My name is Eden. I'm from the most haunted town in Kansas, Atchison! My major is Philosophy and my minor is in history. I think my goal for this class is to learn more about leadership in a conceptual way, so that I can apply it in more real-life situations. My walk up song would be Wake up by Young the giant!

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Reply to the reviewers

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

The manuscript by Neville et al addresses the link between the localization and the activity of the so-called "Pins complex" or "LGN complex", that has been shown to regulate mitotic spindle orientation in most animal cell types and tissues. In most cell types, the polarized localization of the complex in the mitotic cell (which can vary between apical and basolateral, depending on the context) localizes pulling forces to dictate the orientation. The authors reexplore the notion that this polarized localization of the complex is sufficient to dictate spindle orientation, and propose that an additional step of "activation" of the complex is necessary to refine positioning of the spindle.

The experiments are performed in the follicular epithelium (FE), an epithelial sheet of cell that surrounds the drosophila developing oocyte and nurse cells in the ovarium. Like in many other epithelia, cell divisions in the FE are planar (the cell divides in the plane of the epithelium). The authors first confirm that planar divisions in this epithelium depends on the function of Pins and its partner mud, and that the interaction between the two partners is necessary, like in many other epithelial structures. Planar divisions are often associated with a lateral/basolateral "ring" of the Pins complex during mitosis. The authors show that in the FE, Pins is essentially apical in interphase and becomes enriched at the lateral cortex during mitosis, however a significant apical component remains, whereas mud is almost entirely absent from the apical cortex. Pins being "upstream" of mud in the complex, this is a first hint that the localization of Pins is not sufficient to dictate the localization of mud and of the pulling forces.

The authors then replace wt Pins, whose cortical anchoring strongly relies on its interaction with Gai subunits, with a constitutively membrane anchored version (via a N-terminal myristylation). They show that the localization of myr-Pins mimics that of wt-Pins, with a lateral enrichment in mitosis, and a significant apical component. Since a Myr-RFP alone shows a similar distribution, they conclude that the restricted localization of Pins in mitosis is a consequence of general membrane characteristics in mitosis, rather than the result of a dedicated mechanism of Pins subcellular restriction. Remarkably, Myr-Pins also rescues Pins loss-of-function spindle orientation defects.

They further show that the cortical localization of Pins does not require its interaction with Dlg (unlike what has been suggested in other epithelia). However, spindle orientation requires Dlg, and in particular it requires the direct Dlg/Pins interaction. The activity of Dlg in the FE appears to be independent from khc73 and Gukholder, two of its partners involved in its activity in microtubule capture and spindle orientation in other cell types. Based on all these observations, the authors propose that Dlg serves as an activator that controls Pins activity in a subregion of its localization domain (in this case, the lateral cortex of the mitotic FE cell). They propose to test this idea by relocalizing Pins at the apical cortex, using Inscuteable ectopic expression. With the tools that they use to drive Inscuteable expression, they obtain two populations of cells. One population has a stronger apical that basolateral Insc distribution, and the spindle is reoriented along the apical-basal axis; the other population has higher basolateral than apical levels of Insc distribution, and the spindle remains planar. The authors write that Pins localization is unchanged between the two subsets of cells (although I do not entirely agree with them on that point, see below), and that although Mud is modestly recruited to the apical cortex in the first population, it remains essentially basolateral in both. In this situation, the localization of Insc in the cell is therefore a better predictor of spindle orientation than that of Pins or Mud. Remarkably, removing Dlg in an Insc overexpression context leads to a dramatic shift towards apical-basal reorientation of the spindle, suggesting that loss of Dlg-dependent activation of the lateral Pins complex reveals an Insc-dependent apical activation of the complex. Overall, I find the demonstration convincing and the conclusion appropriate. One of the limitations of the study is the use of different drivers and reporters for the localization of Pins, which makes it hard to compare different situations, but not to the point that it would jeopardize the main conclusions. I do not have major remarks on the paper, only a few minor observations and suggestion of simple experiments that would complete the study.

Minor:

What happens to Pins and Mud in Dlg mutant cells that overexpress Insc and behave as InscA? Are they still essentially lateral, or are they more efficiently recruited to the apical cortex?

This is a terrific question. Of course we would love to know and intend to find out.

One way to do this (consistent with the manuscript) would be to generate flies that are Dlg[1P20], FRT19A/RFP-nls, hsflp, FRT19A; TJ-GAL4/+; Pins-Tom, GFP-Mud/UAS-Insc. (Note that these flies would only allow us to image Mud; we would have to repeat the experiment using GFP FRT19A; hsflp 38 to see Pins. This isn’t ideal given that we’d like to image both together). Generating these flies is a major technical challenge because of the number of transgenes and chromosomes involved.

Our preferred way to do this would be to generate flies that are Dlg[1P20]/Dlg[2]; TJ-GAL4/+; Pins-Tom, GFP-Mud/UAS-Insc. So far, we’ve been unsuccessful. We are now undertaking a modified crossing scheme that we hope will solve the problem, though we aren’t overly optimistic about the outcome. We find that the temperature-sensitive mutation Dlg[2] presents an activation barrier; while we are able to generate flies that are Dlg[2] / FM7 in combination with transgenes and/or mutations on other chromosomes, we do not always recover the Dlg[2] / Y males (which must develop at 18degrees) from these complex genotypes.

In the longer term (outside the scope of revision), we are working to develop more tools for imaging Mud and Pins that we hope will help answer this question.

Regarding the competition between Pins and Insc for dictating the apical versus basolateral localization of Insc, the Insc-expression threshold model could be easily tested in Pins62/62 mutants, where it is expected that only InscA localization should be observed, even at 25{degree sign}C (unless Pins is required for the cortical recruitment of Insc, as it is the case in NBs - see Yu et al 2000 for example).

This is another great experiment and one we’d love to carry out. Again, the genetics are currently challenging, only because both UAS-Inscuteable and FRT82B pinsp62 are on the third chromosome. (Right now we’re trying to hop UAS-Inscuteable to the second).

However, we do have another idea for testing the threshold model, which is to repeat the experiment in which we express UAS-Insc in cells that are DlgIP20/IP20 at 25oC. Because the relevant cells (UAS-Insc OX in Dlg mitotic clones) are relatively rare, we have not yet been able to collect enough examples to make a firm conclusion. However, our preliminary results (only six cells so far!) suggest that more InscB cells are observed at the lower temperature, consistent with the threshold model.

I do not agree with the authors on P.10 and Figure 6A-D, when they claim that the apical enrichment of Pins is equivalent in both InscA and InscB cells. The number of measured cells is very low, and the ratio of apical/lateral Pins differs between the two sets of cells. The number of cells should be increased and the ratios compared with a relevant statistic method.

Totally fair. We are working to add more data to these panels (6B and 6D). The trend observed in 6D may be softening in agreement with the reviewer’s prediction, although we currently don’t yet have enough new data points to be confident in that conclusion. Therefore, we have not yet updated the manuscript, though we expect to do so during the revision period. We will also add a statistical comparison. Importantly, as the reviewer suggested, this does not alter our conclusions.

A lot of the claims on Pins localization rely on overexpression (generally in a Pins null background) of tagged Pins expressed from different promoters or drivers, and fused to different fluorescent tags. Therefore, it is difficult to evaluate to which extent the localization reflects an endogenous expression level, and to compare the different situations. As the cortical localization of Pins relies on interaction with cortical partners (mostly GDP-bound Gai) which are themselves in limiting quantity in the cell, and in the case of Gai-GDP, regulated by Pins GDI activity, this poses a problem when comparing their distribution, because the expression level of Pins may contribute to its cortical/cytoplasmic ratio, but also to its lateral/apical distribution. Although I understand that the authors have been using tools that were already available for this study, I think it would be more convincing if all the Pins localization studies were performed with endogenously tagged Pins, even those with Myr localization sequences. In an age of CRISPR-Cas-dependent homologous recombination, I think the generation of such alleles should have been possible. Although this would probably not change the main claims of the paper, it would have made a more convincing case for the localization studies.

We don’t disagree at all with this point. We did indeed try to stick with the published UAS-Pins-myr-GFP, not only for convenience but because it allows us to make comparisons to other studies using the same tool (Chanet et al Current Biology 2017 and Camuglia et al eLife 2022). Another consideration is that we used only one driver across our experiments (Traffic jam-GAL4). It is quite weak at the developmental stages that we examine, meaning that overexpression is not a major concern. (Indeed we have struggled with the opposite problem).

We certainly take the reviewer’s comment seriously and we therefore described it in the manuscript. We are currently working to develop endogenous tools using CRISPR.

Paragraph added to Discussion – Limitations of our Study:

“Another technical consideration is that our work makes use of transgenes under the control of Traffic jam-GAL4. While this strategy allows us to compare our results with previous work employing the same or similar tools, a drawback is that we cannot guarantee that Traffic jam-GAL4 drives equivalent expression to the endogenous Pins promoter (Chanet et al., 2017, Camuglia et al., 2022). However, given that Traffic jam-GAL4 is fairly weak at the developmental stages examined, we are not especially concerned about overexpression effects.”

The authors should indicate in the figure legends or in the methods that the spindle orientation measurements for controls for Pins62/62 are reused between figures 1, 3, 4, 5, 6 , and between figure 3, 4 and 5, respectively.

Absolutely. Added to the Methods section.

Reviewer #1 (Significance (Required)):

Altogether, this study makes a convincing case that the localization of the core members of the pulling force complex, Pins and Mud, is not entirely sufficient to localize active force generation, and that the complex must be activated locally, at least in the FE. The notion of activation of the Pins/LGN complex has probably been on many people's mind for years: Pins/LGN works as a closed/open switch depending on the number of Gai subunits it interacts with, it must be phosphorylated, etc... suggesting that not all cortical Pins/LGN was active and involved in force generation. However the study presented here shows an interesting case where localization and activation are clearly disconnected. The authors show how Dlg plays this role in physiological conditions in the FE, and use ectopic expression of Insc to show that, at least in an artificial context, Insc can have the same "activating activity" (or at least an activating activity that is stronger than its apical recruitment capability and stronger than Dlg's activating activity). It is to my knowledge the first case of such a clear dissociation. In their discussion, the authors are careful not to generalize the observation to other tissues. Although I did not reexplore all that has been published on the Pins/LGN-NuMA/Mud complex over the last 20 years, my understanding is that despite interesting cases of distribution of the complex like that of Mud in the tricellular junction in the notum, the localization model can still explain most of the phenotypes that have been described without invoking an activation step. If it is the case, then the activation model is another variation (an interesting one!) on the regulation of the core machinery, which are plentiful as the authors indicate in their introduction, and is maybe specific to the FE; if not, then it would be interesting to push the discussion further by reexamining previous results in other systems, and pinpointing those phenotypes that could be better explained with an activation step.

Overall, I find this is an elegant piece of work, which should be of interest to many cell and developmental biologists beyond the community of spindle orientation aficionados.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): Summary: The manuscript by Neville et al. addressed the mechanism how conserved spindle regulators (Pins/Mud/Gai/Dynein) control spindle orientation in the proliferating epithelia by revising "the canonical model", using the Drosophila follicular epithelium (FE). The authors examined the epistatic relationship among Pins, Mud and Dlg in FE and found that Pins controls the cortical localization of Mud by utilizing mutant analyses, and suggested their localization does not fully overlap using the newly generated knock-in allele. They also showed that Pins relocalization during mitosis depends on cortical remodeling, or passive model, where Pins localization changes with other membrane-anchored proteins. Their data further suggest that Pins cortical localization is not influenced by Dlg, but Pins-interacting domain of Dlg does affect spindle orientation. Based on these results, the authors propose that Dlg controls spindle orientation not by redistributing Pins, but by promoting (or "activating" from their definition) Pins-dependent spindle orientation. Interestingly, ectopic expression of Inscuteable (Insc) suggested that Insc localization, either apical or lateral, correlates with spindle orientation, and their localization is a dominant indicator of spindle orientation, compared to the localization of Pins and Mud, implicating potentially distinct roles of activation and localization of the spindle complex. Overall their genetic experiments are well-designed and provide stimulation for future research. However, their evidence is suggestive, but not conclusive for their proposal. I have several concerns about their conclusion and would like to request more detailed information as well as to propose additional experiments.

Major concerns: 1. This report lacks technical and experimental details. As the typical fly paper, the authors need to show the exact genotypes of flies they used for experiments. This needs to be addressed for Figures 1-6, and Supplemental Figures. Especially, which Gal4 drivers were used for UAS-Pins wt or mutant constructs in Figure 4 with pins mutant background, Khc73, GUKH mutant backgrounds. Which exact flies were used for mutant clone experiments for Supplemental Figure 3? (A for typical mosaic, and B for MARCM). Without these details, it is impossible to evaluate results and reproduce by others.

We take this concern very seriously!

• We listed the GAL4 driver (Traffic jam-GAL4) in the first section of the Materials and Methods: Expression was driven by Traffic Jam-GAL4 (Olivieri et al., 2010). The transgene and relevant citation have been added to Table 1.
• We explained the background stock for the MARCM experiment in the Materials and Methods: Mosaic Analysis with a Repressible Cell Marker (after the method of Lee and Luo) was carried out using GFP-mCD8 (under control of an actin promoter) as the marker. The transgene and relevant citation have been added to Table 1.
• In line with other fly studies (eg. Nakajima et al., Nature 2013) and our own Drosophila work (Bergstralh et al Current Biology 2013, Bergstralh*, Lovegrove*, St Johnston NCB 2015, Bergstralh et al Development 2016, Finegan et al EMBO J 2019, Cammarota*, Finegan* et al Current Biology 2020) we were careful to show the relevant genotype components in each figure.
• We included a fully referenced Supplementary Table (Table 1 – Drosophila genetics) listing every mutant allele or transgene with a citation and a note about availability. We have expanded this table in response to the author’s concern (see above).

  Related to the comment 1, how did the author perform "clonal expression of Ubi-Pin-YFP" in page 5? As far as I understand, Ubi-Pin-YFP is expressed ubiquitously by the ubiquitin promoter.

The reviewer makes a good point. We regret that we did not make this experiment more clear. Ubi-Pins-YFP was recombined onto an FRT chromosome (FRT82B). We made mitotic clones.

We have clarified this in the Methods section as follows:

“Mitotic clones of Ubi-Pins-YFP were made by recombining the Ubi-Pins-YFP transgene onto the FRT82B chromosome”

1. In page 6, if Pins relocalization is passive and is associated with membrane-anchored protein remodeling during mitosis, its relocalization can be suppressed by disrupting the process of mitotic remodeling (mitotic rounding). The authors should test this by either genetic disruption or pharmacological treatment for the actomyosin should cause defects in Pins relocalization, which bolster their conclusion.

We agree that this is a cool experiment and are happy to give it another shot. However, we do note that interpretation could be difficult. We don’t know that mitotic rounding and membrane-anchored protein remodeling during mitosis are inextricably linked. Notably, the remodeling we describe reflects cell polarity; apical components are evidently moved to the lateral cortex. This is contrary to understanding of rounding, which reflects isotropic actomyosin activity (Chanet et al., (2017) Curr.Biol. & Rosa et al., (2015) Dev. Cell.). Therefore we don’t understand what a “negative” result would mean, or for that matter that a “positive” result would be safe to interpret.

We have attempted many strategies to prevent cell rounding in the follicular epithelium, none of which have successfully prevented rounding. 1) We attempted to genetically knockdown Moesin in the FE and did not see an effect on cell rounding. However we couldn’t confirm knockdown and therefore are not confident in this manipulation. 2) It is difficult to interpret the result of genetically disrupting Myosin, because it causes pleiotropic effects, such as inhibition of the cell cycle, and disruption of monolayer architecture. 3) We treated egg chambers with Y-27632 (a Rok-inhibitor) and examined its effect on mitotic cell rounding and on cytokinesis, which are Rok-dependent processes. Our experiments were performed using manually-dissociated ovarioles treated for 45 minutes in Schneider Cell Medium supplemented with insulin. Even at our maximum concentration of 1mM Y-27632, several orders of magnitude above the Ki, we are unable to see any effect on mitotic cell shape or actin accumulation at the mitotic cortex and did not observe any evidence of defective cytokinesis. We also did not observe defects in spindle organization or orientation, as would be expected from failed rounding. We therefore do not believe that the inhibitor works in this tissue. One possible explanation is that the follicle cells are secretory, and likely to pass molecules taken up from the media quickly into the germline. Therefore, we do not anticipate that we can perform this experiment to our satisfaction.

1. The critical message in this manuscript is that the core spindle complex mediated by Pins-Mud controls spindle orientation by "activation", but not localization. The findings that Pins and Mud localization is not influenced by Insc and that ecotpic Insc expression and genetic Mud depletion (Figure 6) might support their proposal, but these results just suggest their localization does not matter. I wonder how the authors could conclude and define "activation". What does this activation mean in the context of spindle orientation? Can the authors test activation by enzymatic activity or assess dynamics of spindle alignment?

We intend for the critical message of the manuscript to be that “The spindle orienting machinery requires activation, not just localization.” We absolutely do not make the claim that localization is not important, only that it is not sufficient. The reviewer recognizes this point here and in a subsequent comment: “The authors showed that Pins and Mud localization themselves are not sufficient for the control of spindle orientation with genetic analyses.”

We also do not claim that Pins and/or Mud localization are not impacted by Inscuteable. On the contrary, we plainly see and report that they are; the intensity profiles in Figure 6 are distinct from those in Figure 2, as discussed in the text.

We appreciate the reviewer’s point about activation. Since we do not understand these proteins to be enzymes, we aren’t sure what enzymatic activity would be tested. The dynamics of spindle alignment in this slowly developing system are prohibitively difficult to measure: the mitotic index is very low (~2%) and only a very small fraction of those cells will be in a focal plane that permits accurate live imaging in the apical-basal axis. Alternative modes of activation include conformational change and/or a connection with other important molecules. The simplest possibility would be that Dlg allows Pins to bind Mud, but so far our data do not support it. We have added the following paragraph to our discussion:

“The mechanism of activation remains unclear. While the most straightforward possibility is that Dlg promotes interaction between Pins and Mud, our results show that Mud is recruited to the cortex even when Dlg is disrupted (Figure 4D). Alternatively, Discs large may promote a conformational change in the spindle-orientation complex and/or a change in complex composition. Furthermore, the Inscuteable mechanism is not likely to work in the same way. Dlg binds to a conserved phosphosite in the central linker domain of Pins and should therefore allow for Pins to simultaneously interact with Mud (Johnston et al., 2009). Contrastingly, binding between Pins and Inscuteable is mediated by the TPR domains of Pins, meaning that Mud is excluded (Culurgioni et al., 2011; 2018). While a stable Pins-Inscuteable complex has been suggested to promote localization of a separate Pins-Mud-dynein complex, our work raises the possibility that it might also or instead promote activation.”

1. In page 7-8, although Pins-S436D rescue spindle orientation, but Pins-S436A does not in pins null clones background, Pins localization is not influenced by Dlg. This questions how exactly Pins and Dlg can interact, and how Dlg affect Pins function. Related to this observation, in the embryonic Pins:Tom localization in dlg mutant does not provide strong evidence to support their conclusion given the experimental context is different from previous study (Chanet et al., 2017).

We agree with the reviewer. Our data (this paper and previous papers) and the work of others indicate that this interaction is important for spindle orientation (Bergstralh et al., 2013a; Saadaoui et al., 2014; Chanet et al., 2017). However, we show here that Dlg doesn’t obviously impact Pins localization (as proposed in our earlier paper), but does impact the ability of the spindle orientation machinery to work (hence activity).

The reviewer makes a very good point. Our experimental context is different from the previous study concerning Pins and Dlg in embryos: Chanet et al (2017) performed their work in the embryonic head, whereas we look at divisions in the ventral embryonic ectoderm. These are distinct mitotic zones (Foe et al. (1989) Development) and exhibit distinct epithelial morphologies. We show that Pins:Tom localizes at the mitotic cell cortex in Dlg[2]/Dlg[1P20] in cells in the ventral embryonic ectoderm. Our only conclusion from this experiment is that Pins:Tom can localize without the Dlg GUK domain in another cell type (outside the follicular epithelium). In the current preliminary revision we have softened our claim as follows:

“We also examined the relationship between Pins and Dlg in the Drosophila embryo. A previous study showed that cortical localization of Pins in embryonic head epithelial cells is lost when Dlg mRNA is knocked down (Chanet et al., 2017). We find that Pins:Tom localizes to the cortex in the ventral ectoderm of early embryos from Dlg1P20/Dlg2 mothers, indicating that Pins localization in the ventral embryonic ectoderm epithelium does not require direct interaction with Dlg. We therefore speculate that Dlg plays an additional role in that tissue, upstream of Pins (Figure 4G).”

Our intention is to elaborate on our findings with additional data from embryos. To this end we have already acquired preliminary control data investigating the spindle angle with respect to the plane of the epithelium, and are in the process of examining spindle angles in dlg mutant embryonic tissue.

In page 11, the authors state "... that activation of pulling in the FE requires Dlg". I was not convinced by anything related to "pulling". There is no evidence to support "pulling" or such dynamic in this paper, just showing Mud localization, correct?

We appreciate the reviewer’s concern. The original sentence read that “We interpret [our data] to mean that interaction between Pins and Dlg, which is required for pulling, stabilizes the lateral pulling machinery even if Dlg is not a direct anchor.” This statement is based on work across multiple systems, including the C. elegans embryo (Grill et al Nature 2001), the Drosophila pupal notum (Bosveld et al, Nature 2016), and HeLa cells (Okumura et al eLife 2018), which shows that Mud/dynein-mediated pulling (on astral microtubules) orients/positions spindles. This is described in the introduction.

To address the reviewer’s particular concern, we have replaced “pulling” with “spindle-orentation machinery,” so that this sentence now reads …“activation of the spindle-orientation machinery in the FE requires Dlg.”

1. Ectopic expression of Insc (Figure 6) provided a new idea and hypothesis, but the conclusion is more complicated given that Insc is not expressed in normal FE. For example, the statement that "Inscuteable and Dlg mediate distinct and competitive mechanism for activation of the spindle-orienting machinery in follicle cells" is probably right, but it does not show anything meaningful since Insc does not exist in normal FE. Is Dlg in a competitive situation during mitosis of FE? If so, which molecules are competitive against Dlg? The important issue is to provide a new interpretation of how spindle orientation is controlled epithelial cells. I strongly recommend to add models in this manuscript for clarity.

We considered the addition of model cartoons very carefully in preparing the original manuscript, and again after review. While we are certainly not going to “dig in” on this point, our concern is that model figures would obscure rather than clarify the message. As the reviewer points out, we do not understand how activation works, and as discussed in the manuscript we don’t think it’s likely to work the same way in follicle cells (Dlg) as it does in neuroblasts (Insc). Therefore model figure(s) are premature.

We do not agree with the statement that "Inscuteable and Dlg mediate distinct and competitive mechanism for activation of the spindle-orienting machinery in follicle cells… does not show anything meaningful.” This is a remarkable finding because it suggests that there is more than one way to activate Pins. Given the critical importance of spindle orientation in the developing nervous system, and the evolutionary history of the Dlg-Pins interaction, we think that this finding supports a model in which the Dlg-Pins interaction evolved in basal organisms, and a second Inscuteable-Pins interaction evolved subsequently to support neural complexity. These ideas are raised in the Discussion.

The reviewer also writes that “The important issue is to provide a new interpretation of how spindle orientation is controlled epithelial cells.” We find this concern perplexing, since the reviewer clearly recognizes that we have provided a new interpretation: Dlg is not a localization factor but rather a licensing factor for Pins-dependent spindle orientation.

Minor comments: 8. Some sections were not written well in the manuscript. "It does not" in page 6. "These predictions are not met". I just couldn't understand what they stand for. Their writing has to be improved.

Again, we are not going to dig in here, but we would prefer to retain the original language, which in our opinion is fairly clear. Our study is hypothesis-driven and based on assumptions made by the current model. We used direct language to help the reviewer understand what happened when we tested those assumptions.

1. In page 9, Supplementary Figure 4 should be cited in the paragraph (A potential strategy for..), not Supplemental Figure 1A, and 1B.

Good catch, thank you! We have corrected this.

