer et al., (2022) Trends Biochem. Sci. 47, 1009-1022). In the revised manuscript we now clearly mention that we designed our positional frequency matrices to search for “bipartite DREAM binding sites”, i.e. sites that might be referred to as CDE/CHR, E2F/CLE or E2F/CHR sites in various publications.

      (B) The authors identified putative CDE-CHR in the promoters of genes by building two position weight matrices (PWMs) based on 10 or 22 "validated CDE-CHR elements". However, since they include several genes that are clearly expressed in G1/S and contain E2F-(CLE) sites (e.g. Mybl2/B-myb, Rad51, Fanca, Fen1), it is not surprising that they identify a lot of putative CDE-CHR sites in genes that do not contain such elements.

      As discussed above, both CDE/CHR and E2F/CLE are bipartite DREAM binding sites, and we now clearly state that we used bipartite DREAM binding sites to generate our positional frequency matrices and predict DREAM binding.

      (C) Finally, in the discussion, the authors state: "A recent update (2.0) of the Target gene regulation database of p53 and cell cycle genes (www.targetgenereg.org) was recently reported to include putative DREAM binding sites for human genes (Fischer et al., 2022). However, this update only suggests potential E2F or CHR binding sites independently, a feature of little help to identify CDE/CHR elements. For example, targetgenereg 2.0 suggests several potential E2F sites, but no CHR site close to the transcription start site of FANCD2, despite the fact that we previously identified a functionally CDE/CHR element near the transcription start site of this gene (Jaber et al., 2016)." This statement highlights again that the authors don't seem to be aware of what specific properties distinct DREAM binding sites have, and that analyzing promoters for CHR and E2F sites separately generates much more meaningful results than the approach they chose. Also, the FANCD2 promoter binds DREAM as well as E2F:RB complexes and contains a highly conserved E2F binding site - which Jaber et al. mutated together with a potential downstream CLE element and named it "CDE/CHR".

      In the revised manuscript, we provide a more detailed comparison between the bipartite DREAM binding sites predicted with our positional frequency matrices for 151 genes and the separate E2F and CHR predicted sites reported in the Target gene regulation database for the same set of genes. We now mention: “The Target gene regulation (TGR) database of p53 and cell-cycle genes was reported to include putative DREAM binding sites for human genes, based on separate genome-wide searches for 7 bp-long E2F or 5 bp-long CHR motifs [23]. We analyzed the predictions of the TGR database for the 151 genes for which we had found putative bipartite DBS. A total of 342 E2F binding sites were reported at the promoters of these genes, but only 64 CHR motifs. The similarities between the predicted E2F or CHR sites from the TGR database and our predicted bipartite DBS appeared rather limited: only 14/342 E2F sites overlapped at least partially with the GC-rich motif of our bipartite DBS, while 27/64 CHR motifs from the TGR database exhibited a partial overlap with the AT-rich motif. Importantly, most E2F and CHR sites from the TGR database mapped close to E2F4 and LIN9 ChIP peaks, but only 16% of E2Fs (54/342), and 33% of CHRs (21/64) mapped precisely at the level of these peaks (Figure S11), compared to 55% (83/151) of our bipartite DBS (Figure 3a). Thus, at least for genes with bipartite DREAM binding sites, our method relying on PFM22 appeared to provide more reliable predictions of DREAM binding than the E2F and CHR sites reported separately in the TGR database. Importantly however, predictions of the TGR database may include genes regulated by a single E2F or a single CHR that would most likely remain undetected with PFM22, suggesting that both approaches provide complementary results.”

      1. The experimental approach chosen to validate CDE-CHR elements in a set of twelve promoters by luciferase reporter assays is not adequate.<br /> (A) Since the authors introduce point mutations in putative CDE and CHR elements in parallel, it is impossible to identify functional CDE elements. As explained above, a functional CDE is not required for binding of MuvB complexes and gene repression, and mutating the CHR alone would already lead to a loss of DREAM binding and to de-repression of a promoter. Thus, without mutating both sites of CDE-CHR elements separately, it is impossible to provide evidence that a putative CDE is functional.<br /> (B) As the putative CDE-CHR elements identified by the authors with a computational approach can overlap with functional E2F-(CLE) elements, the authors inactivate such sites by introducing mutations which leads to loss of DREAM binding and upregulation of the promoters, however, because of the problems described above, this experimental approach in the best case identifies DREAM binding sites, but does not differentiate between (CDE)-CHR and E2F-(CLE) elements.

      Yes, we agree with this comment. As discussed above, our goal was to identify DREAM-binding sites, not to differentiate between CDE/CHR and E2F/CLE elements. In other words, we wanted to identify genes regulated by p53 and DREAM, but not distinguish between genes regulated by p53, DREAM and E2F/Rb versus those regulated by p53, DREAM and BMyb-MuvB or FoxM1-MuvB.

      (C) The authors analyze the activities of wild-type and mutant promoters in proliferating NIH3T3 cells. Since the mutated promoters showed increased activity (about 2-3 fold), which would be expected when binding of DREAM gets abolished, they conclude: "...these experiments indicated that we could identify functional CDE/CHRs for 12/12 tested genes." In addition to the problems described above, a slight upregulation of promoter activities caused by the introduction of multiple point mutations close to the TSS is not sufficient to verify these elements. The increase in activity could occur independent of DREAM-binding by unrelated mechanisms. The authors should at least analyze the activities of the promoters with and without induction of p53. A loss of p53-dependent repression of the mutated promoters would prove that the elements are essential for p53-dependent repression. Furthermore, there are several experimental approaches to analyze whether DREAM binds to the putative promoter element and whether the introduced mutations disrupt binding (ChIP, DNA affinity purification, etc.).

      In the revised manuscript, we show that the promoters of 7 of the tested genes, when cloned in luciferase reporter plasmids and transfected into NIH3T3 cells, exhibited a significant (> 1.4 fold) repression upon p53 activation by cell treatment with Nutlin, the Mdm2 antagonist. For these promoters, we showed that the p53-dependent repression was abrogated by mutating the identified DREAM binding site, which provided direct evidence that our positional frequency matrices can identify functionally relevant DREAM binding sites essential for p53-mediated repression. These experiments were added in Figures 2e and 2i.

      Furthermore, as previously mentioned in response to referee #1, in the revised manuscript we precisely mapped the predicted DREAM binding sites for 151 genes in 50 bp windows within regions bound by E2F4 and/or LIN9, an analysis included in new Figure 3a. The distribution of these peaks clearly indicates that most predicted DREAM binding sites map precisely within a 50 bp-window encompassing the ChIP peaks, which represents an enrichment of at least a 1300-fold compared to the rest of the genome. This mapping strongly suggests that our predicted DREAM binding sites are functionally relevant.

      Importantly, as shown in the new Figure S11, we carried out a similar mapping of the predicted E2F and CHR sites reported in the Target gene regulation (TGR) database and found that our predicted DREAM binding sites co-mapped with E2F4/LIN9 ChIP peaks more frequently than the E2F and CHR sites of the TGR database, which supports the conclusion that our positional frequency matrices bring new and improved predictions for DREAM binding.

      1. Taken together, while over-simplifying mechanisms of cell cycle gene regulation, the authors largely ignore recent findings and publications regarding gene regulation by p53, E2F:RB, and DREAM/MuvB complexes:<br /> (A) Publications that show how DREAM binds to (CDE)-CHR sites and that experimentally defined a consensus motif for CHR elements (e.g. PMID: 27465258, PMID: 25106871).<br /> (B) Publications that identify p53-DREAM target genes by activating p53 in cells with or without functional DREAM complex (e.g. PMID: 31667499, PMID: 31400114).<br /> (C) Identification and comparison of (CDE)-CHR and E2F-(CLE) DREAM binding sites that have distinct functions in the activation of cell-cycle expression in G1/S and G2/M (e.g. PMID: 29228647, PMID: 25106871).<br /> These findings have been summarized in several review articles (e.g. PMID: 29125603, PMID: 28799433, PMID: 35835684). All of them describe the mechanisms I have mentioned above in detail, and since Rakotopare et al. cite one of the papers (Engeland 2018), I wonder even more why they did not design their experiments based on current knowledge.

      The points (A) and (C) of this comment were largely discussed in our response to points 2 and 3 of the same referee. Briefly, in the revised manuscript we clearly mention CDE/CHR, E2F/CLE and E2F/CHR sites, as well as the functional differences between E2F and CHR sites with regards to cell cycle regulation, but all these sites were considered together in our positional frequency matrices because our goal was to identify genes regulated by p53 and DREAM, not to distinguish between genes regulated by p53, DREAM and E2F/Rb versus those regulated by p53, DREAM and BMyb-MuvB or FoxM1-MuvB.

      Regarding point (B) of this comment, in the revised manuscript we performed a detailed comparison of our results with those of Mages et al. who analyzed, in murine cells with a CRISPR-mediated KO of Lin37 (a subunit of DREAM), the transcriptomic changes that follow a reintroduction of Lin37 (Mages et al. (2017) elife 6, e26876). This comparison is detailed in the discussion section, with New Figure S10 and Table S35. As mentioned in response to referee #2, this comparison is perfectly consistent with DREAM regulating the 151 genes for which we identified DREAM binding sites.

      Minor concerns:

      1. The authors state: "Importantly however, the relative importance of the p53-p21-DREAM pathway (called below p53-DREAM) remains controversial, because multiple mechanisms were proposed to account for p53-mediated gene repression (Peuget and Selivanova, 2021)." Even though Peuget & Selivanova do not agree that genes get repressed in response to p53 activation exclusively by the p21-DREAM pathway, they do not question that this mechanism is essential for the p53-dependent repression of a core set of cell cycle genes. Since I am also not aware of any publications that challenge the importance of the p53-p21-DREAM pathway, I do not agree with this statement.

      As the referee pointed out, in the first version of the manuscript we wrote that “the relative importance of the p53-p21-DREAM pathway (called below p53-DREAM) remains controversial, because multiple mechanisms were proposed to account for p53-mediated gene repression (Peuget and Selivanova, 2021)”. The term “relative” was crucial in this sentence, because we wanted to say that the relative proportion of genes regulated by DREAM remained controversial. It seems to us that the title of the review by Peuget & Selivanova (“p53-dependent repression: DREAM or reality?”) emphasizes this controversy. Nevertheless, in the revised manuscript, we now mention : “The relative importance of this pathway remains to be fully appreciated, because multiple mechanisms were proposed to account for p53-mediated gene repression [18]”. We hope the referee will find this phrasing more acceptable.

      1. Some parts of the manuscript are tiring to read - for example, pages 6, 7, and 8 which contain long listings and numbers of genes that are downregulated in differentiated BMC, found to be mutated in various disorders, bind DREAM components, were identified as downregulated by p53, etc. The authors may consider combining central parts of these data in a table that they show in the main manuscript which would make it easier to digest the information and at the same time significantly shorten the manuscript.

      We apologize if some parts of the article were tiring to read. We hope that the addition of Tables S17 and S26, as well as the Venn-like diagram in Figure 3c, will improve the reading of the manuscript.

      1. The supplementary tables (S1-S26) are combined in one Excel file with multiple tabs. The authors should label the tabs accordingly to make it easier for the reader to find a particular table.

      We labelled the Excel tabs in the revised manuscript, as suggested.

      1. At the end of page 6, the authors show that 17 genes found to be downregulated in differentiated BMCs are mutated in multiple bone marrow disorders, however, since they don't include references, it remains unclear where these mutations were originally described.

      In the revised manuscript, we included a supplementary table (Table S36) with appropriate references for blood and/or brain related phenotypes for the 106 genes associated with blood or brain abnormalities.

      1. On page 9, the authors state: "As a prerequisite to luciferase assays, we first verified that the expression of these genes, as well as their p53-mediated repression, can be observedin mouse embryonic fibroblasts (MEFs), because luciferase assays rely on transfections into MEFs (Figure 3b)." The authors don't explain why luciferase assays rely on transfections into MEFs and based on the caption of Fig. 3C, the luciferase assays were not performed in MEFs, but in NIH3T3 cells: "WT or mutant luciferase reporter plasmids were transfected into NIH3T3 cells..."

      According to the American Type Culture Collection (ATCC), the NIH3T3 cell line is a mouse embryonic fibroblastic (MEF) cell line, which explains why we had tested the expressions of candidate target genes in MEFs. However, as we now clearly mention in the manuscript, this cell line exhibits an attenuated p53 pathway, which improves cell survival after transfection but leads to decreased p53-mediated repression. These points are now clearly mentioned in the text and in a new supplemental Figure (Figure S9).

      Reviewer #3 (Significance):

      While the study supports a large body of publications proving that repression of cell cycle genes by the DREAM complex is crucial for cell cycle arrest and exit, it is noted that none of the main conclusions here are unexpected or particularly exciting. All the analyses are based on data sets that compare gene expression in highly proliferative cells with cells that underwent terminal cell cycle exit. Thus, a large portion of the genes that are downregulated in differentiated BMCs are cell cycle genes and well-established targets of DREAM and E2F:RB complexes. Furthermore, it is not surprising that some of these pro-proliferative genes are mutated in diseases connected to proliferation defects like anemias or microcephaly.

      Again, we agree with the referee that the DREAM complex is well known to regulate cell cycle genes, but many scientists or clinicians specialized in bone marrow failure syndromes or microcephaly diseases are not familiarized with the p53-DREAM pathway, and we think our study will be particularly useful to them. As for DREAM specialists, our strategy relying on disease-based ontology terms rather than cell cycle regulation led to identify many DREAM targets that were not reported in previous studies, and our positional frequency matrices led to identify DREAM binding sites not predicted by previous approaches. We hope that, by considering all these points together, the referee will acknowledge that our study provides a valuable resource for different types of readerships.

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      Referee #3

      Evidence, reproducibility and clarity

      In their work submitted to Review Commons, Rakotopare et al. aim to identify p53-DREAM target genes associated with blood or brain abnormalities. To this end, they utilize published data generated with a cellular model that results in cell-cycle exit and differentiation of murine bone marrow progenitor cells upon inducible expression of Hoxa9. By analyzing this gene expression data set published by Muntean et al., they find that multiple of the 3631 genes which are downregulated more than 1.5-fold in differentiated BMCs are also mutated in several disorders connected to proliferation and differentiation defects during hematopoiesis and brain development. By screening ChIP-seq data sets available at ChIP-Atlas, they find that the promoters of many of these genes are bound by DREAM complex components, and most of them were identified as genes indirectly repressed by p53 before (Fischer et al. 2016, targetgenereg.org). They then use a computational approach to identify putative CDE/CHR DREAM-binding sites in the promoters of 372 genes associated with blood/brain abnormalities which are downregulated in differentiated BMCs and bound by DREAM components. Out of the 173 candidate genes, they select twelve to analyze whether mutation of the putative DREAM binding sites results in increased activity of the promoters in luciferase reporter assays. The authors conclude that their findings suggest a general role for the p53-DREAM pathway in regulating hematopoiesis and brain development.

      While the study supports a large body of publications proving that repression of cell cycle genes by the DREAM complex is crucial for cell cycle arrest and exit, it is noted that none of the main conclusions here are unexpected or particularly exciting. All the analyses are based on data sets that compare gene expression in highly proliferative cells with cells that underwent terminal cell cycle exit. Thus, a large portion of the genes that are downregulated in differentiated BMCs are cell cycle genes and well-established targets of DREAM and E2F:RB complexes. Furthermore, it is not surprising that some of these pro-proliferative genes are mutated in diseases connected to proliferation defects like anemias or microcephaly.

      Additionally, I am not very enthusiastic about this manuscript because of several major concerns:

      1. The authors draw conclusions about the p53-DREAM pathway based on data that was generated in a cellular differentiation model without convincingly showing that p53 plays a central role in gene repression in this experimental setup.

      (A) Rakotopare et al. define p53-DREAM target genes based on RNA expression data from proliferating precursor cells and non-proliferating, differentiated BMCs (Muntean et al., 2010). This paper has not studied whether p53 gets activated in the particular experimental setup during Hox9a-induced BMC differentiation. On page 4 of their manuscript, the authors state: "Consistent with the fact that BMC differentiation strongly correlates with p53 activation..." without citing any literature or explaining why this is supposed to be a fact. Furthermore, they imply that cell cycle gene repression in this model system depends on p53 because mRNA expression of the p53 targets p21 and Mdm2 was found to be increased in the differentiated cells (Fig. 1A, 5-fold and 2-fold, respectively). However, defining a large set of "p53-DREAM target genes" based on the moderate increase in mRNA levels of two genes that are known to be activated by p53 without showing any evidence that p53 is even involved in this effect during BMC differentiation is not appropriate.

      (B) Interestingly, p53 is among the genes that get repressed on mRNA level in differentiated BMCs (Fig. 1B; Trp53), and the authors also identify the DREAM components E2F4 and LIN9 as bound to the p53 promoter by screening ChIP-Atlas data (Fig. 1C). Given that p53 has never been described as a DREAM target, I find this rather surprising and it makes me wonder whether appropriate parameters were selected for analyzing the ChIP data, particularly since the authors do not provide binding data for sets of non-cell cycle genes as a negative control.

      (C) Finally, the authors utilize the targetgenereg.org database to show that many of the genes they describe as p53-repressed were already identified as p53 targets. This database (Fischer et al. 2016) was created by performing a meta-analysis integrating a plethora of RNA-seq and ChIP-seq datasets with the aim to identify whether a particular gene gets up- or downregulated by p53, shows cell-cycle-dependent expression, is a DREAM/MuvB or E2F:RB target, etc. For example, 57 datasets analyzing p53-dependent RNA expression in human and 15 datasets generated with mouse cells were included, and a positive or negative score shows in how many of these experiments the gene was found to be up (positive score) or downregulated (negative score). Combining a large number of datasets in such a study is very helpful to get an idea if a gene is indeed generally regulated by a transcription factor, or if it just showed up in a few experiments - either as a false positive or because the regulation depends on a particular biological setting. The authors find most of the genes they identify as repressed in differentiated BMCs also as downregulated by p53 in targetgenereg.org, however, it remains unclear what parameters they used to define a gene as p53-repressed. For example, in the caption of Fig. 1C, they state: "According to the Target gene regulation database, 72/76 genes are downregulated upon mouse and/or human p53 activation." The four exemptions are SLX1B (human score: 0, mouse score : na), PML (+41, +9), RAD50 (0, na), and TNKS2 (+17, +4). However, there are several other genes that do not appear to be generally repressed by p53, e.g. HMBOX1 (+4, -2); UPF1 (+1, -2), SMG6 (+18, -2), CTC1 (-5, +11), etc. Thus, without providing details regarding the parameters they use to define p53-target genes, such statements are rather misleading. An easy way to solve this problem would be to show the p53 scores in the tables together with the E2F4/LIN9 ChIP data.<br /> 2. The authors define a large set of genes containing "CDE-CHR" promoter elements and thereby ignore how these elements are defined and what properties they have.

      (A) At the beginning of the introduction, the authors state: "The DREAM complex typically represses the transcription of genes whose promoter contain a bipartite CDE/CHR binding site, with a cell cycle-dependent element (CDE) bound by E2F4 or E2F5, and a cell cycle gene homology region (CHR) bound by LIN54, the DNA binding subunit of MuvB (Zwicker et al., 1995; Müller and Engeland, 2010)."

      This statement is incorrect. The authors ignore that the CDE/CHR tandem site is just one of four promoter elements that have been shown to recruit DREAM for the transcriptional repression of several hundred genes. It has been studied in detail that DREAM can bind to the following promoter sites:

      (I) CHR elements - bound by DREAM via LIN54; also bound by the activator MuvB complexes B-MYB-MuvB and FOXM1-MuvB which results in maximum gene expression in G2/M

      (II) CDE-CHR tandem elements - like (I) but binding of DREAM can be stabilized via E2F4/DP interacting with a truncated E2F binding site. Since CDE elements do not represent functional E2F sites, E2F:RB complexes do not bind.

      (III) E2F binding sites - bound by DREAM via E2F4/DP; also bound by E2F:RB complexes and activator E2Fs which results in maximum gene expression in G1/S

      (IV) E2F-CLE tandem elements - like (III) but binding of DREAM can be stabilized via LIN54 interacting with a non-canonical CHR-like element. Since CLE elements do not represent functional CHR sites, B-MYB-MuvB and FOXM1-MuvB do not bind.

      Thus, these promoter sites have different functions and can be clearly distinguished from each other based on their properties - a fact that is completely ignored by the authors. Since the authors do not differentiate between G1/S and G2/M expressed genes and (CDE)-CHR and E2F-(CLE) sites, they identify CDE-CHR elements in G1/S genes that are functional E2F-(CLE) sites. A good example of this is the Rad51ap1 gene (and also the Rad51 gene that the Toledo lab described before as a CDE-CHR gene (Jaber et al. 2016)): these genes get expressed in G1/S and the promoters contain highly conserved E2F sites (parts of which the authors define as CDEs), and CLEs (which the authors define as CHRs). Furthermore, E2F:RB complexes bind to the promoters. Again: even though (CDE)-CHR and E2F-(CLE) sites both bind DREAM, they are otherwise functionally different in their ability to recruit non-DREAM complexes.

      (B) The authors identified putative CDE-CHR in the promoters of genes by building two position weight matrices (PWMs) based on 10 or 22 "validated CDE-CHR elements". However, since they include several genes that are clearly expressed in G1/S and contain E2F-(CLE) sites (e.g. Mybl2/B-myb, Rad51, Fanca, Fen1), it is not surprising that they identify a lot of putative CDE-CHR sites in genes that do not contain such elements.

      (C) Finally, in the discussion, the authors state: "A recent update (2.0) of the Target gene regulation database of p53 and cell cycle genes (www.targetgenereg.org) was recently reported to include putative DREAM binding sites for human genes (Fischer et al., 2022). However, this update only suggests potential E2F or CHR binding sites independently, a feature of little help to identify CDE/CHR elements. For example, targetgenereg 2.0 suggests several potential E2F sites, but no CHR site close to the transcription start site of FANCD2, despite the fact that we previously identified a functionally CDE/CHR element near the transcription start site of this gene (Jaber et al., 2016)." This statement highlights again that the authors don't seem to be aware of what specific properties distinct DREAM binding sites have, and that analyzing promoters for CHR and E2F sites separately generates much more meaningful results than the approach they chose. Also, the FANCD2 promoter binds DREAM as well as E2F:RB complexes and contains a highly conserved E2F binding site - which Jaber et al. mutated together with a potential downstream CLE element and named it "CDE/CHR".<br /> 3. The experimental approach chosen to validate CDE-CHR elements in a set of twelve promoters by luciferase reporter assays is not adequate.

      (A) Since the authors introduce point mutations in putative CDE and CHR elements in parallel, it is impossible to identify functional CDE elements. As explained above, a functional CDE is not required for binding of MuvB complexes and gene repression, and mutating the CHR alone would already lead to a loss of DREAM binding and to de-repression of a promoter. Thus, without mutating both sites of CDE-CHR elements separately, it is impossible to provide evidence that a putative CDE is functional.

      (B) As the putative CDE-CHR elements identified by the authors with a computational approach can overlap with functional E2F-(CLE) elements, the authors inactivate such sites by introducing mutations which leads to loss of DREAM binding and upregulation of the promoters, however, because of the problems described above, this experimental approach in the best case identifies DREAM binding sites, but does not differentiate between (CDE)-CHR and E2F-(CLE) elements.

      (C) The authors analyze the activities of wild-type and mutant promoters in proliferating NIH3T3 cells. Since the mutated promoters showed increased activity (about 2-3 fold), which would be expected when binding of DREAM gets abolished, they conclude: "...these experiments indicated that we could identify functional CDE/CHRs for 12/12 tested genes." In addition to the problems described above, a slight upregulation of promoter activities caused by the introduction of multiple point mutations close to the TSS is not sufficient to verify these elements. The increase in activity could occur independent of DREAM-binding by unrelated mechanisms. The authors should at least analyze the activities of the promoters with and without induction of p53. A loss of p53-dependent repression of the mutated promoters would prove that the elements are essential for p53-dependent repression. Furthermore, there are several experimental approaches to analyze whether DREAM binds to the putative promoter element and whether the introduced mutations disrupt binding (ChIP, DNA affinity purification, etc.).<br /> 4. Taken together, while over-simplifying mechanisms of cell cycle gene regulation, the authors largely ignore recent findings and publications regarding gene regulation by p53, E2F:RB, and DREAM/MuvB complexes:

      (A) Publications that show how DREAM binds to (CDE)-CHR sites and that experimentally defined a consensus motif for CHR elements (e.g. PMID: 27465258, PMID: 25106871).