1. In page 10, the authors examine aPKC localization in Insc expressing context of FE. Does aPKC localization correlate with Insc localization (Insc dictates aPKC?)? aPKC is not involved in spindle orientation from the author's report (Bergstralh et al., 2013), so it does not likely provide any supportive evidence.

I’m afraid we don’t entirely understand this comment. The interdependent relationship between aPKC and Inscuteable localization is long-established in the literature and was previously addressed in the follicle epithelium (Bergstralh et al. 2016). We do not make the claim here that aPKC governs spindle orientation. We are emphasizing that the difference between InscA and InscB cells extends to the relocalization of polarity components involved in Insc localization. As described in the manuscript, these data are provided to support our threshold model:

“In agreement with interdependence between Inscuteable and the Par complex, we find that aPKC is stabilized at the apical cortex in InscA cells but enriched at the lateral cortex in InscB cells (Figure 6E). This finding is consistent with an Inscuteable-expression threshold model; below the threshold, Pins dictates lateral localization of Inscuteable and aPKC. Above the threshold, Inscuteable dictates apical localization of Pins and aPKC.”

1. In Dicussion page 12, "In addition, we find that while the LGN S408D (Drosophila S436D) variant is reported to act as a phosphomimetic, expression of this variant has no obvious effect on division orientation (Johnston et al., 2012)". Where is the evidence for this? I interpret that this phosphomimetic form can rescue like wt-Pins not like unphospholatable mutant S436A, so it means that have an effect on spindle orientation, correct?

The reviewer makes a good point. We regret the confusion. We mean to point out that the S436D variant is no different from the wild type. We have amended the text to clarify:

“In addition, we find that while the LGN S408D (Drosophila 436D) variant is reported to act as a phosphomimetic, this variant does not cause an obvious mutant phenotype in the follicular epithelium (Johnston et al., 2012). What then is the purpose of this modification? Since the phosphosite is highly conserved through metazoans, one possibility to consider is that the phosphorylation regulates the spindle orientation role of Pins, whereas unphosphorylated Pins plays a different role (Schiller and Bergstralh, 2021).”

Reviewer #2 (Significance (Required)):

The authors showed that Pins and Mud localization themselves are not sufficient for the control of spindle orientation with genetic analyses. While the authors tried to challenge the concept of "canonical model", there is no clear demonstration of "activation" of the spindle complex. I appreciate their genetic evidence and new results, and understand the message that Pins and Mud effects are beyond localization, but there is no alternative mechanism to support their model. At the current stage, their evidence provides more hypothesis, not conclusion. Based on my expertise in Developmental and Cell biology, I suggest that the work has an interest in audience who studies spindle machinery, but for general audience.

We think that the reviewer fundamentally shares our perspective on the study. Our work tests assumptions made by the canonical model and shows that they aren’t always met (meaning that the question of how spindle orientation works in epithelia at least is still unsolved), and that in the FE at least one component (Dlg) has been misunderstood. We reach a major conclusion, which is that localization of Pins is not enough to predict spindle orientation in the FE.

It’s absolutely true that the precise molecular role of Dlg has not been solved by our study. This is a major question for the lab, and we are currently undertaking biochemical work to address it. It’s probably more work than we can (or should) do on our own, which is just one reason to share our current results with colleagues.

One fundamental reason for undertaking this study is that 25 years of spindle orientation studies released into an environment in which “positive” conclusions are the bar for publication success may have burdened the field with claims that are overly-speculative. We appear to have contributed to this problem ourselves in 2013. With that in mind we contend that providing an alternative molecular mechanism at this point is premature and would impair rather than improve the literature.

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

Neville et al re-examine the role and regulation of Pins/LGN in Drosophila follicular epithelial cells. They argue that polar or bipolar enrichment of Pins localisation at the plasma membrane is not crucial for spindle orientation, and therefore propose that Pins must be somehow activated to function. These interpretations are not supported by the data. However, the data strongly suggest an alternative interpretation which is of major biological significance.

As an initial point, we disagree with the summary above. We do not argue that enrichment of Pins is not crucial for spindle orientation. We argue that enrichment of Pins is not sufficient. This is why we titled the paper “The spindle orienting machinery requires activation, not just localization” instead of “The spindle orienting machinery requires activation, not localization.”

Although we disagree with reviewer, we appreciate their criticism of our manuscript and are glad for the opportunity to clarify our findings. In our responses to the specific comments (below) we explain why our data contradict the reviewer’s model and what we will do to improve the manuscript.

Comments:

1. In the experiments on Dlg mutants (Fig 4D, S3) visualising Pins:Tom, the wild-type needs to be shown next to the Dlg mutant image, otherwise a comparison cannot be made. For example, Pins:Tom looks strongly enriched at the lateral membranes in the wild-type shown in Fig 2B&C, but much more weakly localised at the lateral membranes in Dlg1P20/2 mutants in Fig 4D. Thus, it looks like the Dlg GUK domain is required for full enrichment of Pins:Tom at lateral membranes, even if some low level of Pins can still bind to the plasma membrane in the absence of the Dlg GUK domain. Quantification would likely show a reduction in Pins:Tom lateral enrichment in the Dlg1P20/2 mutants - consistent with the spindle misorientation phenotype in these mutants.

The reviewer raises a reasonable concern about Figure 4D. We noted the difficulty of imaging Pins:Tom, which is exceedingly faint, in our original manuscript. For technical reasons, only one copy of the transgene was imaged in the experiment presented in 4G (two copies were used in Figure 2B), and the lack of signal presented an even greater challenge. In the manuscript we went with the clearest image. To address the reviewer’s concern, we have added signal intensity plots to this figure showing that Pins:Tom and Pins-myr are both laterally enriched at mitosis in Dlg[1P20]/Dlg[2] mutants. These data have been added as a new panel (E) in Figure 4. We were also able to replace the pictures in 4D with new ones generated after review.

1. In Fig 4E, the phosphomutant PinsS436A-GFP looks more strongly apical and less strongly lateral than the wildtype Pins-GFP, consistent with the spindle misorientation phenotype in S436A rescued pins mutants.

The reviewer has an eagle eye! We did not detect a difference in localization across the three transgenes, though we were certainly looking for it (that’s why we generated these flies in the first place). Again, the strength of signal was a major challenge in these experiments, and we therefore went with the cleanest image. In response to the reviewer’s concern, we note that the S436A and S436D examples shown have equivalent apical signal, but only the S436A fails to rescue spindle orientation.

Together, Reviewer Comments 1 and 2 suggest a model in which Dlg is required for lateral enrichment of Pins at mitosis. As described in the manuscript, this is the very model proposed in our own previous study (Bergstralh, Lovegrove, and St Johnston; 2013), and reiterated in a subsequent review article (Bergstralh, Dawney, and St Johnston; 2017). We point these publications out because the senior author of the current manuscript is not especially enthusiastic about showing himself to be wrong (twice!) in the literature. He therefore insisted on seeing multiple lines of evidence before making the counterargument:

• The reviewer’s model (the 2013 model) is firstly challenged by work shown in Figure 3. We find that membrane-anchored proteins (even just myristoylated RFP!) demonstrate lateral enrichment at mitosis, regardless of whether or not they interact with the Dlg-GUK domain.
• Even stronger evidence is shown in Figure 4F. Pins-myr-GFP is very plainly enriched at the lateral cortex in Dlg[IP20]/Dlg[2] mutant cells (now demonstrated with signal intensity plots in FIGURE 4E). However, the spindle doesn’t orient correctly (quantified in 4C). Since Dlg is impacting spindle orientation independently of Pins localization, these data support our “claim in the final sentence of the abstract ‘Local enrichment of Pins is not sufficient to determine spindle orientation; an activation step is also necessary’.”

  In the InscA examples, Pins:Tom looks apical. In the InscB examples, Pins:Tom looks more laterally localised, consistent with the spindle orientations in these experiments.

These figures (6A-D) don’t only show/quantify Pins:Tom localization. They also show localization of GFP-Mud. Whereas Pins:Tom is certainly apically enriched in the InscA examples, the interesting finding is that GFP-Mud is not. In strong contrast, it instead shows a weak apical localization and a strong lateral enrichment. As described in the manuscript, this pattern of Mud localization predicts normal spindle orientation, which is not observed in these cells.

Thus, these data appear to support the existing model that Pins enrichment at the plasma membrane is a key factor directing mitotic spindle orientation in these cells. The author's claim in the final sentence of the abstract "Local enrichment of Pins is not sufficient to determine spindle orientation; an activation step is also necessary" is not supported by the data.

We are pleased that the reviewer shared this quote; our claim is that Pins localization is not sufficient, not that it is unnecessary (see above). We absolutely do not dispute that “Pins enrichment at the plasma membrane is a key factor directing mitotic spindle orientation.”

The open question posed by the data is why GFP-Mud is excluded apically & basally during mitosis, while Pins:Tom is not. The simple alternative model is that Mud only localises to the plasma membrane where Pins is most strongly concentrated, such that Mud strongly amplifies any Pins asymmetry. Thus, even myr-Pins can still rescue a pins n mutant, because myr-Pins is still enriched laterally compared to apically (or basally).

Thus, I would strongly suggest re-titling the manuscript to: "Mud/NuMA amplifies minor asymmetries in Pins localisation to orient the mitotic spindle".

Well, that is a good-looking title, and we’re therefore sorry to decline the suggestion. However, as described above, Figure 4D shows that Pins enrichment does not always predict spindle orientation. More importantly, Figure 6A (cited by the reviewer in Comment 3) very plainly shows that Mud does not “only locali[ze] to the plasma membrane where Pins is most strongly concentrated.” In this picture – and across multiple InscA cells (Figure 6B) - Pins is strongly concentrated at the apical surface, whereas Mud is not.

Mud/NuMA presumably achieves this amplification by binding to the plasma membrane only where Pins is concentrated above a critical threshold level. This would mean a non-linear model based on cooperativity among Pins monomers that increases the binding avidity to Mud above the threshold concentration of Pins monomers.

This is essentially a minor revision of the standard model, which we expected would hold true in the FE. As described above, it is not supported by our data.

Reviewer #3 (Significance (Required)):

The manuscript is focused on the question of mitotic spindle orientation in epithelial cells, which is a fundamental unsolved problem in biology. The data reported are impressive and important, providing new insights into how the key spindle orientation factors Mud/NuMA and Pins/LGN localise during mitosis in epithelia. I recommend publication after major revisions.

We are delighted that the reviewer finds our data impressive and important, and our experiments insightful. We understand that the “major revisions” requested are meant to bring the paper in line with their model (our own earlier model). Since the data in our original manuscript contradict that model, the revisions are instead focused on clarifying and strengthening our message.

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Reply to the reviewers

We thank for reviewers for their feedback and were pleased they think that the manuscript is “of great interest to the scientific community”. The reviewers agree that the manuscript addresses an important question and that the identification of ASNS as a potential vulnerability of late-stage colorectal cancer is significant. The reviewers agree that our findings would be substantially strengthened by validation in state-of-the-art organoid model systems. We agree with this and are currently liaising with collaborators (Owen Sansom, Beatson Institute and Laura Thomas, Swansea University) to replicate our findings in both mouse and human colorectal organoid models. We will determine the sensitivity of colorectal organoid models to ASNS inhibition across a range of tumorigenicities and mutational profiles representing different stages of the adenoma-carcinoma progression. We believe these experiments will substantially strengthen the manuscript and lend weight to our finding that late-stage adenocarcinoma cells are vulnerable to ASNS inhibition.

This is the predominant concern across reviewers, we are confident we can address this and all other, relatively minor, concerns as detailed below.

Please find below a point-by-point reply to the reviewer’s comments. Reviewer comments are in italicized text and our responses follow.

Reviewer #1

• All of the findings in this manuscript are limited to in vitro observations, we know that most of the in vitro findings can not be translated in vivo. The manuscript would significantly benefit from in vivo experiments using the cells described in Fig.1 A and confirming the in vitro findings.*

We agree that validation of our results in a more physiological context would significantly elevate our manuscript. In order to address this, we intend to use both human and mouse colorectal organoid models (please see detailed description of this in response to reviewer 2). We have decided to take this approach rather than conduct in vivoexperiments using the AA series (C1, SB, 10C and M) for two main reasons. Firstly, the C1 and SB cell lines do not form tumours in mice, consistent with them representing early colorectal adenoma cells. As such, we are not able to use the entire series in in vivo experiments. Secondly, we are keen to demonstrate replication of our findings in an alternative model. An organoid model would offer increased functional relevance, whilst allowing us to retain the ability to validate our observed metabolic dependencies across the adenoma to carcinoma sequence. We hope the reviewer agrees that these experiments would address their concerns.

• The authors should provide proliferation data for the cell lines they used in this manuscript (C1, SB, 10C and M). In Fig. 1 B they show clear differences in EACR, can the authors provide data on glucose uptake differences in these analyzed cell lines.*

We agree that proliferation and glucose uptake data would be a useful addition to the manuscript. We will provide doubling times for the cell lines used in this study and will measure glucose uptake by analysing extracellular glucose levels in the cell culture media from each of the cell lines.

• In Figure 2 C the authors should provide isotope tracing data for the upper glycolysis (e.g. glucose and glucose-6-P) and alanine. In Figure 2 D the authors should provide the isotope tracing data for glutamine and glutamate.*

We have data for glycolytic intermediates; glycerol-3-phosphate and dihydroxyacetone phosphate (DHAP) and alanine and will add them to the figures as requested.

• Do the authors see any sign of reductive carboxylation in their U-13C glutamine experiments?*

We observe only a low level of reductive carboxylation across the AA series cell lines (

• Can the authors speculate how the C1, SB, 10C and M cell lines would react when glucose would be replaced with galactose in the culture environment and forcing the cells to increase oxidative phosphorylation (OXPHOS).*

We would speculate that the cells would react similarly to our experiments in low glucose conditions displayed in Fig 3A-K. Given that M cells were shown to be the most flexible with regards to fuel source, we would expect them to be able to survive and proliferate more efficiently than the other cell lines in challenging culture conditions. Additionally, we would expect the C1s to survive well in galactose conditions, given that they rely less on glycolysis for ATP production and have significantly higher spare respiratory capacity compared to the more progressed cell lines.

• Can the authors comment whether C1, SB, 10C and M cell lines show differences in coping with oxidative stress?*

Again, we would speculate that the M cells would cope with exposure to oxidative stress best, given their metabolic flexibility. However, we would aim to test this by measuring the cellular response to hydrogen peroxide (which would induce oxidative stress) across all cell lines.

• In the ASNS knockdown experiments do the authors detect an increase in glucose uptake in ASNS deficient cells.*

This is an interesting question; we will address it by comparing extracellular glucose levels in C1 and M cells transfected with control and siRNA targeting ASNS.

• Can the authors provide gene expression data that would explain the metabolic switch between early and late-stage adenocarcinoma? Do the authors detect any differences in mTORC1 activation among the C1, SB, 10C and M cell lines? ASNS is an ATF4 target, can the authors provide any expression data on ATF4 in their cell lines.*

To address the first question, using our proteomics data, we have generated heatmaps showing protein abundance data from key metabolic pathways including glycolysis, the TCA cycle and the electron transport chain in the C1, SB and M cell lines. These data show an array of variation in protein expression of these pathways between the C1, SB and M cells, with no clear up or downregulation of these pathways as a whole, but rather more intricate regulation of clusters of proteins within the pathways. These data align well with the metabolomic data presented in Figure 2 and will allow us to investigate the mechanisms underlying the metabolic switch. These heat maps will be incorporated into the manuscript. Using the heatmaps we will identify and discuss key nodes we predict to explain the metabolic switch between early and late-stage adenocarcinoma. We will then determine whether manipulation of these nodes impact the metabolic phenotype of the cells experimentally. For example, the heat maps have highlighted that ENO3 or enolase 3 is strongly upregulated in the SB and M cells in comparison to the C1 cells and may be involved in driving the metabolic switch. Indeed, ENO3 has previously been found to promote colorectal cancer progression by enhancing glycolysis (Chen et al, Med Oncol, 2022), consistent with what we see here. To test this, we will knock down ENO3 across the cell line series and determine the impact on cellular phenotype and metabolism (using Seahorse extracellular flux analysis).

With regards to mTORC1 activation, we have further analysed our proteomics data from C1, SB and M cells and have found that the M cells show significantly higher serine 235/236 phosphorylation of ribosomal S6 protein, a common readout for mTORC1 activation, compared to C1 and SB cells. Further, we aim to carry out immunoblotting across the four cell lines to analyse phospho-S6 (relative to total S6), 4E-BP1 and phospho-ULK-1 (relative to total ULK-1) levels.

With regards to ATF4, using our proteomics data we have generated a heatmap of gene expression changes of ATF4 target genes in C1, SB and M cells that we will provide in supplementary material . These data suggest that there does not appear to be any clear pattern of enhanced or reduced ATF4 transcriptional activity across the cell lines, with different clusters of genes within this signature up or downregulated across the series. Moreover, Ingenuity Pathway Analysis (IPA) revealed that the ATF4 pathway showed an activation z-score of -0.41 (p=0.0134) in SB versus C1 cells, and 0.35 (p=0.00051) in M versus C1 cells (where a threshold of +/- 2 indicates activation/suppression of the pathway, respectively), confirming there is no clear regulation of this pathway between the cell lines. In addition, we will carry out immunoblotting for ATF4 expression levels across the cell line series.

Reviewer #2

*Major comments: *

*Early CRC *

*Molecular understanding of CRC is obviously of great interest and importance for the clinics. However, tumors of early stages are almost exclusively resected and not treated with systemic agents. Hence, the argument by the authors that the metabolic understanding of early CRC is of clinical relevance is somewhat misleading. Overall, it would have been much more clinically relevant to investigate the multiple steps of later stages during CRC progression. How about metabolic changes during metastasis. Deep mechanistic understanding of process during metastasis has striking clinical relevance. *

We agree with the reviewer that understanding metastatic progression is of clinical relevance and should indeed be investigated in more detail. Using our model, we do shed light on a vulnerability of late-stage adenocarcinoma cells (sensitivity to asparagine synthetase (ASNS) inhibition). Indeed, we show that ASNS expression is elevated in both colorectal tumour and metastatic tissue in comparison to normal suggesting that our study may have revealed a vulnerability with utility for treating late stage (and potentially metastatic) tumours. The reviewer raises an important issue with the way we frame the utility of the model in the manuscript text. We will rewrite this to emphasise its utility in identifying late-stage vulnerabilities and the clinical value of this approach. We maintain that the molecular understanding of colorectal cancer across all stages of its progression will provide a valuable contribution to the field but agree that we should be more specific with regards to the clinical utility of our findings.

*Model system *

The cell lines used in this study are not state-of-the-art to investigate the complex process during CRC progression. The original paper is from 1993 in which the cell lines were generated does not allow understanding of the characteristics of these cell lines. Recently, multiple models have been established, for example in organoids, to investigate the progression of CRC much more reliably. There are systems that use CRISPR/CAS9 edited human organoids that follow the genetic alterations of CRC progression with accompanied phenotypes. Further, extensive biobanks of organoids from patients are available (also commercially) which better represent the stages of CRC. Similarly, the question raised above of how representative this progression cell line set is needs to addressed. The mutagen-induced progression could generate various alterations that are not detected in patients, hence create an artificial system. Overall, biological replicates are missing.

We thank the reviewer for their critique and agree that our manuscript would be significantly strengthened if we were able to replicate our key findings in another model. We agree that the cell line series we have used here has limitations and we will make sure these are discussed by adding a ‘Limitations’ section to the ‘Discussion’. We maintain that the cell line series is a valuable tool in which to effectively identify metabolic vulnerabilities for further research. A key advantage of this system is that it is a human cell line series of the same lineage. In addition, we can easily conduct metabolomics and stable isotope tracer analysis allowing us to investigate cellular metabolic activity and manipulate any identified pathways easily. As such, the cell line series is an effective tool in which to identify potential vulnerabilities, but we agree that these vulnerabilities need to be validated in state-of-the-art organoid systems for the impact of the work to be clearer.

To address this, in collaboration with Owen Sansom (Beatson Institute) and Laura Thomas (Swansea University), we aim to validate our identified metabolic dependency in mouse and human colorectal organoids respectively. We will determine the sensitivity of colorectal organoid models across a range of tumorigenicities and mutational profiles representing different stages of the adenoma-carcinoma progression to asparagine synthetase (ASNS) inhibition. We believe these experiments will substantially strengthen the manuscript and lend weight to our finding that late-stage adenocarcinoma cells are vulnerable to ASNS inhibition.

*Gene Expression analysis *

In Figure 5 C and D is the expression of ASNS to stages and overall survival from online available datasets correlated. Its unclear what the difference between tumor and metastatic in C means. The labelling in D is too small. Is the difference between the two groups significant? Are these patients only at a specific stage? It seems not that ASNS is a strong prognosticator; further stratification is needed to clarify the role of ASNS in CRC.

The data displayed in Fig 5C and 5D are from separate datasets so are not correlated. In Fig 5C ‘Tumour’ refers to gene expression from the primary tumour site (in this case the colorectum), whereas ‘Metastatic’ refers to gene expression from a metastatic tumour (from which the primary tumour was of colorectal origin). We will make this clearer in the text and figure legend. We will also make the labelling on the survival plot in D clearer, indicating that the difference between the two groups is significant and displaying the p value clearly.

The data included in the survival plots in 5D encompass all tumour stages. We have further analysed these data, adjusting for tumour stage. We found that high ASNS expression in later stage tumours (stage 3 and 4) is associated with poorer overall survival, whereas there is no significant difference in overall survival in earlier stage tumours (stage 1 and 2) in relation to ASNS expression. We plan to add this to the supplementary materials and discuss in the main text as it is consistent with our findings from the AA cell line series.

*Western Blot controls *

For the Western Blots in Figure 6 A and C the total S6 and ULK1 controls are missing what is needed to assess the effect on pS6 and pULK1 correctly.

We will add total S6 and ULK1 controls to these figures.

In the same panels, the KO efficacy is not very high in A (-ASN). However, this is crucial to make the conclusion that this cell line (C1) is not dependent on ASNS.

The average knockdown efficiency in the C1 cells is 72% across n=3 experiments. Therefore, levels of ASNS are significantly reduced. However, to further validate this finding, we will use L-Albizziine, a competitive inhibitor of ASNS, at the same concentration in both C1 and M cells to eliminate any issues surrounding variation in knockdown efficiency and to replicate the results obtained using ASNS siRNA. These data will be included in supplementary material.

*Minor comments: *

*Statistical analysis of proliferation assays *

The statistical significance for proliferation assays are missing.

The statistical significance at the final timepoints of the proliferation assays are displayed on bar graphs in Supplementary Figure 5 (Figure S5B and C). We will add these to the proliferation curves in the main figure.

Reviewer #3

*A major concern is the model used in this study: *

Sodium butyrate and the carcinogen N-methyl-N-nitro-nitrosoguanidine (MNNG) were used for the transformation. I believe this model was developed by one of the co-authors of the study, A.C. Williams in the 1990s. The relevance of the model for in vivo colon carcinogenesis is not entirely clear to me and I miss information why in particular sodium butyrate and MNNG were used. I am not an expert on colon carcinogenesis but I did not have the impression that this model has been widely adopted and I miss detailed information on the model itself as well as a critical discussion of its limitations.

We thank the reviewer for raising these concerns and will include a ‘Limitations’ section in the manuscript ‘Discussion’ to elaborate on both the utility and the limitations of this model system. As described in response to concerns raised by reviewer #1 and reviewer #2, we plan to validate our findings in organoid models of colorectal tumourigenesis to strengthen the discoveries made using the AA cell line series.

With regards to the use of sodium butyrate and MNNG for transformation of the C1 cells, justification was provided in the original paper describing generation of the cell line model series (Williams et al, Cancer Research. 1990). Sodium butyrate is naturally occurring in the gut and was used for the transformation of the C1 cells as it had been proposed to play a role in promoting colorectal tumorigenesis through upregulating carcinoembryonic antigen (CEA) expression and enhancing proliferation in adenoma cells able to resist growth arrest following treatment (Berry et al, Carcinogenesis. 1988). At the time of generating the cell line series, few reagents were known to induce transformation in human epithelial cells. However, MNNG was one of those and had been previously used to transform keratinocytes (Rhim et al, Science. 1986). Crucially, tumours formed in mice from xenografted 10C cells were found to be heterogeneous, displaying areas of differentiation with glandular organisation, the presence of functional goblet cells enabling mucin production, as well as areas of poorly or undifferentiated cells. Furthermore, cytogenetic analyses revealed that genetic changes in the cell line progression model such as chromosome 18q loss and KRAS activation replicate those seen in CRC patients (Williams et al, Oncogene. 1993). Together, these characteristics recapitulate human tumours in vivo, validating the use of sodium butyrate and MNNG in generating an in vitro CRC cell line model that represents human colorectal tumorigenesis.

Figure 6: total levels of ribosomal S6 protein and ULK1 should be detected, quantified and used for normalization.

We agree with the reviewer, we will add total S6 and ULK1 controls to these figures.

Can you measure ASN upon inhibition of autophagy? Does it go down further?

This is an interesting question, and we will address this experimentally by measuring ASN levels following treatment with chloroquine in the C1 and M cell lines. We will do this using stable isotope labelling and mass spectrometry and include the results in supplementary material.

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Reply to the reviewers

General Comments

We thank all reviewers for providing very detailed, knowledgeable, and informative reviews.

All reviewers were complementary about the data and the rigor of the study. Reviewers 2 and 3 commented on the significance of the work, and their assessments were complementary, specifically about the fact that it bridges several previous studies and links these to kinase-phosphatase regulation on the BUB complex. We agree that this a major strength of the work. That is why we also believe the comment by reviewer 1 that “most of the phenotypes/observations are consistent with the literature and not surprising” is actually a strength and not a weakness. Sometimes manuscripts that bring together various different findings into one conceptual model can be very powerful, even if each finding in isolation is not so surprising. In this case, the concept that a dual kinase-phosphatase module integrates two major mitotic processes will, we believe, prove to be a significant breakthrough that helps to explain how these processes are properly integrated at kinetochores.

The main criticism of all reviewers related to the interpretations and writing style, which in general, we felt were valid. We will take on board all these comments, reword the manuscript during revision, and provide a detailed response to each of these points at resubmission.

In terms of points requiring new experiments, there were 3 in total:

1) Reviewers 1 and 3 raised an important issue about the feedback loop which will be addressed with new experiments to uncouple the feedback.

2) Reviewer 2 made an important point about KNL1 levels, including a good suggestion to perform FRAP analysis to examine BUB complex dynamics when MELT numbers are increased. We will carry out this experiment prior to revision.

3) Reviewer 1 had a second major comment regarding the modulation of MELT number and how this cannot be directly linked to PLK1/PP2A levels. We have 3 new experiments to add regarding this, performed already, which are discussed in the section below.

All other comments were textual points that in most case we felt were valid. They showed that all reviewers had a very good grasp of the paper, the concepts, and the field in general. So, we finish by thanking all reviewers again for their thorough and detailed assessments of our manuscript. The comments they raised will help us to improve the manuscript after revision.

Description of the planned revisions

Three main points:

1) The role of the feedback loop [reviewers 1 and 3]:

The general issue is explained succinctly by reviewer 3’s comment:

“The argument linking the negative feedback loop to biological functions is not straightforward. The authors provide evidence in Figure 1 for regulatory pathways between docked PLK1 and bound PP2A. However, their assays in Figure 2 bypass the feedback loop by directly modulating PP2A activity. These experiment supports an argument that the kinase/phosphatase activity balance is important, but do not address the feedback loop specifically (which could potentially be done using mutations that disrupt the feedback regulation). The claim that "a homeostatic feedback loop maintains an optimal balance of PLK1 and PP2A on the BUB complex" is too strong because there is no direct evidence connecting the feedback loop to optimal function.”

This is a good point that we will address at revision. We demonstrate that the enzymes regulate each other on the BUB complex in figure 1 (PLK1 recruits PP2A, and PP2A removes PLK1), which balances their levels on the BUB complex. To determine consequences of upsetting this balance we locked either the kinase-bound or phosphatase-bound states (Figure 2). Importantly, this is required to assess direct phenotypes associated with each, but it does not directly demonstrate the role of the feedback loop. To do this we will generate mutants, as suggested, and analyse their phenotypes.

We will mutate the PLK1 binding site (T620A) and recover the PLK1-regulated sites in the KARD motif to phospho-mimicking aspartates (S676D/T680D), analyzing effects on PLK1/PP2A recruitment, chromosome alignment and SAC strength. We predict that this will remove PLK1 and recover some PP2A, but to lower levels overall than the BUBR1-B56 fusion. In that case the phenotypes will probably be milder, but that would not change the overall conclusions.

We maintain that locking PLK1 on its phospho-binding site (in BUBR1-DPP2A) is the ideal scenario to test direct PLK1 roles, but we will also now create alanine mutants of the PLK1 site (S676A/T680A) and the CDK1 site (S670A) to address the feedback loops controlled by CDK1 and PLK1. Our prediction is that these will skew the balance towards PLK1, without fully removing PP2A, again likely to produce milder intermediate phenotypes.

It is definitely worth testing these predictions, because it would directly address the role of the feedback loop and it would avoid relying solely on “artificially high levels” as mentioned by reviewer 1. One final point on this however, the PLK1 recruitment in DPP2A cells is not artificial – it is PLK1 bound to its native phospho-motif when PP2A is unbound (without any feedback from PP2A this phospho-site and PLK1 binding increase to the observed maximal levels). The fusion of B56 is admittedly less optimal, but this does still lock the phosphatase-bound state in a set stoichiometry, crucially in the absence of kinase. This is required to assess direct phosphatase effects. These PLK1/PP2A levels may well be higher than observed physiologically on the BUB complex when considering the behavior of all BUBR1 molecules, since we doubt they ever reach 1:1 stoichiometry with either PLK1 or PP2A. However, the feedback loop is operating within individual molecules (figure 1), which may well individually flip between PLK1 or PP2A bound states. This may occur on certain molecules at specific times. Therefore, locking the PLK1.PP2A-bound state on all molecules is, in our opinion, still a valid and useful perturbation to assess function of these two states.

2) The increase recruitment of BUB1-PLK1/PP2A when MELT numbers are increased [reviewer 2]

"While in the 12x and 19x mutant conditions there are more molecules of BUBs per Knl1, the overall BUB levels are the same as in wild-type controls. Since the MELT repeat used throughout the paper is a consensus sequence that is likely optimal for BUB binding, it is possible that the phenotypes of the 12x and 19x mutants are explained because of an increase in the affinity of BUBs for Knl1 rather than overall levels. This would also help explain why Knl1 and BUBs are observed at the spindle midzone in the 19x mutant (Fig. S4)"

The reviewer raises an important issue here, when stating that increasing MELT numbers decreases KNL1 kinetochore recruitment. This has the net effect of normalizing overall BUB1-PLK1/PP2A kinetochore levels, even though BUB1-PLK1/PP2A recruitment per KNL1 molecule is increased. That is why we were careful to state BUB1-PLK1/PP2A were increased “on each KNL1 molecule” and not “on kinetochores” when referring to the effect in the 12x/19x MELT mutant. However, this could easily be misinterpreted so this point will be clarified at revision.

The issue of why the phenotype is so dependent on kinase/phosphatase level per KNL1 molecules is an important one however, which has puzzled us until now. We think the suggestion to look at turnover by FRAP is a good one, because enhanced binding strength could underlie the phenotype here, and potentially explain the lack of disassembly at anaphase. We will perform these experiments at revision to see if they can clarify the issue.

3) The link between MELT number and PLK1/PP2A levels [Reviewer 1]

“My second comment relates to the fact that the two parts of the paper are not directly linked although the authors try to do this. They nicely manipulate the MELT repeats on KNL1 to change the number of Bub complexes. However, they cannot directly link the data to changes in Plk1 and PP2A-B56 levels only as many other things are changing. By increasing MELT numbers Bub complex and Mad1/Mad2 levels increase as well as an example and this makes interpretations complicated. To me these experiments are not addressing the main conclusions of the paper.”

We do not agree with this overall assessment, but there are two elements to this comment: the effect of modulating MELT number on SAC strength (and its link to PLK1) or on KT-MT stability (and link to P2A). We will therefore discuss each separately:

For SAC regulation, we feel that the data is clear and the interpretations are justified, although we will add new data to support this point after revision. Increasing MELT number causes defects in MELT-BUB dissociation and SAC silencing (4a-c). Importantly, these phenotypes can be completely rescued by inhibiting PLK1 (4d-e). So, we do link the effects of high MELT number to PLK1 activity. Our interpretation is that when MELT numbers are increased the ability of PLK1 to phosphorylate these motifs and maintain the SAC platform is enhanced (when MPS1 is inhibited pharmacologically or upon KT-MT attachment). So, whilst it is true that many factors, such as the kinetochore levels of BUB/MAD1/MAD2, are crucial for the SAC, the ability of PLK1 to maintain these levels (via pMELT-BUB1) is crucial and that changes as MELT number increases. This contributes directly to the observe SAC silencing phenotype, as confirmed by the complete rescue of this phenotype after PLK1 inhibition.

We did also explore the possibility that increased BUB1 activity could also contribute to SAC strengthening, for example, by enhancing Aurora B recruitment to centromeres. However, BUB1 inhibition did not alter SAC strength or MELT dephosphorylation kinetics. We will add this data after revision.

We also evaluated the levels of phosphorylated MAD1-pT716, which is important for MCC assembly (Ji et al. 2017, Ji et al. 2018, Faesen, 2017). Our data show that WT and 19xMELT exhibit similar MAD1-pT716 levels during a nocodazole arrest and following MPS1 inhibition. In summary, the main changes we observe are elevated BUB1 levels due to MELT phosphorylation, and increased BUB1 phosphorylation on pT461 (as shown in Figure 4h). All this points towards a localized effect of PLK1 on/around the BUB complex. We will add this data and make this point clearly at revision.

For KT-MT attachment regulation, we agree that we do not have a similar way to inhibit PP2A-B56 activity to rescue hyperstable microtubule attachment when MELT numbers are high. For this, we require a way to rapidly inhibit PP2A-B56 activity after attachments have formed, something that is not technical feasible at the present. We can also not say for certain that reduced MELT numbers destabilize microtubule due to lack of PP2A, however we feel this is the most like interpretation for the following reasons. The phenotype of removing PP2A from BUBR1 or removing the MELT from KNL1 (along with all associated factors), is identical: mutant cells have comparable chromosome misalignment due to unattached kinetochores (compare 2F-I with 5A-D). Therefore, the additional factors lost by removing the MELTs cannot be having such a strong impact in KT-MT attachment. The obvious factor that could affect attachment strength is again BUB1, via Aurora B recruitment to centromeres. However, loss of BUB1 (after MELT removal) is predicted to enhance attachment stability (reduced Aurora B) and not decrease it, as we observe. So, whilst we cannot definitely conclude that modulating MELT number affect attachment stability via PP2A, we feel that this is certainly the most likely explanation. We will state this clearly in the revised text.

Description of analyses that authors prefer not to carry out

“SAC strength of BubR1 WT, ΔC and B56γ was analysed in the presence of nocodazole + MPS1i. It would be interesting to see what the phenotypes are without MPS1i [Reviewer 1]”

In the absence of MPS1i basal MELT phosphorylation increases (DC) or decreases (B56g) as predicted (Figure 2d; compare timepoint 0 all conditions). This does not cause any change to SAC strength when all kinetochores are unattached in nocodazole (not shown). The sensitize SAC assay (nocodazole + MPSi) has been used by many groups (originally Santaguida et al, 2011; Saurin et al, 2011), because it reduces SAC signals from all unattached kinetochores which would otherwise produce a saturated response. In this case, we specifically chose a dose of MPS1 inhibitor that gave a partial SAC response from which we could observe either strengthening or weakening – a key point of the assay. Indeed, this showed that the SAC was strengthened (DC) or weakened (B56g), as predicted (Figure 2E). The only other way to do this, which has been used by some in the literature, is to use a low dose of nocodazole which prevents all kinetochore from signaling to the SAC. We specifically wanted to avoid this situation because then you cannot untangle the effects on SAC and KT-MT attachment stability – this was crucial in our case.

Why do we even care about big numbers is there any use?

I really appreciate this project overall as this will really mean a lot to some scientists that may not ever be in a textbook when they should be. Even if they don't see it I think it's awesome we can do something about giving more people appreciation for their work they may not get.

Author Response

Reviewer #1 (Public Review):

“The synthesis and metabolism of sphingolipid (SL) are involved in wide range of biological processes. In the present study, the authors investigate the role of SPTLC1, one of the essential subunits of serine palmitoyl transferase complex, in both physiological and pathophysiological angiogenesis, via using inducible endothelial-specific SPTLC1 knockout mice. They found SPTLC1 deficiency in ECs inhibited retinal angiogenesis along with reducing several SL metabolites in plasma, red blood cells, and peripheral organs. In addition, the authors found SPTLC1 EC-KO mice are resistant to APAP-induced liver injury. Overall, the in vivo findings in the present study are of potential interest and the authors have given clear evidence that endothelial SPTLC1 is critical to retinal angiogenesis. However, the underlying mechanisms are completely lacking in the present study. Most of the evidence provided is circumstantial, associative, and indirect.”

We appreciate the positive comments of the reviewer. We have addressed the reviewer’s concern regarding underlying mechanisms as detailed below.

“To be specific,

1. The authors found endothelial SPTLC1 is important to both angiogenesis and the plasma lipid profile. However, the authors did not present the data to demonstrate the relationship between them. The in vivo findings about the phenotype and the plasma lipid profile might be true and unrelated. It would be important to know whether supplementing the reduced lipid induced by SPTLC1 KO could rescue the angiogenesis related phenotype in mice, or, whether the alternative way to inhibit the SL synthesis could mimic the phenotype of KO mice.”

In the manuscript, we discussed the possibility whether S1P is involved, since it is one of the most down-regulated SL in the plasma and a major regulator of angiogenesis. We think it is unlikely that reduced plasma S1P is responsible for the phenotype. First, the retinal angiogenesis defect in Sptlc1 ECKO mice is the opposite of S1pr1 ECKO as we have published previously (PMID: 22975328, PMID: 32059774). Moreover, deletion of sphingosine kinase, the enzyme produces S1P, in the endothelium does not influence retinal angiogenesis at P6 (Figure 3 Supplement 2 A and B). Loss of S1P chaperone ApoM- i.e., Apom KO, which exhibits 50% reduction of plasma S1P, does not show change in retinal vascular development (Figure 3 Supplement 2 C and D). Taken together, our results strongly suggest that reduction in plasma S1P is not the cause of vascular defect in Sptlc1 ECKO retinas.

Based on our results in the manuscript, loss of SPT enzyme activity in endothelial cells reduced SL species in the endothelial cells and the plasma. Our in vitro and VEGF intraocular injection experiments (new data) suggests that the angiogenic defects seen in Sptlc1 ECKO mice is due to cell intrinsic defects in VEGF signaling and not due to changes in plasma SL levels. We have edited the discussion section to address this issue.

“2. A major issue is that the present study did not reveal is a real downstream target. It is possible that VEGF signaling might be impaired by SPTLC1 knockout as discussed by the authors. However, the authors did not demonstrate this point with data. Including both in vivo and in vitro data to evaluate the effects of SPTLC1 deficiency on VEGF signaling might further strengthen the hypothesis. Besides, with in vitro experiments, the authors might further find the critical metabolite(s) involved in VEGF signaling and angiogenesis.”

As discussed above, we agree with the review’s critique and have addressed this essential point with new experiments (both in vitro and in vivo) in Figure 5. Our new data shows that SPT pathway supplies the glycosphingolipid GM1, which is needed for efficient VEGF-induced ERK phosphorylation and tip cell formation.

Reviewer #2 (Public Review):

“Andrew Kuo et al. investigated the role of endothelial de novo sphingolipids (SL) synthesis using endothelial cell specific SPTLC1 knockout (ECKO) mice. They showed that these mice exhibited low concentration of various SL species in not only ECs but also RBC, circulation, and other non-EC tissues. They also showed that ECKO mice exhibited impaired angiogenesis in normal and oxygen-induced retinopathy models, consistent with the decrease of endothelial proliferation and tip cell formation. They finally revealed that these mice were resistant to acetaminophen-induced acute liver injury in early phase. The experiments were well-designed, and the results were clear and convincing. The authors concluded that endothelial cells were the major source of SL in circulation and various organs (liver and lung) other than retina (and probably brain). The weakness of the current version of the manuscript is that the authors did not elucidate the mechanisms underlying the observed phenomena.

1) The authors showed impaired angiogenesis in ECKO mice using neonatal retina model. Based on the fact that this phenotype was similar to that in endothelial VEGFR2 deficient mice, they suggested that VEGF responsiveness is altered in ECKO mice. Although this hypothesis is plausible, the authors would need to prove it by evaluating VEGFR signaling (VEGFR phosphorylation, Akt activation etc.) in ECKO mice.”

We thank the reviewer for positive comments. As for the weakness identified, we have addressed this point by conducting new in vitro and in vivo experiments (detailed above). The new Figure 5 addresses this issue directly.

“2) The acetaminophen-induced liver injury was reduced in ECKO mice in early phase. However, it is still unclear whether SL production itself affects liver injury. The authors discussed the possibility that gene deficiency increases unconsumed serine resulting in GSH increase, but it is essentially independent to SL. If possible, it would be good if the authors could investigate the effect of SL administration on the liver injury progression.”

We appreciate the reviewer’s concern about liver injury model in the Sptlc1 ECKO mice. Our data suggests that SL species supplied from EC impacts hepatocyte response to stress. Since the acetaminophen induced liver injury is highly dependent on reactive oxygen species, our finding that increased glutathione levels in the Sptlc1 ECKO mice may be involved in the phenotype. However, we are simply considering them as biochemical markers of liver injury. This has been addressed in the discussion.

“3) This paper showed the impaired cell proliferation in Sptlc1 KO EC mice, and discussed it. Authors described that this phenotype was similar to that of Nos3 KO mice, but its inconsistency with Sptlc2 ECKO adult mice was only justified by a word "isoform-selective function". Authors could quantify eNOS expressions in Sptlc1 KO mice, compared results and then discuss this matter. “

In figure 1C, we used eNOS as an EC marker to show purity during our EC isolation process. In fact, we did not observe change of eNOS expression in Sptlc1 ECKO. We also did not detect elevated phospho-eNOS in Sptl1c ECKO in contrast to Sptlc2 ECKO adult mice (Figure1 supplement 4). Additionally, our work in the retina was performed in postnatal-genedeletion pups from P6-P17 which is different from the published Sptlc2 ECKO study. The differences in gene deletion strategy (early postnatal vs. adult) could result in differences in eNOS expression . We have added discussion about this issue.

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Reply to the reviewers

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

Summary:

Ciliates extensively rearrange their somatic genome every time a new somatic nucleus develops from the zygotic germline nucleus. In this manuscript, Feng et al report the sequencing, assembly and annotation of the germline and somatic genomes of Euplotes woodruffi and the germline genome of Tetmemena sp. (whose somatic genome was sequenced and assembled by the same lab in 2015). They present a comparative analysis of developmentally programmed genome rearrangements in these two species and in the model ciliate Oxytricha trifallax. Their major findings are that:

(i) E. woodruffi and Tetmemena sp. eliminate a smaller fraction of their germline genome (~54%) from their somatic macronucleus (MAC) than O. trifallax (>80%)

(ii) Transposable elements (TE) represent a smaller fraction of the germline genome (~2%) in the first two ciliates than in O. trifallax (~15%). TEs are mainly located at the boundaries of germline chromosomes and in intergenic regions, but can also be found inside IESs

(iii) Several thousands of genes are scrambled in the germline genome of all three species

The authors have also addressed the possible origin of gene scrambling. They report an interesting association with local paralogy and propose a model for the emergence of the odd-even pattern of gene unscrambling between two paralogous copies.

Major comments:

1. Based on the statistics presented in Table 1, genome assemblies are of good quality, with a reasonable N50 size of germline (MIC) contigs. It seems, however, that no entire MIC chromosome could be assembled, since no two-telomere contig is mentioned in the list. As proposed by the authors (p.7) the presence of numerous TEs at the boundaries of MIC contigs (Fig S1) may have hindered the assembly of MIC chromosome ends. I would have appreciated to have more information on the "other repeats" (which seem to differ from tandem repeats according to Fig 2) and their location along MIC contigs.

   Subcategories of “other repeats” were included in Table S2 based on Repeatmasker annotations. We now analyzed the locations of other repeats in MIC contigs and include those as well in new Figure S1B. About 30% of “other” transposable elements are present at the boundaries of MIC contigs, which may also hinder the assembly. Notably, 35-45% of “other TEs” are in assembled, intergenic regions.

The definition of "Internal Eliminated Sequences" (IES) is not clear. The authors make a distinction between IESs and TEs. I understand that IESs are DNA segments that separate two macronuclear-destined sequences (MDS) in the germline genome. Thus they appear to be restricted to those regions that eventually yield gene-sized MAC chromosomes. IESs are eliminated between two pointers that may not be identical on both sides in case of scrambled genes. Some clarification is needed here.

To illustrate my point: I found the statement "with many TE insertions within IESs, suggesting that TE insertions may have generated IESs" particularly confusing (p. 9 lines 5-6). Does this mean that IESs extend beyond the ends of inserted TEs? The legend of Fig S1 should also be clarified.

We clarified the text and legend. IESs can extend beyond the ends of inserted TEs, even if the original IES is a decayed TE, due to subsequent sequence evolution at the boundaries or if the original insertion was into an existing IES. David Prescott referred to sequence evolution at the edges of IESs as “pointer sliding” (ref.36).

p. 10 lines 2-4 and Fig S2: Could the authors explain the difference they make between MDS (in the text) and CDS (in Fig S2)? My understanding is that a CDS is the entire gene coding sequence and may be made of multiple MDSs. If this is correct, the sentence should read "We compared the number of MDSs between single-copy orthologs for single-gene MAC chromosomes across the three species and found that the orthologs have similar CDS lengths".

Yes, we made the correction.

p. 12 lines 10-15: the discovery that paralogous MDSs can be found in scrambled genomic loci is interesting. If the two paralogs can be distinguished based on the number of substitutions, it would be informative to go back to individual reads and check whether each of the two copies can be incorporated in the unscrambled CDS (and at which frequency). Would the pointers be compatible with this?

The paralogous MDSs in the MIC are often not identical. The copy with the highest similarity is assigned as “preliminary match” by SDRAP (ref. 52), and others are assigned as “additional matches”. To validate SDRAP assignments, we did pairwise BLASTN alignments (“-task megablast”) of paralogous MIC MDSs and their corresponding MAC MDSs. We confirmed that in the three species, the preliminary match has the best or equally best pid (percentage of identity) in most cases. Therefore, the MDS assigned as preliminary match is more likely the paralog incorporated into the MAC chromosome.

We used genome assemblies of Euplotes woodruffi, which had the highest Nanopore coverage, to further investigate the frequency of MDS incorporation. We followed the reviewer’s suggestion and called SNP variants on both MAC and MIC genomes. For MAC SNP calling, we used Illumina reads as input for freebayes (ref a). For MIC SNP calling, we used Nanopore reads, instead of Illumina reads, to avoid non-specific short-read mapping on paralogous MDSs and to avoid the presence of any contaminating MAC reads. Variants were called and phased by PEPPER-Margin-DeepVariant (ref b), a new tool published in 2021 in Nature Methods, which has been reported to have similar accuracy to Illumina read variant calling, especially at high read coverage. We used the parameter “--pepper_min_coverage_threshold 20” to call confident variants when at least 20 reads cover the position. Only 92 MIC SNPs in the paralogous MDSs passed all filters of the program. Using this small set of MIC SNPs, we were unfortunately unable to distinguish which paralogous MIC MDS was incorporated into the MAC. Therefore, we cannot infer with what frequency one paralogous MDS is incorporated over another, until they become sufficiently diverged, which is compatible with the model.

a. Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. arXiv preprint arXiv:1207.3907. 2012 Jul 17.

b. Shafin K, Pesout T, Chang PC, Nattestad M, Kolesnikov A, Goel S, Baid G, Kolmogorov M, Eizenga JM, Miga KH, Carnevali P. Haplotype-aware variant calling with PEPPER-Margin-DeepVariant enables high accuracy in nanopore long-reads. Nature methods. 2021 Nov;18(11):1322-32.