      (B) Publications that identify p53-DREAM target genes by activating p53 in cells with or without functional DREAM complex (e.g. PMID: 31667499, PMID: 31400114).

      (C) Identification and comparison of (CDE)-CHR and E2F-(CLE) DREAM binding sites that have distinct functions in the activation of cell-cycle expression in G1/S and G2/M (e.g. PMID: 29228647, PMID: 25106871).

      These findings have been summarized in several review articles (e.g. PMID: 29125603, PMID: 28799433, PMID: 35835684). All of them describe the mechanisms I have mentioned above in detail, and since Rakotopare et al. cite one of the papers (Engeland 2018), I wonder even more why they did not design their experiments based on current knowledge.

      Minor concerns:

      1. The authors state: "Importantly however, the relative importance of the p53-p21-DREAM pathway (called below p53-DREAM) remains controversial, because multiple mechanisms were proposed to account for p53-mediated gene repression (Peuget and Selivanova, 2021)." Even though Peuget & Selivanova do not agree that genes get repressed in response to p53 activation exclusively by the p21-DREAM pathway, they do not question that this mechanism is essential for the p53-dependent repression of a core set of cell cycle genes. Since I am also not aware of any publications that challenge the importance of the p53-p21-DREAM pathway, I do not agree with this statement.
      2. Some parts of the manuscript are tiring to read - for example, pages 6, 7, and 8 which contain long listings and numbers of genes that are downregulated in differentiated BMC, found to be mutated in various disorders, bind DREAM components, were identified as downregulated by p53, etc. The authors may consider combining central parts of these data in a table that they show in the main manuscript which would make it easier to digest the information and at the same time significantly shorten the manuscript.
      3. The supplementary tables (S1-S26) are combined in one Excel file with multiple tabs. The authors should label the tabs accordingly to make it easier for the reader to find a particular table.
      4. At the end of page 6, the authors show that 17 genes found to be downregulated in differentiated BMCs are mutated in multiple bone marrow disorders, however, since they don't include references, it remains unclear where these mutations were originally described.
      5. On page 9, the authors state: "As a prerequisite to luciferase assays, we first verified that the expression of these genes, as well as their p53-mediated repression, can be observed<br /> in mouse embryonic fibroblasts (MEFs), because luciferase assays rely on transfections into MEFs (Figure 3b)." The authors don't explain why luciferase assays rely on transfections into MEFs and based on the caption of Fig. 3C, the luciferase assays were not performed in MEFs, but in NIH3T3 cells: "WT or mutant luciferase reporter plasmids were transfected into NIH3T3 cells..."

      Significance

      While the study supports a large body of publications proving that repression of cell cycle genes by the DREAM complex is crucial for cell cycle arrest and exit, it is noted that none of the main conclusions here are unexpected or particularly exciting. All the analyses are based on data sets that compare gene expression in highly proliferative cells with cells that underwent terminal cell cycle exit. Thus, a large portion of the genes that are downregulated in differentiated BMCs are cell cycle genes and well-established targets of DREAM and E2F:RB complexes. Furthermore, it is not surprising that some of these pro-proliferative genes are mutated in diseases connected to proliferation defects like anemias or microcephaly.

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      Referee #2

      Evidence, reproducibility and clarity

      The authors used various systems including Hoxa9-indubible BMCs, human and mouse cells, WT and p53 knockout MEF, glioblastoma cells to screen p53-DREAM targets and observed distinct finding for each system. Since different cell types have various p53 activation and p53 target genes expression, the authors might want to select proper cell type(s) to screen p53-DREAM target genes and design experiments to confirm that these genes are really p53-DREAM target genes.

      Specific comments:

      The authors need to describe and define HSC and Diff in Figure 1.

      Are Figure 1B and 1D list genes p53 targets in bone marrow cells?

      Where is the detailed information for mouse and human cells in Figure 1 and Figure 2?

      Are Figure 3B list genes also p53 target genes in other cell types such as bone marrow cells and glioblastoma?

      Does BRD8high has high p53 and p21?

      Are genes listed in Figure 4B all p53 target genes? can some validation be done?

      Significance

      This is a potentially interesting study. The major limitation is the absence of validation from the screening. This study would definitely benefit the research community as long as some of the key findings are validated.

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      Referee #1

      Evidence, reproducibility and clarity

      Summary

      In this paper the authors describe a data driven approach to identify and prioritise p53-DREAM targets whose repression might contribute to abnormal haematopoiesis and brain abnormalities observed in p53-CTD deleted mice. The premise is that in these mice, (where they have previously demonstrated p53 to be hyperactive in at least a subset of tissues), that the p53-p21-E2F/DREAM axis is at least in part responsible for observed phenotypes due to the repression of E2F and CDE/CHE element containing genes. Their approach to home in on relevant genes is based on transcriptomic gene ontology analysis of genes repressed in these disease settings where they primarily use publicly available data from HOXA9-ER regulated model of HSC expansion wherein they observe increases on p53-p21 expression upon differentiation where they demonstrate that p53-p21 DREAM target genes are suppressed as we would expect in this scenario where p53-p21 is activating withdrawal from cell cycle. They then spend a lot of effort analysing this datasets combining "gene-ontology", "disease phenotype" and "meta-ChIP-seq" analysis of public data to support the observation that mutations of genes suppressed in this manner are disproportionately linked to heritable haematopoetic and brain disorders. While these results are interesting in terms of framing a hypothesis about how mutations in p53-p21-DREAM regulated targets contribute to such conditions, they are to be expected given the now very well described impact of p53-p21 on both E2F4/DREAM targets. The natural progression of this work would be to go on to show this occurs in relevant cells or tissues derived from the p53-CTD mice as well as look at modulating target genes to understand underlying mechanisms and consequences.<br /> Rather than this, they focus on validating that a sub-set of these targets are indeed suppressed by specific p53 activation by MDM2 inhibitor Nutlin-3A in MEFs by qPCR and that mutation of predicted CDE CHR elements in luciferase constructs leads to increase luciferase activity. While these findings support their predictions, the results are entirely expected based on what is known about such targets and demonstrating that this occurs in MEFs does not closely relate to haematopoietic and brain cells they suggest this regulation is important. In fact, in the discussion, the authors comment on the importance of cell type context specificity in terms of discordance between predictions of TF binding sites and public datasets.<br /> Finally, they try and contextualise effects in glioblastoma data by correlating target gene expression with levels of BRD8 since it has recently been shown to attenuate p53 function in glioblastoma and show that some of the brain disease associated genes are expressed at higher levels in BRD8 high patient samples. It seems strange here that they do not also look at expression of p21 or other p53 targets that would help ascertain if p53 activity is indeed suppressed. Moreover, much more elegant methods for predicting transcription factor activity could be applied to this data.

      Major Comments

      The major result of this paper as it stands is the prioritisation of candidate genes in the p53-DREAM pathway involved in these conditions, and their refined approach used to identify and prioritise these genes and is such more of a starting point for further investigation. They fall short of demonstrating the relevance of their predictions physiologically in tissues from the mice and do not demonstrate functional importance of regulation of targets they put forward. Given that these genes will be co-ordinately regulated, without a mechanistic experiment in physiologically relevant model it is impossible to infer causality. For example, depleting individual targets in the HOXA9 model and evaluating impact on survival, proliferation and differentiation may be a (relatively) simple way to explore this, perhaps comparing to effects of p53 activating agents such as Nutlin-3A. Of note the authors (Jaber 2016 PMID: 27033104) and several other groups had (Fischer 2014 PMID: 25486564 McDade 2014 PMID: 24823795) previously demonstrated the link between p53-p21 and suppression of DNA-repair/Damage related genes (as is also observed here in particular FA-related genes that they discuss briefly here. I would have thought that this would be an obvious starting point for some mechanistic experiments and in fact I note this has been demonstrated before (Li et al 2018 PMID: 29307578)<br /> The analysis of brain specific targets and the link to BRD8 sits largely as an aside and the analysis of patient data from glioblastomas is underdeveloped as noted above.<br /> The computational methods applied are robust, albeit predominantly coorelative, in terms of identifying regulation of potential causative target genes, validated across human and mouse cell lines, and this indicates a role of these genes in the relevant conditions. However, further validation through application in a bulk or single cell RNAseq patient cohort, or at least an in vivo model would strengthen these conclusions and complement the work presented here which is based on in vitro mouse and human cells. This is pertinent as this study improves upon previously published approaches by focusing on "clinically relevant target genes". Additionally, this would exhibit the potential applications of the findings presented.<br /> In terms of statistical analysis, the hypergeometric test should be applied to assess significant enrichment of genes for example with CDE/CHR regions within the previously identified lists.

      Minor Comments

      References are required for the genes listed which play a role in the diseases of interest. This paper would benefit from the inclusion of summary schematics and tables throughout (rather than relying only on somewhat unwieldy heatmaps which show little other than all these genes are co-ordinately regulated), this could include summaries of the methods applied, gene or CDE/CHR inclusion criteria, and Venn diagrams indicating the subsets of final genes identified through this approach.

      Significance

      In its current form this is a very limited study that would require significant additional work to move conclusions beyond correlation and hypothesis generation.

      Overall, while limited largely to target prioritisation, this research nicely exemplifies how genes affected by the p53-DREAM pathway can be robustly identified, providing a potential resource for individuals working on this pathway or on abnormal haematopoiesis and brain abnormalities. These results are complementary to work previously published by Fischer et al, which has been referenced throughout the analysis (highlighting Target Gene Regulation Database p53 and DREAM target genes) and discussion.

      This paper will be of interest to researchers of blood/neurological diseases who can assess if these genes are dysregulated in their datasets, or those investigating the p53-DREAM pathway. This work represents a useful resource detailing genes affected by this pathway in these disease settings, however researchers of the p53-DREAM pathway may find this paper useful when planning an approach to identify and prioritise genes of interest.

      My expertise is in the field of transcription factor and p53 family biology in cancer and disease. Our group utilises functional genomics and computational approaches to harness this information to identify causal regulators of downstream effects or indeed novel ways to exploit p53 family

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      Reply to the reviewers

      We thank the referees for their interest, comments and advice on our manuscprit. The reply to the reviewers follows the revision plan proposed Review Commons.

      1. Description of the planned revisions

      R#1 major comments :

      R#1 raised three major points relative to quantitative data. For the two first, we have optimized our quantification methods, as explained in the next section, which clearly improved the results, but some data still have to be re-analyzed for figures 5,6,7.

      For the third one, here is the comment and our proposed additional experiments to answer it :

      c) Quantifications of lateral fraction Col IV in mosaic experiments do not support decreased lateral secretion in Rab8 OE (3G) or Dys- (S5C), which are central tenets of the study. “

      We have endeavoured to detect such differences in Dys mutants and Rab8 OE and do not see any possible improvement in the quantification method and therefore propose, instead, additional experiments.

      With respect to Rab8 OE, we suspect that this gain of function is not sufficiently effective under the specific conditions of the experimental setup described in Figure 3, as its effect appears to be more subtle than that of Rab10 OE in Figure 2. We therefore propose to repeat this experiment on a sensitised background in which Rab10 function is partially affected. Unpublished data indicate that this downregulation of Rab10 is not sufficient to induce significant differences in this experimental setup. However, based on the genetic interactions described in the other figures, an additive/synergistic effect between rab8 OE and Rab10 KD can be expected, which would allow to confirm the involvement of Rab8 in basal secretion.

      With regard to Dys mutant, we considered another possible explanation for this observation, namely that DAPC could affect the secretion of other BM proteins but not that of collagen IV. It should be noted that Perlecan and LamininA, which are also found in BM fibrils, are ligands for Dystroglycan, which is not the case for collagen IV. Unfortunately, there is no existing transgene with a UAS promoter and a tagged version of these proteins that would allow this hypothesis to be tested within a reasonable timeframe using the same method as that described in Fig.3. Therefore, we propose an alternative approach that would determine whether the secretion of endogenous laminin and/or perlecan is affected by Dystroglycan overexpression (i.e. secreted more laterally) and test whether this effect is Dys-dependent. It should be noted that this hypothesis would be fully consistent with all our data and in particular with that shown in Fig. 5.

      R#2 major comments

      From the data presented in Figure S1B, the authors state that the basement membrane mislocalization observed in Rab8/10KD has no major impact on polarity maintenance. They based this statement only on the localization of the apical marker aPKC. Although the aPKC data are convincing, it would be more compelling if the authors observe the distribution of other polarity proteins such as Dlg, E-Cadherin, and armadillo to better assess if the overall epithelial polarity is maintained in this condition.

      We will complete fig S1 and perform ECad and Dlg staining to provide a better description of apical-basal polarity in the different Rab knock-down conditions.

      R#3 comments

      Results Figure 4

      The authors suggest basal Rab10 expression domain near the Golgi exit point. Can the authors use a Trans-Golgi marker in order to confirm this statement other than the references stated?

      Such a staining will be included in the final version with for instance golgin245 staining.

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

      R#1 major comments :

      a) Rab8 KD does not significantly increase apical fraction of Collagen IV with respect to control (Fig. 1H). The image in 1C clearly shows that Col IV is present apically, something that has been shown by others and that never occurs in the wild type. Failure of the quantifying method to detect a difference can only mean the quantifying method is not adequate. A 10% average in the control when it's clear that no Col IV at all is found apically in the wild type suggests that the authors are quantifying background signal that they should not be acquiring, and, if acquired, they should be subtracting Rab8/Rab10 double knock down is said to show a synergistic effect, when an additive effect would be more consistent with alternative routes. Other problematic deductions drawn from apical fraction quantifications are found in Fig. 5J (Dys- enhancing Rab8 KD but not Rab10 KD) and Fig. 7D (Exo70- enhancing Rab10 KD but not Rab8 KD)

      We agree that this quantification was not optimal. We improved it by quantifying a narrower and more precise region for each domain. The new results are shown in Figure 1H. This improvement reduces the apical signal in the control from 10% to 6% and allows us to detect a significant increase between the control and Rab8 KD, thus resolving the problem raised. After verification, we did not subtract the background because there was no electronic background in our images (i.e. black is really black and equal to zero). Thus, the remaining signal is the true cytoplasmic GFP signal and it may not be appropriate to subtract it. Other data (fig 5J and 7D, now named fig 5L and 7H) were also re-analyzed with no major change.

      b) Similar to apical fraction, measurements of planar polarization (trailing/lateral ratio) show average ratios near 1 for Dg, Rab10 and Dys, which is striking given that the localization of these proteins is so clearly polarized. Ratios lower than 1, which are reported for many individual cells in these graphs, should mean reversed polarity. In light of this, I would not be too confident on the effects reported in 5O-Q. In fact, on two occasions, the authors obtain significant differences in these planar polarization measurements that they themselves disregard: Fig. 6J (Rab10 in Exo70-) and Fig. 7I (Dys in Rab8 KD).

      We agree that this quantification could be improved. Our initial quantification of the planar polarised proteins, Rab10 and Dys, found at the trailing edge, was confounded by their lateral spread. We have now reported with only the front half of the lateral side. By doing this in Figure 5, we increase the ratio in the control conditions, with almost no points below the value of 1, while the conditions in which polarity is visually affected are unchanged and still close to 1. We did not have time to re-analyze all the data (Figures 6 and 7), but will do so in the final revised version.

      R#1 minor comments :

      All the minor points raised by R#1 have been addressed by changes in the main text and/or the figures with the exception of the following :

      • Fig. S1B does not seem to make a significant point in the context of this study.

      Although we understand this comment, we followed suggestion of R#2 who asked in its major comments for more details with other cell polarity markers. These data are not yet included but will be generated for the fully revised version.

      • I suggest drawing a summary scheme to aid readers better assess interpretations alternative to the ones given in the text.

      While we will be happy to provide such a scheme in the final version, we prefer to wait for the results of the proposed complementary experiments to be as accurate as possible.

      R#2 major comments

      In the text for Figure 1G-H (page 4), the authors stated that the basal secretion was not restored in Rab8, 10, and 11 triple KD, in our opinion, it is unclear how the authors came to this strong conclusion from the presented data. It would be good if the authors explicitly explain how they come to this conclusion. Is it only based on the weak Coll-IV-GFP signal in the Rab8, 10, and 11 triple KD data compare to the control? If so, the authors should statistically quantify the difference with the control. In Figure 1H, no statistical analysis is provided between the control and triple KD conditions.

      We agree that it was not entirely appropriate to give such conclusions on the basis of the quantifications available. A new graph showing basal fluorescence intensity (new Figure 1H) (and not just the ratio of apical to apical plus basal as in Figure 1I) has been added to better support the text. A relevant statistical comparison has been added to Figure 1H (old Figure 1I). We apologize for this oversight.

      R#2 minor comments :

      We took in account all these comments and changed accordingly the text and the figures

      R#3 comments :

      Results figure 1

      The authors use RNAi lines to arrive at their conclusions, however, the extent of inhibition of gene expression achieved by the RNAi, has not been justified. Also observations from only one RNAi stock may not be completely conclusive:

      i) Efficiency of RNAi has not been tested or shown. No supporting data. Rab10-RNAi stock is 26289 BDSC which is in Valium10, which is a weak RNAi line and needs a Dicer.

      ii) Can same observations be made using classic alleles or generate somatic clones on follicular epithelial cells?<br />

      R#3 raised several questions regarding the efficiency of RNAi, the use of different lines and/or the use of classical mutants as an alternative method.

      For Rab10, we tested three different lines with similar results as shown now in Figure S1A-B. These data are also consistent with those obtained by overexpression of a dominant-negative form of Rab10 (Lerner et al, 2013). Unfortunately, Rab10 is located extremely close to the X chromosome centromere and is even more proximal than the FRT transgenes. It is therefore impossible to generate somatic mutant clones.

      Regarding Rab8, it is already published that Rab8 RNAi, expression of a dominant-negative form of Rab8 and Rab8 mutant cells obtained by somatic clones give similar defects (Devergne et al, 2017). The text has been modified to better illustrate the available data validating our approach.

      In addition, mutant clones would not allow analysis of genetic interactions in complex genetic contexts such as double and triple KDs. Similarly, the choice of the Rab10 line was motivated by the ease of obtaining the appropriate genetic combination according to their genomic location.

      iii) Intensity of Collagen IV in the basement membrane in Rab11 knock-down mutants seems to be significantly low as compared to the Rab8 and Rab10 knock downs in supplementary Fig 1B. Are the authors very sure that Rab11 has no functions in basement membrane basal organization?

      Good catch! Indeed, Rab11 RNAi significantly reduces basal secretion as now shown on fig 1H. Rab11 has pleiotropic functions in epithelial cells notably for their polarity (Choubey and Roy, 2017, Fletcher et al, 2012…) and, accordingly aPKC is partially disrupted in Rab11 RNAi conditions (Fig S1). Thus, the reason for such a decrease is unclear and could be an indirect consequence of an overall abnormal epithelial structure. Thus, we now report this observation but have not taken its interpretation too far.

      iv) Authors need to show where and how fluorescence intensities have been measured.

      Magenta rectangles with dashed lines on figure 1A illustrate the ROIs used for this analysis and more details have been added in the ‘experimental procedures’ section.

      Results Figures 2 and 3:

      Texts and figures have been modified as suggested.

      Results Figure 4 :

      The authors suggest a UAS-Rab10-RFP transgene show same results as endogenous Rab10-YFP as compared to spatial expression pattern. This is worrisome as expression of full length functional gene tagged with a fluorophore may be an overexpression. A control experiment would be helpful in suggesting/comparing with the Rab10 OE phenotype and that will be more convincing.

      We are not sure that we fully understand the reviewer's comment. However, we initially compared endogenous Rab10 and UAS-RAB10 at 25°C, a temperature at which the latter has no visible impact on BM structure (Cerqueira-Campos et al, 2020). Furthermore, even when higher expression was induced (by increasing the temperature and therefore Gal4 activity) and this had an impact on BM structure, this did not change the subcellular localization of Rab10, i.e. it was still planarly polarized, as shown in Fig 5S. The text has been modified to emphasize this point.

      Result Figure 5

      The authors may provide a Rab10 expression profile in DAPC null or KD mutants which would make their claims more comprehensive.

      Data showing Rab10 localization in Dys mutant cells were already shown on Figure S5A-B. Of notice, we also tried similar experiments using RAB10 knock-in line. However, for unexpected reason, having one copy of the chromosome with YFP insertion in Rab10 strongly enhanced DAPC mutant phenotypes in terms of F-actin orientation and follicle elongation ((as described in Cerqueira-Campos et al, 2020). We therefore considered these data as inappropriate.

      General comment

      In general some immunostainings should be carried out if not in all at least in some experiments with some cell domain specific markers, more specifically PCP markers such as Flamingo/Vangl and basolateral markers such as Lgl/Dlg. This makes the positions specific claims of the authors more valid in the eyes of the reader.

      We agree that this may help the reader but the pcp markers mentioned are not expressed in this tissue. However, the tissue planar orientation is now systematically indicated and consistent in all figures. We did not generally perform immunostaining for lateral markers but routinely included F-actin staining to detect cellular cortex. Our quantifications or cortical segmentations were based on the cell outline provided by this stain. On the basis of this staining, the outline of the cells was added on certain figures to facilitate understanding of the images.

      3. Description of analyses that authors prefer not to carry out

      R#3 comments :

      Result Figure 6 :

      Exo 70 is a versatile molecule and Rho kinases such as Cdc42 can direct Polarised exocytosis through interaction of Rab effectors with Exo 70. Have the authors considered this?

      We agree that it is an interesting prospect, but we consider it as beyond the scope of this article.

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      Referee #3

      Evidence, reproducibility and clarity

      In the present work the authors have elucidated a novel mechanistic model of basement membrane morphogenesis using Drosophila ovarian follicle cells as a model. The authors have employed extensive quantification approaches to justify the spatio-temporal expression of the molecules under study such as Collagen IV, Rab8, Rab10, DAPC, etc. The authors suggest distinct exit domains of BM protein Collagen IV facilitated by Rab8 and Rab10 via distinct routes as they interact with each other. The authors further show that DAPC plays an essential role in Rab10 mediated baso-lateral fibrillar BM synthesis whereas Rab8 functions are more Exocyst (Exo-70 dependent).

      Major comments:

      Result 1:

      The authors use RNAi lines to arrive at their conclusions, however, the extent of inhibition of gene expression achieved by the RNAi, has not been justified. Also observations from only one RNAi stock may not be completely conclusive:

      1. Efficiency of RNAi has not been tested or shown. No supporting data.<br /> Rab10-RNAi stock is 26289 BDSC which is in Valium10, which is a weak RNAi line and needs a Dicer.
      2. Can same observations be made using classic alleles or generate somatic clones on follicular epithelial cells?
      3. Intensity of Collagen IV in the basement membrane in Rab11 knock-down mutants seems to be significantly low as compared to the Rab8 and Rab10 knock downs in supplementary Fig 1B. Are the authors very sure that Rab11 has no functions in basement membrane basal organization?
      4. Authors need to show where and how fluorescence intensities have been measured.

      Result 2:

      Confusing diagram. The authors should clarify whether the BM fibrils indicate lateral or planar BM components which they show to be more prominently expressed in Rab10 over-expression mutants.

      A short note or an accompanying explanatory diagram on the source of the BM fibrils in the cellular context should make things less confusing.

      FF calculation is an ingenious way of trying to look into functions.

      The term Opposite effects/functions may be reconsidered as Rab8 and Rab10 compete with each other to deposit Collagen at spatially distinct domains. Opposite functions may give an impression that Rab8 actually represses Rab10 activity or vice versa, which may not be the case here.

      Result 3:

      Why was anti-GFP Ab detected with Cy3-Cy5 secondary Ab. GFP itself is green so why detect it with a Red secondary? Logic? How clone Collagen GFP and ECM collagen GFP was differentiated? Please justify

      A panel with a dotted line joining the peripheral or lateral Collagen as shown in panels D' E' of Fig 3 would support the cartoon provided and link the cartoon to the actual microscopic images.

      Result 4:

      The authors suggest a UAS-Rab10-RFP transgene show same results as endogenous Rab10-YFP as compared to spatial expression pattern. This is worrisome as expression of full length functional gene tagged with a fluorophore may be an overexpression. A control experiment would be helpful in suggesting/comparing with the Rab10 OE phenotype and that will be more convincing.

      When the authors mention back of cells, where do the authors exactly mean? A cartoon of "the back of follicle cells", wrt the entire ovarian follicle would be helpful.

      The authors suggest basal Rab10 expression domain near the Golgi exit point. Can the authors use a Trans-Golgi marker in order to confirm this statement other than the references stated?

      Result 5:

      The authors may provide a Rab10 expression profile in DAPC null or KD mutants which would make their claims more comprehensive.

      Result 6:

      Exo 70 is a versatile molecule and Rho kinases such as Cdc42 can direct Polarised exocytosis through interaction of Rab effectors with Exo 70. Have the authors considered this?

      In general some immunostainings should be carried out if not in all at least in some experiments with some cell domain specific markers, more specifically PCP markers such as Flamingo/Vangl and basolateral markers such as Lgl/Dlg. This makes the positions specific claims of the authors more valid in the eyes of the reader.

      Significance

      The findings impinge on a critical cellular process of Rab protein interactions in the genesis of the basement membrane which is of potential interest.

      This falls under basic research. Since Rab molecules have emerged as molecules governing membrane morphogenesis, Cell and Molecular Biologists as well as a wide audience including clinicians will be interested on this.

      Our group works on the roles of Rab11 in membrane morphogenesis in Drosophila model and we are now trying to venture with Rab11 in mammalian wound healing.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary:

      In this manuscript, Dennis et al. identify different secretory routes and cell exit sites involved in basement membrane secretion and diversification in epithelial cells. Using the follicular epithelium of the Drosophila ovary as their model system coupled with genetics, imaging, and image analysis approaches, they show that two previously identified RabGTPases, Rab8 and Rab10, work in parallel routes for basement membrane secretion. These two small GTPases work in a partially redundant manner, where Rab8 promotes basal secretion leading to a homogenous basement membrane, while Rab10 promotes lateral and planer-polarized secretion, leading to the formation of fibrils. The authors also show that Rab10 and the dystrophin-associated protein act together to regulate lateral secretion, and dystrophin (Dys) is necessary for dystroglycan (Dg) to recruit Rab10. Furthermore, DAPC is shown to be essential for fibril formation and is sufficient to reorient Collagen IV to the Rab10-dependent secretory route. Dys was also shown to interact directly with exocyst subunit Exo70. Using overexpression and loss of function approaches the authors claim that Exo70 limits the planer polarization of Dys, and as a result, Rab10, hence limiting basement membrane fibril formation. Finally, the authors state that the Exocyst (Exo70) is also required for the Rab8-dependent basement membrane route. Overall, the data described in this manuscript are convincing and the authors' claims are supported by the presented data. We have mainly minor comments and only a few major comments that need to be addressed.

      Major Comments:

      • In the text for Figure 1G-H (page 4), the authors stated that the basal secretion was not restored in Rab8, 10, and 11 triple KD, in our opinion, it is unclear how the authors came to this strong conclusion from the presented data. It would be good if the authors explicitly explain how they come to this conclusion. Is it only based on the weak Coll-IV-GFP signal in the Rab8, 10, and 11 triple KD data compare to the control? If so, the authors should statistically quantify the difference with the control. In Figure 1H, no statistical analysis is provided between the control and triple KD conditions.
      • From the data presented in Figure S1B, the authors state that the basement membrane mislocalization observed in Rab8/10KD has no major impact on polarity maintenance. They based this statement only on the localization of the apical marker aPKC. Although the aPKC data are convincing, it would be more compelling if the authors observe the distribution of other polarity proteins such as Dlg, E-Cadherin, and armadillo to better assess if the overall epithelial polarity is maintained in this condition.

      Minor Comments:

      General comments:

      • In the text describing their data, we recommend that the authors clearly indicate which panel(s) they are referring to.
      • The authors should also be consistent with the diction throughout the manuscript when referring to the cortical domain or region of the cell (back/rear/trailing edge/leading edge).
      • Several references are missing in the manuscript.

      The following specific comments are in order of appearance in the manuscript.

      Introduction Section:

      The following statements in the introduction should be supported by specific references:

      • "BM is critical for tissue development, homeostasis and regeneration, as exemplified in humans by its implication in many congenital and chronic disorders."
      • "BM is assembled from core components conserved throughout evolution: type IV collagen (Col IV), the heparan sulfate proteoglycan perlecan, and the glycoproteins laminin and nidogen."
      • "During development, the dynamic interplay between cells and BM participates in sculpting organs and maintaining their shape."
      • "BM protein secretion shows some specificities, mainly because of the large size of the protein complexes (e.g., procollagen) that must transit from the endoplasmic reticulum to the cell surface". This statement could be supported with references including specific Drosophila references. Additionally, the authors need to clarify what they mean by "some specifies".

      Results section:

      • In the text describing Fig. 2 (page 5), the authors describe two different basement membrane types: fibrils and homogenous. Moreover, the manuscript focuses on the role of Rab8 and Rab10 in the formation of these two structures. Thus, the authors must better describe the two different types of basement membrane structures and their known roles. This will be helpful for the readers to analyze the presented data, especially for those that are not familiar with the system. In Figure 2A, the authors describe stage 3 basement membrane as uniform BM, do they mean homogenous?
      • In the text describing the data for Fig. 3 (page 6), the authors should clearly explain the reason to use anti-GFP antibodies in a non-permeabilized condition (i.e., to detect specifically the extracellular secretion of BM proteins). This will help the readers to interpret the data presented.
      • On page 9, the authors stated that the precise localization of Dg in follicle cells is unknown. This statement is incorrect. It has been shown, using a Dg antibody, that Dg localizes at a high level at the basal side of the follicle cells and at a lower level at the apical side (Deng et al, 2003 and Denef et al. 2008).

      Discussion Section:

      • The following statement is not clear: "Thus, three different Rab proteins are targeted towards the three distinct domains of epithelial cells defined by apical basal polarity, and at least of them is also planar polarized". The authors should rephrase and describe specifically which Rabs they are talking about.
      • This statement is vague: "These three Rab GTPases have been jointly involved in different processes (Knödler et al, 2010; Sato et al, 2014; Vogel et al, 2015; Eguchi et al, 2018; Häsler et al, 2020)". The authors could also mention the processes in which Rab8, 10, and 11 are involved.
      • The following statements need to be supported by references. "Therefore, more investigations are required to define exactly how the DAPC allows the formation of BM fibrils. Nonetheless, given the importance of the DAPC and BM proteins in muscular dystrophies, our results will pave the way to determine whether a similar function is present also in muscle cells. Interestingly, the extracellular matrix is different between the myotendinous junction and the interjunctional sarcolemmal basement membrane and may provide another developmental context where several routes targeted to different subcellular domains may be implicated".

      Experimental Procedure Section:

      • In the dissection and immunostaining section (p14), there is a typo: it should be for "20 min" instead of "2for 0 min"
      • For the GST pulldown experiments, the authors mention that they use a standard protocol to produce S35 Exo 70 and the GST pulldown experiments. The authors should provide references.

      Figure and Figure Legend:

      • General comment: The orientation of the images showing the rotation and leading and trailing edges need to be consistent in the different figures (e.g., In Figures 3 and 7, the leading edge is oriented to the top while in Figures 4, S4, 5, 6, the leading edge is oriented to the bottom). This will help the readers to analyze the data.
      • In Figure 1 C-G the scale bars are missing and should be added as Fig. 1B.
      • Figure S1A: The data presented in Figure S1A is convincing. However, a control panel should be added showing the absence of apical Coll IV for comparison. This information will help with the interpretation of the data.
      • In Figure 3 legend: it should be "immunostained" for GFP instead of stain for f-actin and GFP.
      • In Figure 4, some scale bars are missing.
      • In Figure 4 legend: it should be "(A, E)" after (i.e 0.8 µm above the basal surface) instead of "(C, G)"
      • In Figure 5A-E, the authors show quantification of the fibril fraction for Dys-, Rab10 OE, and Rab10OE+Dys, Rab8KD, and Rab8KD+Dys-, and images of the collagen fibril for all the conditions except Dys-, it will be informative that the authors present a representative image of the Coll IV fibril in Dys- condition for comparison. The above comment also applies to Figure 5F-J, and it will be also informative to have a representative image of Dys- condition.
      • In Figure 5 legend (p23), it should be "plane" and not "plan".
      • Overall, the legend for Fig. S5 is not clear and we recommend the authors to clearly described the different panels. (e.g., it should be "(D)" instead of "(H-J)")
      • In Figure 6, some scale bars are missing.

      Significance

      Despite the important roles of the basement membrane for mechanical support, tissue and organ development, and function, the mechanisms that control the polarized deposition of basement membrane proteins are largely unknown. The contribution of Rab 8 and Rab 10 in the polarized deposition of the basement membrane was previously shown. However, by identifying two competitive secretory routes for the basal secretion of the basement membrane proteins that required these two different RabGTPases, controlled by the DAPC and the exocyst complexes, the authors make a novel contribution to our understanding of the mechanism that leads to the polarized secretion of basement membrane proteins (in that case Collagen IV). Since the basement membrane has critical roles in tissue and organ morphogenesis and functions, and its misregulation has been associated with developmental defects and pathological conditions, this research sheds light on the mechanisms important in these morphogenetic processes and will give insights into their deregulations in pathological conditions.

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      Referee #1

      Evidence, reproducibility and clarity

      This manuscript by Dennis et al. reports a study of the polarized secretion of basement membrane Collagen IV in the Drosophila (fruit fly) follicular epithelium. Using genetic manipulations and confocal imaging, the authors show that Rab-GTPases Rab8 and Rab10, both known to be required for proper basal secretion of Collagen IV (work by the labs of Sally Horne-Badovinac and Trudi Schupbach, respectively), mediate two alternative secretion routes: Rab8 mediates basal-most secretion of soluble Collagen IV that is incorporated homogenously into the basement membrane, whereas Rab10 mediates basal-lateral secretion of Collagen IV that produces insoluble fibers. The authors additionally study the relation between Rab10 and Dystroglycan/Dystrophin (Dystrophin-associated protein complex, DAPC), which they previously showed to be essential for fibril formation (Cerqueira-Campos et al., 2020). They show here that Dystrophin and Rab10 colocalize at the basal trailing side of follicle cells and that overexpressed Dystroglycan can recruit Rab10 to the plasma membrane; however, they also show that Dystrophin mutants fail to display an effect on Rab10 localization, leaving the significance of the proposed Rab10-DAPC interaction unresolved. Finally, the authors present convincing evidence that the exocyst complex opposes fibril formation, and suggestive but comparatively weaker results pointing that this opposition is due to two independent separate exocyst roles: an inhibitory interaction exocyst-Dystrophin (Dystrophin being required for fibril formation), and a positive role in the alternative Rab8 non-fibril route.

      Major comment:

      • There are several instances throughout the study in which the authors seem to have problems quantifying results. This affects some assertions central to the message of the paper that are not supported by the quantifications presented. It also casts doubts on accessory points deduced from quantitative differences (or lack of difference) that do not seem fully reliable. I would urge the authors to reevaluate their quantification methods.

      a) Rab8 KD does not significantly increase apical fraction of Collagen IV with respect to control (Fig. 1H). The image in 1C clearly shows that Col IV is present apically, something that has been shown by others and that never occurs in the wild type. Failure of the quantifying method to detect a difference can only mean the quantifying method is not adequate. A 10% average in the control when it's clear that no Col IV at all is found apically in the wild type suggests that the authors are quantifying background signal that they should not be acquiring, and, if acquired, they should be subtracting. Rab8/Rab10 double knock down is said to show a synergistic effect, when an additive effect would be more consistent with alternative routes. Other problematic deductions drawn from apical fraction quantifications are found in Fig. 5J (Dys- enhancing Rab8 KD but not Rab10 KD) and Fig. 7D (Exo70- enhancing Rab10 KD but not Rab8 KD).

      b) Similar to apical fraction, measurements of planar polarization (trailing/lateral ratio) show average ratios near 1 for Dg, Rab10 and Dys, which is striking given that the localization of these proteins is so clearly polarized. Ratios lower than 1, which are reported for many individual cells in these graphs, should mean reversed polarity. In light of this, I would not be too confident on the effects reported in 5O-Q. In fact, on two occasions, the authors obtain significant differences in these planar polarization measurements that they themselves disregard: Fig. 6J (Rab10 in Exo70-) and Fig. 7I (Dys in Rab8 KD).

      c) Quantifications of lateral fraction Col IV in mosaic experiments do not support decreased lateral secretion in Rab8 OE (3G) or Dys- (S5C), which are central tenets of the study.

      Minor comments:

      • It is stated that Rab10 and Dys associate with tubular endosomes, but no data here support identification as endosomes of these tubular structures, to my understanding.

      • The authors call sup-basal the cell region immediately apical to the most basal. Is there sufficient reason to not call this lateral? If a new term is needed, shouldn't it be supra-basal?

      • In Fig. S1A and B, Col IV is labeled as green but represented in cyan.

      • Fig. S1A should present a wild type control.

      • Fig. S1B does not seem to make a significant point in the context of this study.

      • Fig. 3C'-E' label suggests a gradient made from multiple images, but it looks like just two images and two colors.

      • Graphs in Fig. 3H-J, S5D and 7B are not legible.

      • It is not clear where Y2H results in Fig 6A come from.

      • I suggest drawing a summary scheme to aid readers better assess interpretations alternative to the ones given in the text.

      Significance

      This study reports important new information on the secretion of Collagen IV by polarized cells of the Drosophila follicular epithelium. It complements previous studies on the roles of Rab8, Rab10 and Dystroglycan/Dystrophin, additionally uncovering a role for the exocyst complex. Addressing some issues with quantitative imaging should increase confidence in its most critical conclusions.

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      Reply to the reviewers

      Reviewer #1 (Evidence, reproducibility and clarity):

      1) It is interesting MxDnaK1 seems to prefer cytosolic proteins while Mx-DnaK2 prefers inner membrane proteins. The domain-swapping experiments seem to suggest that the NBD is important for this difference. How NBD is important is not addressed. Is it due to ATP hydrolysis, NBD-SBD interaction, or co-chaperone interactions?

      Answer: Thanks for your comments. We speculate that the co-chaperone interaction might be the key factor contributing to substrate differences. According to the working principle of Hsp70, its functional diversity is largely determined by substrate differences. Co-chaperones, such as JDPs, play a crucial role in this process as they possess the ability to bind substrates and facilitate their targeted delivery. Therefore, much of the functional diversity of the HSP70s is driven by a diverse class of JDPs 1,2. We found that NBD played important roles in cochaperone recognition of MxDnaKs. Additionally, it is generally accepted that the efficiency of ATP hydrolysis does not significantly impact the substrate recognition of Hsp70. Furthermore, if the NBD-SBD interaction is crucial, the substitution of either the NBD or SBDβ domain might result in similar cell phenotypes, as both alterations disrupt the original NBD-SBDβ interaction. We believe the DnaK proteins and their cochaperones both determine the substrate spectrums. We made corresponding modifications in the revised manuscript. (Page22; Line 488-494 in the marked-up manuscript)

      2) About the interactome analysis, since apyrase was added to remove ATP, it's surprising multiple Hsp40s were found in their analysis. Hsp70-Hsp40 interaction is known to require ATP. This may suggest some of the proteins found in their interactome analysis are artifacts. The authors should perform negative controls for their interactome analysis, such as using a control antibody for their CO-IP and analyze any non-specific binding to their resin.

      In addition, since JDPs were pull-down, is it possible some of the substrates identified are actually substrates for JDPs, not binding directly to DnaKs?

      Answer: This is an interesting question. As you correctly noted, the interaction between Hsp70 and Hsp40 requires ATP. In our experiment, we used apyrase to remove ATP in order to promote tight binding of substrate by DnaK. This methodology was initially described by Calloni, G. et al in 20123, and the authors also identified the co-chaperone protein DnaJ, but with a concentration higher than 77% of the interactors. In our opinions, the incomplete removal of ATP could be the underlying cause of this phenomenon.

      We apologize for the undetailed description in Methods. Actually, we implemented negative controls for each MxDnaK in order to eliminate the potential non-specific interactions with Protein A/G beads or antibodies. Specifically, we conducted a CO-IP experiment without the presence of antibodies to assess any non-specific binding to the Protein A/G beads. To further investigate non-specific binding to the antibodies of MxDnaK2 and MxDnaK1, we utilized the mxdnak2-deleted mutant (strain YL2216) and the MxDnaK1 swapping strain with the MxDnaK2 SBDα (strain YL2204), respectively. As the SBDα of MxDnaK1 was employed as antigen to generate antibodies, and YL2204 can’t be recognized by anti-MxDnaK1 (Figure S5). We believe these controls allowed us to evaluate and exclude the non-specific interactions in our CO-IP. We have improved our description in methods. (Page 27; Line 596-607)

      While one of the main functions of JDPs is to interact with unfolded substrates and facilitate their delivery to Hsp70, there may still be substrates that do not directly bind to Hsp70. It’s thus possible that some of the substrates identified only bind to JDPs. We made corresponding modifications in the revised manuscript. (Page 14; Line 290-292)

      3) For Figure S7, the pull-down assay used His6-tagged JDPs. Ni resin is known to bind Hsp70s non-specifically. It's not surprising DnaK showed up in all the pull-down lanes, especially considering how much DnaK was over-expressed. For some pull-down lanes, the amount of DnaK is much more than that of JDPs, further indicating artifact. The author should include negative controls such as JDPs without His6-tag or any irrelevant protein with His6 tag.

      Answer: Thanks for your suggestion. As you and another reviewer pointed out, there were some flaws in the experimental design of the pulldown assay. These include the non-specific binding of Hsp70 proteins to nickel resin, the absence of a negative control without a tag, and the inappropriate selection of the MBP tag. Thus, we employed the nLuc assay as an alternative to the pulldown experiment to validate the interaction between DnaK and JDP (Figure S9). While our manuscript employed nLuc to confirm protein dimerization, it is worth noting that nLuc assay was originally devised for investigating protein interactions 4.

      4) For the proposed dimer formation in Fig. 4C, there are multiple bands above the monomer bands. What are these forms? It seems the majority of the Cys residues that could form disulfide bonds are in the NBD of MxDnaK2 since constructs with MxDnaK2-NBD form some sort of high-MW bands above the monomer. Does MxDnaK1-NBD also contain Cys at the analogous positions? The fact that MxDnaK1 didn't show disulfide-bonded bands doesn't mean it doesn't form dimer. It depends on where the Cys residues are.

      It's nice the authors did Fig. 4D. However, the authors should include a positive control to show how strong the signal is for a true interaction before interpreting their results.

      Answer: Thank you very much for your comments. In at least three independent experiments, we consistently observed two unidentified bands within the molecular weight range of 70-100 kDa during the purification process of His6-MxDnaK2. These bands appeared to be intermediate in size between the monomeric and dimeric forms of His6-MxDnaK2, and disappeared upon DTT treatment. the unidentified band compositions have been confirmed by LC/MS. The upper band included MxDnaK2 (65.3 kDa) and anti-FlhDC factor of E. coli (WP_001300634.1, 27 kDa). In the lower band, we detected the presence of MxDnaK2 and the 50S ribosomal protein L28 of E. coli (WP_000091955.1, 9 kDa). Based on these findings, we conclude that these two additional bands are the result of the interaction between His6-MxDnaK2 and these two E. coli proteins. The related explanations have been added in the legend of Figure 5. (Page 42; Line 938-942)

      We analyzed the presence of Cys in MxDnaK1 and MxDnaK2. The NBD region of MxDnaK2 contains two Cys, located at positions 15 and 319. MxDnaK1-NBD contain a Cys at position of 316, which is the analogous position of 319-Cys of MxDnaK2. The analogous position of 15-Cys of MxDnaK2 is a Val in MxDnaK1, which might be an important factor contributing to the inability of MxDnaK1 to form oligomers.

      Thanks for your suggestion to add the positive control. We re-performed the nLuc assays including a positive control(αSyn). According to the working principle of the nLuc assay, the amount of fluorescent substrate is limited. Therefore, even for proteins that interact with each other, the fluorescence value gradually decreases and reaches a plateau, similar to the negative control. This gradual decline in fluorescence is a significant indicator of protein interaction. In Figure 4D (Figure 5D in the revision version), we only presented the results of the first 20 minutes of detection. The complete two-hour detection results have been added in the supplementary figure (Figure S14).

      5) line 48: "human HSC70 and HSP70 are 85% identical, and the phenotypes of their knockout mutants are different, which is consistent with their largely nonoverlapping substrates" The authors completely ignored that the promoters of HSC70 and HSP70 are very different.

      Answer: This is our carelessness. Yes, HSC70 and HSP70 exhibit distinct expression patterns, which play important roles in their functional diversity. We modified the sentence in the new version (Page 5; Line 58)

      6) Line 69: "The two PRK00290 proteins, not the other Myxococcus Hsp70s, could alternatively compensate the functions of EcDnaK (DnaK of E. coli) for growth." Please add references for this statement.

      Answer: Added, thanks.

      7) line 191: What's the mechanism for DnaK's role in oxidative stress? Is the disulfide bond formation in Fig. 4 related? Does disulfide-bond change the activity of DnaK?

      Answer: Thanks for your pertinent comments. Honestly, we have no idea about the mechanism for MxDnaK2's role in oxidative stress. In our previous studies, we determined that the deletion of mxdnaK2 resulted in a longer lag phase after H2O2 treatment. Here, our aim was to investigate the impact of region swapping on the cellular function of MxDnaK2. In other bacteria, the critical role that DnaK plays in resistance to oxidative stress stems from the pleotropic functions of this chaperone. By shortening the dwelling time that proteins spend in the unfolded state, the DnaK/DnaJ chaperone system minimizes the risk of metal-catalyzed carbonylation of the side chains of proline, lysine, arginine, and threonine residues, but none of them linked to the dimerization characteristic of DnaK 5-7.

      8) Fig. S9 seems redundant.

      Answer: Deleted, thanks.

      9) line 263, "but the NBD exchange was almost equal to the deletion of the gene with respect to phenotypes." But, the mutant has >50% activity in Fig. 3F.

      Answer: We apologize for the confusing description. The “phenotypes” here indicates “cell phenotypes”. What we really tried to say with this sentence is that the NBD swapping strain of either MxDnaK1 or MxDnaK2 presented identical cell phenotypes with the gene-deleted strain. As we have already provided a detailed description of this result earlier, now we consider this sentence to be redundant and have therefore deleted it. (Page 17; Line 355-356)

      10) line 221-226: the logic is not quite clear.

      Answer: We apologize for the confusing description. In M. xanthus DK1622, MxDnaK1 is essential for cell survival, and an insertion of a second copy of mxdnaK1 in the genome is required for deletion of the in-situ gene. Thus, To verify whether the NBD region is required for the essentiality of MxDnaK1, we performed the region swapping of the in situ MxDnaK1 gene in the att::mxdnaK1 mutant (a DK1622 mutant containing a second copy of mxdnaK1 at attB site), and successfully obtained the MxDnaK1 mutant swapped with the MxDnaK2 NBD region. The experiment indicated that the NBD of MxDnaK1 is essential for the cellular functions of the chaperone. We have added the information and modified the sentences in the manuscript. (Page 15; Line 308-319)

      Minor concerns:

      Please check spelling. There are some typos such as "HPPES" in the Methods.

      Answer: Corrected. Many thanks.

      My areas of expertise are protein biochemistry, genetics, and structural biology on heat shock proteins.

      Reviewer #2 (Evidence, reproducibility and clarity):

      Major comments:

      The manuscript is very nice and interesting, although some of the authors' conclusions are perhaps not well supported by their data. For example:

      1) In the pulldown experiments the lack of interaction between 2747-MxDnaK2, 3015-MxDnaK2 and 1145-MxDnaK1 should be shown in order to support the conclusion made in line 197-198,

      Answer: This is our carelessness. As you and another reviewer pointed out, there are some flaws in the experimental design of the pulldown assay. These include the non-specific binding of Hsp70 proteins to nickel resin, the absence of a negative control without a tag, and the inappropriate selection of the MBP tag. Thus, we employed the nLuc assay as an alternative to the pulldown experiment to validate the interaction between DnaK and JDP (including 2747-MxDnaK2, 3015-MxDnaK2 and 1145-MxDnaK1 interaction) (Figure S9). While our manuscript employed nLuc to confirm protein dimerization, it is worth noting that nLuc assay was originally devised for investigating protein interactions 4.

      2) The only evidence that the NBD of MxDnaK1 is essential for bacterial growth is that this mutation couldn´t be obtained in M. xanthus. However, it could be purified in E. coli. Could the authors do some experiments with the M. xanthus strain without the chromosomal MxDnaK1 and then introduce a plasmid with the mutated gene?