The hypothesis that odd-even scrambled loci have evolved from paralogous genes in E. woodruffi is supported by the existence of paralogous MDSs, length conservation of MDS/IES pairs and sequence similarity between corresponding MDS and IES in a pair. The correlations presented for Oxytricha and Tetmemena are much less convincing (Fig S5D and E). I recommend that the authors are even more cautious in their statement on p.13 ("For Oxytricha and Tememena, the MDS and IES lengths for such MDS/IES pairs also correlate positively, but more moderately").

Thank you, we rephrased the text.

p. 15 last paragraph: Why did the authors focus only on TBEs inserted in non-scrambled IESs to look for orthologous TBE insertions? Is there a reason to believe that no recent TBE insertion occurred at other genomic loci? Or was it only for practical reasons? It is also not clear to me whether the authors have considered full-length TBEs or the presence of at least one TBE ORF.

This analysis was limited for practical reasons, because we identify position conservation of TBEs by aligning protein sequences of MAC genes. We only consider TBEs inserted in non-scrambled IESs in exons. It would be difficult and less meaningful to align completely non-coding MIC-limited regions.

Partial TBEs are also included if they contain at least one TBE ORF (detected by BLAST).

Furthermore, TE insertion cannot explain the origin of scrambled IESs, and TEs rarely map to scrambled IESs (Figure S1A), but there is a clear evolutionary model for the origin of nonscrambled IESs from decay of TBEs (ref. 49). Initial purifying selection would act on the TE to maintain its ability to self-excise, whereas we advocate for a different model for the origin of scrambled IESs by decay of paralogous MDSs.

p. 16: the authors report that some introns of E. woodruffi map "near" Oxytricha/Tetmemena pointers. How near? Based on the information provided by the authors, I don't think this observation necessarily implies that IESs were converted to introns (or reciprocally) during evolution. If this were true, shouldn't at least one intron boundary coincide exactly with a pointer? The authors should clarify this (also in the discussion, on p. 20, top paragraph).

We used a 20bp window (~7 amino acids), as described in the Methods, and added that to the Results. Full detail is provided in the Methods section, “Ortholog comparison pipeline and Monte Carlo simulations”. 103 E. woodruffi introns are within 20bp from the midpoint of Oxytricha/Tetmemena pointers. Among these, 43 intron boundaries overlap an Oxytricha or Tetmemena pointer. We observed 306 cases of precisely matching boundaries between any two species, where the exon junction of one species maps inside the MDS/IES pointer of another species, although we would only expect the boundaries of introns and IESs to coincide so precisely if they were recent conversions. Hence we feel that a window analysis is informative.

p. 19 2nd paragraph: the suggested mechanism explaining the 5' bias of IESs in E. woodruffi genes is unclear. How could germline recombination take place between a MIC chromosome and a MAC reverse transcript or nanochromosome? This would imply that DNA could be imported in the MIC. Is there evidence that this might occur?

The ability of TEs to invade the MIC demonstrates that even foreign DNA can be incorporated into the MIC. Since MAC DNA is present at high copy number, it offers a potential source for a recombination template that could erase IESs, as could an errant reverse transcript of one of the long noncoding template RNAs. Any of these would be infrequent events that would matter on an evolutionary time scale even if developmentally rare.

According to Figure 1, no scrambled genes have been reported in Paramecium tetraurelia. Within the frame of the proposed model, this is somewhat unexpected because this ciliate went through several whole genome duplications during evolution and harbors many paralogous gene pairs. Is there a reason why no gene scrambling took place in Paramecium?

Paramecium uses only TA dinucleotide pointers for IES elimination, unlike the rich diversity of pointers in spirotrichous ciliates. This limitation in its machinery may explain why no scrambled loci have been observed in Paramecium, despite the abundance of paralogs. Our model suggests that local MIC paralogy is associated with the origin of scrambling. But most of the paralogy reported in Paramecium is at the level of whole chromosomes in the MAC (ref. 104) rather than local MIC paralogy.

Minor comments:

p. 4 (4th bottom line): To my knowledge, ref #28 presents a draft (incomplete) MIC assembly of the Paramecium genome.

Thank you, we added reference 29 and adjusted the wording describing the quality of MIC genome draft assemblies.

p. 7 (last paragraph): "encoding" should be replaced by "carrying"

Thank you, we made the change.

p. 10 (2nd paragraph): insert a missing "o" into "nanochromosomes"

Thank you, corrected.

p. 10 (same paragraph): the weak 5' bias of IES distribution in Tetmemena should be shown (either as an additional panel in Fig 3 or in a Sup Figure.

Thank you, we added it as Figure S2C.

p. 24 2nd paragraph: "a" is missing in "Trinity, which is a software..."

Thank you, we made the correction.

CROSS-CONSULTATION COMMENTS

I agree with most comments of reviewer 3.

The authors have actually defined "TE" in the introduction (p. 6). Depending on the journal's rules for abbreviation use, it may not be necessary to define it again in the results section

Reviewer #1 (Significance (Required)):

Ciliates are unicellular models to study developmentally programmed genome rearrangements at the mechanistic, genome-wide and evolutionary levels. These aspects have so far mostly been addressed in three species: P. tetraurelia and Tetrahymena thermophila on the one hand, the spirotrichous ciliate O. trifallax on the other.

One new piece of information that can be found in the present manuscript is the assembly and annotation of the germline genome of two novel species: Tetmemena sp, closely related to Oxytricha, and the more distant E. woodruffi. Feng et al establish that, similar to other ciliates, Tetmemena and Euplotes eliminate TEs and other germline-specific sequences during programmed genome rearrangements. They also undergo extensive gene unscrambling, which results in IES removal and MDS reordering to assemble coding sequences.

A TE origin was discussed previously for Paramecium (Arnaiz et al PLoS Genet; Sellis et al 2021 PLoS Biol) and Tetrahymena IESs (Hamilton et al 2016 eLife). While this may also hold true in spirotrichous ciliatesThe present manuscript proposes a completely new evolutionary scenario for IESs from scrambled genes. Here, Feng et al establish that scrambled genes of spirotrichous ciliates tend to be associated with local paralogy. They provide evidence supporting that IESs from scrambled genes may have evolved from paralogous MDSs.

Although I am more an expert in the molecular mechanisms involved in genome rearrangements, I feel that the work reported here should draw the attention of a broader audience interested in genome dynamics and evolution, beyond the specific field of spirotrichous ciliate biology.

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

Feng et al. provide a solid analysis of the evolution of genome rearrangement in spirotrich ciliates. The authors applied a variety of state-of-the-art sequencing and bioinformatic methods to investigate the intriguing and extremely complex patterns of genome architecture in this protist lineage. Methods (including statistical analyses) are adequate and explained in detail. Results and discussions reflect careful, clever analysis of the data and excellent linkage with the literature. Figures and tables complement the text in a compelling way. I have only minor suggestions:

Summary: more gradually introduce Spirotrichea and the phylogenetic relationship among the three species analyzed. This would better position the reader to understand the evolutionary context you are working in. Also, it would be helpful to more clearly differentiate novel vs. existing data. A suggestion: "This study focuses on three spirotrich species: two in the family Oxytrichidae (Oxytricha trifallax and Tetmemena sp) and Euplotes woodruffi as an outgroup. To complement existing data, we sequenced, assembled and annotated the germiline and somatic genomes of E. woodruffi and the germline genome of Tetmemena sp."

Thank you, we clarified the summary (abstract).

Introduction, first paragraph: Replace "The species in this study..." for a more precise statement, such as "The three spirotrich species studied here..."

Thank you, we have made this statement more precise.

p. 4: This sentence is unclear: "These useful tools provide partial insight to guide selection of species for full genome sequencing, which allows construction of complete rearrangement maps of a MIC genome onto a MAC genome for a reference species."

Thank you, we have clarified this sentence.

p. 8: define TE on first mention.

Defined on page 6.

Table 1. Indicate which MIC and MAC data are from this study.

References are included for published data and a note has been added to indicate data from this study.

Reviewer #3 (Significance (Required)):

The present work represents a significant advance in the field of evolutionary genomics. The focus of the paper is on ciliates, an ancient (2 billion-year old) and highly diverse eukaryotic phylum that presents many peculiarities, including sex, nuclear dimorphism, genome rearrangement, high numbers of paralogs and transposons, etc. While some data exist on a few model ciliates of disparate phylogenetic position, this work focuses on two species taxonomically placed in the same family, plus a more distant outgroup within the same class. This gives a novel dimension to this study, that goes beyond exploring genome architecture in a single clade. Instead, it allows to explore evolutionary trends in genome rearrangement among relatively closely related species. This paper should be of high interest not only for ciliate biologists (like me), but also in relation to comparative genomics of protists/eukaryotes and germ-soma biology. I highly recommend publication.

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Referee #1

Evidence, reproducibility and clarity

Summary:

Ciliates extensively rearrange their somatic genome every time a new somatic nucleus develops from the zygotic germline nucleus. In this manuscript, Feng et al report the sequencing, assembly and annotation of the germline and somatic genomes of Euplotes woodruffi and the germline genome of Tetmemena sp. (whose somatic genome was sequenced and assembled by the same lab in 2015). They present a comparative analysis of developmentally programmed genome rearrangements in these two species and in the model ciliate Oxytricha trifallax. Their major findings are that:

(1) E. woodruffi and Tetmemena sp. eliminate a smaller fraction of their germline genome (~54%) from their somatic macronucleus (MAC) than O. trifallax (>80%)

(2) Transposable elements (TE) represent a smaller fraction of the germline genome (~2%) in the first two ciliates than in O. trifallax (~15%). TEs are mainly located at the boundaries of germline chromosomes and in intergenic regions, but can also be found inside IESs

(3) Several thousands of genes are scrambled in the germline genome of all three species

The authors have also addressed the possible origin of gene scrambling. They report an interesting association with local paralogy and propose a model for the emergence of the odd-even pattern of gene unscrambling between two paralogous copies.

Major comments:

(1) Based on the statistics presented in Table 1, genome assemblies are of good quality, with a reasonable N50 size of germline (MIC) contigs. It seems, however, that no entire MIC chromosome could be assembled, since no two-telomere contig is mentioned in the list. As proposed by the authors (p.7) the presence of numerous TEs at the boundaries of MIC contigs (Fig S1) may have hindered the assembly of MIC chromosome ends. I would have appreciated to have more information on the "other repeats" (which seem to differ from tandem repeats according to Fig 2) and their location along MIC contigs.

(2) The definition of "Internal Eliminated Sequences" (IES) is not clear. The authors make a distinction between IESs and TEs. I understand that IESs are DNA segments that separate two macronuclear-destined sequences (MDS) in the germline genome. Thus they appear to be restricted to those regions that eventually yield gene-sized MAC chromosomes. IESs are eliminated between two pointers that may not be identical on both sides in case of scrambled genes. Some clarification is needed here.

To illustrate my point: I found the statement "with many TE insertions within IESs, suggesting that TE insertions may have generated IESs" particularly confusing (p. 9 lines 5-6). Does this mean that IESs extend beyond the ends of inserted TEs? The legend of Fig S1 should also be clarified.

(3) p. 10 lines 2-4 and Fig S2: Could the authors explain the difference they make between MDS (in the text) and CDS (in Fig S2)? My understanding is that a CDS is the entire gene coding sequence and may be made of multiple MDSs. If this is correct, the sentence should read "We compared the number of MDSs between single-copy orthologs for single-gene MAC chromosomes across the three species and found that the orthologs have similar CDS lengths".

(4) p. 12 lines 10-15: the discovery that paralogous MDSs can be found in scrambled genomic loci is interesting. If the two paralogs can be distinguished based on the number of substitutions, it would be informative to go back to individual reads and check whether each of the two copies can be incorporated in the unscrambled CDS (and at which frequency). Would the pointers be compatible with this?

(5) The hypothesis that odd-even scrambled loci have evolved from paralogous genes in E. woodruffi is supported by the existence of paralogous MDSs, length conservation of MDS/IES pairs and sequence similarity between corresponding MDS and IES in a pair. The correlations presented for Oxytricha and Tetmemena are much less convincing (Fig S5D and E). I recommend that the authors are even more cautious in their statement on p.13 ("For Oxytricha and Tememena, the MDS and IES lengths for such MDS/IES pairs also correlate positively, but more moderately").

(6) p. 15 last paragraph: Why did the authors focus only on TBEs inserted in non-scrambled IESs to look for orthologous TBE insertions? Is there a reason to believe that no recent TBE insertion occurred at other genomic loci? Or was it only for practical reasons? It is also not clear to me whether the authors have considered full-length TBEs or the presence of at least one TBE ORF.

(7) p. 16: the authors report that some introns of E. woodruffi map "near" Oxytricha/Tetmemena pointers. How near? Based on the information provided by the authors, I don't think this observation necessarily implies that IESs were converted to introns (or reciprocally) during evolution. If this were true, shouldn't at least one intron boundary coincide exactly with a pointer? The authors should clarify this (also in the discussion, on p. 20, top paragraph).

(8) p. 19 2nd paragraph: the suggested mechanism explaining the 5' bias of IESs in E. woodruffi genes is unclear. How could germline recombination take place between a MIC chromosome and a MAC reverse transcript or nanochromosome? This would imply that DNA could be imported in the MIC. Is there evidence that this might occur?

(9) According to Figure 1, no scrambled genes have been reported in Paramecium tetraurelia. Within the frame of the proposed model, this is somewhat unexpected because this ciliate went through several whole genome duplications during evolution and harbors many paralogous gene pairs. Is there a reason why no gene scrambling took place in Paramecium?

Minor comments:

• p. 4 (4th bottom line): To my knowledge, ref #28 presents a draft (incomplete) MIC assembly of the Paramecium genome.

• p. 7 (last paragraph): "encoding" should be replaced by "carrying"

• p. 10 (2nd paragraph): insert a missing "o" into "nanochromosomes"

• p. 10 (same paragraph): the weak 5' bias of IES distribution in Tetmemena should be shown (either as an additional panel in Fig 3 or in a Sup Figure.

• p. 24 2nd paragraph: "a" is missing in "Trinity, which is a software..."

CROSS-CONSULTATION COMMENTS

I agree with most comments of reviewer 3.

The authors have actually defined "TE" in the introduction (p. 6). Depending on the journal's rules for abbreviation use, it may not be necessary to define it again in the results section

Significance

• Ciliates are unicellular models to study developmentally programmed genome rearrangements at the mechanistic, genome-wide and evolutionary levels. These aspects have so far mostly been addressed in three species: P. tetraurelia and Tetrahymena thermophila on the one hand, the spirotrichous ciliate O. trifallax on the other.

• One new piece of information that can be found in the present manuscript is the assembly and annotation of the germline genome of two novel species: Tetmemena sp, closely related to Oxytricha, and the more distant E. woodruffi. Feng et al establish that, similar to other ciliates, Tetmemena and Euplotes eliminate TEs and other germline-specific sequences during programmed genome rearrangements. They also undergo extensive gene unscrambling, which results in IES removal and MDS reordering to assemble coding sequences.

• A TE origin was discussed previously for Paramecium (Arnaiz et al PLoS Genet; Sellis et al 2021 PLoS Biol) and Tetrahymena IESs (Hamilton et al 2016 eLife). While this may also hold true in spirotrichous ciliatesThe present manuscript proposes a completely new evolutionary scenario for IESs from scrambled genes. Here, Feng et al establish that scrambled genes of spirotrichous ciliates tend to be associated with local paralogy. They provide evidence supporting that IESs from scrambled genes may have evolved from paralogous MDSs.

• Although I am more an expert in the molecular mechanisms involved in genome rearrangements, I feel that the work reported here should draw the attention of a broader audience interested in genome dynamics and evolution, beyond the specific field of spirotrichous ciliate biology.

Author Response

Reviewer #1 (Public Review):

1) While the authors identify the suppressors in known genetic interactors (GIs) of the yeast SEC53, it is worth testing if the compensatory mutations are rewiring the GIs, thereby explaining the lack of comparable compensations observed in reconstituted strains. If altered GIs explain the suppression, then while yeast serves as an excellent tool to perform these assays, the human context of the disease may require a different set of genetic suppressors and, therefore, a different target than the yeast PGM1 ortholog.

Our data show that pgm1 mutations alone greatly improve growth of sec53-V238M strains. Our data also indicate other pathways of compensation. Whether each of these compensatory mechanisms translate to humans is unknown. However, the observed enrichment of compensatory mutations in genes whose human homologs are associated with Type 1 CDG, suggests that many of these genetic interactions are likely to be conserved.

Also, are Sec53 and Pgm1 proteins directly interacting in yeast and whether these mutations are on the interaction interface?

As we mention above, there is no support for a direct physical interaction between Sec53 and Pgm1.

2) Based on the data obtained between pACT1 and pSEC53-driven expression of the SEC53 mutant alleles, the pattern of suppressors appears to be different. Authors report that the variants expressed from strong pACT1 promoters show more suppressors than those driven by native promoters. Is this a general trend in experimental evolution that slower-growing strains tend to show lesser suppressors? For example, on Page 6, line 154, "compensating for Sec53-F126L dimerization defects are rare or not easily accessible". The statement suggests that the authors did obtain suppressors that compensate for the dimerization defect. At the same time, while rare (also, are authors suggesting suppression of dimerization defect as in better dimerization?), the rate of obtaining suppressors seems to be linked to the severity of the fitness defects of the strains. The lack of suppressors may be a limitation of the evolution experiments. Indeed later in the manuscript, the authors noticed that while PGM1 suppressors obtained in V238M can also suppress F126L alleles, the suppression was not as efficient. Could it be that evolution experiments in slower-growing strains predominantly enrich suppressors in other pathways (i.e., not in the CDG orthologs) that restore the growth better and compete out the relatively weaker suppressors in PGM1? In fact, the authors report similar effects on Page 7, lines 204-210. These two paragraphs are contradictory and should be explained further.

All of our sequencing was performed on strains with sec53 under the control of the pACT1 promoter. While we did not identify unique sec53-F126L suppressors, we cannot exclude that sec53-F126L suppressors exist, so we describe them as “rare or not easily accessible”. While it is possible that the slower growth rate of the sec53-F126L allele could impact the likelihood of observing suppressors, we think it is more likely due to the nature of the variant (dimerization defect versus stability defect) rather than growth rate. In other laboratory evolution experiments the same beneficial mutation typically has a greater effect in slower-growing backgrounds (for example: doi.org/10.1126/science.1250939).

3) Authors report that the LOF of PGM1 compensates for the SEC53 mutations. However, the evolution experiments did not capture any LOFs in PGM1. The fitness comparisons in evolution experiments are different as many different genotypes compete in a mix. Therefore, the fitness assays in a clonal population may not represent these differences well. To test this argument, authors can try to mimic the evolution experiments by mixing two genotypes to check competitive fitness, like the co-culture of pgm1 suppressor obtained via evolution experiments with pgm1Δ.

Though we did not perform a direct head-to-head competition between a pgm1 suppressor and a pgm1Δ, our data suggest that the pgm1 delete would outcompete some of the lower-fitness suppressors. In the Discussion we speculate as to why we do not see deletion mutations: “Given that most of the evolved clones containing pgm1 mutations are more fit than the reconstructed strains, it is possible that other evolved mutations interact epistatically only with non-loss-of-function pgm1 mutations.”. Though it is beyond the scope of the present manuscript, it would be possible to rerun the evolution experiment in sec53-V238M strains carrying either a pgm1 missense suppressor or a pgm1Δ. Under the hypothesis of additional interacting loci, only the pgm1 missense suppressors would be more likely to acquire additional compensatory mutations.

Reviewer #3 (Public Review):

Vignogna et al. used yeast genetics, experimental evolution and biochemistry to tackle human congenital disorders of glycosylation (CDG), a disease mostly caused by mutations in PMM2. They took advantage of the observation that the budding yeast gene SEC53 is almost identical to human PMM2, and used experimental evolution to find interactors of SEC53/PMM2. They found an overrepresentation of mutations in genes corresponding to other human CDG genes, including PGM1. Genetic and biochemical characterizations of the pgm1 mutations were carried out. This work is solid, although authors did not reveal why reduction of pgm1 activity could compensate for defects of a particular mutant allele of sec53.

Out of curiosity, if the authors were to simply focus on the preexisting mutations, would they have gotten the materials for most of the experiments in this article? In other words, how important is the experimental evolution?

The evolution experiment was crucial as the specific pgm1 mutations we identified here have not been reported elsewhere, nor have the orthologous mutations been identified in human PGM1.

A strain table with full genotypes is needed.

We added a strain genotype table (Supplemental Dataset 2).

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Reply to the reviewers

Manuscript number: RC-2022-01541

Corresponding author(s): Hubert Hilbi

1. General Statements

Upon infection of eukaryotic host cells, Legionella pneumophila forms a unique compartment, the Legionella-containing vacuole (LCV). While the role of vesicle trafficking pathways for LCV formation has been quite extensively studied, the role of putative membrane contact sites (MCS) between the LCV and the ER has been barely addressed. In our study, we provide a comprehensive analysis of the localization and function of protein and lipid components of LCV-ER MCS in the genetically tractable amoeba Dictyostelium discoideum.

We would like to thank the 3 reviewers for their thorough and constructive reviews. Overall, the reviewers state that the study is of interest to researchers in the field of Legionella and other intracellular pathogens (Reviewer 2), as well as to cell biologists (Reviewer 3). Reviewer 1 does not ask for additional experiments but is critical about the overall structure of the manuscript and the proteomics approach. As requested by the reviewer, we have substantially restructured the revised manuscript, now clearly outline the hypotheses put forward in the study and streamlined the proteomics data. Reviewer 2 asks for additional experiments to support our model of LCV-ER MCS. In the revised manuscript, we have included additional experiments addressing lipid exchange at the MCS, and we plan to perform further co-localization experiments. Reviewer 3 appreciates the comprehensive LCV proteomics and asks for only minor revisions, which we have incorporated in the revised version of the manuscript. We include below a point-by-point response to all the comments made by the reviewers.

2. Description of the planned revisions

Reviewer #2

Major comment

1) MCS contain protein complexes or a group of proteins, but the proteins here are studied in isolation and do not support the model shown in Figure 7. Co-localization studies of the putative LCV-ER MCS proteins are critical, especially given that the authors hypothesize the proteins are working together to modulate PI(4)P levels.

Response: As suggested by the reviewer, we will perform additional co-localization experiments with MCS components. To this end, we will construct mCherry-Vap, and we will co-transfect the parental D. discoideum strain Ax3 with plasmids producing mCherry-Vap and OSBP8-GFP or GFP-OSBP11. Using these dually fluorescence labelled D. discoideum strains, the co-localization of Vap with the OSBPs will be assessed at 1, 2, and 8 h post infection. The data will be presented as fluorescence micrographs, and co-localization of Vap with the OSBPs will be quantified using Pearson’s correlation coefficient and fluorescence intensity profiles. The data will be outlined in the text (l. 258 ff.) and shown in the new Fig. 2 and__ Fig. S4__.

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

Reviewer #1 (Evidence, reproducibility and clarity):

In the manuscript by Vormittag, et al., the authors perform proteomics identification of proteins associated with the Legionella-containing vacuole (LCV) in the model amoeba Dictyostelium discoideum comparing WT to atlastin knockout mutants. The authors find approximately half the D. discoideum proteome associated with the LCV, but there was enrichment of some proteins on the WT relative to the mutant. They focus on proteins involved in forming membrane contact sites (MCS) that previously were shown to be important for expansion of the Chlamydia-containing vacuole. Most significant are the oxysterol binding proteins (OSBP) and VapA (similar to that seen in Chlamydia). The authors show differential association of these proteins with either the LCV or presumably the ER associated with the LCV. Using a linear scale over 8 days, they show that mutations in some of the MCS reduce yields in two of the OSPB knockout mutants and the growth rate of the vap mutant is slowed but ultimate yield is increased. Using some nice microscopy techniques, they measure LCV size, and the osbK mutant appears particular small relative to other strains, whereas the osbH mutant generates large vacuoles. This doesn't necessarily correlate with the PI4P quantities on the vacuoles (which is higher in all of them), but I am not totally sure how this is measured, and whether is it PI4P/pixel or PI4P/LCV. In all cases, this was reduced by Sac1 mutation. Surprisingly, even though there was uniform increase in PI4P in each of the mutants, loss of PI4P only affects localization of some of the proteins. Finally, in what seems to be a peripherally related experiment, the authors show that a pair of Legionella translocated effectors are required to maintain PI4P levels, although it is not clear how this is related to the other data in the manuscript.