      Answer: We apologize for the confusing description. Actually, we determined the NBD is essential not only from the mutation couldn’t be obtained. In M. xanthus DK1622, MxDnaK1 is essential for cell survival, and in-situ deletion of the gene could be obtained after an insertion of a second copy of mxdnaK1 in the genome at the attB site. To verify whether the NBD region is required for the essentiality of MxDnaK1, we performed the region swapping of the in situ MxDnaK1 gene in the att::_mxdnaK_1 mutant (a DK1622 mutant containing a second copy of _mxdnaK_1), and successfully obtained the MxDnaK1 mutant swapped with the MxDnaK2 NBD region. The experiment indicated that the NBD of MxDnaK1 is essential for the cellular functions of the chaperone. We have added the information and modified the sentences in the manuscript. (Page 15; Line 308-319)

      3) All the experiments with purified proteins were done with MxDnaKs bearing His-tags. It doesn't say explicitly its position, but as they employed a pET28A it is likely that the tag is at the N-terminus, which is close to the linker region. As this tag might interfere, it should be removed for the experiments, or at least a control done with the tag removed.

      Answer: We apologize for the lack of detailed description. As you pointed out, the His-tags are located at the N-terminus of DnaKs. The full lengths of MxDnaK1 and MxDnaK2 are 638 and 607 amino acids. The linker regions are located at amino acid positions 381-386 for MxDnaK1 and 387-392 for MxDnaK2. Therefore, we believe that the His-tag is not close to the linker regions. We have included the information in new manuscript. (Page 24; Line 544-546)

      The purified His6-DnaK proteins were employed for holdase activity assays and in vitro dimerization assays. Several previous studies have utilized the same holdase activity assay method with His-tagged DnaK 8,9. We suggested that the His-tag did not interfere with the holdase activity of DnaK. To exclude the influence of His-tag on oligomerization, we conducted a control with the tag removed in the in vitro dimerization assay and the result show no difference (Figure S13).

      4) The authors state that MxDnaK dimerized in vitro with the NBD, and to disrupt the dimer they used 100 mM DTT, which is a very high concentration. As the protein has the His-tag, it should be removed to corroborate that it is not interfering with the dimerization.

      Answer: Thanks for your suggestion. As mentioned above, to exclude the influence of the His-tag on oligomerization, we conducted a control with the tag removed in the in vitro dimerization assay and the result show no difference (Figure S13).

      5) Why were the pulldown experiments done with MBP-MxDnaKs? Can you show a negative control between the MBP and the JDPs to rule out this interaction? It will be more suitable to do the pulldown assays with the purified MxDnaK´s without the His-tags (and the His-tags JDP that were employed).

      Answer: Thanks for your suggestion. As mentioned above, there are some flaws in the experimental design of the pulldown assay. Thus, we employed the nLuc assay as an alternative to the pulldown experiment to validate the interaction between MxDnaKs and JDPs (Figure S9).

      Minor comments:

      • E. coli´s DnaK is only essential in heat shock conditions and for lambda phage cycle. If MxDnaK1 is similar to this Hsp70, why the substitution of its NBD for the NBD MxDnaK2 would be lethal for bacterial growth?

      Answer: Thanks for the comments. As you correctly point out, DnaK is nonessential in E. coli. But in some other bacteria, DnaK also plays an essential role in cell growth for different reasons 10-12. In our previous studies, we determined that MxDnaK1 is essential in M. xanthus DK1622, and the MxDnaK2 is nonessential. In this study, we performed region swapping and found that only the NBD of MxDnaK1 was unreplaceable. In our opinions, the result indicated that NBD play important roles in the functional diversity between MxDnaK1 and MxDnaK2.

      • I think that the writing should be revised and in the supporting information the captions of the figures should include more information.

      Answer: Thanks a lot for the suggestion. We revised the manuscript and added more information in the legends of supplementary figures.

      Reviewer #2 (Significance):

      -General assessment: This is a nice piece of work which would benefit from revision to address the comments above. The authors showed the roles and differences between two DnaK in the same organism. They track these differences to the subdomains of the MxDnaK´s and co-chaperones. It will be interesting for future works to explore more deeply the co-chaperones and their interactions.

      -Advance: I think that this manuscript fills a gap regarding the role of DnaK duplicated in bacterial strains. -Audience: I would say that the audience is broad and includes scientists interested in protein folding and chaperones, as well as myxobacteria.

      1. Rosenzweig, R., Nillegoda, N. B., Mayer, M. P. & Bukau, B. The Hsp70 chaperone network. Nat Rev Mol Cell Biol 20, 665-680, doi:10.1038/s41580-019-0133-3 (2019).
      2. Kampinga, H. H. & Craig, E. A. The HSP70 chaperone machinery: J proteins as drivers of functional specificity. Nat Rev Mol Cell Biol 11, 579-592, doi:10.1038/nrm2941 (2010).
      3. Calloni, G. et al. DnaK functions as a central hub in the E. coli chaperone network. Cell Rep 1, 251-264, doi:10.1016/j.celrep.2011.12.007 (2012).
      4. Dixon, A. S. et al. NanoLuc Complementation Reporter Optimized for Accurate Measurement of Protein Interactions in Cells. ACS Chem Biol 11, 400-408, doi:10.1021/acschembio.5b00753 (2016).
      5. Fredriksson, A., Ballesteros, M., Dukan, S. & Nystrom, T. Defense against protein carbonylation by DnaK/DnaJ and proteases of the heat shock regulon. J Bacteriol 187, 4207-4213, doi:10.1128/JB.187.12.4207-4213.2005 (2005).
      6. Santra, M., Dill, K. A. & de Graff, A. M. R. How Do Chaperones Protect a Cell's Proteins from Oxidative Damage? Cell Syst 6, 743-751 e743, doi:10.1016/j.cels.2018.05.001 (2018).
      7. Fredriksson, A., Ballesteros, M., Dukan, S. & Nystrom, T. Induction of the heat shock regulon in response to increased mistranslation requires oxidative modification of the malformed proteins. Mol Microbiol 59, 350-359, doi:10.1111/j.1365-2958.2005.04947.x (2006).
      8. Chang, L., Thompson, A. D., Ung, P., Carlson, H. A. & Gestwicki, J. E. Mutagenesis reveals the complex relationships between ATPase rate and the chaperone activities of Escherichia coli heat shock protein 70 (Hsp70/DnaK). J Biol Chem 285, 21282-21291, doi:10.1074/jbc.M110.124149 (2010).
      9. Thompson, A. D., Bernard, S. M., Skiniotis, G. & Gestwicki, J. E. Visualization and functional analysis of the oligomeric states of Escherichia coli heat shock protein 70 (Hsp70/DnaK). Cell Stress Chaperones 17, 313-327, doi:10.1007/s12192-011-0307-1 (2012).
      10. Shonhai, A., Boshoff, A. & Blatch, G. L. The structural and functional diversity of Hsp70 proteins from Plasmodium falciparum. Protein Sci 16, 1803-1818, doi:10.1110/ps.072918107 (2007).
      11. Vermeersch, L. et al. On the duration of the microbial lag phase. Curr Genet 65, 721-727, doi:10.1007/s00294-019-00938-2 (2019).
      12. Burkholder, W. F. et al. Mutations in the C-terminal fragment of DnaK affecting peptide binding. Proc Natl Acad Sci U S A 93, 10632-10637, doi:10.1073/pnas.93.20.10632 (1996).
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      Referee #2

      Evidence, reproducibility and clarity

      Summary: This manuscript describes interesting studies of two paralogues of the E. coli Hsp70, DnaK, from of M. xanthus: MxDnaK1 and MxDnaK2. MxDnaK1 is similar to E. coli DnaK in terms of heat shock response, subcellular localization, etc. while MxDnaK2 is involved with membrane proteins and does not participate in the heat shock response. The interactome of the Mx DnaK´s are larger than that of E. coli DnaK, and their subcellular localization is also different. Regarding the differences between M. xanthus DnaK´s, MxDnaK2 prefers proteins with a higher hydrophobicity score, consistent with its role associated with membrane proteins. The phenotype of diverse mutants with domain swapping showed that the substitution of the NBD of MxDnaK2 for the NBD of MxDnaK1 led to similar phenotypes as the deletion of MxDnaK2 in terms of sporulation and S motility. Consistently, the interactomes of these variants were reduced in number of substrates in comparison with the wild type enzymes. No obvious effect was observed when the SBD´s subdomains were swept. Both MxDnaK interact with JDPs and NEF cochaperones. However, MxDnaK2 interacts only with one of the NEFs, and it depends on the NBD, and has one specific JDP, whichdepends on the beta-subdomain of the SBD (no information provided regarding NBD). MxDnaK1 interacts with both NEFs and has two specific JDPs, which also seems to depend on the beta subdomain of the SBD. Finally, a phylogenetic analysis reveals that the duplication of the dnak gene in Mx is correlated with the complexity of the proteome.

      Major comments:

      • The manuscript is very nice and interesting, although some of the authors' conclusions are perhaps not well supported by their data. For example: 1) In the pulldown experiments the lack of interaction between 2747-MxDnaK2, 3015-MxDnaK2 and 1145-MxDnaK1 should be shown in order to support the conclusion made in line 197-198, 2) The only evidence that the NBD of MxDnaK1 is essential for bacterial growth is that this mutation couldn´t be obtained in M. xanthus. However, it could be purified in E. coli. Could the authors do some experiments with the M. xanthus strain without the chromosomal MxDnaK1 and then introduce a plasmid with the mutated gene?
      • All the experiments with purified proteins were done with MxDnaKs bearing His-tags. It doesn't say explicitly its position, but as they employed a pET28A it is likely that the tag is at the N-terminus, which is close to the linker region. As this tag might interfere, it should be removed for the experiments, or at least a control done with the tag removed.
      • The authors state that MxDnaK dimerized in vitro with the NBD, and to disrupt the dimer they used 100 mM DTT, which is a very high concentration. As the protein has the His-tag, it should be removed to corroborate that it is not interfering with the dimerization.
      • Why were the pulldown experiments done with MBP-MxDnaKs? Can you show a negative control between the MBP and the JDPs to rule out this interaction? It will be more suitable to do the pulldown assays with the purified MxDnaK´s without the His-tags (and the His-tags JDP that were employed).

      Minor comments:

      • E. coli´s DnaK is only essential in heat shock conditions and for lambda phage cycle. If MxDnaK1 is similar to this Hsp70, why the substitution of its NBD for the NBD MxDnaK2 would be lethal for bacterial growth?
      • I think that the writing should be revised and in the supporting information the captions of the figures should include more information.

      Significance

      General assessment: This is a nice piece of work which would benefit from revision to address the comments above. The authors showed the roles and differences between two DnaK in the same organism. They track these differences to the subdomains of the MxDnaK´s and co-chaperones. It will be interesting for future works to explore more deeply the co-chaperones and their interactions.

      Advance: I think that this manuscript fills a gap regarding the role of DnaK duplicated in bacterial strains.

      Audience: I would say that the audience is broad and includes scientists interested in protein folding and chaperones, as well as myxobacteria.

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      Referee #1

      Evidence, reproducibility and clarity

      In this study, Pan et al. characterized two Hsp70 DnaKs from Myxococcus xanthus DK1622. Through determining interactomes, the authors defined the differences and similarities between these two DnaKs in interacting with co-chaperones and substrates. Using domain-swapping, the authors analyzed the domain requirements for their functions. Lastly, their bioinformatics analyses seem to suggest the presence of these two DnaKs (i.e., DnaK duplication) is due to the increase of proteomic complexity. Overall, the results are interesting although not surprising. As the authors pointed out, many organisms have multiple Hsp70s with different but overlapping functions. Although multiple experimental approaches were used, the manuscript is generally descriptive without revealing any major mechanistic insights.

      1. It is interesting MxDnaK1 seems to prefer cytosolic proteins while Mx-DnaK2 prefers inner membrane proteins. The domain-swapping experiments seem to suggest that the NBD is important for this difference. How NBD is important is not addressed. Is it due to ATP hydrolysis, NBD-SBD interaction, or co-chaperone interactions?
      2. About the interactome analysis, since apyrase was added to remove ATP, it's surprising multiple Hsp40s were found in their analysis. Hsp70-Hsp40 interaction is known to require ATP. This may suggest some of the proteins found in their interactome analysis are artifacts. The authors should perform negative controls for their interactome analysis, such as using a control antibody for their CO-IP and analyze any non-specific binding to their resin.<br /> In addition, since JDPs were pull-down, is it possible some of the substrates identified are actually substrates for JDPs, not binding directly to DnaKs?
      3. For Figure S7, the pull-down assay used His6-tagged JDPs. Ni resin is known to bind Hsp70s non-specifically. It's not surprising DnaK showed up in all the pull-down lanes, especially considering how much DnaK was over-expressed. For some pull-down lanes, the amount of DnaK is much more than that of JDPs, further indicating artifact. The author should include negative controls such as JDPs without His6-tag or any irrelevant protein with His6 tag.
      4. For the proposed dimer formation in Fig. 4C, there are multiple bands above the monomer bands. What are these forms? It seems the majority of the Cys residues that could form disulfide bonds are in the NBD of MxDnaK2 since constructs with MxDnaK2-NBD form some sort of high-MW bands above the monomer. Does MxDnaK1-NBD also contain Cys at the analogous positions? The fact that MxDnaK1 didn't show disulfide-bonded bands doesn't mean it doesn't form dimer. It depends on where the Cys residues are.<br /> It's nice the authors did Fig. 4D. However, the authors should include a positive control to show how strong the signal is for a true interaction before interpreting their results.
      5. line 48: "human HSC70 and HSP70 are 85% identical, and the phenotypes of their knockout mutants are different, which is consistent with their largely nonoverlapping substrates." The authors completely ignored that the promoters of HSC70 and HSP70 are very different.
      6. Line 69: "The two PRK00290 proteins, not the other Myxococcus Hsp70s, could alternatively compensate the functions of EcDnaK (DnaK of E. coli) for growth." Please add references for this statement.
      7. line 191: What's the mechanism for DnaK's role in oxidative stress? Is the disulfide bond formation in Fig. 4 related? Does disulfide-bond change the activity of DnaK?
      8. Fig. S9 seems redundant.
      9. line 263, "but the NBD exchange was almost equal to the deletion of the gene with respect to phenotypes." But, the mutant has >50% activity in Fig. 3F.
      10. line 221-226: the logic is not quite clear.

      Minor concerns:

      Please check spelling. There are some typos such as "HPPES" in the Methods.

      Significance

      In this study, Pan et al. characterized two Hsp70 DnaKs from Myxococcus xanthus DK1622. Through determining interactomes, the authors defined the differences and similarities between these two DnaKs in interacting with co-chaperones and substrates. Using domain-swapping, the authors analyzed the domain requirements for their functions. Lastly, their bioinformatics analyses seem to suggest the presence of these two DnaKs (i.e., DnaK duplication) is due to the increase of proteomic complexity. Overall, the results are interesting although not surprising. As the authors pointed out, many organisms have multiple Hsp70s with different but overlapping functions. Although multiple experimental approaches were used, the manuscript is generally descriptive without revealing any major mechanistic insights.

      My areas of expertise are protein biochemistry, genetics, and structural biology on heat shock proteins.

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      Reply to the reviewers

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

      Summary: Kellner and Berlin present their research findings pertaining to the effect of GRIN2B variants that modify NMDA receptor function and pharmacology. While these mutations were published previously, the new manuscript provides a more thorough investigation into the effects that these variants pose when incorporated into heteromeric complexes with either wildtype GluN2B or GluN2A - NMDA receptors containing only a single mutated GluN2B subunits is more relevant to the disease cases because the associated patients are heterozygous for the variant. The authors achieved selective expression of receptor heteromeric complexes by utilising an established trafficking control system. The authors found that while a single variant subunit in the receptor complex is largely dominant in its effect on reducing glutamate potency of the NMDA receptor, it 's effect on receptor pharmacology varied. Unlike diheteromeric receptors containing mutated subunits, polyamine spermine potentiated GluN1/2B (but not GluN1/2A/2B) receptors that contained a single mutated GluN2B. In contrast, the neurosteroid, pregnenolone-sulfate (PS), was effective at potentiating the NMDA receptor currents (to varying degrees) regardless of the subunit composition. The potentiation of NMDA receptor currents by PS was also observed in neurons overexpressing the variants.

      The techniques used in this study were appropriate to address the objectives and the overall effects are large, and generally convincing. I like the way the results are presented, although have a few (easily addressable) comments.

      We thank the reviewer for the positive remarks on our manuscript.

      Major comments:

      #1 When incrementally adding drugs (e.g. traces in figures 5 and 6), it doesn't always appear like the response has plateaued before changing the solutions/drugs. Therefore, I am curious to what extent the effects observed are underestimated.

      The reviewer is correct to note that some responses do not necessarily reach a plateau, despite our efforts reach steady-state (as shown in most figures, e.g., Figs. 1-4, 6b, etc.), in particular when applying pregnenolone-sulfate (PS) (Fig. 5a, all traces in middle and bottom rows). However, in several instances, this was unobtainable due the very slow effect of the neurosteroid (its mode of action is from within the membrane) and the very large size of the cell (~1 mm). For these reasons, these experiments mandated excessively long exposures (~minutes) of oocytes to glutamate and PS (see scale bar- 20 secs) to try to reach steady-state, however this also caused deterioration to some cells (which did not return to baseline- and were therefore discarded). Thus, we eventually converged on settings whereby we did not expose oocytes to more than 4 minutes of the drug. Nevertheless, to try to estimate the extent of the underestimation (if any), we fitted the currents (standard mono-exponential fit, as previously reported1–3 (Suppl. 5a). We found that our application times of PS were, on average, three time the response’s time constants (tau) (Suppl. 5b), and we found a very weak relationship (R2 = 0.09) between the response to PS and time of its application (Suppl. 5c). These are now explicitly mentioned in the text (line #203), and in the legend of Suppl. 5. These thereby suggest that the reaction reached approximately 95% (1 - 1/e^3) of the steady-state value, and we are therefore confident that we have very small, if any, underestimation the extent of PS potentiation.

      2 Also, in relation to figure 6, to what extent does agonist application cause desensitization here? Looking at traces in Figure 6b it appears that there is some desensitization and it isn’t clear to what extent this persists during the solution changes.

      Agonist desensitization of NMDARs-currents is a well-known phenomenon, but it is very well established that it is not always observed in cells, including neurons (e.g., 4–7). In general, we did not observe very frequent desensitization’s (we provide a larger variety of traces of desensitizing and non-desensitizing currents (Fig. 6b Suppl. 7e and Suppl. 8a). Nevertheless, we explicitly note that in neurons, currents that didn’t reach steady-state after application of 100 mM NMDA were excluded from analysis (Methods - Patch clamping of cultured neurons, line #474), and in most cases desensitization was minor (or absent) following application of 100 mM NMDA and 100 mM PS (Fig. 6b).

      3 Could the authors conduct/show the controls where NMDA alone (for 50-60s), or NMDA followed by PE-S (without ifenprodil).

      These recordings are now shown in Fig. 6b and Suppl. 8a, (as opposed to Suppl. 7e).

      #4 Finally, figure 5 shows the effect of the neurosteroid (and ifenprodil) on NMDA-evoked currents in neurons overexpressing the GluN2B variants in neurons. However, there currents probably reflect a mixture of extrasynaptic and synaptic receptors. To what extent are synaptic NMDA receptors affected by the variants?

      To show the extent of the effect of the variants over synaptic receptors, we recorded miniature NMDA-dependent EPSCs; mEPSCNMDA), as described in our previous report8. We find that the varinats completely eliminate the appearance of mEPSCs (Suppl. 7a, b). Change in minis’ frequency is not the result of a presynaptic change or a change in synapse number9, as we have shown that AMPAR-mEPSC frequency was unaffected by the variants (i.e., synapse number and probability of presynaptic release are unchanged by the variants).

        To further address this, we also explored the relative synaptic vs. extrasynaptic distribution of the variants by using the established MK-801-protocol (to block all synaptic receptors during spontaneous activity, leaving extrasynaptic receptors unblocked)10,11. In neurons overexpressing the GluN2B-*wt* subunit, we obtained an extrasynaptic fraction of 38%, highly consistent with previous reports12,13. Overexpression of the variants, however, yielded a significantly and higher fraction (~50%) of the remaining current, supposedly suggesting more variant receptors at extrasynaptic loci (__Suppl. 8b, c__). However, due to the experimental settings we have chosen, the results from this experiment represent quite the inverse when involving extreme LoF variants. Firstly, 100 mM NMDA does not saturate variant receptors (whether pure, mixed di- or tri-heteromers, see __Table 1__). Secondly, normal neurotransmission does not open synaptic receptors containing mutant GluN2B-subunits, attested by the complete absence of mEPSCs (see __Suppl. 7a, b and __8,9). Thus, during the 10 minutes exposure to MK-801, only (mostly) purely *wt* receptors are blocked by spontaneous synaptic activity, and thus the second bout of 100 mM NMDA solely exposes the remaining *wt*-receptors. An increase in the number reflects more *wt*-receptors at the extrasynapse than the synapse. Thus, the observed increase in the fraction of extrasynaptic receptors in neurons overexpressing the variants, implies that the number of *wt*-receptors is necessarily decreased from the synapse and increases at the extrasynapse. We deem this to ensue due to the incorporation of the variants at the synapse. This increase cannot be explained by an overall increase in membrane expression of *wt*-receptors in neurons overexpressing the variants, as these cells show a strong reduction in Imax  (see __Fig. 6c and Suppl. 7e__). This is now detailed in the text (lines #270-290).
      

      Minor comments:

      5 Looking at the fits in the graph of Figure 2b it appears that the slope on the concentration response curves is less steep for the mixed 2B-diheteromeric NMDA receptors. How much are the Hill coefficients changing and can this be interpreted to provide more mechanistic insight? Wouldn't it make sense to include the Hill coefficients in Table 1?

      We agree with the reviewer’s observation. Actually, the mixed di-heteromers have a similar Hill coefficient (nH) as the purely di-heteromeric GluN2Bwt receptors (see Table 1), and these show the typical near nH ~1 (e.g., 14–16). The only diverging groups are the purely di-heteromeric variant-containing channels (G689C/S only containing receptors; nH~2). Although these may suggest positive cooperation between the subunits, we are less inclined to infer insights from the latter owing to the fact that we limited our examination to 10 mM glutamate (we limit exposure of oocytes to 10 mM glutamate due to artifacts arising past this concentration, as discussed in Kellner et al.8: Fig. 2—figure supplement 1). (this description is now mentioned in page lines #149, 318, 319).

      6 The authors illustrate the changes in potency by the shift in the concentration response curves, but is there any change in efficacy? A simple way to illustrate this would be also present a simple graph showing the maximum current amplitudes (i.e. to 10 mM glutamate) for each of the receptor complexes.

      We now provide these data in (Suppl. 2a, b). We would like to note however that the expression pattern of the tailed-receptors (i.e., subunits with carboxy-termini tagged with C1/C2 tails, see Fig. 1a) are less expressive in general when compared with the native subunits (Suppl. 2c). This description is detailed in lines# 162-166.

      #7 The authors characterize the 'apparent' affinity (or potency) of the receptor using concentration-response curves, but numerous points in the manuscript refer to changes in affinity. None of the experiments shown directly measure affinity (which would require ligand-binding assays) and so the use of the word affinity is inaccurate/misleading. I suggest the authors replace the instances of the word 'affinity' with 'potency'.

      We apologize for the confusion surrounding our use of the term affinity. In fact, we do initially define this term in introduction (page #4): “apparent glutamate affinity (EC50)” to differentiate from affinity (KD). Regardless, and to avoid confusion, we replaced all terms, as suggested by reviewer to potency.

      #8 In the third line of the abstract, the authors wrote, 'for which there are no treatments' in relation to GRINopathies. My understanding is that there are symptomatic treatments but that there are no disease-modifying treatments.

      Indeed, all current treatments are supportive, rather than provide a bona fide cure or disease-modifying. These are now better defined in the abstract.

      #9 The authors have interchangeably used the terms NMDAR or GluNRs throughout the manuscript. I suggest sticking to one of these terms. I would suggest NMDARs since this is less likely to be misread as a a specific NMDA receptor subunit.

      Agreed and corrected throughout manuscript.

      #10 Typos: 1) Results paragraph 2 sentence one: 'We thereby produced GluN2B-wt, GluN2B-G689C and GluN2B-G689S subunits tagged with C1 or C2, co-expressed these along with the GluN1a-wt subunits in...') Results paragraph 2: '...but these were mainly noticeable when oocytes are were exposed to high (saturating) glutamate concentrations...'
3) Last sentence in the second to last paragraph of the results section entitled 'Mixed di-and tri-heteromeric channels...': 'This , PS may serve to rescue...'
4) Last sentence in last paragraph of the results section entitled 'Mixed di-and tri-heteromeric channels...': 'Despite the latter, we found no evidence for any direct effect of three different physiologically relevant concentrations of the drug on di- or tri-heteromeric receptors'

      All typos corrected.

      #11 Figures 1e, 2b, 3b: it would be helpful to add a legend to the graph so that the curves can be interpreted without having to read through the figure legend.

      Corrected.

      #12 The bar graphs in Figure 6 show individual data points but those in figures 4b and 5b don't. Can the authors please add the data points to these graph.

      Individual data points have been added.

      #13 It would be helpful to reviewers that future manuscripts by the authors include page numbers and line numbers.

      Included.

      **Referees cross-commenting**

      #14 Reviewers 2 and 3 highlight an important issue concerning figure 6 and the extent to which the overexpressed variants subunits can compete and assemble with endogenous NMDA receptors (unlike the system where the surface expression of specific receptor complexes is controlled). Indeed in the recent paper by the same authors, the two variants differed in their surface expression (in HEK cells), with G689C expressing particularly poorly. With reference to the second minor comment of Reviewer 1, the maximum current amplitudes would of course need to be normalized to cell surface expression of the receptor to gain any insight into efficacy.

      We provide maximal current amplitudes (Imax) as a proxy for expression level as typically done (e.g.,8,17). These are now shown in Suppl. 2a, b (and see our response to comment #6, above). We would like to emphasize that we find it challenging to gain insights about efficacy of the variants in neuronal synapses, as we purposefully express non-C1/C2 tagged subunits in neurons (as we covet assembly of the variants with endogenous subunits). Moreover, the C1/C2-tagged subunits (whether wt or variants) are less expressive compared to their non-tagged NMDAR-counterparts. For instance, tagged GluN2B-wt subunits express at ~50% compared to non-tagged GluN2B wt subunits (Suppl. 2c). Thus, we find that efficacy of the C1/C2 tagged-subunits is less relatable to the non-tagged subunits (which are used in neurons and likely more relevant to the disease).

      Despite the latter, we deem that we have specifically addressed this issue by measuring miniature EPSCs (mEPSCs) (see our reply to comment #4, Suppl. 7a, b). Briefly, even though the non-tagged G689C expresses at ~40% compared to other subunits (in oocytes and mammalian cells8), in neurons it engenders a robust (and highly significant) negative effect over synaptic currents (mEPSCs), as strong as the G689S-variant which expresses much more robustly (non-tagged G689S expresses to same extent as wt subunits). This demonstrates that the reduced efficacy the tagged subunits is less relatable to the non-tagged subunits and, importantly, it does not hinder the variants’ ability to incorporate within the synapse and affect function (i.e., exert a dominant negative effect). Here, we extend these observations towards the major postnatal channel subtype, namely tri-heteromers (2A/2B*), and therefore demonstrate that the robust dominant negative effect of G689C and G689S variants is likely due to their ability to incorporate within the predominant receptor subtype at the synapse (Suppl. 8).

      Reviewer #1 (Significance (Required)):

      This study emphasizes the complex pattern of effects that variants can have on glutamate receptor function and pharmacology, especially considering the context of receptor subunit composition. The authors have followed up their previous findings on the same mutants (Kellner et al, 2021, Elife), but used a trafficking control system here to characterize properties of mutated receptor complexes that are most likely to exist in neurons. The authors show that the defective currents mediated by NMDA receptors containing a loss-of-function GluN2B variant can be enhanced by neurosteroids (and in the case of GluN1/2B receptors, polyamines also). Development and approval of neurosteroids for the clinic would be required for the findings to translate to a therapy for patients. Readers should also be aware that neurosteroids act on other receptors too (e.g. GABA receptors), which could complicate the outcome. The expertise of the reviewer is in glutamate receptors and synaptic transmission.

      We agree with the reviewer’s comment pertaining to challenges in translating PS to the clinic. Indeed, we explicitly mentioned its inhibitory effect on GABAA receptors (see line #366-367 and reference 18), as well as note its potential negative effect over GluN2C/D-containing receptors (line #365 and reference 19). We further describe alternative neurosteroids and means to bypass the limitations of PS, for instance by use of 24(S)-hydroxycholesterol6,18 or synthetic analogues (SGE-201, SGE-301)6. Lastly, we also propose a novel therapeutic approach, for which we did not find any mentions in the literature with regard to GRINopathies, consisting of the use of the FDA-approved Efavirenz (anti-retroviral compound20) to promote activity of cytochrome P450 46A1 (CYP46A1) to increase amounts of 24-S in the brain (discussion, lines #370-383).


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

      #1 The objective of this paper is to assess whether a single mutated subunit of GRIN can affect the function of various forms of NMDA receptors. In particular, this study investigates the functional consequences of a GRIN variant when assembled within tri-heteromers, containing 2 GluN1, 1 GluN2A and 1 GluN2B subunits, the major postnatal receptor type. For this purpose, the authors artificially forced the subunits to associate in predefined complexes, using chimeras of GRIN subunits fused to GABAb receptor retention control sites at the endoplasmic reticulum. This trick allows to control the stoichiometry of the channels at the membrane and thus to focus on the function of a single type of NMDA receptor.The take home message of the paper is that a single GluN2B‐variant, whether assembled with a GluN2B‐wt subunit to form mixed di‐heteromer or with a GluN2A‐wt‐subunit (tri‐heteromer), strongly impairs the receptor functioning, as reported by a decrease of the apparent glutamate affinity of the receptor.

      Altogether, this is a straightforward study of great interest for the GRIN community.

      We greatly appreciate the reviewer’s comment about the relevance of our work towards the GRIN-community.

      2 However, the way the background and purpose of the study (title and abstract) are presented is a bit confusing for non-specialists and could be easily improved. Technical information, which is crucial to validate the conclusions drawn from data analysis, should be added to the article. Some additional experiments are suggested to consolidate the work. Finally, additional discussion points are strongly encouraged.

      We apologize for not making the paper more accessible to a broader readership. We did so for the sake of brevity. Nevertheless, we have re-written major parts of the manuscript to address this issue and retitled the report: “Rescuing Tri-Heteromeric NMDA Receptor Function: The potential of Pregnenolone-Sulfate in Loss-of-Function GRIN2B Mutations”.

      Specific comments

      Abstract / Title:

      3 This work shows that a single GRIN variant impairs the function of various forms of NMDA receptors. Several sentences in the title and the abstract are confusing for a non-specialized audience. "Two extreme Loss‐of‐Function GRIN2B‐mutations are detrimental to triheteromeric NMDAR‐function, but rescued by pregnanolone‐sulfate." "Here, we have systematically examined how two de novo GRIN2B variants (G689C and G689S) affect the function of di‐ and tri‐heteromers." The number of variants tested is not of capital importance in the title, especially because one could believe that both are tested at the same time; similarly, when variants are named in the abstract, the fact that only 1 variant is studied at a time should be clarified (G689C OR G689S). Indeed, the problem is obvious to those familiar with GRIN disorders, but if this paper is to be published in a journal reaching a large audience rather than a specialized audience, the title of the paper should be modified.

      As noted in our reply to comment #1 of this reviewer, we apologize for not making the paper more accessible and have therefore changed the title and re-written major parts of the manuscript to address this issue. We would like to note that we appreciate the reviewer’s comment and intent to increase the readership of our manuscript.

      #4 "We find that the inclusion of a single GluN2B‐variant within mixed di‐ or tri‐heteromeric channels is sufficient to prompt a strong reduction in the receptors' glutamate affinity, but these reductions are not as drastic as in purely di‐heterometric receptors containing two copies of the variants. This observation is supported by the ability of a GluN2B‐selective potentiator (spermine) to potentiate mixed diheteromeric channels." Please, clarify the link between the two sentences. How do spermin potentiation of mixed diheteromeric channels supports the observation that the inclusion of a single GluN2B‐variant has less effect than the inclusion of two variants?

      Our intention was to highlight that mixed di-heteromeric channels (2B/2B*) are less “damaged” (this is the link) than purely di-heteromeric channels (2B*/2B*).Explicitly mixed di-heteromers show less reduction in glutamate potency AND are also spermine-responsive, whereas purely mutant di-heteromers (2B*/2B*) show reduced potency, BUT do not respond to spermine at all. We have rephrased the sentences in our current manuscript to be clearer:

      For instance: The positive responses of mixed di-heteromers, compared to the null effect over pure di-heteromers, is likely the result of the restored pH-sensitivity of mixed di-heteromers (Suppl. 3). This was surprising as the minimal and essential rules of engagement for potentiation by spermine are not well established, particularly in the case of tri-heteromers21,22 (see discussion, lines #341-353).

      Methods

      #5 All this study is based on the use of a unique ER‐retention technique to limit expression of a desired receptor‐population at the membrane of cells. According to the ER system retention of GABAb receptor, used in this study, while C1/C1-fused subunits are retained in the ER, C2/C2 reach the cell surface and the association of C1/C2 in the ER enables cell-surface targeting of the heterodimer. However, GB2 does not contain any retention signal and can reach the cell surface in the absence of GB1, as a functionally inactive homo-dimer (doi: 10.1042/BJ20041435). If there is an experimental trick that prevents the addressing of C2/C2 to the cell membrane, it should be specified and explained. This is critically important for understanding which receptor populations the data are derived from: receptors containing C1/C2 fused subunits only as stated by the authors, or C1/C2 and C2/C2 fused subunits?

      We base our experiments on two seminal reports—23,24—that have developed this unique method (which we also refer to in the text, lines# 112-116). Briefly, the method employs the binding motifs of GABAB1 (GB1) and GB2 subunits and ER-retention motifs (these are now better detailed in Methods section, line # 448). Previous reports explicitly demonstrate that C1/C1- OR C2/C2-containing receptors do not reach the plasma (or very minimally) and we have reproduced these data with our variants (C1/C1: Suppl. 1a-d; C2/C2: Fig. 1a-c).

      Figures #6 NMDA-receptor current amplitude should be normalized by the membrane expression of the receptors. A preliminary experiment should measure the effective cell surface expression of each of the subunits in the different transfection conditions.

      To address the effective cell surface expression, we employed Imax as a proxy for functional expression (e.g.,8,17). These are now shown in Suppl. 2a, b (and see our response to reviewer 1, comments #6 and 14). Expectedly, we find significantly reduced efficacy by the varinats compared to wt-receptors, and the purely mutant di-heteromeric receptors exhibit the weakest efficacy. We have also addressed this issue by measuring miniature EPSCs (mEPSCs) (see our reply to reviewer 1, comment #4,). We find the variants to abolish mEPSCNMDA frequency (Suppl. 7a, b). This shows that the variants’ reduced efficacy translates to elimination of synaptic activity (dominant negative effect) (also seen in Suppl. 8).

      #7 Fig.1a

      The scheme should include C2-C2 complexes and mention whether these complexes are expressed at the cell surface (see previous and following comments).

      As noted in our reply to comment #5 of this reviewer (above), C2/C2-containing receptors do not reach the plasma membrane (Fig. 1a-c). To avoid confusion, we have now added this scheme to the cartoon presented in Fig. 1a and have provided a more detailed description of the method and clones produced in the Methods section (line # 448).

      #8 Fig.1b and c

      Current from cells transfected with GluN2B‐wt‐C1 and GluN2B‐wt‐C2 should be compared to current expressed in cells expressing untagged receptor subunits: GluN2B‐wt Current from cells transfected with GluN2B‐wt‐C1 alone should be shown as well (although expected to be retained in the ER) (as performed for GluN2A‐wt‐C1 GluN2B‐wt‐C1 in suppl Fig. 1a)

      Current comparisons of oocytes expressing tagged GluN2B‐wt‐C1 and GluN2B‐wt‐C2 and non-tagged GluN2B‐wt are now demonstrated in Suppl. 2c. The results indicate that the “tags” (C1 and C2) affect the expression of the subunits. We have also added a sample trace of current from a cell expressing the GluN2B‐wt‐C1 alone (Fig. 1b).

      9 How could you explain the null current from cells transfected with GluN2B‐wt‐C2 alone (Fig.1b middle, and 1c)? since GB2 does not contain any retention signal and can reach the cell surface in the absence of GB1, GluN2B‐wt‐C2 is supposed to reach the cell surface. This is a very important point to clarify (I am probably missing a technical detail) because if the sub-unit tagged with C2 does reach the cell surface, then all the results and conclusions drawn from the C1-C2 conditions are wrong and could be attributed to a mix of complexes containing either C1-C2 or C2-C2.

      We now realize that the reviewer was missing a crucial technical detail, namely how the clones are designed. Briefly, all clones have ER retention motifs and cannot reach plasma membrane unless they necessarily assemble as C1/C223,24. Also, please see our replies to comments #5, 7 to this reviewer (and Methods section, line # 448).

      My following comments are based on the assumption that only receptors containing C1-C2 tagged subunits reach the membrane (as assumed by the authors and suggested in Figure 1b middle), but explanations should absolutely be provided to convince the reader. Fig. 4a and 5a (see our above replies to comments #5, 7 and 9; and references 23,24).

      #10 Please, keep the current scale constant between all current illustrations within the same figure (4a and 5a). Indeed, not only the Spermin- or SP- induced potentiation is an important data (which is presently quantified on the histograms of fig. 4b and 5b) but also knowing whether the amount of current recorded in cells expressing one mutant subunit in presence of SP (for example GluN2A‐wt‐C1 GluN2B‐G689S‐C2) is comparable to the current recorded in wt receptor-expressing cells (GluN2A‐wt‐C1 GluN2B‐wt‐C2) in absence of SP would be an excellent added value for the paper. A special figure could quantify this rescue effect of SP, measuring and comparing the mean currents recorded in these conditions (one current illustration is not sufficient given variations between similar samples). By the way 5mM glutamate might be an excessive concentration. At 1mM, the expected synaptic concentration of glutamate following action potential, according to figures 3 and Suppl1 the response of the mutated receptor is much lower than that of the WT which is already almost maximal. In these conditions, SP-induced potentiation by a factor of two of GluN2A‐wt‐C1 GluN2B‐G689S‐C2 current could be equivalent to control currents recorded in GluN2A‐wt‐C1 GluN2B‐wt‐C2 cells.

      We have rescaled all current amplitudes in Figs. 4 and 5 to be identical in size for easier comparison.

      We have added all current amplitudes to try to examine the rescue effect of the two drugs in cell overexpressing a specific channel subtype, as requested (Suppl. 4). We find that; indeed, the potentiated currents of the mutant receptors reach (or even surpass) the basal Imax (i.e., current before potentiation) of the non-mutated receptors (Suppl. 4, dashed statistics bar).

      In neurons, we address this in two ways. First, we show that the total NMDA-current is reduced by expression of the variants, and this current is “normalized” by PS (Fig. 6a-c). Similar reductions in Imax (by the variants) are shown in Suppl. 7e (to provide more examples). Secondly, neurotransmission (i.e., 1 mM glutamate25,26) is not sufficient for activating mutant receptors, certainly not pre-di-heteromers (see Table1, EC50 and Suppl. 7a, b- no mEPSCs)27–29. Therefore, 5 mM was required. Together, these strongly suggest that PS may normalize the currents of different receptors that respond to PS (under physiological settings and not 1- or 5mM NMDA). As suggested by the reviewer, there are many subtypes, and some may be activated by ambient glutamate (as suggested by application of PS onto neurons without opening the receptors by NMDA; see Suppl. 7c, d).

      #11 Fig. 6

      Figure 6 is not convincing because cultured hippocampal neurons do express endogenous NMDA receptors. To what extent the recording currents are affected by endogenous, non-mutated GluN2B subunits? Western Blots showing an extinction of endogenous subunits expression when transfected tagged subunits are competitively expressed would be required.

      We have previously shown that the two variants incorporate very efficiently within synapses, causing a very robust elimination of synaptic currents (by measuring miniature NMDA-dependent EPSCs; minis) [see Fig. 8 in Kellner et al. eLIFE, 202127, and see review by Sabo et al.9 ). Change in minis’ frequency can be interpreted as either a presynaptic change or a change in synapse number, however we observed that AMPAR-mEPSC frequency was unaffected by these variants. These imply that synapse number and probability of release are unchanged by the variants. As the experiments are performed in wild-type neurons, (which express wild-type GluN2A and -2B), the dramatic effects we observed on minis suggests a dominant-negative effect of these disease-associated GluN2B variants. These are consistent with our observations that mutant subunits can co-assemble with wild-type GluN2B and/or GluN2A subunits. We have now reproduced this experiment (in fact, we employ this strategy prior each experiment to ensure expression of the variants) (Suppl. 7a, b). This thereby shows that there are no available wt-receptors at the synapse.

      As there are various pools of NMDARs at synaptic and extrasynaptic sites, we did not think that a western blot would sufficiently differentiate between the latter, and thereby would not provide insight about extinction of wt-receptors (which could be simply pushed to other sites compared to synapse). Moreover, the intracellular pool of receptors is much larger than the amount of NMDARs that can be detected at the membrane (e.g., 30,31), and therefore electrophysiology seemed to be a better means to monitor membrane receptors only:

      Thus, to examine the distribution of the variants between synaptic- and extrasynaptic loci, we employed a standard procedure consisting of the use of the activity-dependent blocker MK-801 (Methods). Briefly, neurons were persistently bathed in TTX during which they were probed for Imax using 100 mM NMDA (to refrain from activating other GluRs), followed by application of MK-801 for 10 minutes to exclusively blocks synaptic receptors (that open following action-potential independent miniature neurotransmission). This thereby spares all extrasynaptic receptors from being blocked by MK-801, which are subsequently revealed by a second application of 100 mM NMDA (Suppl. 8a, inset)12. In neurons overexpressing the GluN2B-wt subunit, we obtained an extrasynaptic fraction of 38%, highly consistent with previous reports12,13. Overexpression of the variants, however, yielded a significantly and higher fraction (~50%) of remaining current (Suppl. 8b, c), but instead of reflecting a larger pool of extrasynaptic receptors, the experiment represents quite the inverse when involving LoF variants. Firstly, 100 mM NMDA does not saturate variant receptors (whether pure, mixed di- or tri-heteromers, see Table 1). Secondly, normal neurotransmission does not open synaptic receptors containing mutant GluN2B-subunits, attested by the complete absence of mEPSCs (see Suppl. 7). Thus, during the 10 minutes exposure to MK-801, only wt receptors are blocked by spontaneous synaptic activity, and thus the second bout of 100 mM NMDA solely exposes the remaining wt-receptors at the extrasynapse. Thus, the observed increase in the fraction of extrasynaptic receptors, in neurons overexpressing the variants, implies that the number of wt-receptors is necessarily decreased from the synapse and increases at the extrasynapse, most likely due to the incorporation of the variants at the synapse. This increase cannot be explained by an overall increase in membrane expression of wt-receptors in neurons overexpressing the variants, as these cells show, yet again, a strong reduction in Imax as seen above (see Fig. 6c and Suppl. 7e) (lLines #270-291). These thereby suggest that purely wt-receptors are not necessarily eliminated from the membrane (extinct), rather pushed outside of the synapse.

      12 Fig.6b “PE-S” on the graph should be replaced by “PS”

      Typo corrected.

      Discussion #13 The authors are surprised by the fact (Fig.2) that 1 variant reduces the apparent glutamate affinity of the receptor, but not as much as 2 variants, despite the fact that "NMDARs opening requires all four subunits to be liganded (i.e., occupied by a ligand) which implies that the least affine subunit should have dominated the final affinity of the receptor". I agree that the difference is noticeable, however the glutamate affinity for receptors containing 1 variant is much closer to that of receptors containing 2 variants than that of wild-type receptors. Hence, the results obtained do not seem so surprising and could result, as rightly explained by the authors from a possible cooperativity between the subunits.

      We agree with the reviewer that glutamate potency of receptors containing 1 variant subunit is much closer to that of receptors containing 2 variant subunits. However, we maintain our surprise because we expected it to equal (not just close) to the potency of the least affine subunit (the limiting factor). This is based on the notion that all four subunits need to be liganded for channel opening4,32–34. We gently raise the possibility of potential cooperativity (Table 1, see Hill-coefficient and 33,35,36), as well as mention that this may also stem from the variants’ lower proton sensitivity (Suppl. 3), which has also been shown to promote motions of the ATD (amino terminal domain) and increase open probability (positive cooperativity)36. Nevertheless, we are very careful with interpreting the Hill coefficient , as we limited exposure of oocytes to 10 mM glutamate due to artifacts arising past this concentration (see Kellner et al.8: Fig. 2—figure supplement 1). This description is now mentioned in page lines #149, 318, 319. Thus, even the slightest underestimation of the maximal reposnse would surely affect the slope.

      #14 On the other hand, the data in Figure 6 are much more difficult to interpret and reconcile with the nature of the expressed receptor subunits (which this time is not controlled) nor their association within the same receptor. However, this aspect, which is essential to the understanding of the consequences of 1 variant on neuronal signalling, is not discussed: Whatever the stoichiometry of the complexes in the heterozygous disease, the mutated and wild type GluN2B subunits coexist in the same cell: Either within the same di-heteromeric complexes GluN2B-wt + GluN2B-mutant, or in separate complexes but nevertheless expressed in the same cell, in di heteromeric (GluN2B-wt + GluN2B-wt and GluN2B-mutant + GluN2B-mutant); or tri-heteromeric (GluN2A-wt + GluN2B-wt and GluN2A-wt + GluN2B-mutant) complexes. Assuming that half of the complexes remain wild-type, e.g. (GluN2A-wt + GluN2B-wt and GluN2A-wt + GluN2B-mutant) we would expect (Fig. 6) a small decrease in NMDA current (carried only by the half that expresses the mutated subunit, and whose function is not zero but only decreased by about 20% in response to 5 mM Glutamate, Fig. 3b). The same reasoning applies to the di-heteromeric conditions (GluN2B-wt + GluN2B-wt; GluN2B-mutant + GluN2B-mutant), here again the decrease observed Fig. 6b is difficult to reconcile with the responses measured Fig. 2b.

      In other words, how to explain a 50% decrease of the currents, instead of the 10% expected by the previous reasoning. In this experiment we do not know which subunits are expressed, their proportions, nor how they are associated in functional complexes, which makes the interpretation of the data impossible. The only explanation, far-fetched, for 50 % decrease would be that the complexes were to contain all (or the vast majority) 1 wild-type subunits associated with 1 variant, then a homogeneous 50% reduction in current could be expected. But this extreme condition could only be possible in the case of di-heteromers, which is unlikely the case in Fig.6 as GluN2A currents are measured in presence of Ifenprodil. To conclude

      1) the comparison of the currents in transfected and non-transfected neurons does not make sense in figure 6b which is not convincing because we do not know the nature of the currents actually measured. A comparison in controlled condition would make more sense (as I suggested in the criticism of figures 4, 5).

      2) The reality of the combinations of expression and association between subunits within different complexes expressed in the same cell must be considered and taken into account in the interpretation of the data. Undoubtedly, the means of restoring the NMDA current will be different depending on the presence of mutated subunits in all functional channels or not.

      Indeed, neurons express a variety of different combinations of channel stoichiometry, including following transfection with the variants. We do find find that the effect on whole-cell current is indeed ~50% (Fig. 6b, c), thereby safe to assume that 50% remain “wt”, but we do not know how they distribute between synaptic and extrasynaptic loci. Our results however argue against 50% remaining receptors at the synapse. First, mEPSCNMDA disappear (Suppl. 7a, b and see reply to comment #11 of this reviewer), but wt-receptors are still at the membrane, and they seem to be moving out of synapse (Suppl. 8). Thus, we can only state with higher certainty that the variant subunits are very efficient in incorporating within mixed or pure receptors, especially at the synapse.

        We also consider that the reduction in the whole-cell current observed in __Fig. 6b, c__ is not due to the remaining 50% GluN2B-*wt*-containing receptors, rather likely due to other variants, notably GluN2A, which are more prominent at postnatal stages37, such as in our case. In support, we see a large remaining current after saturating ifenprodil application (__Suppl. 7 e, f__)38. Thus, the variants incorporate within all 2A/2B membrane receptors, at the synapse and outside it (i.e., extrasynaptic) (see __Suppl. 8, c__).
      