It is not clear from the manuscript if the authors are just cataloging things or trying to test a hypothesis. This is an extremely difficult manuscript to read and reconstruct what the authors showed. I really think that the only people who will understand what is written are people who are familiar with the work in Chlamydia starting in 2011 in Engel's and Derre's laboratories, which clearly showed that MCS and most specifically Vap/OSBPs are involved in vacuole expansion. If the authors could rewrite the manuscript along these lines, perhaps comparing their data to the Chlamydia data it would help a lot. Otherwise, I don't think anyone else will understand why they are focusing on these things. I don't recommend new experiments (although re-analyzing data is necessary), but the manuscript has to be taken apart and claims removed, and data be interpreted properly. Otherwise, the manuscript seems like just a clearing house for data.

Response: Thank you for the concise summary of our data and pointing out the need to restructure the manuscript and to clearly outline the hypotheses underlying the study. According to the reviewer’s suggestions, we have now re-structured the manuscript. In the revised manuscript the story unfolds from the observation that the ER tightly associates with (isolated) LCVs, and the proteomics approach is used as a validation of the presence of MCS proteins at the LCV-ER MCS.

As suggested by the reviewer, we now highlight the seminal work on Chlamydia by the Engel and Derré laboratories not in the Discussion section (as in the original version of the manuscript) but already in the Introduction section (l. 142-148). We believe that it makes a stronger case to start out an analysis of LCV-ER MCS with a Legionella-specific cell biological finding (LCV-ER association) and an unbiased proteomics approach, as compared to a more derivative and defensive approach starting out with what is known about Chlamydia.

The reviewer’s comment “This is an extremely difficult manuscript to read” appears overly harsh and conflicts with the positive evaluation of Reviewer #2 and Reviewer #3. Finally, we respectfully disagree with the reviewer’s statement that experiments characterizing L. pneumophila effectors implicated in the formation and function of LCV-ER MCS are peripheral. These experiments significantly contribute to a mechanistic understanding of how L. pneumophila forms and exploits LCV-ER MCS, and they are central for studies on pathogen-host interactions. The studies are analogous to the work on Chlamydia effectors by the Engel and Derré laboratories, but the mode of action of Legionella and Chlamydia effectors is obviously different. Another important distinction of our work to the studies on Chlamydia is the use of the genetically tractable amoeba, D. discoideum, which allows an analysis of LCV-ER MCS by fluorescence microscopy at high spatial resolution.

Specific comments

1. The problems start with the first figure, in which the authors state that almost half the D. discoideum proteome is LCV-associated. I doubt that this is correct, and they should base this on some selective criterion. Furthermore in Fig. 1A, they show Venn diagrams for how they whittled this down, but the Supplemental Dataset gives us no clue on how this was done. I can only sit down myself with the dataset and try to figure that out, but that is an unreasonable expectation for the reader. The dataset provided should have a series of sheets, describing how the large protein set was whittled down and how they were sorted, so the reader can evaluate how robust the final results were. To me (at least), if they said: "look we got this surprising result that suggests MCS are involved in promoting LCV formation, and although this is well recognized in Chlamydia but poorly recognized in Legionella", that would be satisfactory to me.

Response: According to the reviewer’s suggestions, we have now thoroughly re-structured the manuscript. In the revised manuscript the story unfolds from the observation that the ER tightly associates with LCVs in infected cells and with isolated LCVs. The proteomics approach is now used as a validation of the presence of MCS proteins at the LCV-ER MCS and relegated to the Supplementary Information section (former Fig. 1, now Fig. S3).

For the proteomics analysis, all protein identifications have been filtered for robustness applying a constant FDR (false discovery rates) of protein and PSM (peptide spectrum match) of 0.01, which is a commonly accepted threshold in the field. Moreover, two identified unique peptides were required for protein identification. The parallel application of both filter criteria results in very robust and reliable data sets. This is outlined in the Material and Methods section (l. 683-693).

In the data set of LCV-associated proteins, 2,434 D. discoideum proteins have been identified (Table S1). This is 18.5% of the total of 13,126 predicted D. discoideum proteins (UniprotKB) and considerably less than “almost half the D. discoideum proteome”, as stated by the reviewer. Moreover, 1,224 L. pneumophila proteins have been identified (among 3,024 predicted L. pneumophila proteins in the database). This is a reasonable number of proteins identified from an intracellular vacuolar pathogen, given the LCV isolation and proteomics methods applied. We now outline these findings more extensively in the Results section (l. 207-213). Moreover, to render Table S1 more reader-friendly, we added to the datasheet “All data” the datasheets “Dictyostelium”, “Legionella” and “Info”.

The Venn diagram in Fig. S3A (previously Fig. 1A) does not show a subset of proteins “whittled down” from the entire proteomes, but simply summarizes LCV-associated proteins, which were either identified exclusively in the parental strain Ax3 but not in the Δsey1 mutant strain, or only in Δsey1 but not in Ax3, thus identifying possible candidates relevant for the LCV-ER MCS. This information is now outlined more clearly in the text (l. 238-241). Moreover, we now explicitly define in the Material and Methods section (l. 697-704) the “on” and “off” proteins shown in Fig. S3A.

The overall rational for the comparative proteomics approach was our previous finding that compared to the D. discoideum parental strain Ax3, the Δsey1 mutant strain accumulates less ER around LCVs (PMID: 28835546, 33583106). This finding suggests that formation of the LCV-ER MCS might be compromised in the Δsey1 mutant strain. This hypothesis is now outlined at the beginning of the Results paragraph (l. 204-207).

I am clueless regarding how Fig. 6 fits with the rest of the manuscript. If this is about MCS, there is no demonstration these effectors are directly involved in MCS other than the somewhat diffuse argument that there is some correlative connection to PI4P levels, that I am not particularly convinced by.

Response: The PtdIns(4)P gradient between two different cellular membranes is an intrinsic feature of MCS. To date, a quantification of PtdIns(4)P levels on LCVs in response to the presence or absence of specific L. pneumophila effectors is lacking. Accordingly, we opted for quantifying the PtdIns(4)P levels on LCVs in presence and absence of an L. pneumophila effector putatively generating PtdIns(4)P on LCVs, the phosphoinositide 4-kinase LepB, or titrating PtdIns(4)P on LCVs, the PtdIns(4)P-binding ubiquitin ligase SidC. To address the concerns of Reviewer 1 and Reviewer 3 (see below), we now outline in detail the rational to assess the role of LepB and SidC for MCS function (l. 385-387). Importantly, we now also provide data that at LCV-ER MCS PtdIns(4)P/cholesterol lipid exchange is functionally important (new Fig. 6 and Fig. S10). In the revised version of the manuscript, this new data is preceding the experiments with the L. pneumophila effectors, which should render our choice of effectors more comprehensible to the reader and increase the flow of the manuscript.

Line 146 and associated paragraph. We don't need a catalog of proteins in narrative. There is more detail in the narrative than there is in the tables and figures, which would be a more appropriate way to present the data.

Response: As suggested by the reviewer, we summarized the LCV-associated D. discoideum proteins and considerably reduced the list in the text (l. 214-230).

Line 186. There is nothing wrong with pursuing MCS based on the idea that this was seen before with Chlamydia and you wanted to test if this was a previously unappreciated aspect of Legionella biology. I don't see the rationale based on the proteomics, partly because I don't understand how the proteomics dataset was parsed.

Response: As suggested by the reviewer, we thoroughly re-structured the manuscript and now highlight the seminal work on Chlamydia by the Engel and Derré laboratories already in the Introduction section (not in the Discussion section as in the original version of the manuscript). We believe that it makes a stronger case to start out an analysis of LCV-ER MCS with a Legionella-specific cell biological finding (LCV-ER association) and an unbiased proteomics approach, as compared to a more derivative and defensive approach starting out with what is known about Chlamydia.

Figure 3: These growth curves are super-weird. I am not used to looking at 8 days of logarithmic growth in a linear scale and seeing no (apparent) growth for 4 days. Considering all the microscopy data are performed in the first 18 hrs of infection, it’s hard to see how this is related to data at 8 days post infection. If this were plotted in logarithmic scale, as microbiologists are used to doing, then perhaps we could see a connection. Also, in some cases, it might be helpful to calculate a growth rate, because it’s possible the author may now see some effects by comparing logarithmic growth rates.

Response: We have been characterizing growth of L. pneumophila in D. discoideum in several studies using growth curves with RFU vs. time plotted in linear scale (e.g., Finsel et al., 2013, Cell Host Microbe 14:38; Rothmeier et al., 2013, PloS Pathog 9: e1003598; Swart et al., 2020, mBio 11: e00405-20). The D. discoideum-L. pneumophila infection model is peculiar, since the amoebae do not survive temperatures beyond 26 degC. This is substantially below the optimal growth temperature of L. pneumophila (35-40 degC). This means that - due to the many genetic tools available - D. discoideum is an excellent model to investigate cell biological aspects of the infection at early time points (ca. 1-18 h p.i.), but the amoebae are not an optimal system to quantify (several rounds) of intracellular growth.

Figure 2: The images don't necessarily show what the bar graphs show. In particular, look at Osp8. That image doesn't make sense to me.

Response: The individual channels of the merged images in Fig. 1 (formerly Fig. 2) are shown in Fig. S2. By looking at the individual channels, it becomes clear that OSBP8-GFP co-localizes with calnexin-mCherry (overlapping signals), but not with P4C-mCherry or AmtA-mCherry (adjacent signals). Co-localization was quantified in a non-biased manner by Pearson’s correlation coefficient. To further visualize co-localization, we now also provide fluorescence intensity profiles for all confocal micrographs (amended Fig. 1).

In summary, I think the authors hit on something that is probably important for Legionella biology, but it’s not clear what they want to show. They are very invested in connecting everything to PI4P levels, which may or may not be correct, but it seems to me that perhaps taking more care in showing the importance of the Vap/OSPB nexus in supporting Legionella growth should be the first priority.

Response: Given the importance of the PtdIns(4)P gradient for lipid exchange at MCS, we believe it is justified to put considerable emphasis on this lipid. To further substantiate a functional role of PtdIns(4)P at LCV-ER MCS, we now also show that an increase in PtdIns(4)P at the LCV correlates with a decrease of cholesterol (new Fig. 6 and Fig. S10). The inverse correlation of these two lipids is in agreement with the notion that cholesterol is a counter lipid of PtdIns(4)P at LCV-ER MCS.

It is not clear from the manuscript if the authors are just cataloging things or trying to test a hypothesis.

Response: In the revised version of the manuscript, we put forward several specific hypotheses, which we then tested in our study (l. 152-155).

If I understand Fig. 1, only one of the candidates (VapA) was verified as being more enriched in WT relative to atlastin mutants. This argues even more strongly that the authors have to describe their criteria for choosing these candidates.

Response: As outlined above (specific point 1), we have now re-structured the manuscript according to the reviewer’s suggestions. In the revised manuscript the story unfolds from the observation that the ER tightly associates with LCVs in infected cells and with isolated LCVs. The proteomics approach is now used as a validation of the presence of MCS proteins at the LCV-ER MCS and relegated to the Supplementary Information section (formerly Fig. 1, now Fig. S3). We consider the proteomics approach a powerful hypothesis generator, and the experimental identification of several MCS proteins by proteomics validated the cell biological and bioinformatics insights.

Reviewer #1 (Significance (Required)):

As stated above, the manuscript can't decide if it’s about MCS or PI4P, and I would argue strongly that the emphasis on PI4P detracts from the manuscript, as well as its inability to draw connection to previous work that is likely to be important.

Response: We respectfully disagree with the reviewer on this important point and hold that proteins as well as lipids are crucial functional determinants of MCS. The PtdIns(4)P gradient is a pivotal process for lipid exchange at MCS. Therefore, we believe it is justified to put considerable emphasis on this lipid. In the Introduction section, we now specify several hypotheses on the localization and function of lipids and proteins at LCV-ER MCS (l. 152-155). Moreover, we now also refer to the previous work on Chlamydia MCS in the Introduction section (l. 142-148).

Reviewer #2 (Evidence, reproducibility and clarity):

Summary of paper and major findings

Membrane contact sites (MCS) are locations where two membranes are in close proximity (10-80nm). MCS have a defined protein composition which tether the membranes together and function in small molecule and lipid exchange. Typically, MCS proteins contain structural (e.g., tethers) and functional (e.g., exchange lipids) proteins, in addition to proteins which regulate the structure and function of the MCS. In this manuscript, Vormittag et al describe protein components of MCS between the Legionella-containing vacuole (LCV) and the host endoplasmic reticulum (ER) in the amoeba Dictyostelium. Proteomics of isolated LCVs followed by microscopy analysis identified several proteins which localize to either the LCV-associated ER (OSBP8), the LCV (OSBP11), or both (VAP and Sac1). The mammalian homologs of these proteins have been shown to play important roles in ER MCS, with VAP serving a structural role, Sac1 a PI(4P) phosphatase regulating PI(4)P levels, and OSBP8 and OSBP11 lipid transferring proteins. Given the importance of PI(4)P in formation and maintenance of the Legionella-containing vacuole, the authors used dicty mutants to determine the importance of these proteins in bacterial growth, LCV size, and PI(4)P levels on the LCV. While VAP and OSBP11 appear to promote Legionella infection, OSBP8 appears to restriction infection, although all identified MCS components appear to play a role in decreasing PI(4P) shortly after infection. Finally, VAP and OSBP8 localization to the LCV is PI(4)P-dependent. Overall, the authors conclude that these MCS components play a role in modulating PI(4)P levels on the LCV.

Overall, this is an interesting study further exploring the role of PI(4)P in LCV-ER interactions, and how PI(4)P levels are regulated. The figures are clearly presented, there is an impressive amount of data, and rigor appears to be strong with appropriate replicates and statistical analysis. The phenotypes are often mild, but the authors are careful to not overinterpret the data. While this is an interesting study, additional experiments are necessary to support the overall model and the text needs to put the findings into the larger context.

Response: We would like to thank the reviewer for this positive and constructive assessment. We performed and planned additional experiments to further strengthen the study and support our model.

Major comments

1) MCS contain protein complexes or a group of proteins, but the proteins here are studied in isolation and do not support the model shown in Figure 7. Co-localization studies of the putative LCV-ER MCS proteins are critical, especially given that the authors hypothesize the proteins are working together to modulate PI(4)P levels.

Response: To further explore the possible interactions between Vap and OSBP proteins, we plan co-localization experiments using D. discoideum strains producing mCherry-Vap and either OSBP8-GFP or GFP-OSBP11, as outlined above (Section 2, new__ Fig. 2__ and Fig. S4).

Moreover, we included additional data on PtdIns(4)P/cholesterol lipid exchange (Fig. 6 __and Fig. S10__), which have been incorporated into the model (amended Fig. 8). Based on the available data, we do not postulate direct interactions between Vap and OSBP proteins. The previous model, which now has been amended, might have been misleading in that respect.

2) The phenotypes are relatively mild, suggesting functional redundancy. Double knockouts, particularly in VAP and OSBP11, may generate a stronger phenotype that better supports the hypothesis and demonstrate the importance during infection.

Response: Thank you for this interesting suggestion. Please see Section 4 below for our arguments, why we believe that this intriguing approach is beyond the scope of the current study.

3) The timing of PI(4)P and MCS protein localization during infection is critical to understanding how MCS might be functioning. Based on Figure 6C, PI(4)P levels decrease on the LCV during infection, but this is not fully explained in the context of what's known in the literature and what is observed the previous figures. How does localization of different MCS components change during infection, and does this correlate with the changes in growth or LCV size? A better description in the Introduction on LCV-associated PI(4)P levels would be beneficial in orienting the reader to why PI(4)P levels are modulated.

Response: As suggested by the reviewer, we added to the Introduction section more detail about the kinetics of PtdIns(4)P accumulation on LCVs (l. 65-71), and we discuss the limited spatial resolution of the IFC approach (formerly Fig. 6C, now Fig. 7C; l. 407-408). Importantly, we also provide new data showing that within 2 h p.i. an increase in PtdIns(4)P at the LCV coincides with a decrease of cholesterol (new Fig. 6 and Fig. S10). The new data is put into this context in the Discussion section (l. 449-454).

4) OSW-1 has other targets besides OSBPs, and depleting Sac1 and Arf1 in A549 cells is not specifically targeting the MCS, as these proteins have other functions. The data in mammalian cells is not convincing and should be removed.

Response: As suggested by the reviewer, we removed the data on depleting Sac1 in A549 cells (Fig. 3D, and Fig. S6BC). We propose to leave the pharmacological data on inhibition of L. pneumophila replication by OSW-1 in the manuscript, but to clearly point out that OSW-1 has other targets besides OSBPs (l. 297-299).

Minor comments

1) Figure 2 is missing details on number of experiments/replicates and statistical analysis.

Response: Thank you for having noted this oversight. The number of independent experiments and statistical analysis have now been added to Fig. 1 (formerly Fig. 2) (l. 1009-1010).

2) Can the authors hypothesize why VAP promotes growth early during infection, but appears to restrict growth at later timepoints (Figure 3A)?

Response: Thank you for raising this intriguing point. The opposite effects on growth of Vap at early and later timepoints during infection might be explained by interactions with antagonistic OSBPs. Vap likely co-localizes with OSBP8 as well as with OSBP11 on the limiting LCV membrane or the ER, respectively (experiment to be performed; Fig. 2 and__ Fig. S4__). The absence of OSBP8 (ΔosbH) or OSBP11 (ΔosbK) causes larger or smaller LCVs, and increased or reduced intracellular replication of L. pneumophila, respectively. Thus, OSBP8 seems to restrict and OSBP 11 seems to promote intracellular replication. Accordingly, if Vap affects or interacts with OSBP11 early and with OSBP8 later during infection, opposite effects on growth of Vap might be explained. These reflections are now outlined in the Discussion section (l. 431-441).

3) There is a large amount of data, which makes it difficult at times to follow. I suggest adding additional information to table 1, including LCV size and whether or not the protein's localization is PI(4)P-dependent.

Response: Thank you for this suggestion. As proposed by the reviewer, we added the additional information to Table 1 (PtdIns(4)P-dependency of protein localization, LCV size).

Reviewer #2 (Significance (Required)):

Membrane contact sites during bacterial infection are a growing area of research. In Legionella, several papers point to the presence of MCS. Further, PI(4)P is known to be an important component on the LCV. This paper shows that MCS protein members are important in modulating LCV PI(4)P levels. The model as presented is not completely supported by the data as co-localization experiments are needed, along with more detailed analysis of how PI(4)P levels change over infection and the role of these MCS proteins in that process. This study will be of interest to those studying Legionella and other vacuolar pathogens. Area of expertise is on membrane contact sites and lipid biology.

Response: Thank you very much for the overall positive and constructive evaluation.

Reviewer #3 (Evidence, reproducibility and clarity):

The authors perform proteomic analysis of Legionella-containing vacuoles. The observe association of membrane contact site (MCS) proteins including VAP, OSBPs, and Sac1. Functional data indicates that these proteins contribute to PI4P levels on LCVs and their ability to acquire lipid from the ER to enable LCV expansion/stability. Overall, the paper is an important contribution to the field and builds upon a growing appreciation for MCS in establishment of intracellular niches by microbial pathogens. I have only minor comments for the authors consideration.

Response: We would like to thank the reviewer for this enthusiastic assessment.

Minor comments:

-line 145, "This approach revealed 3658 host or bacterial proteins identified on LCVs...". This number seems high... how does it compare to prior proteomic studies of pathogen-containing vacuoles?

Response: As outlined above (reviewer 1, point 1), we have now changed the text (l. 207-213): “This approach revealed 2,434 LCV-associated D. discoideum proteins (Table S1), of a total of 13,126 predicted D. discoideum proteins (UniprotKB). Moreover, 1,224 L. pneumophila proteins were identified (among 3,024 predicted L. pneumophila proteins), which is a reasonable number of proteins identified from an intracellular bacterial pathogen within its vacuole with the proteomics methods applied (Herweg et al, 2015; Schmölders et al., 2017).”

• line 160. Can the authors comment on why mitochondrial proteins are observed in their proteomic analysis? Are these non-specific background signals or reflecting relevant organelle contact?

Response: The dynamics of mitochondrial interactions with LCVs and the effects of L. pneumophila infection on mitochondrial functions have been thoroughly analyzed (PMID: 28867389). This seminal work is now cited in the text (l. 227-230).

• line 268. It is reported that LCVs are smaller with MCS disruption at 2 and 8 h p,i.. Does this also lead to instability or rupture of LCVs? And related to this why would LCVs be bigger at 16h with MCS disruption?

Response: MCS components affect LCV size positively or negatively. E.g., the absence of OSBP8 (ΔosbH) or OSBP11 (ΔosbK) causes larger or smaller LCVs, and increased or reduced intracellular replication of L. pneumophila, respectively. However, as outlined in the Discussion section (l. 442-454), we believe that the relatively small size likely reflects a structural remodeling of the pathogen vacuole rather than a substantial LCV expansion. LCV rupture takes place only very late in the infection cycle (beyond 48 h) and is followed by lysis of the host amoeba (PMID: 34314090).

• lines 288 and 299 "data not shown" this data should be included in a supplemental figure.

Response: The data on the localization of GFP-Sac1 and GFP-Sac1_ΔTMD are included in the Figs. 1A, 4A, 5AD, S2A, S7A, and__ S9__ (l. 328, l. 339).

• line 327. The authors choose to focus on the role of LepB and SidC in MCS modulation. The rationale for choosing these two amongst the ca 330 effectors was not given. Were other effectors also examined?

Response: LepB and SidC were chosen due to their activities producing or titrating PtdIns(4)P, respectively, and their LCV localization. This rational is now given in the text (l. 385-387). No other effectors were examined up to this point.

Reviewer #3 (Significance (Required)):

Comprehensive LCV proteomics of interest to field of cellular microbiology. Studies of MCS broadly relevant to cell biologists.

Response: Thank you very much for the overall very positive evaluation.

4. Description of analyses that authors prefer not to carry out

Reviewer #2

Major comment

2) The phenotypes are relatively mild, suggesting functional redundancy. Double knockouts, particularly in VAP and OSBP11, may generate a stronger phenotype that better supports the hypothesis and demonstrate the importance during infection.

Response: Thank you for raising the important question of functional redundancy. We now outline this concept in the Discussion section (l. 427-429). A further analysis of the genetic and biochemical relationship between Vap and OSBP11 or OSBP8 are without doubt some of the most interesting aspects of further studies on the topic of LCV-ER MCS.

The construction of a D. discoideum double mutant strain is time consuming and usually takes 1-2 months. Provided that a Vap/OSBP11 double deletion mutant strain is viable and can be generated, it takes another 1-2 months to thoroughly characterize the strain regarding intracellular replication of L. pneumophila (Fig. 3), LCV size (Fig. 4), and PtdIns(4)P score (Fig. 5). Moreover, there is already a large amount of data in the paper (to quote Reviewer #2), and therefore, adding new data might makes it even harder to follow the story and focus on the key points. Finally, we believe that the planned colocalization experiments (Reviewer #2, point 1) and the new data on lipid exchange kinetics (new Fig. 6 and Fig. S10) fit the current story more coherently, and thus, are more straightforward and informative than the generation and characterization of double mutant strains. For these reasons, we believe that the generation and characterization of D. discoideum double mutant strains is beyond the scope of the current study.

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Referee #1

Evidence, reproducibility and clarity

In the manuscript by Vormittag, et al., the authors perform proteomics identification of proteins associated with the Legionella-containing vacuole (LCV) in the model amoeba Dictyostelium discoideum comparing WT to atlastin knockout mutants. The authors find approximately half the D. discoideum proteome associated with the LCV, but there was enrichment of some proteins on the WT relative to the mutant. They focus on proteins involved in forming membrane contact sites (MCS) that previously were shown to be important for expansion of the Chlamydia-containing vacuole. Most significant are the oxysterol binding proteins (OSBP) and VapA (similar to that seen in Chlamydia). The authors show differential association of these proteins with either the LCV or presumably the ER associated with the LCV. Using a linear scale over 8days, they show that mutations in some of the MCS reduce yields in two of the OSPB knockout mutants and the growth rate of the vap mutant is slowed but ultimate yield is increased. Using some nice microscopy techniques, they measure LCV size, and the osbK mutant appears particular small relative to other strains, whereas the osbH mutant generates large vacuoles. This doesn't necessarily correlate with the PI4P quantities on the vacuoles (which is higher in all of them), but I am not totally sure how this is measured, and whether is it PI4P/pixel or PI4P/LCV. In all cases, this was reduces by Sac1 mutation. Surprisingly, even though there was uniform increase in PI4P in each of the mutants, loss of PI4P only affects localization of some of the proteins. Finally, in what seems to be a peripherally related experiment, the authors show that a pair of Legionella translocated effectors are required to maintain PIF4P levels, although it is not clear how this is related to the other data in the manuscript.

It is not clear from the manuscript if the authors are just cataloging things or trying to test a hypothesis. This is an extremely difficult manuscript to read and reconstruct what the authors showed. I really think that the only people who will understand what is written are people who are familiar with the work in Chlamydia starting in 2011 in Engel's and Derre's laboratories, which clearly showed that MCS and most specifically Vap/OSBPs are involved in vacuole expansion. If the authors could rewrite the manuscript along these lines, perhaps comparing their data to the Chlamydia data it would help a log. Otherwise, I don't think anyone else will understand why they are focusing on these things. I don't recommend new experiments (although re-analyzing data is necessary), but the manuscript has to be taken apart and claims removed, and data be interpreted properly. Otherwise, the manuscript seems like just a clearing house for data.

1. The problems start with the first figure, in which the authors state that almost half the D. discoideum proteome is LCV-associated. I doubt that this is correct, and they should base this on some selective criterion. Furthermore in Fig. 1A, they show Venn diagrams for how they whittled this down, but the Supplemental Dataset gives us no clue on how this was done. I can only sit down myself with the dataset and try to figure that out, but that is an unreasonable expectation for the reader. The dataset provided should have a series of sheets, describing how the large protein set was whittled down and how they were sorted, so the reader can evaluate how robust the final results were. To me (at least), if they said: "look we got this surprising result that suggests MCS are involved in promoting LCV formation, and although this is well recognized in Chlamydia but poorly recognized in Legionella", that would be satisfactory to me.
2. I am clueless regarding how Fig. 6 fits with the rest of the manuscript. If this is about MCS, there is no demonstration these effectors are directly involved in MCS other than the somewhat diffuse argument that there is some correlative connection to PI4P levels, that I am not particularly convinced by.
3. Lin 146 and associated paragraph. We don't need a catalog of proteins in narrative. There is more detail in the narrative than there is in the tables and figures, which would be a more appropriate way to present the data.
4. Line 186. There is nothing wrong with pursuing MCS based on the idea that this was seen before with Chlamydia and you wanted to test if this was a previously unappreciated aspect of Legionella biology. I don't see the rationale based on the proteomics, partly because I don't understand how the proteomics dataset was parsed.
5. Figure 3: These growth curves are super-weird. I am not used to looking at 8 days of logarithmic growth in a linear scale, and seeing no (apparent) growth for 4 days. Considering all the microscopy data are performed in the first 18 hrs of infection, its hard to see how this is related to data at 8 days post infection. If this were plotted in logarithmic scale, as microbiologists are used to doing, then perhaps we could see a connection. Also, in some cases, it might be helpful to calculate a growth rate, because its possible the author may now see some effects by comparing logarithmic growth rates.
6. Figure 2: The images don't necessarily show what the bar graphs show. In particular, look at Osp8. That image doesn't make sense to me.

In summary, I think the authors hit on something that is probably important for Legionella biology, but its not clear what they want to show. They are very invested in connecting everything to PI4P levels, which may or may not be correct, but it seems to me that perhaps taking more care in showing the importance of the Vap/OSPB nexus in supporting Legionella growth should be the first priority.

It is not clear from the manuscript if the authors are just cataloging things or trying to test a hypothesis.

If I understand Fig. 1, only one of the candidates (VapA) was verified as being more enriched in WT relative to atlastin mutants. This argues even more strongly that the authors have to describe their criteria for choosing these candidates

Significance

As stated above, the mansucript can't decide if its about MCS or PI4P, and I would argue strongly that the emphasis on PI4P detracts from the manuscript, as well as its inability to draw connection to previous work that is likely to be important.

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Reply to the reviewers

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

This paper examines the formation and repair of micronuclei in non-cancerous cells, specifically in mouse embryonic fibroblasts. This work was performed completely in culture and used a combination of western blot, confocal and superresolution microscopy to assess the contents of micronuclei over a repair period of 5 hours after 2 hours of induction of double strand breaks by treatment with etoposide. The authors found that the bodies colocalised with LC3, Beclin 1 and lysosomes suggestive of autophagy. However no evidence of autophagic flux has been demonstrated.

Major issues are as follows:

Figure 2

A - Any sense of the autophagic flux? LC3B - I and LC3B - II seem to be in equal quantities most of the time. Maybe using the tandem LC3 in this system could provide further insight. Also remove the violin plots from this graph and from G and H, as there are too few data points.

Thank you for your comment. We have evidence of a functional autophagic flux, since we observed an increasing number of acidic vesicles stained with Lysotracker in response to DNA damage, which were reduced after DNA repair. Some of the micronuclei were also co-stained with Lysotracker, suggesting their lysosomal degradation. We reorganized the data in the revised figure 2A to communicate better these observations. We reproduce here the dynamic of Lysotracker stain, please notice an increase in the abundance of acidic vesicles after 2h of DNA damage. A further evidence of activation of functional autophagy is the dynamic intracellular distribution of both LC3 and BECN1, indicative of autophagy induction. Please notice in revised Figure 2A that LC3 surrounding vesicles increases after 2h of DNA damage and diminish when DNA is repaired. BECN1 in control MEFs is highly concentrated inside the nucleus, predominantly at the nucleolus, and after DNA damage it redistributes towards the cytoplasm. Finally after DNA repair, BECN1 appears highly concentrated at the nucleus again. These dynamic changes correlate with autophagosomes formation and successful fusion with lysosomes. In the revised manuscript we removed the violin plot as suggested. Since the elimination of nuclear components occurs in a subset of cells, the role of the autophagic machinery needs to be analyzed cell by cell. We considered better to eliminate also the Western blot, as an analysis of the whole population does not provide information relevant for this study.

• Can you reduce the brightness in the merge image, as I cannot see DAPI nor a convincing Beclin-1/LC3 co-localisation.

Thank you for the observation. We improved the quality of the images and reorganized Figure 2 to convincingly show BECN1 and LC3 co-localization, together with Lysotracker, in nuclear alterations (buds and micronuclei). We modified the results text accordingly.

• Although the data is convincing, It would be clearer if the brightness of the merge image was reduced.

Thank you for your comment. We improved the images shown, these data is now integrated in new Figure 2A.

• Is the significant result the difference between 5h R Control si and 5h R Atg7? if so, there is no significant change in micronuclei as the same time point, can you explain this disconnect? are the buds being degraded prior to becoming micronuclei?

That is correct, we found no statistical significant difference in the number of micronuclei formed silencing Atg7, although there was a trend to reduce them. To consolidate the role of autophagy in nuclear buds and micronuclei formation, we studied Atg4-/- MEFs. We confirmed a statistical significant reduction of buds formation when autophagy is impaired (new Figure 2G). However, we observed that the number of micronuclei increased after 2h of DNA damage in Atg4-/- MEFs, suggesting that autophagy does not contribute to micronuclei formation but elimination. Together, our results suggest that the origin of buds and micronuclei are mechanistically different. A difference in the biogenesis of buds and micronuclei has been previously suggested studying cells cultured under strong stress conditions that induce DNA amplification, as well as in cells under folic acid deficiency. While interstitial DNA without telomere was more prevalent in buds than in micronuclei, telomeric DNA was more frequently observed in micronuclei (Fennech et al. 2011, Mutagenesis 26:125-132). We agree with the reviewer, it seems that not all the buds become micronuclei.

Figure 3 A - nice microscopy showing the co-localisation of TOP2A and LC3-GFP. I'm interested in DAPI being on some bodies and not others. Do you have any sense of the dynamics of this?

Thank you for the interesting question. Since removal of nuclear alterations as nuclear buds and micronuclei is a very dynamic process, we detect nuclear damaged material in the cytoplasm are at different degradation stages. Nucleases could be degrading DNA in micronuclei. Another possibility to the lack of DAPI signal in some micronuclei containing TOP2A and GFP-LC·is that TOP2A could be expelled from the nucleus with undetectable fragments of DNA or even without DNA, as a renewal process. We believe that nuclear buds can form without extruding DNA in some cases, perhaps to modulate proteostasis in addition to protect genome stability. In the revised manuscript we discuss this possibility further.

G - c shows a strand of mostly TOP2B coming from the nucleus. Is there any evidence that this occurs using either confocal microscopy or super resolution approaches. Could you try Z-stack to find these?

Thank you for the suggestion, we analyzed Z-stack images and tried to observe it also by immunofluorescence. We could detect some tubular signal connecting the nucleolus with a micronucleus containing TOP2B and BECN1 (arrow head in Fig 3B reproduced below), although we cannot be certain we are detecting the same nuclear extrusion mechanism by Electron Microscopy than by immunofluorescence.

Figure 4 C - is there a significant increase in FBL negative bodies, this would make sense if FBN is being degraded in the micronuclei during the repair process

We found that the number of micronuclei without FBL increased with statistical significant difference by Two-way-ANOVA followed by Dunnett´s multiple comparison test (P=0.463 comparing cells with 2h of DNA damage with control cells; P=0.0017 comparing cells after 5h of DNA repair with untreated cells; n=5). We agree with the reviewer, a possible explanation is that FBL is being degraded in micronuclei during the repair process. Although it could also be possible that nucleolar is less sensitive to Etoposide poisoning, or that nucleolar DDR is mechanistically different.

• Would it be possible to increase the n of these experiments to confirm either no change in FBL/LC3 co-loc, or evidence of increase?

Thank you for the suggestion. We repeated the experiment two more times to increase the n to 5. We found no statistical difference in the number of nuclear buds or micronuclei containing both FBL and LC3 during DNA damage and repair. Therefore it seems that the release of nucleolar components is not enhanced by Etoposide-induced DSB, suggesting that nucleolar DDR is a unique response, independent of DDR elsewhere in the genome (reviewed in Nucleic Acids Research, 2020, Vol. 48, No. 17 9449–9461 doi: 10.1093/nar/gkaa713).

Minor issues:

Figure 4 and 5 legends are in a different font.

Thank you. We correct the font in the current manuscript.

Reviewer #1 (Significance (Required)):

There is little specific data on the role of autophagy in clearing micronuclei in cancer cells, so this may be suggestive of a new mechanism that occur during normal cellular homeostasis. There are known links between lamin A defects and the formation of micronuclei, but not explicitly that the micronuclei are also Lamin A positive. it is likely that analogous processes occur in both cancer and non-cancer, so the impact of these data is not clear to me. This paper may be of interest to researchers interested in nuclear structure and DNA damage, but based on the data presented the significance is limited.

The significance of the present work is to discover that autophagy is relevant both during physiological DNA damage and in response to an exogenous DNA damaging agent, to extrude damaged DNA, TOP2cc and Fibrillarin from the nucleus. This knowledge is relevant since insufficiencies on autophagy imply a risk of genomic instability, which in turn could drive the cell into a senescent or malignant state. We present data showing that autophagy regulates the dynamic formation and elimination of nuclear buds and micronuclei in a mechanistically differentiated way. While autophagy contributes to nuclear buds formation, it is necessary for micronuclei elimination. Our data suggest that nucleophagy could be also a mechanism to alleviate basal nucleolar stress. As the reviewer noticed, some micronuclei did not have DNA. It is conceivable then that nuclear buds and micronuclei form also for a proteostatic function, not necessarily involving DNA damage elimination. We believe the significance of our work contributes to our understanding of the cell, as well as to cancer research. Whether common mechanisms between cancerous and normal cells occur is relevant to know, to consider the specificity of potential therapeutic approaches.

I don't have sufficient expertise to evaluate the super resolution microscopy beyond assessing the images.

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

Peer review of the manuscript with the number RC-2021-01181 by Muciño-Hernandez G et. al. at Review Commons and with the tittle "Nucleophagy contributes to genome stability 1 though TOP2cc and nucleolar components degradation"

1. Summary Muciño-Hernandez G et. al. show in this manuscript that mouse embryonic fibroblasts (MEFs) have basal levels of nuclear buds and micronuclei, which are indicators of genomic DNA damage. These basal levels of nuclear buds and micronuclei in MEFs increased after Etoposide treatment, which is known to induce DNA Double stranded Breaks (DSD). Interestingly, the nuclear buds and micronuclei co-localize with makers for nucleophagy (BECN1 and LC3) and acidic vesicles, suggesting that they are cleared by nucleophagy. The authors propose that basal levels of nucleophagy clear basal levels of genomic DNA damage that occurs as result from DNA-dependent biological processes in the cell nucleus, thereby contributing to nuclear stability of MEFs under physiological conditions. These basal levels of nucleophagy increase after the action of factors that induce DNA damage and nuclear stress. The concepts proposed by Muciño-Hernandez G et. al. are novel, since most of the current published data on nucleophagy related to DNA damage have been obtained under pathological conditions, e.g. implementing cancer cells.

The authors use in their manuscript various molecular biology techniques to obtain data that support their claims, including Western Blot analysis of protein extracts from MEFs, immunostaining on MEFs and neutral comet assays, complemented with state of the art imaging techniques, such as confocal microscopy, immunoelectron microscopy and super resolution microscopy. The quality of the data is sound. The structure of the manuscript support the understanding of the reader. However, I would like to suggest several improvements that will help to increase the quality of the manuscript, in order that fits to the standards of articles recently published in journals affiliated to Review Commons, such as the Journal of Cell Biology, the EMBO Journal or eLife.

1. Major comments

2.1 The authors have to improve the description of the results. Especially the description of those Figure panels containing plots that were generated using data from several experiments has to be improved.

One example is the description of the Figure 1D, which is in the lanes 137-151 of the current version of the manuscript. Whereas the authors describe in lanes 137-147 observations related to representative pictures of confocal microscopy after immunostaining presented in Figure 1D (left), the description of the quantification from 9 independent experiments presented in the plots in Figure 1D (right) comes relatively short in lanes 147-150 without mentioning any of the values implemented for creating the plots.

"Interestingly, while the frequency of nuclear buds gradually increased after DNA damage and during DNA repair, the frequency of micronuclei also increased after DNA damage, but diminished upon DNA repair."

The other plots presented in the different figure panels across the manuscript are described in a similar manner. I would like to suggest to the authors to improve their manuscript by including during the description of their results the values that were implemented for the degeneration of the plots presented in the manuscript. For example, in the specific case of Figure 1D above:

"Interestingly, the percentage of MEFs with nuclear buds gradually increased from XY% ({plus minus} XY SD) in control non-treated (Ctrl) MEFs to XY% ({plus minus} XY SD; P=XY) after 2 h Etoposide-induced DSB in MEFs and XY% ({plus minus} XY SD; P=XY) after DNA repair take place in MEFSs 5 h upon stop of Etoposide treatment (Figure 1D, right). In contrast, the percentage of MEFs with micronuclei significantly increased from XY% ({plus minus} XY SD) in Ctrl MEFs to XY% ({plus minus} XY SD; P=XY) after 2 h Etoposide-induced DSB, whereas it was reduced to XY% ({plus minus} XY SD; P=XY) 5 h after stop of Etoposide treatment (Figure 1D, right)."

Descriptions of the plots as mentioned above will make the text more intuitive for the reader, and they will make possible to read the Results Section without switching to the Figure Legends or the Material and Methods Section or to Supplementary Files. Even though the representative pictures from different microscopy techniques presented in the manuscript are of good quality and support the claims of the authors, it is important to mention that the quantifications presented in the plots demonstrate the statistical significance of these representative pictures. Thus, the authors should consistently include in the manuscript during the description of theirs results all the information (mean values, standard error of the means, P values, n values, etc.) that support their interpretation of the results and demonstrate the statistical significance of their claims.

Thank you for your clear and valuable advice. We followed it and in the revised manuscript we included the data in the results section.

2.2 Following a similar line of argumentation as in the previous point, the authors should provide as Supplementary Material an Excel file containing a statistical summary, including all statistical relevant information from each one of the plots presented in each Figure panel, such as n values, P values, Test implemented, values used for the plots, numbers of experiments, etc. The information could be organized in the Excel file in different data sheets according to the Figure panels, in order that the reader can easily navigate through the data. In the current version of the manuscript, one cannot find the values used for the generation of the plots presented in the manuscript in any of the submitted files.

Thank your for this suggestion. We have included in Table S1 an Excel file with a data sheet for each Figure panel, containing all the data collected and the statistical analysis performed.

Minor comments

3.1 In general, prior studies were appropriately referenced. Only few references has to be added.

Line 48: Add to the already included reference "Dobersch et al., 2021" also the reference Singh et al., 2015 PMID 26045162.

Thank you, we added this reference.

Line 53: Add the corresponding reference after the word "respectively".

We added the corresponding reference.

Line 82: Add the corresponding reference after the word "them".

We added the corresponding reference.

Line 125: Add the corresponding reference after the word "cells".

We added the corresponding reference.

Line 130: The expression "...by analyzing the recruitment of the phosphorylated histone γH2AX..." is the first time that the authors mention in the manuscript the DNA damage maker γH2AX. I suggest that is better introduced as " ... by analyzing the recruitment of the DNA damage marker γH2AX (histone variant H2A.X phosphorylated a serine 139, Rogakou EP, et al., 1998, PMID 9488723) to DSB sites."

Thank you very much for your suggestion. In the revised manuscript we corrected the text as suggested.

Line 199: Add the corresponding reference after the word "formation".

We added the corresponding reference.

Line 205: Add the corresponding reference after the word "cells".

We added the corresponding reference.

3.2 The use of the English language is appropriate throughout the manuscript. However, there are minor errors in the use of punctuation marks, in the use of prepositions and typos. I will list some of them below. However, I would like to recommend that manuscript is corrected by an English native speaker.

Thank you for your careful review of our manuscript. We corrected all the errors listed. A college proficient in English has reviewed the revised manuscript.

Line 41: "...and reproductive systems; genome instability also..." the semicolon can be replaced by a period.

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Line 43: "Since early in development DNA is under constant endogenous..." between "development" and "DNA" there should a comma.

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The sentence in lanes 53-55 has to be rephrased.

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Lines 62-63: the expression "...throughout life." should be substituted.

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Line 70: The abbreviation "rDNA" has to be explained the first time that is used.

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Lines 81-82: It has to be explained for the scientist that is not specialized in the field of nucleophagy, how the integrity of the genome is threatened by micronuclei and nuclei-derived material.

√ Lines 106-110: The sentence is long. It would be easier to understand for the reader if this sentence is divided into two sentences.

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Lines 121-122: The subtitle should be rephrased.

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Lines 132-138: The sentence is long. It would be easier to understand for the reader if this sentence is divided into two sentences, e.g. with a period before the word "hence".

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Lines 143-144: "... in a subpopulation of healthy, untreated cells...". The interpretation of "healthy" might be subjective. I would like to suggest substituting in the complete manuscript the word "healthy" by "control".

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Line 163: The abbreviation for γH2AX was already introduced in line 130.

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Line 182: A comma after "cell lines" is missed.

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Line 183: delete "either". √ Lines 190-194: The sentence is long. It would be easier to understand for the reader if this sentence is divided into two sentences, e.g. with a period after the word "decreased" in line 191.

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Line 218: I assume that instead of "bus", it should be "buds".

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Line 220: I assume that instead of "iRNA", it should be "siRNA". In addition, it is the first time that the abbreviation is used. Thus, I suggest introducing it as "...was silenced by specific small interfering RNA (siRNA) previous to ..."

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Line 327: delete the word "chronic".

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Line 344: I assume that instead of "(figures 4C)", it should be "(Figure 4D)".

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3.3 The structure of the Figures is ok for the peer review process and it might be optimized during editing of the manuscript. Nevertheless, I would like to suggest to the authors to increase the lettering size throughout all the figures. It will make the figures more intuitive.

Thank you for the suggestion. We increase the font size of the figures.

Reviewer #2 (Significance (Required)):

Significance

The work presented by Muciño-Hernandez G et. al. will be clearly a significant contribution to the scientific community working on autophagy, DNA damage repair and cancer, among others. It will be of interest to a broad spectrum of scientists, as I will elaborate in the following lines. The authors propose that MEFs have basal levels of genomic DNA damage under physiological conditions, which are cleared by basal levels of nucleophagy. On one hand, these findings are in line with various publications demonstrating that DNA-dependent biological processes in the cell nucleus, such as transcription, replication, recombination, and repair, involve intermediates with DNA breaks that may compromise the integrity of DNA. Thus, there must be mechanisms that ensure the integrity of the genome during these processes under physiological conditions, one of them seems to be nucleophagy. This perspective might explain the fact that proteins and histone modifications that were initially characterized during DNA repair also play a role during transcription, recombination, and replication. For example, phosphorylated H2AX at S139 (γH2AX) is often used as a marker for DNA-DSB [PMID 9488723]. However, accumulating evidences suggest additional functions of this histone modification [PMIDs 19377486; 22628289; 23382544]. In addition, McManus et al. [PMID 16030261] analyzed the dynamics of γH2AX in normal growing mammalian cells and found γH2AX in all phases of cell cycle with a maximum during M phase, suggesting that γH2AX may contribute to the fidelity of the mitotic process, even in the absence of ectopic- induced DNA damage. Further, Singh et al [PMID 26045162] and Dobersch et al [PMID 33594057] report that γH2AX plays a role in transcriptional activation in response to TGFB-signaling. Moreover, classical DNA-repair complexes have been linked to DNA demethylation and transcriptional activation [PMIDs 17268471; 28512237; 25901318], and DNA-DSB is known to induce ectopic transcription that is essential for repair, supporting a tight mechanistic correlation between transcription, DNA damage, and repair [PMID 24207023]. Perhaps, the authors might consider introducing several of the aspects and the citations written above into the Discussion section of the revised version of their manuscript. On the other hand, most of the published data related to nucleophagy have been obtained from cancer cells. Muciño-Hernandez G et. al. obtained their data implementing MEFs to demonstrate that the proposed mechanisms take also place under non-pathological conditions, what is one of the novel aspects of the present work.

I hope that my suggestions help the authors to improve their manuscript, thereby reaching the standards of manuscripts recently published in journals affiliated to Review Commons AND increasing the impact of their contribution to the scientific community.

Thank you very much for your suggestions. They helped us to present now a much-improved manuscript. We hope the revised work is now suitable for publication in the Journal of Cell Science.

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

In this manuscript, Muciño-Hernández and colleagues suggest that basal formation of nuclear buds and micronuclei increases in primary mouse embryonic fibroblasts following etoposide-induced double strand breaks (DSBs). The study combines the use of biochemical methodologies with confocal and super resolution microscopy in an effort to explore the contribution of nucleophagy to genome stability. The authors provide evidence that autophagy is induced upon etoposide treatment. They detected GFP-LC3 and BECN1 signals in nuclear buds and micronuclei even in untreated control and to a higher extent in etoposide-treated cells. Then, the authors examined whether nucleophagy is required for the removal of nuclear buds and micronuclei, by treating fibroblasts with control and Atg7 siRNA. The authors claim that the percentage of cells with micronuclei or nuclear buds decrease upon Atg7 knockdown, suggesting that components of the autophagy machinery induce the formation of these nuclear abnormalities. Moreover, Type II DNA Topoisomerases (TOP2A and TOP2B) and the ribosomal protein fibrillarin were detected in nuclear buds and micronuclei in fibroblasts treated or not with etoposide. Again in this case, GFP-LC3 was detected in fibrillarin-containing nuclear alterations. Based on these observations, the authors suggest that nucleophagy contributes to the elimination of chromosomal fragments or nucleolar bodies exiting the nucleus under DNA damage -inducing conditions. Specifically, they propose a key role for nucleophagy in maintaining genome stability by eliminating Type II DNA Topoisomerase cleavage complex (TOP2cc) and nucleolar components such as fibrillarin.