      **Referees cross-commenting**

      The referees' comments are highly relevant. In particular, referee 3's comment 1 seems very interesting because it may help to better understand the discrepancy in the results observed in neurons, i.e. a 50% decrease in the currents induced by the expression of the mutant and wild type subunits in the same cells, whereas theoretically one would expect a 10% decrease of this current (cf. referee 2's 2nd comment in the discussion section). This comment 1 of referee 3 indeed stresses the fact that the control (non-transfected neurons) to which the heterozygous condition is compared is not the correct control, which should rather be neurons transfected with wild type receptor subunits. More generally, this comment underlines the importance of monitoring the effective membrane expression of the different subunits in each of the experimental conditions in order to be able to compare conditions and draw conclusions.

      We initially did not perform this control as the literature paints a clear picture whereby expression of the GluN2B-subunit (without adding excess of the GluN1 subunit) does not instigate a robust increase in surface expression of NMDARs (and thus current remains the ~same) 4,39–43, and see our reply to comment #14 (above), and reviewer 3 comment 1 (below). Nevertheless, we have now performed this test by overexpressing GluN2B-wt. In support of previous reports, we do not find any statistical difference in current size between non-transfected neurons and neurons solely overexpressing the GluN2B-wt subunit (Fig. 6a, b). Furthermore, application of PS onto naïve or GluN2Bwt expressing neurons yields identical currents (Imax) and potentiation (Fig. 6c, d). These argue that we did not obtain “overexpression”.

      We suggest that the 50% reduction in current size between neurons expressing the mutant and wt expressing neurons stems from the integration of mutant subunits and their dominant negative effect. Evidence for this incorporation is provided by the very strong reduction in synaptic currents (suppl 7a, b), and the supposedly higher abundance of wt-containing receptors in extrasynaptic regions (see reviewer 1 comment 4 and suppl 8). This is

      Reviewer #2 (Significance required):

      The novelty of the study, is to evaluate the consequences of a single mutated subunit within NMDA receptors affected by GRIN variant, to mimic the heterozygous condition of GRIN encephalopathies, this is of potential value for the field and the interest could also be extended to other genetic diseases (at least the experimental way to study the functioning of only one desired stoichiometric configuration). The strength of this paper is precisely to isolate technically and to study the functioning of a desired stoichiometric configuration only. The main limitation of the paper is the interpretation that the authors make of their data in a physio-pathological context. This work could be of interest for general audience, providing the title and summary are slightly modified. My area of expertise could not be closer to the topic of the article: Glutamate receptors; GRIN; molecular tinkering, cell culture, electrophysiology, receptor stoichiometry...

      We thank the reviewer for noting the value in our work and its potential contribution and interest to the field and other diseases. Per reviewer’s suggestion, we have modified the title and text to suit a larger audience.

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

      This paper is a follow up of an earlier paper published by the group (Kellner et al., eLife 2021), which aimed at characterizing the functional properties of two de novo GluN2B mutations in patients suffering from severe pediatric diseases, GluN2B-G689C and -G689S. NMDA receptors (NMDARs) are tetramers composed of two GluN1 and two GluN2 subunits. A single receptor can incorporate either two identical GluN2 subunits (di-heteromers) or two different GluN2 subunits (tri-heteromers), leading to a large diversity of NMDAR subtypes. The main NMDAR subtypes in the adult forebrain are GluN1/GluN2A and GluN1/GluN2B di-heteromers, as well as GluN1/GluN2A/GluN2B tri-heteromers. While the exact proportions of these three subtypes are still contentious, there are evidence that in the adult N1/2A/2B tri-heteromers form the major population of synaptic NMDARs in the adult forebrain. In addition, patients bearing pathogenic mutations are often heterozygous for the mutation, giving rise to mixed NMDARs incorporating one mutated and one intact GluN2 subunit. In their previous paper, Kellner et al. had shown that purely di-heteromeric GluN1/GluN2B-G689C and -G689S mutants display a drastic (> 1,000-fold) decrease of glutamate sensitivity and a decrease of surface expression. In the current paper, the authors characterize the effects of the -G689C and -G689S mutations on N1/2A/2B tri-heteromeric receptors, as well as on mixed di-heteromeric GluN1/GluN2B receptors containing one copy of the wild-type GluN2B subunit and one copy of the mutated GluN2B subunit. They show that one copy of the mutant subunit, either within mixed diheteromeric or tri-heteromeric receptors, is sufficient to decrease drastically glutamate sensitivity, although the shift in glutamate EC50 is not as strong as in pure di-heteromeric receptors (≈ 500-fold). They furthermore explore strategies to counteract the hypofunction induced by these mutations by testing the effect of positive allosteric modulators (PAMs). They show that spermine, a GluN2B-specific PAM, can potentiate the activity of mixed diheteromeric N1/2B but not N1/2A/2B tri-heteromers. However pregnenolone sulfate (a 2A/2B-specific PAM) can potentiate both the activity of mixed diheteromeric and tri-heteromeric NMDAR populations, either in oocytes or cultured neurons.I have very few major comments to make. The experiments are straightforward and the adequate controls have been made. Here are my two only major comments:

      We thank the reviewer for the very detailed overview of our work and for appreciating our controls and methods.

      #1 About the experiment on cultured neurons. The authors compare the currents of cultured neurons transfected with GluN2B-G689C and -G689S to non transfected neurons. The adequate control is rather neurons transfected with the wild-type GluN2B subunit to even out any phenomenon linked to transfection of the neuron. Given the overexpression that can occur after transfection, the effect of the mutations on the size of NMDAR currents might be even stronger than what the authors show. However in that case PS might not completely rescue mutant NMDAR currents to wild-type levels.

      We initially did not perform this control as the literature paints a clear picture whereby expression of the GluN2B-subunit (without adding excess of the GluN1 subunit) does not instigate a robust increase in surface expression of NMDARs (and thus current remains the ~same) 4,39–43, and see our reply to comment #14 (above), and reviewer 3 comment 1 (below). Nevertheless, we have now performed this test by overexpressing GluN2B-wt. In support of previous reports, we do not find any statistical difference in current size between non-transfected neurons and neurons solely overexpressing the GluN2B-wt subunit (Fig. 6a, b). Furthermore, application of PS onto naïve or GluN2Bwt expressing neurons yields identical currents (Imax) and potentiation (Fig. 6c, d). These argue that we did not obtain “overexpression”. Thus, our results and interpretations hold true, and are therefore not underestimation of the effects of PS in neurons.

      2 How come high concentrations of glutamate (>100µM) produce additional current on wt GluN1/GluN2B (with retention signals) compared to 100 µM glutamate, which is supposed to be saturating? It does not seem to stem from an osmotic effect since 10 mM glutamate does not produce any current on uninjected oocytes. Knowing that this "artefactual" effect might also occur in the mutant receptors, how do you take this effect into account when calculating the glutamate EC50s of the mutants? Given the drastic shift in EC50 produced by the mutant, taking into account this artefact is not going to change the conclusion, but the actual EC50s will be affected.

      GluN1/GluN2B-wt receptors (with or without retention signals) are indeed saturated at 100 mM glutamate. However, excessively large concentrations of glutamate (>100 mM) may yield artefacts even in non-injected oocytes (in 10 mM, this occurs in ~20% of the cells, see Kellner et al 20218—Fig. 2 and Suppl. 1c, d) as well as in GluN2B-wt injected oocytes (supplementary Table 1 in 44). This is not due to osmolarity, as rightly mentioned by the reviewer (and below), rather possibly by endogenous glutamate receptors and transporters that do not readily contribute to current amplitude (these are extremely small currents), but can cause deterioration of the cell (and enhance ‘leak’) when activated for prolonged times by very large concentrations (e.g.,45). In fact, we explicitly report these to highlight potential artefacts, as these are often overlooked in the field. Regardless, most reports do no go past 100 mM glutamate, not even when describing GRIN2 mutations since most mutations do not cause such drastic shifts in potency as we observed (to the best of our knowledge only one report describes such an extreme LoF mutation for a GluN2A variant46). Of note, these effects are not seen when glycine is applied at high concentrations (supporting lack of effect by osmolarity)47. Thus, we refrained from testing concentrations past 10 mM, aware that it may yield a slight underestimation of glutamate potency (and perhaps the reason for the larger Hill coefficient, nH; see our reply to reviewer #1, comment #5). Importantly, despite the potential underestimation of the EC50, it does not change our conclusions as all groups are measured side-by-side (thus, the same underestimation equally applies to all other groups as well). We now mention this more in detail in the methods under the section – “Two Electrode Voltage Clamp recordings in Xenopus Laevis oocytes”.

      Minor comments:

      3 In the first paragraph of the "Results" section, when describing the design of the constructs used to force a heteromeric stoichiometry in recombinant systems, the authors do as if they had designed the constructs themselves "Briefly, we tagged...are retained in the ER (Fig. 1a)". Please rewrite this paragraph to show that you used constructs that had been previously designed by another group (Hansen et al., 2014).

      We apologize. We did not mean to express that we have developed the method and indeed refer readers to the seminal works of those who did (Stroebel et al., 2014 and Hansen et al. 2014, lines #109-116). We did not go into details for the sake of brevity. We have rewritten this part to give proper acknowledgement to the method’s developers (also see Methods, line# 448).

      4 I do not see any evidence of "positive cooperativity" between subunits in ref. 32. Ref. 32, to the best of my knowledge, states that in N1/2A/2B tri-heteromers, the 2A subunit sets the biophysical properties of the tri-heteromer. But there is no account of mixed di-heteromers. In addition, the cooperativity between the glutamate and glycine binding sites is negative.

      The reviewer is correct, and we apologize for the mis-citation. Indeed, the cooperativity between glutamate- and glycine-binding is typically reported as negative48,49, and our intention was to highlight the strong cooperativity (whether positive or negative) observed between NMDAR-subunits and meant to cite the works of: 33,35,50 (lines . We have now rephrased the sentence: The divergence from this scenario suggests that the slight amelioration in potency could stem from positive cooperativity between the subunits50 (but see Hill coefficients in Table 1). Indeed, mixed receptors show restored proton sensitivity (Suppl. 3), which has been suggested to be coupled to other receptor features, notably increase in open probability.

      5 Interpretation of spermine action within the Results section: it is striking indeed to observe that the mutations in the context of a mixed di-heteromer still allow spermine potentiation, while they abolish this potentiation in pure di-heteromers. As rightly said in the discussion, the regain of spermine potentiation in the mixed compared to the pure diheteromers is likely due to a more favorable transduction of spermine signaling to the pore, likely via a higher pH sensitivity of mixed di-heteromers compared to di-heteromers. I would thus avoid the terms of "one single intact interface" for the mixed di-heteromer, since both spermine binding sites are likely intact in this NMDAR configuration. How is pH sensitivity affected in the mixed di-heteromers?

      We have performed a detailed pH dose-responses for the various channel types (Suppl 3). We find that GluN2B mixed di-heteromers exhibit similar IC­50 as pure GluN2B-wt di-heteromers, thus explaining their ability to undergo potentiation by spermine via alleviation of proton inhibition. We therefore further suggest that mixed di-heteromers’ have higher pH-sensitivity compared to pure mutant di-heteromers and this mat also contributes to their higher spermine sensitivity. Lastly, we observed that all GluN2A-wt-containing tri-heteromeric receptors were non-responsive to spermine (Fig. 4a). In fact, under our experimental conditions tri-heteromers underwent slight inhibition by spermine, regardless the identity of the GluN2B subunit (whether wt or variant) (Fig. 4b). Thus, as the tri-heteromers used here exhibit identical pH-sensitivity as 2B-di-heteromers, the only diverging aspect is the missing interface between the GluN1a and GluN2B subunits, demonstrating that potentiation by spermine requires at least one GluN2B-subunit with an intact proton sensitivity, and mandates two intact interfaces between GluN1-wt and GluN2B-wt subunits (Table 1)21.

      6 In the methods section, the oocyte recording solution (likely Ringer and not Barth) does not contain any potassium. This is probably a typo. Could you correct the composition of your Ringer?

      Corrected. We record NMDARs currents by use of a Barth solution containing (in mM): 100 NaCl, 0.3 BaCl2, 5 HEPES, pH 7.3 (adjusted with KOH, at ~2.5 mM) (as in 4,51).

      7 There are several typos, especially in the Discussion.

      We have corrected the typos throughout the publication.

      **Referees cross-commenting**

      I overall agree with the comments of reviewers 1 and 2. In particular, I agree that it is pointless to compare the absolute currents of non transfected neurons vs mutant-transfected neurons without an idea of receptor cell-surface expression.

      We have performed this experiment (Fig. 6) and please see our reply to this reviewer’s comment #1.

      I would like however to give some precisions about some comments of Reviewer 2. About the ER retention technique to express tri-heteromers: I didn't know that the C2 signal could be addressed to the membrane on its own. The lack of leak current stemming from C1-C1 or C2-C2 combinations has been demonstrated in the paper establishing the technique (Hansen et al, 2014), as well as in another paper that developed an analog technique based on GABAB retention signals (Stroebel et al., J Neurosci 2014). So it is fair to consider that the authors were not surprised by the lack of current when co-expressing two GluN2B subunits carrying the C2 signal.

      We thank you for this addition and support for our observations.

      About the comparison about absolute currents wt vs mutants, +/- spermine (Fig. 4a and 5a). I agree with reviewer 2 that being able to compare absolute currents of wt without spermine to mutant + spermine would be very interesting to see if spermine can actually rescue mutant hypofunction. However, to the defense of the authors, comparing absolute current values of recordings from Xenopus oocytes is meaningless. Indeed the variability of currents for the same construct and same day of experiment is too high (there can be up to a ten-fold difference between the lowest and the highest current of oocytes expressing the same construct the same experimental day). A way to investigate this aspect would be to estimate the open probability of the different constructs with or without spermine via the inhibition kinetics of an open channel blocker (e.g. MK801) and measure surface expression by Western blot, but I am not sure these experiments are worth it for the spermine experiment.

      We agree with this reviewer about current size. It is quite variable among cells and would therefore introduce an additional variable and variability: the expression of these modified (C1/C2-tagged) subunits is dually affected by the mutation itself (Kellner et al. 2021) and by the introduction of the tagging (which really hampers there trafficking to membrane, Suppl. 2c); with unknown contribution of each variable. We thereby do not think these provide an added value to our conclusions, yet to grant reviewers’ no 2 request we have added __Suppl. 4 __which shows the rescue effect of the different drugs.

      Reviewer #3 (Significance (Required)):

      This paper is not of high significance since most of the characterization of the 2B-G689C and -G689S de novo mutants found in patients has already been published (Kellner et al., eLife 2021). However, this paper is worth publishing since it brings new data on the effect of the mutations on tri-heteromeric and mixed di-heteromeric NMDAR populations, which are likely the most abundant NMDAR populations in the patient's brain at adult stage. Tri-heteromeric and mixed NMDAR populations have often been overlooked when studying pathogenic NMDAR mutations due to the difficulty to express them specifically in recombinant systems. This paper (in addition to other papers in the field, see for instance Elmasri et al., Brain Sci. 2022; Li et al., Hum. Mutat. 2019) shows that the effect of the mutations on the receptor biophysical and pharmacological properties (but also on trafficking) differ whether the receptor contains one or two copies of the mutant subunit. This paper is of interest to scientists interested in NMDA receptor structure-function and pharmacology, as well as clinicians interested in GRINopathies (pathologies linked to NMDAR mutations).

      I, the reviewer, am an expert in NMDAR structure-function and pharmacology. I believe I have sufficient expertise to evaluate the entirety of the paper.

      We thank the reviewer for appreciating and acknowledging the merits of our work for publication.

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      Referee #3

      Evidence, reproducibility and clarity

      This paper is a follow up of an earlier paper published by the group (Kellner et al., eLife 2021), which aimed at characterizing the functional properties of two de novo GluN2B mutations in patients suffering from severe pediatric diseases, GluN2B-G689C and -G689S. NMDA receptors (NMDARs) are tetramers composed of two GluN1 and two GluN2 subunits. A single receptor can incorporate either two identical GluN2 subunits (di-heteromers) or two different GluN2 subunits (tri-heteromers), leading to a large diversity of NMDAR subtypes. The main NMDAR subtypes in the adult forebrain are GluN1/GluN2A and GluN1/GluN2B di-heteromers, as well as GluN1/GluN2A/GluN2B tri-heteromers. While the exact proportions of these three subtypes are still contentious, there are evidence that in the adult N1/2A/2B tri-heteromers form the major population of synaptic NMDARs in the adult forebrain. In addition, patients bearing pathogenic mutations are often heterozygous for the mutation, giving rise to mixed NMDARs incorporating one mutated and one intact GluN2 subunit. In their previous paper, Kellner et al. had shown that purely di-heteromeric GluN1/GluN2B-G689C and -G689S mutants display a drastic (> 1,000-fold) decrease of glutamate sensitivity and a decrease of surface expression. In the current paper, the authors characterize the effects of the -G689C and -G689S mutations on N1/2A/2B tri-heteromeric receptors, as well as on mixed di-heteromeric GluN1/GluN2B receptors containing one copy of the wild-type GluN2B subunit and one copy of the mutated GluN2B subunit. They show that one copy of the mutant subunit, either within mixed diheteromeric or tri-heteromeric receptors, is sufficient to decrease drastically glutamate sensitivity, although the shift in glutamate EC50 is not as strong as in pure di-heteromeric receptors (≈ 500-fold). They furthermore explore strategies to counteract the hypofunction induced by these mutations by testing the effect of positive allosteric modulators (PAMs). They show that spermine, a GluN2B-specific PAM, can potentiate the activity of mixed diheteromeric N1/2B but not N1/2A/2B tri-heteromers. However pregnenolone sulfate (a 2A/2B-specific PAM) can potentiate both the activity of mixed diheteromeric and tri-heteromeric NMDAR populations, either in oocytes or cultured neurons.

      I have very few major comments to make. The experiments are straightforward and the adequate controls have been made. Here are my two only major comments:

      1. About the experiment on cultured neurons. The authors compare the currents of cultured neurons transfected with GluN2B-G689C and -G689S to non transfected neurons. The adequate control is rather neurons transfected with the wild-type GluN2B subunit to even out any phenomenon linked to transfection of the neuron. Given the overexpression that can occur after transfection, the effect of the mutations on the size of NMDAR currents might be even stronger than what the authors show. However in that case PS might not completely rescue mutant NMDAR currents to wild-type levels.
      2. How come high concentrations of glutamate (>100µM) produce additional current on wt GluN1/GluN2B (with retention signals) compared to 100 µM glutamate, which is supposed to be saturating? It does not seem to stem from an osmotic effect since 10 mM glutamate does not produce any current on uninjected oocytes. Knowing that this "artefactual" effect might also occur in the mutant receptors, how do you take this effect into account when calculating the glutamate EC50s of the mutants? Given the drastic shift in EC50 produced by the mutant, taking into account this artefact is not going to change the conclusion, but the actual EC50s will be affected.

      Minor comments:

      1. In the first paragraph of the "Results" section, when describing the design of the constructs used to force a heteromeric stoichiometry in recombinant systems, the authors do as if they had designed the constructs themselves "Briefly, we tagged...are retained in the ER (Fig. 1a)". Please rewrite this paragraph to show that you used constructs that had been previously designed by another group (Hansen et al., 2014).
      2. I do not see any evidence of "positive cooperativity" between subunits in ref. 32. Ref. 32, to the best of my knowledge, states that in N1/2A/2B tri-heteromers, the 2A subunit sets the biophysical properties of the tri-heteromer. But there is no account of mixed di-heteromers. In addition, the cooperativity between the glutamate and glycine binding sites is negative.
      3. Interpretation of spermine action within the Results section: it is striking indeed to observe that the mutations in the context of a mixed di-heteromer still allow spermine potentiation, while they abolish this potentiation in pure di-heteromers. As rightly said in the discussion, the regain of spermine potentiation in the mixed compared to the pure diheteromers is likely due to a more favorable transduction of spermine signaling to the pore, likely via a higher pH sensitivity of mixed di-heteromers compared to di-heteromers. I would thus avoid the terms of "one single intact interface" for the mixed di-heteromer, since both spermine binding sites are likely intact in this NMDAR configuration. How is pH sensitivity affected in the mixed di-heteromers?
      4. In the methods section, the oocyte recording solution (likely Ringer and not Barth) does not contain any potassium. This is probably a typo. Could you correct the composition of your Ringer?
      5. There are several typos, especially in the Discussion.

      Referees cross-commenting

      I overall agree with the comments of reviewers 1 and 2. In particular, I agree that it is pointless to compare the absolute currents of non transfected neurons vs mutant-transfected neurons without an idea of receptor cell-surface expression.

      I would like however to give some precisions about some comments of Reviewer 2. About the ER retention technique to express tri-heteromers: I didn't know that the C2 signal could be addressed to the membrane on its own. The lack of leak current stemming from C1-C1 or C2-C2 combinations has been demonstrated in the paper establishing the technique (Hansen et al, 2014), as well as in another paper that developed an analog technique based on GABAB retention signals (Stroebel et al., J Neurosci 2014). So it is fair to consider that the authors were not surprised by the lack of current when co-expressing two GluN2B subunits carrying the C2 signal. About the comparison about absolute currents wt vs mutants, +/- spermine (Fig. 4a and 5a). I agree with reviewer 2 that being able to compare absolute currents of wt without spermine to mutant + spermine would be very interesting to see if spermine can actually rescue mutant hypofunction. However, to the defense of the authors, comparing absolute current values of recordings from Xenopus oocytes is meaningless. Indeed the variability of currents for the same construct and same day of experiment is too high (there can be up to a ten-fold difference between the lowest and the highest current of oocytes expressing the same construct the same experimental day). A way to investigate this aspect would be to estimate the open probability of the different constructs with or without spermine via the inhibition kinetics of an open channel blocker (e.g. MK801) and measure surface expression by Western blot, but I am not sure these experiments are worth it for the spermine experiment.

      Significance

      This paper is not of high significance since most of the characterization of the 2B-G689C and -G689S de novo mutants found in patients has already been published (Kellner et al., eLife 2021). However, this paper is worth publishing since it brings new data on the effect of the mutations on tri-heteromeric and mixed di-heteromeric NMDAR populations, which are likely the most abundant NMDAR populations in the patient's brain at adult stage. Tri-heteromeric and mixed NMDAR populations have often been overlooked when studying pathogenic NMDAR mutations due to the difficulty to express them specifically in recombinant systems. This paper (in addition to other papers in the field, see for instance Elmasri et al., Brain Sci. 2022; Li et al., Hum. Mutat. 2019) shows that the effect of the mutations on the receptor biophysical and pharmacological properties (but also on trafficking) differ whether the receptor contains one or two copies of the mutant subunit. This paper is of interest to scientists interested in NMDA receptor structure-function and pharmacology, as well as clinicians interested in GRINopathies (pathologies linked to NMDAR mutations). I, the reviewer, am an expert in NMDAR structure-function and pharmacology. I believe I have sufficient expertise to evaluate the entirety of the paper.

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      Referee #2

      Evidence, reproducibility and clarity

      The objective of this paper is to assess whether a single mutated subunit of GRIN can affect the function of various forms of NMDA receptors. In particular, this study investigates the functional consequences of a GRIN variant when assembled within tri-heteromers, containing 2 GluN1, 1 GluN2A and 1 GluN2B subunits, the major postnatal receptor type. For this purpose, the authors artificially forced the subunits to associate in predefined complexes, using chimeras of GRIN subunits fused to GABAb receptor retention control sites at the endoplasmic reticulum. This trick allows to control the stoichiometry of the channels at the membrane and thus to focus on the function of a single type of NMDA receptor. The take home message of the paper is that a single GluN2B‐variant, whether assembled with a GluN2B‐wt subunit to form mixed di‐heteromer or with a GluN2A‐wt‐subunit (tri‐heteromer), strongly impairs the receptor functioning, as reported by a decrease of the apparent glutamate affinity of the receptor.

      Altogether, this is a straightforward study of great interest for the GRIN community. However, the way the background and purpose of the study (title and abstract) are presented is a bit confusing for non-specialists and could be easily improved. Technical information, which is crucial to validate the conclusions drawn from data analysis, should be added to the article. Some additional experiments are suggested to consolidate the work. Finally, additional discussion points are strongly encouraged.