While it seems that there is a relationship between nuclear-extruded TOP2 with endogenous BECN1 and GFP-LC3 suggesting autophagic engagement, inconsistencies of fluorescent images between different figures indicate possible technical problems/limitations (please see specific comments, below), compromising authors' claims. LC3 immunoblotting and GFP-LC3 localization results appear over-interpreted (comments below). Neither TOP2 nor Fibrillarin have been shown to be actual autophagic substrates. Also, the link between genomic stability, micronuclei formation and autophagy has been previously reported (Zhao et al., PMID: 33752561).

An additional major concern is relates to nucleophagy being a selective type of autophagy. As such it requires efficient recognition and sequestration of the nuclear material destined to be degraded. Cargo specificity is mediated by receptor proteins, but no evidence for such receptors is provided in this study. Moreover, there is no real mechanistic insight on how nucleophagy mediates genome stability and how this can be interpreted in terms of cell survival under physiological and stress conditions. In other words, the biological significance of the findings presented has not been addressed.

Specific comments are summarized below:

The authors suggest that autophagy is induced after etoposide treatment and during the DNA repair process. However, the Western blot presented in Fig. 2A is not convincing and quantification does not support a significant autophagy induction in any of these cases. Autophagy appears to be induced 1h after etoposide removal, as evidenced LC3II/LC3 I increase (Fig. 2A and S2A). Nevertheless, all these changes should be more rigorously assessed.

Thank you for the observation. We removed the analysis of LC3II/LC3I by Western blot in the revised manuscript because a basal and induced elimination of nuclear components by the autophagic machinery occurs only in a subset of cells. It needs to be analyzed cell by cell. Pooling together all the cells dilutes the observation. Nevertheless, the dynamic intracellular distribution of both LC3 and BECN1 indicate autophagy induction. Please notice in revised Figure 2A that LC3 surrounding vesicles increases after 2h of DNA damage and diminish when DNA is repaired. BECN1 in control MEFs is highly concentrated inside the nucleus, at the nucleolus as it co-localized with Fibrillarin (new Figure 4E), and after DNA damage it redistributes towards the cytoplasm. Finally after DNA repair, BECN1 appears highly concentrated at the nucleus again. A further evidence of a functional autophagic flux, is the observation of an increasing number of acidic vesicles stained with Lysotracker in response to DNA damage, which were reduced after DNA repair. Some of the micronuclei were also co-stained with Lysotracker, suggesting their lysosomal degradation.

Line 190 and Fig. 2A: It is totally unclear whether "autophagy activation" takes place during the two waves described. There is no LC3B-I to LC3B-II conversion to initially suggest "autophagy activation". It rather suggests that autophagy is stalled. Fig. 2F shows that GFP-LC3 is strongly fluorescent into the lysotracker-stained lysosomes, further pointing to possible functional or technical problems.

As pointed out by reviewer 1, the images presented in original Figure 2F were over-exposed. In the current version we replaced those images with new images of better quality. We also reorganized the presentation of the data, and in revised Figure 2A we present photos where more convincingly can be observed a co-localization of BECN1 with LC3, with o without Lysotracker signal in nuclear buds and micronuclei. We also performed immunolocalization of endogenous LC3 (new Figure 2D) to rule out a possible misinterpretation of GFP-LC3 aggregates. As explained before, we removed original Figure2A.

Fig. 2B and Sup. Fig. 2B: BECN1 staining looks problematic. There is extreme BECN1 accumulation in the nucleus. Are those nuclear patterns of endogenous BECN1 and GFP-LC3 normal (see also minor comment 6 and 7)? Is there literature supporting such a distribution?

Yes, it has been documented BECN1 localization in the nucleus during development and in response to DNA damage stimuli such as ionizing radiation, and with a function related to DNA repair alternative to autophagosome formation (Fei Xu, et al. 2017, Scientific Reports | 7:45385 | DOI: 10.1038/srep45385). In the current manuscript we also detected endogenous LC3, to avoid a possible artifact with GFP-LC3 expression. We observed endogenous LC3 also localized in the nucleus (new Figure 2D).

It is hard to imagine how BECL1 is implicated in a (here hypothetical) nuclear lamina degradation event driven by LC3-lamin B1 direct interaction (Dou et al., 2015). BECL1 is an upstream to LC3 component and is a subunit of the PI3K complex catalyzing the local PI3P generation. The above should cause recruitment of the downstream autophagic machinery. Other subunits of the same complex or downstream effectors should be identified at the same spots to support authors' claims.

Our proposal that BECN1 is contributing to nucleophagy is supported by its co-localization with LC3 and Lysotracker stained vesicles (new figure 2A), as well as with TOP2 (Figure 3A-C). We appreciate the interesting idea of the reviewer; we certainly did not analyze the presence of BECN1 interacting partners. We agree, further studies analyzing their localization could complement our current findings. Supporting our work, others have observed UVRAG in the nucleus, specifically in centromeric regions, and it also has a role in DNA repair through its interaction with DNA-PK (Dev Cell. 2012 May 15; 22(5): 1001–1016. doi: 10.1016/j.devcel.2011.12.027). Given the anti-tumorigenic role of several autophagic molecules, it is tempting to speculate that several of them could have triple roles in the nucleus: directly interacting with DNA repair machinery, eliminating unrepairable DNA damaged and preventing excessive protein accumulation in the nucleus. Further experiments are necessary to probe this hypothesis, but are beyond the scope of the present manuscript.

U, 2h D and 5h R images of whole cells are necessary. The authors should also provide representative images of cells under different conditions i.e. control, etoposide-treatment and during DNA repair. Along similar lines, untreated control cells are not included in Fig. 2E and F. These images are needed for a better comparison between normal and DNA damage-inducing conditions.

The reviewer is right. In the revised Figure 2 we included representative images of control cell, Etoposide-treatment and during DNA repair cells. Images of whole cells are now shown in supplementary Figure 2S.

The authors state that autophagy is required for nuclear buds and micronuclei formation. However, the data shown in Fig. 2G and H are hardly convincing given that the statistical difference between cells treated with control and Atg7 siRNA is not strong (for example, *p˂0.5, 5h after etoposide removal). To provide further support to this notion, they should use cells from autophagy defective mutants and examine the appearance of nuclear abnormalities across different conditions compared to control cells.

We agree with the reviewer and followed his/her suggestion. We established collaboration with Dr. Sandra Cabrera, who kindly shared with us Atg4b-/- mice from which we isolated MEFs to compare side by side with WT MEFs the appearance of nuclear abnormalities. We confirmed a statistical significant reduction in the formation of nuclear buds in both conditions: silencing the expression of Atg7 by siRNA and in Atg4b-/- MEFs, suggesting that the autophagic machinery contributes to buds formation (new Figure 2F-G). Interestingly, we observed a different result analyzing micronuclei. While we found no statistical significant difference in the percentage of cells with micronuclei silencing the expression of Atg7 by siRNA, we found a statistical significant increment of cells with micronuclei in Atg4b-/- MEFs (new Figure 2F-G). This apparently discrepant result suggests that nuclear buds and micronuclei have a different mechanistic origin. A difference in the biogenesis of buds and micronuclei has been previously suggested studying cells cultured under strong stress conditions that induce DNA amplification, as well as in cells under folic acid deficiency. While interstitial DNA without telomere was more prevalent in buds than in micronuclei, telomeric DNA was more frequently observed in micronuclei (Fennech et al. 2011, Mutagenesis 26:125-132).

Lines 223-228: The role of autophagic machinery in the formation of nuclear buds is not supported and furthermore hard to conceptualize. How the components of autophagy are implicated during the nuclear buds and micronuclei formation? Colocalization of autophagic proteins might mean that autophagy is engaged at some point after or during the above formation. The causal, mechanistic and temporal aspects of the above budding and nucleophagic events need experimental support and/or more accurate interpretation.

We agree with the reviewer, and now we expressed our interpretation with more caution. The role of autophagic machinery in the formation of nuclear buds is supported by the following findings: a) the localization of LC3 and BECN1 in nuclear buds; b) the inhibition of Atg7 expression by specific siRNAs reduced the number of cells with buds and c) Atg4b-/- MEFs had reduced number of cells with buds (new Figure 2G). How the components of autophagic machinery are implicated in nuclear buds formation is an interesting question and deserves further investigation, beyond the scope of the present manuscript.

The authors claim that nucleophagy eliminates topoisomerase cleavage complex because TOP2A and TOP2B appear to more extensively co-localize with GFP-LC3 and BECN1 after etoposide-induced DSBs. However, the quantification presented in Fig. 3D-F to support this statement does not, in general, show a statistically significant difference in fibroblasts across different conditions (normal, etoposide treatment, etoposide removal).

Autophagic elimination of TOP2 protein is supported by the following findings: 1) both BECN1 and LC3 were detected in micronuclei in acidic vesicles (labeled with Lysotracker), which is indicative of the autolysosomal nature of the cytoplasmic compartment containing TOP2 (Figure 2A); 2) TOP2B was found by electron microscopy in some cells exiting the nucleus surrounded by LC3 (Figure 3G); 3) TOP2B accumulated in cells lacking ATG4, as expected if it is degraded by autophagy (Figure 3H).

Why would BECLIN colocalise with TOP2B in Figure 3g, given that beclin is involved in the initiation process?

We think that BECN1 is involved in additional functions to the initiation process of bud formation. For example, it has been shown by others that BECN interacts with TOP2 (Dev Cell. 2012 May 15; 22(5): 1001–1016. doi: 10.1016/j.devcel.2011.12.027). It could be working as an autophagic receptor targeting TOP2cc to buds and micronuclei. We are aware that further studies are necessary to test this hypothesis, but they are beyond the scope of this manuscript.

Fig. 4A and B: There is no enrichment of GFP-LC3 in "the nuclear alterations containing Fibrillarin" as stated in lines 341-343 comparing to the rest of the cellular GFP fluorescence.

It is true that there is not a local enrichment of GFP-LC3 as those normally reported as LC3 puncta in response to autophagy induction by starvation, for example. Nevertheless we are confident of the specificity of the observation, as not every nuclear alteration was found having GFP-LC3. We detected GFP-LC3 in 72% (mean ± 3.61 SD) of the nuclear alterations containing Fibrillarin in untreated cells, in 65.7% (mean ± 1.97 SD) of cells with 2h of DNA damage and in 90.33% (mean ±6.36 SD) after 5 h of DNA repair (in 5 independent experiments).

Moreover, there is no statistical significance in Fig. 4C and D measurements limiting the safety of authors' conclusions in lines 341-346.

We agree with reviewer´s observation. We repeated these experiments two more times and did not find a statistical significant difference in the percentage of cells with nuclear lesions containing Fibrillarin and GFP-LC3 after DNA damage nor after DNA repair. These results suggest that nucleolar DDR is a particular response, independent of DDR elsewhere in the genome, as has been suggested (reviewed in Nucleic Acids Research, 2020, Vol. 48, No. 17 9449–9461; doi: 10.1093/nar/gkaa713). An alternative is that the release of nucleolar components is not enhanced by Etoposide at the dose and time used in this work.

Lines 368-370: As discussed by the authors and reported in previous publication (Xu et al., 2017), "BECN1 interacts directly with TOP2B, which leads to the activation of DNA repair proteins, and the formation of NR and DNA-PK repair complexes", independent of its role in autophagy. Currently, there are no rigorous findings supporting the contribution of BECN1 (as a functional constituent of the core autophagic machinery) to nuclear damaged material extrusion (lines 382-384).

We agree with the reviewer in that we did not perform an assay to demonstrate that BECN1 is contributing to TOP2 nuclear extrusion as a functional constituent of the core autophagic machinery. Nevertheless, the following data support the proposal of an autophagic elimination of TOP2cc: 1) TOP2B was detected in micronuclei containing BECN1 (Figure 3B); 2) BECN1 was found in micronuclei containing LC3 and in an acidic vesicle (labeled with Lysotracker), indicative of the autolysosomal nature of the compartment (Figure 2A); 3) TOP2 was found in some cells exiting the nucleus surrounded by LC3 (Figure 3G); d) TOP2 accumulated in cells lacking ATG4, suggesting its autophagic degradation (Figure 3H).

Lines 435-441 and Fig. 5: The current findings do not support the proposed model. It is hard to support and conceptualize the statement "proteasome and nucelophagy function in a dynamic way inside the nucleus".

The reviewer is right. We made a mistake integrating an interpretation within the summary of the actual findings of this work. We correct the text in the current version.

In Fig. 5, LC3 appears to decorate inner nuclear membrane and probably to interact with some of the other proteins depicted, which is misleading.

We agree with the reviewer. We removed the scheme in the current manuscript.

Beclin-1 appears to interact with Fibrillarin (Nucleolus).

This is correct. We observed by immunofluorescence a co-localization of BECN1 with Fibrillarin (new Figure E), and demonstrated by co-immunoprecipitation that they are constituents of a complex (new Figure F).

Most of the differences in Sup. Fig. 3 lack statistical significance compromising the authors' claims.

We agree with the reviewer. To perform a separated statistical analysis of the percentage of cells with nuclear buds or micrnonuclei did not provide further information. We eliminated this analysis in the current version.

Many conclusions are drawn by colocalisation-immunofluorescence analysis. Co-immunoprecipitation experiments should also be performed to show that TOP2B and fibrillarin interact with LC3/autophagic machinery.

Thank you for your suggestion. We performed immunoprecipitation analysis and confirmed an interaction of Fibrillarin with BECN1, this result is now presented in Figure 4F. We found no co-immunoprecipitation of LC3 with either Fibrillarin or TOP2A, nor of TOP2B with BECN1.

Additionally, colocalisation analysis should be performed using tools such as Pearson's correlation and is an initial indication of nucleophagy. In the case of fibrillarin, immunofluorescence images do not indicate colocalisation, they need to be repeated.

The transport of Fibrillarin out of the nucleus by micronuclei formation and its autophagic degradation implies that both proteins are contained in the same vesicular compartment, it does not necessarily requires a direct interaction of Fibrillarin with LC3. Therefore, a co-localization detected by Pearson´s analysis is not a necessary confirmation of the nucelophagic degradation of Fibrillarin. Actually, Fibrillarin does not seem to interact with LC3, since we could not detect both proteins by co-immunoprecipitation. Nevertheless, we observed a nucleolar localization of BECN1 overlapping with Fibrillarin (new Figure 4E), and we confirm by co-immunoprecipitation the presence of both BECN1 and Fibrillarin in a complex (new Figure 4F). Following reviewer´s advice, we repeated two more times the analysis of Fibrillarin immunolocalization. We corroborated its localization in micronuclei and nuclear buds in 5.86% (mean ± 5.03 SD) of untreated cells, indicating a basal level of nucleolar material exclusion from the nucleus. Interestingly, the percentage of cells with Fibrillarin in nuclear alterations did not increased with statistical significance with Etoposide treatment. At 2 h of DNA damage we observed only a slight increase to 6.8% (mean ± 4.03 SD) of cells having nuclear buds and micronuclei with Fibrillarin, while the number of cells with nuclear lesions increased to 30.6% (mean ± 4.2 SD). Similarly, the proportion of cells having Fibrillarin in nuclear lesions after 5 h of DNA repair increased only to 7.66 % (mean ±6.08 SD), while the total number of cells having nuclear buds and micronuclei increased to 38.42% (mean ± 9.3SD). These results suggest that nucleolar components are constantly sent out of the nucleus as a homeostatic process, and not significantly in response to Etoposide-induced DSB.

Measurement of LC3/fibrillarin positive puncta should be performed, under basal conditions, genotoxic, and nucleolar stress under control and Atg7 knockdown conditions.

Since we observed no statistical significant change in the number of micronuclei with Fibrillarin under Etoposide-induced DSB nor DNA repair, we did not perform the suggested experiment.

Moreover, if nuclear proteins described are substrates of autophagy, then their levels would decrease upon autophagic induction i.e. starvation or in this case DNA damage and nucleolar stress. Thus, western blot analysis of relative protein levels can be performed.

Thank you for the suggestion. Since only 5% of the cells have micronuclei with Fibrillarin, and this proportion did not increased significantly in response to DNA damage, it is unlikely to detect a difference in the amount of Fibrillarin in response to autophagy manipulation performing a population analysis (as it is in a Western blot). Nevertheless, we compared Fibrillarin abundance by Western blot in WT MEFs vs. Atg4-/- MEFs untreated (U), treated for 2 h with Etoposide (D) and after 5 h of DNA repair (5) shown in the top panel of the follow figure. As expected, we found no statistical significant difference determined by 2way-ANOVA followed by Sidak´s multiple comparisons test (n=3). Ajusted P values are shown for each comparison (left graph).

On the other hand, since the percentage of cells with TOP2B in micronuclei and nuclear buds increased in response to DNA damage and during DNA repair, it was possible to detect a statistical significant accumulation of TOP2B in cells lacking ATG4 after 5h of DNA repair (bottom panel and right graph in the figure above). This observation is now included in new Figure 3H. Supporting our finding, TOP2A is reduced in cancerous cells grown under glucose deprivation (Alchanati, I., et al. 2009. PLoS One. 4:e8104).

Endogenous LC3 nuclear buds should also be detected to verify nucleophagy as GFP-LC3 has been shown to aggregate, causing artifacts under certain conditions.

We agree with the reviewer. We detected endogenous LC3 by immunofluorescence. This result is now included in Figure 2D.

Minor comments

In the Discussion section, the paragraph focused on the role of the ubiquitin-proteasome system is not substantiated by the data presented in the manuscript. Along similar lines, formation of aggresomes following etoposide treatment and their subsequent removal has not been monitored.

We apologized for the confusion, we corrected the text to now clearly distinguish which are our findings and which are published data that we just attempt to relate.

Western blots of better quality should be provided with assigned markers of protein size.

The Western blots shown have markers of protein size.

There are several language errors in the text that need to be corrected. Several sentences are too long and confusing or must be re-phrased. For example, see the lines: 123-125, 209-210,212, 218,221-222.

We apologize for our language errors. We corrected all errors indicated and asked colleges proficient in English to review our text.

Fig. 1B. Place "μm" into parenthesis.

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Sup. Fig. 1B: Replace "gH2AX" with "γH2AX".

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Fig. 1D: Separate DAPI and γH2AX channel images would be informative.

We now show also separated channels.

Fig. 2E: Enlarged separate DAPI, GFP-LC3 and lamin A/C channel images would be informative.

We now show also separated channels.

Line 218: Replace "bus" with "buds".

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Fig. 2B, 2E, 2F, 3A and probably Sup. Fig. 2B represent MEFs treated for 2h with etoposide. The pattern of GFP-LC3 in 2B looks extensively nuclear and almost absent from cytoplasm.

We confirmed our finding detecting endogenous LC3.

In addition, Fig. 2B and 3B represent MEFs treated for 2h with Etoposide. The pattern of endogenous BECN1 in Fig. 2B looks extensively nuclear and almost absent from cytoplasm. In Fig. 3B the pattern is notably different.

BECN1 pattern of distribution is rather similar, predominantly in the nucleolus. We demonstrate it further by detecting BECN1 overlapping localization with Fibrillarin (new Figure 4E) and co-immunoprecipitation (new Figure 4F).

Sup. Fig. 2C: Index box is not properly aligned.

Thank you. We reviewed the alignment of each index box and reorganized the figure in the revised manuscript to add the whole blots of the new experiments we performed to analyze MEFs Atg4-/-.

Lines 154, 343 and 837: Replace "DBS" with "DSB".

Thank you, we corrected these typos.

Fig. 4 panels are not clearly cited at the text.

We apologize, we reviewed that they are clearly cited now.

Line 220: siRNA

Thank you, we corrected the text.

Lines 373-374: References "Lenain et al., 2015" and "Li et al., 2019" are missing.

Thank you for noticing it, we added the missing references. We use EndNote X9, we did not expect it to fail.

Lines 400-401 and 407: Probably the second "Latonen, 2011" reference needs "et al".

It is correct. We now cite this paper properly.

Line 427: Do authors refer to Fig. 1E rather than Fig. 2B?

Yes, we are sorry for this mistake. Thank you for pointing it out.

Line 434: Correct "clearance" spelling.

Thank you, we corrected it.

Reviewer #3 (Significance (Required)):

The authors suggest that nucleophagy contributes to the elimination of chromosomal fragments or nucleolar bodies exiting the nucleus under DNA damage -inducing conditions. Specifically, they propose a key role for nucleophagy in maintaining genome stability by eliminating Type II DNA Topoisomerase cleavage complex (TOP2cc) and nucleolar components such as fibrillarin.

However, neither TOP2 nor Fibrillarin have been shown to be actual autophagic substrates. Also, the link between genomic stability, micronuclei formation and autophagy has been previously reported (Zhao et al., PMID: 33752561).

We found nuclear buds and micronuclei with markers of different stages of the autophagic pathway, suggesting an active role of autophagy proteins in buds formation, and micronuclei removal. We detected TOP2 and Fibrillarin in micronuclei and propose their elimination by nucleophagy by the following findings: 1) both BECN1 and LC3 were detected in micronuclei in acidic vesicles (labeled with Lysotracker), which is indicative of autolysosomes (Figure 2A); 2) TOP2B was found by electron microscopy in some cells exiting the nucleus surrounded by LC3 (Figure 3G); 3) TOP2B accumulated in cells lacking ATG4, as expected if it is degraded by autophagy (Figure 3H); 4) BECN1 has a dynamic cytoplasmic-nucelar traffic in response to DNA damage; 5) BECN1co-localized with Fibrillaron in nucleolus and both proteins were co-immunoprecupitated.

The link between genomic stability, micronuclei formation and autophagy has been previously reported only in cancerous cells. Considering that physiological DNA damage occurs constantly in the cell, basal nucleophagy is potentially fundamental to maintain cells healthy.

Author Response

Reviewer #1 (Public Review):

This paper addresses an important question: whether the conduction velocity in white matter tracts is related to individual differences in memory performance. The authors use novel MRI techniques to estimate the "g-ratio" in vivo in humans - the ratio of the inner axon relative to the inner axon plus its outer myelin sheath. They find that autobiographical recall is positively related to the g-ratio in a specific white matter tract (the parahippocampal cingulum bundle) in a population of 217 healthy adults. This main finding is extended by showing that better memory is associated with larger inner axon diameters and lower neurite dispersion, which suggests more coherently organised neurites. The authors also argue that their results show that the magnetic resonance (MR) g-ratio can reveal novel insights into individual differences in cognition and how the human brain processes information.

The study is exploratory in nature and the analyses were not pre-registered. The technique has not been used before to associate cognitive performance with MR estimates of conduction velocity in candidate white matter tracts. It is therefore unknown how strong any associations are likely to be and what sort of sample size might be needed to observe them. Nevertheless, if the technique proves to be reliable, then it certainly offers a valuable new tool to understand individual differences in cognitive abilities. However, brain structure to behavior associations are notoriously variable across studies and have been argued to require very large sample sizes to obtain reproducible results.

We respectfully disagree that the study was exploratory. We had distinct aims and hypotheses from the outset. Our prime interest is in autobiographical memory, the hippocampus and its connectivity. This motivated our focus on three specific white matter tracts. We also planned from the time of study design to examine the MR g-ratio, and even contributed to refining the pre-processing pipeline for this approach, as reported in a previous paper (Clark et al., 2021, Frontiers in Neuroscience). Moreover, in the current manuscript we outlined well thought through possible outcomes and declared specific predictions.

Regarding pre-registration, due to the scope of this work, the experiment was planned eight years ago, and data collection commenced seven years ago. At that time, formal pre-registration was not common practice. However, it has been a long-standing feature of our Centre that proposed studies and their analysis plans undergo rigorous internal peer review, including presentation to the whole Centre, before data acquisition can commence. The proposal for the research under consideration here was presented on 26th September, 2014.

As noted in our response to the Editors’ Public Evaluation Summary above, someone has to be the first to report a novel result, and we believe that the depth and transparency of our approach permits confidence in the findings. Not least, and to reprise, because we employed the most widely-used and best-validated method of testing autobiographical memory recall that is currently available – Levine’s Autobiographical Interview. Our primary analyses were performed using the behavioural outcome measure from this test, the results of which were directly compared to those from a closely-matched control measure to test whether significantly larger effects were observed for our variable of interest. The potential for false positives was further reduced by extracting microstructure data from hypothesised tracts of interest (instead of performing whole brain voxel-wise analyses), with statistical correction performed on all structure-behaviour analyses. Moreover, we performed partial correlations with age, gender, scanner and number of voxels in a region of interest (ROI) as covariates. Complementary investigations were also conducted using other commonly-reported measures, providing supporting evidence. We report all analyses (and provide all the source data), including those finding no relationships. The consistent results throughout were associations between autobiographical memory recall ability and the microstructure of the parahippocampal cingulum bundle only. Moreover, thanks to the excellent suggestions of the Reviewers, the revised version reports additional analyses that allow us to further corroborate and interpret our findings.