      Specific comments

      Abstract / Title:

      This work shows that a single GRIN variant impairs the function of various forms of NMDA receptors. Several sentences in the title and the abstract are confusing for a non-specialized audience. "Two extreme Loss‐of‐Function GRIN2B‐mutations are detrimental to triheteromeric NMDAR‐function, but rescued by pregnanolone‐sulfate." "Here, we have systematically examined how two de novo GRIN2B variants (G689C and G689S) affect the function of di‐ and tri‐heteromers." The number of variants tested is not of capital importance in the title, especially because one could believe that both are tested at the same time; similarly, when variants are named in the abstract, the fact that only 1 variant is studied at a time should be clarified (G689C OR G689S). Indeed, the problem is obvious to those familiar with GRIN disorders, but if this paper is to be published in a journal reaching a large audience rather than a specialized audience, the title of the paper should be modified.

      Introduction:

      "We find that the inclusion of a single GluN2B‐variant within mixed di‐ or tri‐heteromeric channels is sufficient to prompt a strong reduction in the receptors' glutamate affinity, but these reductions are not as drastic as in purely di‐heterometric receptors containing two copies of the variants. This observation is supported by the ability of a GluN2B‐selective potentiator (spermine) to potentiate mixed diheteromeric channels." Please, clarify the link between the two sentences. How do spermin potentiation of mixed diheteromeric channels supports the observation that the inclusion of a single GluN2B‐variant has less effect than the inclusion of two variants?

      Methods

      All this study is based on the use of a unique ER‐retention technique to limit expression of a desired receptor‐population at the membrane of cells. According to the ER system retention of GABAb receptor, used in this study, while C1/C1-fused subunits are retained in the ER, C2/C2 reach the cell surface and the association of C1/C2 in the ER enables cell-surface targeting of the heterodimer. However, GB2 does not contain any retention signal and can reach the cell surface in the absence of GB1, as a functionally inactive homo-dimer (doi: 10.1042/BJ20041435). If there is an experimental trick that prevents the addressing of C2/C2 to the cell membrane, it should be specified and explained. This is critically important for understanding which receptor populations the data are derived from: receptors containing C1/C2 fused subunits only as stated by the authors, or C1/C2 and C2/C2 fused subunits?

      Figures

      NMDA-receptor current amplitude should be normalized by the membrane expression of the receptors. A preliminary experiment should measure the effective cell surface expression of each of the subunits in the different transfection conditions.

      Fig.1a - The scheme should include C2-C2 complexes and mention whether these complexes are expressed at the cell surface (see previous and following comments).

      Fig.1b and c - Current from cells transfected with GluN2B‐wt‐C1 and GluN2B‐wt‐C2 should be compared to current expressed in cells expressing untagged receptor subunits: GluN2B‐wt - Current from cells transfected with GluN2B‐wt‐C1 alone should be shown as well (although expected to be retained in the ER) (as performed for GluN2A‐wt‐C1 GluN2B‐wt‐C1 in suppl Fig. 1a) - How could you explain the null current from cells transfected with GluN2B‐wt‐C2 alone (Fig.1b middle, and 1c)? since GB2 does not contain any retention signal and can reach the cell surface in the absence of GB1, GluN2B‐wt‐C2 is supposed to reach the cell surface. This is a very important point to clarify (I am probably missing a technical detail) because if the sub-unit tagged with C2 does reach the cell surface, then all the results and conclusions drawn from the C1-C2 conditions are wrong and could be attributed to a mix of complexes containing either C1-C2 or C2-C2. My following comments are based on the assumption that only receptors containing C1-C2 tagged subunits reach the membrane (as assumed by the authors and suggested in Figure 1b middle), but explanations should absolutely be provided to convince the reader.

      Fig. 4a and 5a - Please, keep the current scale constant between all current illustrations within the same figure (4a and 5a). Indeed, not only the Spermin- or SP- induced potentiation is an important data (which is presently quantified on the histograms of fig. 4b and 5b) but also knowing whether the amount of current recorded in cells expressing one mutant subunit in presence of SP (for example GluN2A‐wt‐C1 GluN2B‐G689S‐C2) is comparable to the current recorded in wt receptor-expressing cells (GluN2A‐wt‐C1 GluN2B‐wt‐C2) in absence of SP would be an excellent added value for the paper. A special figure could quantify this rescue effect of SP, measuring and comparing the mean currents recorded in these conditions (one current illustration is not sufficient given variations between similar samples). By the way 5mM glutamate might be an excessive concentration. At 1mM, the expected synaptic concentration of glutamate following action potential, according to figures 3 and Suppl1 the response of the mutated receptor is much lower than that of the WT which is already almost maximal. In these conditions, SP-induced potentiation by a factor of two of GluN2A‐wt‐C1 GluN2B‐G689S‐C2 current could be equivalent to control currents recorded in GluN2A‐wt‐C1 GluN2B‐wt‐C2 cells.

      Fig. 6 - Figure 6 is not convincing because cultured hippocampal neurons do express endogenous NMDA receptors. To what extent the recording currents are affected by endogenous, non-mutated GluN2B subunits? Western Blots showing an extinction of endogenous subunits expression when transfected tagged subunits are competitively expressed would be required. - Fig.6b "PE-S" on the graph should be replaced by "PS"

      Discussion

      The authors are surprised by the fact (Fig.2) that 1 variant reduces the apparent glutamate affinity of the receptor, but not as much as 2 variants, despite the fact that "NMDARs opening requires all four subunits to be liganded (i.e., occupied by a ligand) which implies that the least affine subunit should have dominated the final affinity of the receptor". I agree that the difference is noticeable, however the glutamate affinity for receptors containing 1 variant is much closer to that of receptors containing 2 variants than that of wild-type receptors. Hence, the results obtained do not seem so surprising and could result, as rightly explained by the authors from a possible cooperativity between the subunits.

      On the other hand, the data in Figure 6 are much more difficult to interpret and reconcile with the nature of the expressed receptor subunits (which this time is not controlled) nor their association within the same receptor. However, this aspect, which is essential to the understanding of the consequences of 1 variant on neuronal signalling, is not discussed: Whatever the stoichiometry of the complexes in the heterozygous disease, the mutated and wild type GluN2B subunits coexist in the same cell: Either within the same di-heteromeric complexes GluN2B-wt + GluN2B-mutant, or in separate complexes but nevertheless expressed in the same cell, in di heteromeric (GluN2B-wt + GluN2B-wt and GluN2B-mutant + GluN2B-mutant); or tri-heteromeric (GluN2A-wt + GluN2B-wt and GluN2A-wt + GluN2B-mutant) complexes. Assuming that half of the complexes remain wild-type, e.g. (GluN2A-wt + GluN2B-wt and GluN2A-wt + GluN2B-mutant) we would expect (Fig. 6) a small decrease in NMDA current (carried only by the half that expresses the mutated subunit, and whose function is not zero but only decreased by about 20% in response to 5mM Glutamate, Fig. 3b). The same reasoning applies to the di-heteromeric conditions (GluN2B-wt + GluN2B-wt; GluN2B-mutant + GluN2B-mutant), here again the decrease observed Fig. 6b is difficult to reconcile with the responses measured Fig. 2b. In other words, how to explain a 50% decrease of the currents, instead of the 10% expected by the previous reasoning. In this experiment we do not know which subunits are expressed, their proportions, nor how they are associated in functional complexes, which makes the interpretation of the data impossible. The only explanation, far-fetched, for 50 % decrease would be that the complexes were to contain all (or the vast majority) 1 wild-type subunits associated with 1 variant, then a homogeneous 50% reduction in current could be expected. But this extreme condition could only be possible in the case of di-heteromers, which is unlikely the case in Fig.6 as GluN2A currents are measured in presence of Ifenprodil. To conclude 1) the comparison of the currents in transfected and non-transfected neurons does not make sense in figure 6b which is not convincing because we do not know the nature of the currents actually measured. A comparison in controlled condition would make more sense (as I suggested in the criticism of figures 4, 5). 2) The reality of the combinations of expression and association between subunits within different complexes expressed in the same cell must be considered and taken into account in the interpretation of the data. Undoubtedly, the means of restoring the NMDA current will be different depending on the presence of mutated subunits in all functional channels or not.

      Referees cross-commenting

      The referees' comments are highly relevant. In particular, referee 3's comment 1 seems very interesting because it may help to better understand the discrepancy in the results observed in neurons, i.e. a 50% decrease in the currents induced by the expression of the mutant and wild type subunits in the same cells, whereas theoretically one would expect a 10% decrease of this current (cf. referee 2's 2nd comment in the discussion section). This comment 1 of referee 3 indeed stresses the fact that the control (non-transfected neurons) to which the heterozygous condition is compared is not the correct control, which should rather be neurons transfected with wild type receptor subunits. More generally, this comment underlines the importance of monitoring the effective membrane expression of the different subunits in each of the experimental conditions in order to be able to compare conditions and draw conclusions.

      Significance

      The novelty of the study, is to evaluate the consequences of a single mutated subunit within NMDA receptors affected by GRIN variant, to mimic the heterozygous condition of GRIN encephalopathies, this is of potential value for the field and the interest could also be extended to other genetic diseases (at least the experimental way to study the functioning of only one desired stoichiometric configuration).

      The strength of this paper is precisely to isolate technically and to study the functioning of a desired stoichiometric configuration only. The main limitation of the paper is the interpretation that the authors make of their data in a physio-pathological context

      This work could be of interest for general audience, providing the title and summary are slightly modified My area of expertise could not be closer to the topic of the article: Glutamate receptors; GRIN; molecular tinkering, cell culture, electrophysiology, receptor stoichiometry...

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      Referee #1

      Evidence, reproducibility and clarity

      Summary:

      Kelner and Berlin present their research findings pertaining to the effect of GRIN2B variants that modify NMDA receptor function and pharmacology. While these mutations were published previously, the new manuscript provides a more thorough investigation into the effects that these variants pose when incorporated into heteromeric complexes with either wildtype GluN2B or GluN2A - NMDA receptors containing only a single mutated GluN2B subunits is more relevant to the disease cases because the associated patients are heterozygous for the variant. The authors achieved selective expression of receptor heteromeric complexes by utilising an established trafficking control system. The authors found that while a single variant subunit in the receptor complex is largely dominant in its effect on reducing glutamate potency of the NMDA receptor, it 's effect on receptor pharmacology varied. Unlike diheteromeric receptors containing mutated subunits, polyamine spermine potentiated GluN1/2B (but not GluN1/2A/2B) receptors that contained a single mutated GluN2B. In contrast, the neurosteroid, pregnenolone-sulfate (PS), was effective at potentiating the NMDA receptor currents (to varying degrees) regardless of the subunit composition. The potentiation of NMDA receptor currents by PS was also observed in neurons overexpressing the variants.

      The techniques used in this study were appropriate to address the objectives and the overall effects are large, and generally convincing. I like the way the results are presented, although have a few (easily addressable) comments.

      Major comments:

      • When incrementally adding drugs (e.g. traces in figures 5 and 6), it doesn't always appear like the response has plateaued before changing the solutions/drugs. Therefore, I am curious to what extent the effects observed are underestimated.
      • Also, in relation to figure 6, to what extent does agonist application cause desensitization here? Looking at traces in Figure 6b it appears that there is some desensitization and it isn't clear to what extent this persists during the solution changes. Could the authors conduct/show the controls where NMDA alone (for 50-60s), or NMDA followed by PE-S (without ifenprodil).
      • Finally, figure 5 shows the effect of the neurosteroid (and ifenprodil) on NMDA-evoked currents in neurons overexpressing the GluN2B variants in neurons. However, there currents probably reflect a mixture of extrasynaptic and synaptic receptors. To what extent are synaptic NMDA receptors affected by the variants?

      Minor comments:

      • Looking at the fits in the graph of Figure 2b it appears that the slope on the concentration response curves is less steep for the mixed 2B-diheteromeric NMDA receptors. How much are the Hill coefficients changing and can this be interpreted to provide more mechanistic insight? Wouldn't it make sense to include the Hill coefficients in Table 1?
      • The authors illustrate the changes in potency by the shift in the concentration response curves, but is there any change in efficacy? A simple way to illustrate this would be also present a simple graph showing the maximum current amplitudes (i.e. to 10 mM glutamate) for each of the receptor complexes.
      • The authors characterize the 'apparent' affinity (or potency) of the receptor using concentration-response curves, but numerous points in the manuscript refer to changes in affinity. None of the experiments shown directly measure affinity (which would require ligand-binding assays) and so the use of the word affinity is inaccurate/misleading. I suggest the authors replace the instances of the word 'affinity' with 'potency'.
      • In the third line of the abstract, the authors wrote, 'for which there are no treatments' in relation to GRINopathies. My understanding is that there are symptomatic treatments but that there are no disease-modifying treatments.
      • The authors have interchangeably used the terms NMDAR or GluNRs throughout the manuscript. I suggest sticking to one of these terms. I would suggest NMDARs since this is less likely to be misread as a a specific NMDA receptor subunit..
      • Typos: 
1) Results paragraph 2 sentence one: 'We thereby produced GluN2B-wt, GluN2B-G689C and GluN2B-G689S subunits tagged with C1 or C2, co-expressed these along with the GluN1a-wt subunits in...'
2) Results paragraph 2: '...but these were mainly noticeable when oocytes are were exposed to high (saturating) glutamate concentrations...'
3) Last sentence in the second to last paragraph of the results section entitled 'Mixed di-and tri-heteromeric channels...': 'This , PS may serve to rescue...'
4) Last sentence in last paragraph of the results section entitled 'Mixed di-and tri-heteromeric channels...': 'Despite the latter, we found no evidence for any direct effect of three different physiologically relevant concentrations of the drug on di- or tri-heteromeric receptors'
      • Figures 1e, 2b, 3b: it would be helpful to add a legend to the graph so that the curves can be interpreted without having to read through the figure legend.
      • The bar graphs in Figure 6 show individual data points but those in figures 4b and 5b don't. Can the authors please add the data points to these graph.
      • It would be helpful to reviewers that future manuscripts by the authors include page numbers and line numbers

      Referees cross-commenting

      Reviewers 2 and 3 highlight an important issue concerning figure 6 and the extent to which the overexpressed variants subunits can compete and assemble with endogenous NMDA receptors (unlike the system where the surface expression of specific receptor complexes is controlled). Indeed in the recent paper by the same authors, the two variants differed in their surface expression (in HEK cells), with G689C expressing particularly poorly. With reference to the second minor comment of Reviewer 1, the maximum current amplitudes would of course need to be normalized to cell surface expression of the receptor to gain any insight into efficacy.

      Significance

      This study emphasizes the complex pattern of effects that variants can have on glutamate receptor function and pharmacology, especially considering the context of receptor subunit composition. The authors have followed up their previous findings on the same mutants (Kellner et al, 2021, Elife), but used a trafficking control system here to characterize properties of mutated receptor complexes that are most likely to exist in neurons. The authors show that the defective currents mediated by NMDA receptors containing a loss-of-function GluN2B variant can be enhanced by neurosteroids (and in the case of GluN1/2B receptors, polyamines also). Development and approval of neurosteroids for the clinic would be required for the findings to translate to a therapy for patients. Readers should also be aware that neurosteroids act on other receptors too (e.g. GABA receptors), which could complicate the outcome. The expertise of the reviewer is in glutamate receptors and synaptic transmission.

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      Reply to the reviewers

      Reviewer #1

      Major:

      - The statement (line 149'Together, our data suggest that systemic ecdysone levels are unlikely to be involved in modulating tumour-induced muscle detachment or to mediate the role of fatbody Insulin signalling in regulating muscle detachment.') is derived from an experiment with sterol free diet (in which 20HE is genetically addressed) and a pleiotropic experiment (PG>RasG12V). In neither paper nor the current manuscript, 20HE levels have been directly addressed.

      Therefore, this statement needs further experimental support and discussion. Ecdysone is a critical hormone during development and especially growth-related effects central to this study. The authors should consider doing pharmacology or augment their claims here with genetic manipulation experiments of 20HE related genes in larvae (Leopold, Rewitz, Rideout, Drummond-Barbosa, Schuldiner labs) and adult animals using genetics, pharmacology or direct assessment of 20HE levels (RIPA, Edgar and Reiff labs).

      The main point we were trying to convey is that we do not think global ecdysone levels plays a role in modulating fatbody insulin or tgfb signalling, which in turn affects muscle detachment. We are not claiming that edysone levels is not changing in control vs. tumour bearing animals. In fact, we predict that 20HE levels will be different in tumour bearing vs. control animals (as tumour bearing animals undergo developmental delay), but this is not the main point of our conclusions. We believe that our conclusions are supported by the experiment demonstrating global ecdysone alterations (via feeding sterol-free food) did not affect how fatbody Akt activation altered tgfb signalling and enhanced muscle integrity (Figure S1). Therefore, we don’t think measuring 20HE helps to support our conclusions. Pharmacological inhibition via feeding ecdysone inhibitors effectively demonstrate a similar point to feeding sterol-free food which we have already performed. We are happy to try direct manipulation of 20HE related genes (eip75B-RNAi) in the fatbody to see if this affects muscle detachment or pAkt and pMad levels in tumour bearing animals.

      - In Fig.7 the authors used a sog-LacZ stock to show transcriptional activation in fatbody cells. This stock is based on P-element insertion in the according regulatory regions and supposed to express lacZ with an nls. I can clearly see lacZ in nuclei in Fig. 7H, whereas this is very hard to see in nuclei in Fig7i in the tumour model. In addition, lacZ is known for its high stability and not the best option. As this finding is vital for central claims of this study, it should be complemented by either qPCR for sog on fat body cells or using another readout by converting one of the two Mimic lines (BL42189/44958) into GFP sensors for sog.

      We will add a counterstain to these images. We will also perform qPCR in the fatbody of control and cachectic animals to assess whether Sog transcription is altered. We agree converting one of the Mimic lines to a GFP sensor would be a good option, but this experiment would require getting new fly lines into Australia, which takes at least 2 months because of quarantine laws. We don’t believe this experiment would change the general conclusions of the paper, therefore would prefer not to do this experiment.

      - I have similar problems with Fig.7B-F, as phosphorylated Mad should be translocated to the nucleus. In 7F the authors measure pMad over Dapi, which is the right way but it is hard to see pMad in the nucleaus apart from Fig7B, wheras in D and E, where the authors measure higher levels, I cannot identify clear pMad in nuclei. These images either need to show the Dapi channel or more representative images should be chosen like in Fig.4 with arrows pointing to measured nuclei. Fig.7C something went wrong with the compression of this image.

      We will show more representative examples and fix Fig 7C.

      - The proper function of RNAi stocks targeting genes like sog, mad, etc. is vital for this study as these lines are used throughout the study. Functional evidence of specific knockdown efficiency should be provided or references given in which these stocks were shown to provide functional knockdown on transcript or protein level.

      We agree with the reviewer that this is an important point. We will demonstrate the knockdown of sog and mad (and other RNAis) used in the study by either referring to published data or demonstrate knockdown ourselves.

      - Fig.S7 discusses appearance of gbb/Bmp7 and Sog/CHRD in human patients. The analysis the authors performed shows a correlation between both factors, but is hampered by the fact that datasets for peripheral tissues of cachexia patients are unavailable. The authors may consider sorting these after tumor entities in which cachexia occurs frequently vs. low occurrence and then check for both genes.

      We will try this analysis.

      Fig.5 M-P pMAd is not indicated in the Panels only the legend.

      We will fix this error.

      - Please follow FlyBase nomenclature, e.g. dlg1 for discs large 1 and unify in the whole manuscript and figure for all genes.

      We will fix this error.

      - For endogenous fusion proteins like Viking-GFP (e.g. vkg::GFP) choose a format to clearly decipher them from transcriptional readout stocks like sog-lacZ.

      We will fix this error.

      - The quantifications in most figures are quite small with tiny lettering and XY axis are difficult to read in letter/A4 size.

      We will enlarge font size.

      Minor:

      1. Adjust in-figure caption alignments

      2. Line 104: add comma RasV12, dlgRNAi

      3. Line 114: replace little  not significant (n.s.)

      4. Line 334: 'sogRNAi overexpression' to my knowledge, RNAi are expressed, not overexpressed.

      5. Line 454: italicize r4>

      6. Fig S4E: remove frame

      7. Figures 6: It would be better to number and explain the pathway presented in the figure in text and fig legend.

      8. Just a personal preference. Lettering of images in images is commonly done horizontally, here it appears like a mix between vertical and horizontal.

      We will fix these minor errors.

      Reviewer #2

      Major comment

      Their genetic experiments clearly showed that the reduction of insulin signaling activity in the fatbody induces upregulation of TGF-β signaling and Collagen accumulation. Then, how does TGF-β signaling induce Collagen accumulation?

      From the experiments we have carried out, we do not have insights into how TGF-B signalling induce Collagen accumulation.

      They showed that Rab10 knockdown and SPARC overexpression reduced the accumulation of fatbody ECM. Are Rab10 and SPARC expression regulated by TGF-β signaling?

      We can address this point by assessing if Rab10 and SPARC expression is altered in cachectic fatbody.

      Minor comments

      Line 90: "Disc Large (Dlg) RNAi in the eye" must be "Discs Large (Dlg1) RNAi in the eye imaginal discs".

      we will fix this error.

      Figures 1D and 1L are from the same image. Also, Figures 1C and 1M are from the same image. Are both of them necessary to be shown in the different panels?

      The duplication of 1C and 1M, was an error, we thank the reviewer for picking this up. We will fix this error. We will use different images for 1D and 1L.

      Why are the staining patterns of anti-pAkt shown in Figures 1L and 1U so different? pAkt is not detected in the nuclei in Fig. 1L but its nuclear signal is clear in Fig. 1U.

      We will show more representative images of these staining.

      Figure 1: Images of counter staining for nuclei like DAPI should be also included for all these fatbody images.

      We will show counter staining for DAPI.

      Line 101: "Tumour specific ImpL2 inhibition was sufficient to reduce fatbody pAkt levels." Is this correct? ImpL2 inhibition in tumors should elevate the pAKT level in fatbody.

      This was a mistake, we will fix this error.

      Figure S1~S4: These figures and their legends do not correspond to each other. We thank the reviewer in picking up this error, there was an error in inserting the images into the text. S2 and S3 were swapped.

      We will fix this error.

      Line 189: The pAkt level in the muscle of tumour-bearing animals should be examined to confirm the activity of the insulin signaling is downregulated.

      We will include this data.

      Line 189: If the authors conclude that muscle insulin signaling predominantly regulates translation and atrophy, OPP assay for the muscle cells should be examined in the same experimental settings.

      We will carry out OPP assay upon Akt overexpression in the muscle.

      Line 247: The expression level of Rab10 and SPARC should be examined in the fatbody of tumour-bearing animals to see whether Rab10 is upregulated and SPARC is downregulated.

      Line 247: If Rab10 upregulation and SPARC downregulation are the causes of the accumulation of ECM proteins in the fatbody of tumour-bearing animals, how the overexpressed Collagen proteins can be secreted from the fatbody cells?

      We are not sure, but the overexpression of Collagen proteins is at an extremely high level, therefore, it is possible that some of it can be processed and secreted despite Rab10 upregulation and SPARC downregulation. We have carried out an experiment to overexpress Collagen proteins in the muscle, in this case, this manipulation did not rescue. This indicates that processing of Collagen in the fatbody is important, however, we do not know how the processing is regulated.

      Line 347: Sog is a secreted BMP antagonist. Thus, it can be expected that the Sog overexpression downregulates TGF-β signaling in fatbody and muscle tissues. If the rescued phenotypes with Sog overexpression can be explained by this logic, pMad level should be examined in these experiments.

      We have shown this data in Figure R-T. We will refer back to this data in Line 347.

      Reviewer #3

      Major comments:

      - Are the key conclusions convincing?

      Most of the conclusions are convincing. It is not clear however whether the ECM accumulation in the fat body of tumor animals is fibrotic and whether it is extracellular or in the cell cortex.

      - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

      -The authors state in line 71 'This deposition of disorganized ECM leads to fibrotic ECM

      accumulation.' The authors haven't really provided evidence for the ECM being fibrotic. The authors could either rephrase this or provide additional experimental evidence of fibrosis in the fat body.

      We will tone down the claim that the ECM accumulation is fibrotic.

      - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

      -The authors state in line 147" Finally, in tumor-bearing animals fed a sterol-free diet, that underwent a prolonged 3rd instar stage due to reduced ecdysone levels (Parkin and Burnet, 1986), we activated insulin signalling in the fatbody via Akt overexpression (QRasV12, scribRNAi). We found that this manipulation caused a significant decrease in pMad levels in the fatbody and a rescue of muscle detachment (Figure S1 D-I), similar to animals fed a standard diet (Figure 1 O-Q, Figure 2 F-H)." Since it's not already known what the extent of muscle integrity defect there is in tumors with additional sterol free diet, it would be important to show a non-tumor control for comparison in FigS1F. This would also then make it clear to what extent the defect is rescued by Akt overexpression.

      We will include a non-tumour control for Fig S1F.

      -The authors state in line 158 'Upon the knockdown of Impl2, we found that tumor gbb was not significantly altered (Figure S3A).' Even though this shows an indication that Gbb levels are not reduced, the n number is too low to state that it is non-significant. The authors should increase the n number here.

      N=3 is generally enough to see a difference, we will include data done in parallel which shows Impl2 RNAi is sufficient to induce a reduction in Impl2 RNA levels. This will demonstrate that n=3 is sufficient to demonstrate a reduction in transcript levels if there is a reduction.

      -The authors state in line 171 'Conversely, knockdown of gbb alone or knockdown of gbb together with ImpL2 significantly rescued the Nidogen overaccumulation defects observed at the plasma membrane of fatbody from tumor-bearing animals, while ImpL2RNAi alone did not (Figure S2 Q-U).' This is a somewhat misleading representation, since again no non-tumor control was used, so the extent of the rescue by gbb knowdown is not obvious. In FigS2P Nidogen levels in the tumor seem ~100% higher than in control. But in FigS2U, in which no control was included, the tumor+gbb knowdown seems ~ 20% lower than tumor. So it is probably a more moderate rescue, but that's only possible to assess by including a non-tumor control in FigS2U. Also the images in FigS2Q-T don't seem representative since they appear to show a much bigger difference in fluorescence intensity than ~20%. Please show more representative images.

      We will include a non-tumour control for S2Q-T and show more representative pictures.

      -The authors state in line 174 'Finally, co-knockdown of gbb and ImpL2 in the tumor significantly rescued the reduction in OPP and Nidogen levels observed in the muscles of tumor-bearing animals (Figure S3 B-I).'

      Again, the single knockdowns and the non-tumor control are not shown in FigS3E and I and should be included for comparison and to see the contribution of each knockdown and to be able to judge the extent of the rescue.

      We will include the single knockdowns and a wildtype control

      -Regarding Fig3O: Is there a significant tumor muscle attachment defect here? In this graph the tumor only looks about 10% lower than the WT (rather than 40% in Fig2E). The other issue is the extremely low n number for WT. I would recommend increasing the n number for WT here and to indicate in the graph whether the tumor is significantly different to WT (or non-significant, in which case RabRNAi wouldn't actually 'rescue' the defect). In the present form, this graph is not very convincing.

      We will increase the n number for WT for this experiment. The reduction in muscle detachment is 10% rather than 40% here is because this experiment was done at day 6, which we will indicate in the figure legend. The 40% reduction in Fig2E is because these samples were processed at day7. Rab10RNAi experiment was carried out at day 6, because by day7, the Rab10RNAi rescue is so good, most of the tumour bearing animals have pupated, thus the experiment could only be carried out at day6.

      - Regarding Fig3W: A non-tumor control would be important to include to be able to judge the extent of muscle attachment defects and the extent of the rescue for UAS-Sparc. This will allow to assess the severity of muscle integrity defect in this particular experiment (since it appears to vary in different experiments e.g. muscle defect in tumor 40% in Fig2E and ~10% in Fig3O) and to assess the extent of rescue for the various genotypes.

      We will include a non-tumour control for 3W.

      -The authors show an accumulation of ECM in the fat body of tumors. It is not clear, whether this ECM accumulates intracellularly near the cell surface or extracellularly. The authors should assess this, maybe by doing electron microscopy.

      We do not have an EM facility that can accommodate this experiment, thus doing EM is not an option for us. However, we can address whether the accumulation of ECM is intracellular or extracellular by performing an experiment, where we try perform antibody staining against Viking-GFP without permeabilizing the cells. If Viking is detected without permeabilization, it would indicate the accumulations are extracellular. This approach has been previously used to address this question in Zang et al., elife, 2015.

      - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.

      -These suggested experiments should be quite straightforward since they are mostly just repeating previous experiments with the appropriate controls and n numbers. I would think that they can be done within a few months. The electron microscopy should not take more than a few weeks and not be costly.

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

      -The details on how old animals used in each experiment were, are not easy to find and not written very clearly. They should be included in the each figure legend rather than summarising those details in the methods.

      We will add the number of days in the figure legend.

      -Also, in line 788 in the methods, several stocks are indicated as coming from particular labs (e.g. UAS-FOXO (Kieran Harvey), UAS-GFP (Kieran Harvey), UAS-lacZRNAi (Kieran Harvey), UAS-RasV12 (Helena Richardson), UAS-cg25C;UAS-Vkg (Brian Stramer)).

      However, it is not clear whether these labs actually made these stocks and if so whether it has already been described in their papers how the lines were made. If the lines are unpublished, the detailed information should be given on how the lines were made. Or if the lines are published, the authors should provide the reference.

      We will fix these references.

      - Are the experiments adequately replicated and statistical analysis adequate?

      In general, the n number is rather low in several experiments, especially n of 3 for many controls. And as I mentioned before, rescues of tumor phenotypes are often shown without including a non-tumor control, making it hard to judge the extent of the rescue. Sometimes this information can be found in other figures, but the reader should not have to search for it. And also the severity of the phenotype can vary from experiment to experiment.

      We will include a non-tumour control when appropriate to address this.

      Minor comments:

      - Specific experimental issues that are easily addressable.

      - Are prior studies referenced appropriately?

      Yes, as far as I can tell.

      - Are the text and figures clear and accurate?

      -In the literature, people usually call it 'fat body' rather than 'fatbody'.

      We will fix this error.

      -The authors state in line 265 "Vkg accumulated in the membranes of fatbody where p60 was overexpressed using r4-GAL4 (Figure 5 A-C)."

      This must be a typo. I think it is shown in Fig5E-G. Unless it's labelled wrongly in the figure and B, C and D show p60 rather than TorDN.

      We will fix this error.

      -The authors state in line 188 'This manipulation significantly rescued muscle integrity (Figure S4 A-C) and muscle atrophy (Figure S4 D-F), without affecting muscle ECM levels (Figure S4 G-H).' According to the graph in FigS4H this does actually 'affect muscle ECM levels' significantly, as in that it reduced Nidogen levels further. The authors could rephrase this.

      We will reword this statement.

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      Referee #3

      Evidence, reproducibility and clarity

      Summary:

      Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

      This paper uses a Drosophila tumor model induced by the expression of RasV12+Scrib-IR or RasV12+Dlg-IR in the eye imaginal disc to understand how inter-organ communication affects cachexia in the fat body and muscle. The tumor has previously been shown to secrete the factors ImpL2 and Gbb which decreases insulin signalling and increases TGF-beta signalling in the fat body, respectively, and results in fat body and muscle defects. Here they dissect the role of insulin and TGF-beta signalling in the fat body in regulating muscle integrity further. They show that these two pathways converge via Sog in the fat body of tumor-bearing animals and result in aberrant ECM accumulation in the fat body which hinders ECM secretion. This then results in the muscle receiving less fat body-derived ECM which causes muscle attachment defects. Interestingly, these muscle defects can be ameliorated by activating insulin signalling or inhibiting TGF-beta signalling or even by increasing ECM secretion in the fat body. The authors also provide some evidence that the insulin and TGF-beta signalling pathways can converge in non-tumor settings.

      Major comments:

      • Are the key conclusions convincing?

      Most of the conclusions are convincing. It is not clear however whether the ECM accumulation in the fat body of tumor animals is fibrotic and whether it is extracellular or in the cell cortex.<br /> - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?<br /> - The authors state in line 71 'This deposition of disorganized ECM leads to fibrotic ECM<br /> accumulation.' The authors haven't really provided evidence for the ECM being fibrotic. The authors could either rephrase this or provide additional experimental evidence of fibrosis in the fat body.<br /> - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.<br /> - The authors state in line 147" Finally, in tumor-bearing animals fed a sterol-free diet, that underwent a prolonged 3rd instar stage due to reduced ecdysone levels (Parkin and Burnet, 1986), we activated insulin signalling in the fatbody via Akt overexpression (QRasV12, scribRNAi). We found that this manipulation caused a significant decrease in pMad levels in the fatbody and a rescue of muscle detachment (Figure S1 D-I), similar to animals fed a standard diet (Figure 1 O-Q, Figure 2 F-H)." Since it's not already known what the extent of muscle integrity defect there is in tumors with additional sterol free diet, it would be important to show a non-tumor control for comparison in FigS1F. This would also then make it clear to what extent the defect is rescued by Akt overexpression.<br /> - The authors state in line 158 'Upon the knockdown of Impl2, we found that tumor gbb was not significantly altered (Figure S3A).' Even though this shows an indication that Gbb levels are not reduced, the n number is too low to state that it is non-significant. The authors should increase the n number here.<br /> - The authors state in line 171 'Conversely, knockdown of gbb alone or knockdown of gbb together with ImpL2 significantly rescued the Nidogen overaccumulation defects observed at the plasma membrane of fatbody from tumor-bearing animals, while ImpL2RNAi alone did not (Figure S2 Q-U).' This is a somewhat misleading representation, since again no non-tumor control was used, so the extent of the rescue by gbb knowdown is not obvious. In FigS2P Nidogen levels in the tumor seem ~100% higher than in control. But in FigS2U, in which no control was included, the tumor+gbb knowdown seems ~ 20% lower than tumor. So it is probably a more moderate rescue, but that's only possible to assess by including a non-tumor control in FigS2U. Also the images in FigS2Q-T don't seem representative since they appear to show a much bigger difference in fluorescence intensity than ~20%. Please show more representative images.<br /> - The authors state in line 174 'Finally, co-knockdown of gbb and ImpL2 in the tumor significantly rescued the reduction in OPP and Nidogen levels observed in the muscles of tumor-bearing animals (Figure S3 B-I).'<br /> Again, the single knockdowns and the non-tumor control are not shown here in Fig3E and I and should be included for comparison and to see the contribution of each knockdown and to be able to judge the extent of the rescue.<br /> - Regarding Fig3O: Is there a significant tumor muscle attachment defect here? In this graph the tumor only looks about 10% lower than the WT (rather than 40% in Fig2E). The other issue is the extremely low n number for WT. I would recommend increasing the n number for WT here and to indicate in the graph whether the tumor is significantly different to WT (or non-significant, in which case RabRNAi wouldn't actually 'rescue' the defect). In the present form, this graph is not very convincing.<br /> - Regarding Fig3W: A non-tumor control would be important to include to be able to judge the extent of muscle attachment defects and the extent of the rescue for UAS-Sparc. This will allow to assess the severity of muscle integrity defect in this particular experiment (since it appears to vary in different experiments e.g. muscle defect in tumor 40% in Fig2E and ~10% in Fig3O) and to assess the extent of rescue for the various genotypes.<br /> - The authors show an accumulation of ECM in the fat body of tumors. It is not clear, whether this ECM accumulates intracellularly near the cell surface or extracellularly. The authors should assess this, maybe by doing electron microscopy.<br /> - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments.<br /> - These suggested experiments should be quite straightforward since they are mostly just repeating previous experiments with the appropriate controls and n numbers. I would think that they can be done within a few months. The electron microscopy should not take more than a few weeks and not be costly.<br /> - Are the data and the methods presented in such a way that they can be reproduced?<br /> - The details on how old animals used in each experiment were, are not easy to find and not written very clearly. They should be included in the each figure legend rather than summarising those details in the methods.<br /> - Also, in line 788 in the methods, several stocks are indicated as coming from particular labs (e.g. UAS-FOXO (Kieran Harvey), UAS-GFP (Kieran Harvey), UAS-lacZRNAi (Kieran Harvey), UAS-RasV12 (Helena Richardson), UAS-cg25C;UAS-Vkg (Brian Stramer)).<br /> However, it is not clear whether these labs actually made these stocks and if so whether it has already been described in their papers how the lines were made. If the lines are unpublished, the detailed information should be given on how the lines were made. Or if the lines are published, the authors should provide the reference.<br /> - Are the experiments adequately replicated and statistical analysis adequate?<br /> In general, the n number is rather low in several experiments, especially n of 3 for many controls. And as I mentioned before, rescues of tumor phenotypes are often shown without including a non-tumor control, making it hard to judge the extent of the rescue. Sometimes this information can be found in other figures, but the reader should not have to search for it. And also the severity of the phenotype can vary from experiment to experiment.

      Minor comments:

      Specific experimental issues that are easily addressable.

      • Are prior studies referenced appropriately?

      Yes, as far as I can tell.<br /> - Are the text and figures clear and accurate?<br /> - In the literature, people usually call it 'fat body' rather than 'fatbody'.<br /> - The authors state in line 265 "Vkg accumulated in the membranes of fatbody where p60 was overexpressed using r4-GAL4 (Figure 5 A-C)."<br /> This must be a typo. I think it is shown in Fig5E-G. Unless it's labelled wrongly in the figure and B, C and D show p60 rather than TorDN.<br /> - The authors state in line 188 'This manipulation significantly rescued muscle integrity (Figure S4 A-C) and muscle atrophy (Figure S4 D-F), without affecting muscle ECM levels (Figure S4 G-H).' According to the graph in FigS4H this does actually 'affect muscle ECM levels' significantly, as in that it reduced Nidogen levels further. The authors could rephrase this.<br /> - Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

      Significance

      • Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

      The field of inter-organ communication in cancer is a very interesting and trending research field. Several labs including this one have provided new insights into how the tumor, the fat body and the muscle communicate and affect each other and how this can cause cachexia. Previous work from the Chen lab already showed that the tumor secretes the factors ImpL2 and Gbb which decreases insulin signalling and increases TGF-beta signalling in the fat body, respectively and results in fat body and muscle defects. Here they dissect this role of insulin and TGF-beta signalling in the fat body in regulating muscle integrity during cachexia further. They show that these two pathways converge via Sog in the fat body of tumor-bearing animals and result in aberrant ECM accumulation in the fat body which hinders ECM secretion. As a result of this, the muscle receives less fat body-derived ECM and displays muscle attachment defects. Interestingly, the authors show that these muscle defects can be ameliorated by activating insulin signalling or inhibiting TGF-beta signalling or even by increasing ECM secretion in the fat body. This has potentially important implications for the clinic since it suggests that targeting ECM secretion or ECM remodeling in the fat tissue could be a promising treatment for cachexia.<br /> Moreover, the authors also provide some evidence that the insulin and TGF-beta signalling pathways can converge in tumor and non-tumor settings. This might also reveal new drug targets to treat cachexia.<br /> - Place the work in the context of the existing literature (provide references, where appropriate).

      The Chen lab showed previously that MMP1 secreted from the tumor induces ECM disruption in the fat body as well as muscle, ultimately causing fat body remodeling and muscle wasting (Lodge et al. 2021). They showed that this is via TGF-beta activation in the fat body. Another contributing factor is tumor-secreted Impl2 which decreases Insulin signalling in the fat body and tumor. However, it remained unknown, how ECM accumulation in the fat body might cause muscle wasting. In this paper, the authors look into this.<br /> - State what audience might be interested in and influenced by the reported findings.

      This paper would be of interest for scientists and clinicians interested in inter-organ communication in cancer, particularly in the context of cachexia.<br /> - Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

      My expertise lies in the field of Drosophila fat body and ECM, and to some extent tumors but less so signalling pathways.

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      Referee #2

      Evidence, reproducibility and clarity

      Summary

      In this paper, the authors show how the interaction of two signaling pathways, insulin/PI3K and TGF-β signaling, in the fatbody plays an important role in cachectic muscle detachment in tumor-bearing animals. The Drosophila tumor models and the genetic experimental tools are sophisticated and the conclusion is well supported by the data from these genetic experiments. They found that the TGF-β signaling activation (phosphorylation of Mad) is negatively regulated by insulin/PI3K signaling in the fatbody. They also identified the functional involvements of two molecules secreted from tumour tissues, Impl2 (a negative regulator of insulin signaling) and Gbb (one of the TGF-β ligands), in protein synthesis and ECM accumulation in the fatbody, respectively. They also showed that the cachectic fatbody traps ECM proteins and prevents ECM secretion to the muscle, causing muscle degradation. Finally, they identified a secreted BMP antagonist, Sog, as an important player in this process. They found that Sog is reduced in the hemolymph of tumour-bearing animals and that Sog expression is regulated by insulin signaling. Furthermore, Sog overexpression in the tumours, fatbody, and muscle rescues cachectic muscle detachment.

      Major comment

      Their genetic experiments clearly showed that the reduction of insulin signaling activity in the fatbody induces upregulation of TGF-β signaling and Collagen accumulation. Then, how does TGF-β signaling induce Collagen accumulation? They showed that Rab10 knockdown and SPARC overexpression reduced the accumulation of fatbody ECM. Are Rab10 and SPARC expression regulated by TGF-β signaling?

      Minor comments

      1. Line 90: "Disc Large (Dlg) RNAi in the eye" must be "Discs Large (Dlg1) RNAi in the eye imaginal discs".
      2. Figures 1D and 1L are from the same image. Also, Figures 1C and 1M are from the same image. Are both of them necessary to be shown in the different panels?
      3. Why are the staining patterns of anti-pAkt shown in Figures 1L and 1U so different? pAkt is not detected in the nuclei in Fig. 1L but its nuclear signal is clear in Fig. 1U.
      4. Figure 1: Images of counter staining for nuclei like DAPI should be also included for all these fatbody images.
      5. Line 101: "Tumour specific ImpL2 inhibition was sufficient to reduce fatbody pAkt levels." Is this correct? ImpL2 inhibition in tumors should elevate the pAKT level in fatbody.
      6. Figure S1~S4: These figures and their legends do not correspond to each other.
      7. Line 189: The pAkt level in the muscle of tumour-bearing animals should be examined to confirm the activity of the insulin signaling is downregulated.
      8. Line 189: If the authors conclude that muscle insulin signaling predominantly regulates translation and atrophy, OPP assay for the muscle cells should be examined in the same experimental settings.
      9. Line 247: The expression level of Rab10 and SPARC should be examined in the fatbody of tumour-bearing animals to see whether Rab10 is upregulated and SPARC is downregulated.
      10. Line 247: If Rab10 upregulation and SPARC downregulation are the causes of the accumulation of ECM proteins in the fatbody of tumour-bearing animals, how the overexpressed Collagen proteins can be secreted from the fatbody cells?
      11. Line 347: Sog is a secreted BMP antagonist. Thus, it can be expected that the Sog overexpression downregulates TGF-β signaling in fatbody and muscle tissues. If the rescued phenotypes with Sog overexpression can be explained by this logic, pMad level should be examined in these experiments.

      Significance

      I found these results from their genetic experiments described here very interesting and of high quality. Although the mechanism by which the TGF-β signaling induces ECM accumulation in fatbody is not clear, this study represents several important advances to understand the key processes in tumor-induced muscle degradation. These data will attract broad audiences not only from cancer biology but also from the research fields including interorgan interactions, systemic signaling in homeostasis, and developmental biology.

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      Referee #1

      Evidence, reproducibility and clarity

      In this manuscript, Bakopolous et al. investigated on the function of Insulin and TGF beta signaling in the converging regulation of sog (BMP antagonist) and how it controls ECM remodeling. Therefore, the authors used a Drosophila model of cachexia established in Lodge et al., 2021. The authors have shown that the tumors increase Impl2 and Gbb in the fatbody leading to the inhibition of insulin signaling and activation of TGF-β signaling respectively. This lead to the accumulation of ECM proteins that contributes to muscle ECM deficit and muscle detachment. These findings are a major advance in the field of cachexia and of broad interest. The authors demonstrate that state-of-the-art genetics in flies allows acquisition of genetically precise data along with important and complex discoveries on signaling pathways with relevance not only for basic, but for biomedical research as well.

      The manuscript is concise and very well written. The experiments overt a clear logical order and are comprehensively described. The authors provide exhaustive data to support their novel claims of broad interest to the scientific community. Please find below some minor recommendations and experiments that could shed further light on some aspects of this manuscript.

      Major:

      • The statement (line 149'Together, our data suggest that systemic ecdysone levels are unlikely to be involved in modulating tumour-induced muscle detachment or to mediate the role of fatbody Insulin signalling in regulating muscle detachment.') is derived from an experiment with sterol free diet (in which 20HE is genetically addressed) and a pleiotropic experiment (PG>RasG12V). In neither paper nor the current manuscript, 20HE levels have been directly addressed.<br /> Therefore, this statement needs further experimental support and discussion. Ecdysone is a critical hormone during development and especially growth-related effects central to this study. The authors should consider doing pharmacology or augment their claims here with genetic manipulation experiments of 20HE related genes in larvae (Leopold, Rewitz, Rideout, Drummond-Barbosa, Schuldiner labs) and adult animals using genetics, pharmacology or direct assessment of 20HE levels (RIPA, Edgar and Reiff labs).
      • In Fig.7 the authors used a sog-LacZ stock to show transcriptional activation in fatbody cells. This stock is based on P-element insertion in the according regulatory regions and supposed to express lacZ with an nls. I can clearly see lacZ in nuclei in Fig. 7H, whereas this is very hard to see in nuclei in Fig7i in the tumour model. In addition, lacZ is known for its high stability and not the best option. As this finding is vital for central claims of this study, it should be complemented by either qPCR for sog on fat body cells or using another readout by converting one of the two Mimic lines (BL42189/44958) into GFP sensors for sog.
      • I have similar problems with Fig.7B-F, as phosphorylated Mad should be translocated to the nucleus. In 7F the authors measure pMad over Dapi, which is the right way but it is hard to see pMad in the nucleaus apart from Fig7B, wheras in D and E, where the authors measure higher levels, I cannot identify clear pMad in nuclei. These images either need to show the Dapi channel or more representative images should be chosen like in Fig.4 with arrows pointing to measured nuclei. Fig.7C something went wrong with the compression of this image.
      • The proper function of RNAi stocks targeting genes like sog, mad, etc. is vital for this study as these lines are used throughout the study. Functional evidence of specific knockdown efficiency should be provided or references given in which these stocks were shown to provide functional knockdown on transcript or protein level.
      • Fig.S7 discusses appearance of gbb/Bmp7 and Sog/CHRD in human patients. The analysis the authors performed shows a correlation between both factors, but is hampered by the fact that datasets for peripheral tissues of cachexia patients are unavailable. The authors may consider sorting these after tumor entities in which cachexia occurs frequently vs. low occurrence and then check for both genes.
      • Fig.5 M-P pMAd is not indicated in the Panels only the legend.
      • Please follow FlyBase nomenclature, e.g. dlg1 for discs large 1 and unify in the whole manuscript and figure for all genes.
      • For endogenous fusion proteins like Viking-GFP (e.g. vkg::GFP) choose a format to clearly decipher them from transcriptional readout stocks like sog-lacZ.
      • The quantifications in most figures are quite small with tiny lettering and XY axis are difficult to read in letter/A4 size.

      Minor:

      1. Adjust in-figure caption alignments
      2. Line 104: add comma RasV12, dlgRNAi
      3. Line 114: replace little  not significant (n.s.)
      4. Line 334: 'sogRNAi overexpression' to my knowledge, RNAi are expressed, not overexpressed.
      5. Line 454: italicize r4>
      6. Fig S4E: remove frame
      7. Figures 6: It would be better to number and explain the pathway presented in the figure in text and fig legend.
      8. Just a personal preference. Lettering of images in images is commonly done horizontally, here it appears like a mix between vertical and horizontal.

      Significance

      In this manuscript, Bakopolous et al. investigated on the function of Insulin and TGF beta signaling in the converging regulation of sog (BMP antagonist) and how it controls ECM remodeling. Therefore, the authors used a Drosophila model of cachexia established in Lodge et al., 2021. The authors have shown that the tumors increase Impl2 and Gbb in the fatbody leading to the inhibition of insulin signaling and activation of TGF-β signaling respectively. This lead to the accumulation of ECM proteins that contributes to muscle ECM deficit and muscle detachment. These findings are a major advance in the field of cachexia and of broad interest. The authors demonstrate that state-of-the-art genetics in flies allows acquisition of genetically precise data along with important and complex discoveries on signaling pathways with relevance not only for basic, but for biomedical research as well.

      The manuscript is concise and very well written. The experiments overt a clear logical order and are comprehensively described. The authors provide exhaustive data to support their novel claims of broad interest to the scientific community