Our sample of 217 participants allowed for sufficient power to identify medium effect sizes when conducting correlation analyses at alpha levels of 0.01 and when comparing correlations at alpha levels of 0.05 (Cohen, 1992, Psychological Bulletin). While it has recently been suggested that thousands of participants are required in order to investigate brain structure-behaviour associations (Marek et al., 2022, Nature), other, more sophisticated, analyses suggest that samples of ~200 participants can be sufficient, in line with our estimates (Cecchetti and Handjaras, https://psyarxiv.com/c8xwe; DeYoung et al., https://psyarxiv.com/sfnmk). Given that our study was principled, well-controlled, analysed appropriately and produced very specific and consistent findings, we are confident that the findings are robust.

The authors decided to analyse performance on a single task - the Autobiographical Memory Interview - and identified three candidate white matter tracts that connect the hippocampal region with other brain regions. While it is clear why these three tracts were chosen, it is less obvious why the authors chose to investigate associations with the Autobiographical Memory Interview and not other memory tests that were part of the battery of tests administered to the participants. It is reasonable to assume that something as general as the conduction velocity of a white matter tract would have an effect on memory ability across a range of tasks, so to single out one seems an unnecessarily narrow focus.

Our main interest over many years, and hence the focus of this study, is autobiographical memory recall because it directly relates to how people function in real life. As noted above, autobiographical experiences occur in dynamic, multisensory, multidimensional, non-linear, ever-changing contexts; they involve actively engaging with the environment and other people; they are embodied; they span milliseconds to decades. Many of these features cannot be captured by laboratory-based episodic memory tests. This issue is increasingly being discussed (for example, see recent reviews by Nastase et al., 2020, NeuroImage; Mobbs et al., 2021, Neuron; Miller et al., 2022, Current Biology). It is further laid bare in McDermott et al.’s (2009, Neuropsychologia) meta-analysis of functional MRI studies which showed that laboratory-based and autobiographical memory retrieval tasks differ substantially in terms of their neural substrates. Consequently, we were not surprised to find that when we analysed laboratory-based memory test performance, there were no correlations with the MR g-ratio. Recall of vivid, detailed, multimodal, autobiographical memories may rely on inter-regional connectivity to a greater degree than simpler, more constrained laboratory-based memory tests. Therefore, as well as speaking to conduction velocity, these findings also contribute to wider discussions about real-world compared to laboratory-based memory tests. We thank the Reviewer for making the excellent suggestion to include these additional data, analyses and discussion points.

The results of the study are interesting and highlight a key role of the parahippocampal cingulum bundle in autobiographical memory recall. The results are corrected for multiple comparisons across the three fiber tracts of interest and the recall of "external details" provides a nice control compared to the "internal details" which are the measure of interest. The main findings are extended to show that it is likely to be an increase in axon diameter and an increase in neurite coherency that characterize those individuals with better autobiographical recall. Despite these positives, it remains unclear whether memory recall, in general, is better in people with higher g-ratios in this tract (as implied in the Abstract), or if this effect is specific to scores on the Autobiographical Memory Interview.

Our interest is in autobiographical memory, and so we employed the most widely-used and best-validated method of testing autobiographical memory recall that is currently available – Levine’s Autobiographical Interview. Not only does this test include a control measure, external details (as noted by the Reviewer), but we had independent raters score the autobiographical memory descriptions, and found that the inter-class correlation coefficients were very high (see Materials and Methods). Despite using this current, gold standard approach, at the request of the Reviewer we have now analysed data from eight additional laboratory-based memory tests. These are standard memory tests that are often used in neuropsychological studies: testing recall - the immediate and delayed recall of the Logical Memory subtest of the Wechsler Memory Scale IV, the immediate and delayed recall of the Rey Auditory Verbal Learning Test, the delayed recall of the Rey–Osterrieth Complex Figure; testing recognition memory - the Warrington Recognition Memory Tests for Words and Faces; testing semantic memory - the “Dead or Alive Test”. While these tests can assess some aspects of memory recall, they cannot be regarded simply as proxies for autobiographical memory recall, for the reasons we outlined in our response to the previous point. They do not capture key aspects of autobiographical memories. It is therefore all the more interesting that we found no associations between these laboratory-based memory tasks and the MR g-ratio of the parahippocampal cingulum bundle, in contrast to the relationship identified with autobiographical memory recall ability. Recall of vivid, detailed, multimodal, autobiographical memories may rely on inter-regional connectivity to a greater degree than simpler, more constrained laboratory-based memory tests. Therefore, as well as speaking to conduction velocity, these findings also contribute to wider discussions about real-world compared to laboratory-based memory tests. We thank the Reviewer once again for making the excellent suggestion to include these additional data, analyses and discussion points.

Reviewer #2 (Public Review):

In this study, Clark and colleagues tackle a very intriguing question: how differences in autobiographical recall abilities reflect in the human brain structure and function? To answer this question, they interviewed a large cohort of subjects and proceeded to acquire MRI data, specifically diffusion-weighted imaging and magnetization transfer data, to estimate the g-ratio, a measure of myelination deeply linked to conduction velocity. Looking at three specific white matter pathways of interest, all interconnecting the hippocampus with other brain structures, they studied the relationship between the g-ratio and the autobiographical recall abilities, together with many more measures from MRI. They found a significant positive association between the g-ratio of the parahippocampal cingulum bundle and the number of inner details from the interviews. These results can provide new potential directions to further study the underlying neural features beyond memory.

I think that this is a very interesting article, it is well written, the methods are extensively explained, and the appendix provides further details for more expert readers. The authors put an effort into providing a comprehensive context in the introduction and in the discussion, and as a result, the paper seems overall quite suitable for both general and specialistic readerships.

Thank you.

The main issue I can currently see in the paper is that the mentioned relationship between g-ratio and recall abilities is then used to infer that better recall abilities are associated with higher conduction velocity and larger axons. The authors' line of reasoning is that given the hypothesized association, the increase in the g-ratio implies increases in myelin and axonal diameter. Despite this scenario being indeed possible given the current result, an increased g-ratio may also not indicate higher conduction velocity. In fact, the first potential inference would be that, without having any information on the axon size, the quantify of myelin can indeed be lower and as result, the conduction velocity would decrease. I understand that the authors expected higher conduction velocity associated with better autobiographical memory recall, but it is hard to see any experimental outcome that could have disproved this hypothesis: from the possible scenarios depicted in the introduction, any change in the g-ratio (and even not any change at all) could indicate higher conduction velocity. What would be then needed to corroborate one of these scenarios is some independent or complementary measure, which unfortunately is missing.

The mentioned issue does not mean that the paper loses relevance - I think that it should focus on the very practical result, a change in myelination and microstructure, and discuss what are the potential implications, including the one that currently dominates the discussion section.

Thank you for these comments and the opportunity to provide further clarification.

First, we have now provided additional background information regarding the relationship between the MR g-ratio and conduction velocity. We explicitly note that while finding a significant relationship between the MR g-ratio and autobiographical memory recall suggests the existence of an association between autobiographical memory recall and parahippocampal cingulum bundle conduction velocity, it cannot speak to the direction of this association.

Second, we have further noted that interpretation of the parahippocampal cingulum bundle MR g-ratio in relation to the underlying microstructure requires knowledge, or an assumption, about whether the associated change in conduction velocity is faster or slower. Given that faster conduction velocity is thought to promote better cognition (e.g. Brancucci, 2012; Dicke and Roth, 2016; Miller, 1994; Reed and Jensen, 1992), we interpreted our MR g-ratio findings under the assumption of faster conduction velocity, and now explicitly note in several places in the revised manuscript that this is an assumption.

Third, we thank the Reviewer for the excellent suggestion that a complementary measure could help to further inform the findings. Consequently, we now also include additional analyses examining the relationship between the extent of myelination and autobiographical memory recall ability. This is possible using the magnetisation transfer saturation maps, which are optimised to assess myelination. Given our assumption of faster conduction velocity when interpreting our positive MR g-ratio correlations, then no relationship between parahippocampal cingulum bundle magnetisation transfer saturation and autobiographical memory recall would be expected. On the other hand, if conduction velocity is actually decreasing, then a negative correlation between magnetisation transfer saturation values and autobiographical memory recall ability would be observed. In fact, we found no relationship between parahippocampal cingulum bundle magnetisation transfer saturation and autobiographical memory recall. This suggests that myelin was not associated with autobiographical memory recall ability, supporting our assumption that relationships with the MR g-ratio were indicative of faster rather than slower, conduction velocity.

We have now added these new data, analyses and discussion points to the revised manuscript.

It would also be helpful to include some paragraphs on both interpretation and methodological issues when it comes to MRI-based microstructural imaging, which at the moment is lacking. This would provide a better picture of the results for a more general readership.

We agree, and additional consideration of interpretational and methodological limitations have now been included in the manuscript.

As one of the first works using an MRI-based microstructural measure of myelin, the g-ratio, to study cognition in a large cohort of subjects, I think this work will be a needed and significant step towards merging the neuroscience and MRI physics community - the methodology presented here is robust and could be used in many other applications.

Thank you.

Reviewer #3 (Public Review):

The manuscript adds useful information about how structural properties of the brain are related to individual differences in autobiographical memory. A novel metric is used to assess features of white matter in tracts that are important for information exchange between the hippocampus and other brain regions. In one of these, the parahippocampal bundle, a relationship between the MR g-ratio and autobiographical memory recall is identified. This represents new and interesting information. The authors interpret the results in line with the theory that speed of signal transmission is important for cognitive function.

Thank you for this positive summary.

Author Response

Reviewer #1 (Public Review):

Rasicci et al. have developed a FRET biosensor that is designed to light up when cardiac myosin folds. This structure is extremely important to understand, and its link to the super-relaxed (SRX) state has not been fully shown. Their study provides a comprehensive review of the literature and provides compelling data that the 15 heptad+leucine zipper+GFP construct does function well and that the DCM mutant E525K has a similar IVM velocity despite a reduced ATPase compared with HMM. They rely on the ionic strength-dependent changes in the rate of MantATP release to argue that the E525K mutation stabilizes the 'interacting heads motif' (IHM) state, which makes logical sense.

Strengths:

Well written and comprehensive.

Utilizes the appropriate fluorescence-based sensor for measuring the folding of the myosin structure. Provides a detailed range of techniques to support the premise of the study

Weaknesses:

Over-interpretation of the outcomes from this study means that the IHM and SRX are the same. Similar studies, e.g. Anderson 2018 and Chu 2021 support the opposite view that IHM and SRX are not necessarily the same, Anderson (and Rohde 2018) point out that S1 has some element of a reduced ATPase, this clearly cannot be due to folding of the molecule. Also, mavacamten was used in these studies to show that even S1 is inhibited suggesting that SRX and IHM are not connected. This is not to say that with enough supporting evidence that these observations cannot be over-ridden, it is just not clear that there is enough in this study to support this conclusion.

We have revised our discussion to emphasize that our results support a model in which the SRX state is enhanced by formation of the IHM, but given the S1 and 2HP data the IHM may not be required for populating the SRX biochemical state (see page 8).

I felt that the authors passed over the recent Chu 2021 paper too quickly, the Thomas group used a FRET sensor as well and provides a direct comparison as a technique, but with opposite conclusions. They also have supporting data in Rohde 2018 that their constructs were less ionic strength sensitive. It would be useful to understand what the authors think about this.

We have discussed the Rohde and Chu papers in more detail in the discussion (see page 8). In the Rhode paper they used proteolytically prepared HMM and S1. Rohde found 20% SRX at all KCl concentrations in S1, while HMM shifted from 50% to 20% SRX in low and high salt conditions, respectively. Our results are different in terms of the absolute fraction of the SRX state but the trend is similar in terms of S1 being salt-insensitive and HMM being salt-sensitive. The difference could be proteolytic HMM, which is a longer construct, and proteolytic S1, which is prone to internal cleavage that can impact ATPase activity. Another difference could be the mixed isoform of mantATP used in previous studies and the single isoform of mantATP used on our study (see page 5)

Reviewer #2 (Public Review):

The paper by Rasicci et al. examines the impact of the DCM mutation E525K in beta-cardiac myosin on its function and regulation by autoinhibition. The role of the auto-inhibited state of beta-cardiac myosin in fine-tuning cardiac contractility is an active and exciting area of current research related to muscle biology and cardiomyopathies. Several studies in the past have linked the destabilization of the autoinhibited, super-relaxed (SRX) state of myosin to the pathogenesis of hypertrophic cardiomyopathy. This timely study provides one of the first few examples where the hypocontractile phenotype of a DCM mutation has been linked to the stabilization of the SRX state.

One of the strengths here is the utilization of a wide variety of both pre-existing and novel biochemical and biophysical assays for the study. The authors have characterized a new two-headed long-tailed myosin construct containing 15-heptad repeats of the proximal S2 (15HPZ), which they show allows myosin to form the SRX state in vitro using single ATP turnover assays. The authors go on to compare the E525K and WT proteins using the 15HPZ myosin construct in terms of their steady-state actin-activated ATPase activity, in-vitro actin-sliding velocity and single ATP turnover measurements. These assays reveal that the predominant effect of this mutation is the stabilization of the SRX state which is maintained even at 150 mM salt concentration where the WT SRX is largely disrupted. This is an important observation because DCM mutations so far have been believed to only affect the force-generating capacity of myosin.

One of the biggest strengths of this study is the attempt to develop a FRET-based approach to directly ask if the biochemical SRX state here correlates well with the structural IHM state, which is an important unresolved question in the field. The authors have designed a FRET pair (C-terminal GFP and Cy3ATP bound to the active site) that is sensitive to the relative position of the heads and the tail, allowing them to distinguish between the low-FRET closed IHM conformation and the no-FRET open conformation. Remarkably, the authors show that the salt dependence of the FRET efficiency values closely follows their results from the salt dependence of the percent SRX for both WT and E525K proteins. The authors then attempt to substantiate their FRET results by a direct visual analysis of the conformational states populated by both WT and E525K proteins at low salt using negative staining EM analysis. The authors have optimized conditions to allow the deposition of the IHM state on grids without adding the small molecule mavacamten, which was found to be necessary in an earlier study to visualize the closed state using EM. The authors conclude that the SRX state correlates well with the IHM state and that the E525K mutation indeed stabilizes the folded-back conformation of myosin.

This study significantly strengthens the previously illustrated correlation between the SRX and IHM states and provides methodological advances (especially visualization of the IHM state by negative EM in the absence of cross-linking agents) that will be very useful to the field going forward. The observation that a DCM mutation can lead to stabilization of the folded back state is a novel insight that should spark interest in the field to test how broadly this applies to other DCM mutations. The conclusions of the paper are mostly supported by the data; however, some clarifications and qualifications are needed.

Weaknesses:

The extremely low enzymatic activity of the M2β 15HPZ myosins as compared to the WT S1 control (which is a historical control not assayed in parallel with the 15HPZ proteins), is concerning for the low protein quality of the 15HPZ myosins. The authors attribute the low kcat to the high proportion of SRX population in their ensembles. However, the DRX rates reported for the WT and E525K 15HPZ proteins in the single ATP turnover assay are ~3-4 fold lower than those of their S1 counterparts. These rates reflect basal turnover of ATP in the open state and thus should not be affected by the presence of the S2 tail, which leads to concerns about the 15HPZ protein activity. In addition, the very high percentage of stuck filaments in the in vitro motility assay for the 15HPZ constructs (despite the use of dark actin) is also concerning for significant amounts of enzymatically inactive protein.

We thank the reviewer for pointing out the differences in the S1 and HMM DRX rates. We performed additional single turnover measurements with S1, adding two sets of measurements from one additional preparation (N=3), and we demonstrate that there is a significant increase in the DRX rates of WT S1 compared to WT HMM (see pages 4-5, Table 3, Figure 3- figure supplement 3). A faster rate in S1 was also reported in Rohde et al. 2018. Indeed, the DRX rates of E525K S1 are significantly higher than WT in S1, which we also now report in the results (see page 5, Figure 3 – figure supplement 3). We addressed the concerns about 15HPZ activity by performing NH4+ ATPase assays to demonstrate that the number of active heads was similar in S1 and 15HPZ HMM (see page 4). It is possible that the higher percentage of stuck filaments in the HMM motility is due to myosin heads in the IHM state on the motility surface, which generate a drag force by non-specifically interacting with actin, but further study is necessary to examine this question.

The authors assert that the E525K mutation represents a new mechanism by which DCM-causing mutations lead to decreased contractility - by stabilizing the sequestered state rather than affecting motor function. However, there is no evaluation of the motor function (actin-activated ATPase activity or in vitro motility) of the E525K S1, which would reveal the effects of the mutation without confounding effects due to the sequestering of heads. Interestingly, in the single ATP turnover assay, the DRX rate of the E525K S1 is >2-fold higher than the WT control, suggesting that the mutation may have effects beyond stabilization of the SRX state. The conclusion that the E525K mutation's effect on myosin function is mediated via stabilization of the SRX state would be strengthened if the effects of the mutation on the motor domain alone were also known.

We thank the reviewer for this suggestion. We performed actin-activated ATPase assays with WT and E525K S1 and found that E525K increases kcat and lowers KATPase, demonstrating enhanced intrinsic motor activity in the mutant S1 construct (see page 4, Figure 2B). This adds an interesting dimension to the manuscript because we report a mutant that enhances the intrinsic motor activity but stabilizes the SRX/IHM (see Discussion page 10). We did not perform in vitro motility, because this assay depends on the surface attachment strategy, and we would like to compare all constructs with the same attachment strategy using a C-terminal GFP tag (mutant and WT S1 and 15HPZ HMM). Therefore, we are making the S1 construct with a C-terminal GFP tag for this purpose, to be examined in a future study.

While the authors show strong qualitative correlations between the SRX and IHM states using single ATP turnover, FRET, and EM experiments, attempts to quantitatively compare the fraction of heads in the IHM state using the various experimental approaches is problematic. For example, the R0 value of the FRET pair used here doesn't allow precise measurement of the distances being probed here to be made, but the distances are reported and compared to predicted distances. The authors report that the R0 for their FRET pair is 63 Å. Surprisingly the authors go on to use the steady-state FRET efficiency values to determine the average D-A distance (Fig 5B) which is 100 Å when all heads are in the IHM configuration and becomes larger than that when heads open. R0 of 63 Å allows a precise distance measurement to be made in the 31.5-94.5 Å range which corresponds to 0.5-1.5 R0. It is therefore technically incorrect to use the steady-state FRET efficiency values to determine the D-A distance here. Besides, there are several unknown factors here like orientation factor (κ2) which further complicate these calculations. Similarly, the quantification of IHM state molecules from the negative stain EM experiments is significantly hampered by the disruptive effect of the grid surface on the structure of the IHM state. The authors find that limiting the contact time with the grid to ~ 5s is necessary to preserve the IHM state.

Despite that, only ~15% WT molecules were seen in the IHM state at low salt (Fig. 6B). In contrast, ~56% E525K molecules were in the IHM state. Both these proteins have similar SRX proportions (Fig. 3C) and similar FRET efficiency values (Fig. 5A) at this salt concentration. This mismatch highlights the problem arising due to not having a measure of the populations from the FRET data. It is not clear if the hugely different proportions of the IHM state in EM experiments are indicative of the relative stability of this state in the two proteins or a random difference in the electrostatic interactions of WT vs mutant with the grid. These experiments do not provide a correct idea of the %IHM in the two proteins. In the absence of any IHM population measurement, it is important to proceed with caution when quantitatively correlating the SRX and IHM.

We thank the reviewer for pointing out that measuring precise distances by FRET can be difficult. We agree that the low FRET efficiency makes precise distance determination even more challenging. However, FRET is quite good at measuring a change in distance given a specific donor-acceptor pair. We feel our FRET biosensor clearly demonstrates FRET efficiencies that are salt-insensitive in E525K but a clear decrease in FRET at higher salt concentrations in WT. In order to compare the trend in the predicted FRET, based on the single turnover measurements, and the actual FRET we thought it was important to plot the two together on the same graph. We understand that this could have been misleading that we were reporting actual distances. We have now plotted the FRET efficiency instead of distance as a function of KCl concentration (Figure 5B), to prevent any confusion with reporting distances. In addition, we have emphasized that the data are plotted to allow for a comparison of the trend in the single turnover and FRET data (see page 6, 10, Figure 5B).

We agree that it is important to proceed with caution when comparing the EM to the FRET and single turnover data. The EM does not give a quantitative estimate of the fraction of IHM molecules, due to the disruptive effect of the grid surface on protein conformation. However, it does provide direct (though qualitative) evidence that the conformation underlying SRX and enhanced FRET is the IHM, and it is consistent with our interpretation that the E525K mutation enhances FRET and SRX by stabilizing the IHM. To consolidate this result, we have performed EM experiments now with a total of 3 preparations of WT and mutant (see page 6-7 and Figure 6D). We find that while there is variability from experiment to experiment, likely because the grid surface is slightly different each time the experiment is performed, in all cases there was a ~4-fold higher fraction of folded molecules in the mutant. Since each WT/mutant experimental pair was studied in parallel, using identically prepared grids, the results provide further evidence that the mutant stabilizes the IHM. However, we agree that a quantitative, direct visual correlation of the SRX and IHM is not possible based on the current EM data.

Finally, the utility of the methods described in the paper to the field would be greatly enhanced if they were described in more detail. As currently written, it would be difficult for others to replicate these experiments.

Thank you for the comment. We have made significant changes in the methods to clarify the details of the experiments (see pages 11-14). In addition, we have added details to the results and figure legends.

Author Response

Reviewer #1 (Public Review):

“This study investigates the dynamics of brain network connectivity during sustained experimental pain in healthy human participants. To this end, capsaicin was applied to the tongues of two cohorts of participants (discovery cohort, N=48; replication cohort, N=74). This procedure resulted in pain for several minutes. During sustained pain, pain avoidance/intensity ratings and fMRI scans were obtained. The analyses (i) compare the pain state with a resting state, (ii) assess the dynamics of brain networks during sustained pain, and (iii) aim to predict pain based on the dynamics of brain networks. To this end, the analyses focus on community structures of time-evolving networks. The results show that sustained pain is associated with the emergence of a brain network including somatomotor, frontoparietal, basal ganglia and thalamic brain areas. The somatomotor area of the tongue is particularly involved in that network while this area is decoupled from other parts of the somatomotor cortex. Moreover, the network configuration changes over time with the frontoparietal network decoupling from the somatomotor network. Frontoparietal-cerebellar connections were predictive of decreases of pain. Together, the findings provide novel and convincing insights into the dynamics of brain network during sustained pain.

Strengths

• The brain mechanisms of sustained pain is a timely and relevant topic with potential clinical implications.

• Assessing the dynamics of sustained pain and relating it to the dynamics of brain networks is a timely and promising approach to further the understanding of the brain mechanisms of pain.

• The study includes discovery and replication cohorts and pursues a cutting-edge analysis strategy.

• The manuscript is very well-written and the results are visualized in an exemplary manner including a graphical outline and summary of the findings.”

We thank the reviewer for the thoughtful summarization and evaluation of our study.

“Weaknesses

• It remains unclear whether the changes of brain networks over time simply reflect the duration of sustained pain or whether they essentially reflect different levels of pain intensity/avoidance.”

We appreciate the editor and reviewer’s comment on this issue. With the current experimental paradigm, it is difficult to dissociate the pain duration from the level of pain because the delivery of oral capsaicin commonly induces initial bursting and then a gradual decrease of pain over time. That is, the pain duration is correlated with the pain intensity in our task.

However, when we examined the time-course of the ratings at each individual level (as shown in Figure S2), the time duration explained 53.7% of the rating variance, R2 = 0.537 ± 0.315 (mean ± standard deviation). In addition, if we constrain the beta coefficient of the time duration to be negative (i.e., ratings should decrease over time), the explained variance decreases to 48.2%, R2 = 0.482 ± 0.457, leaving us enough variance (i.e., greater than 50%) for examining the distinct effects of time duration and ratings on the patterns of functional brain reorganization.

Indeed, the two main analyses included in the manuscript—consensus community detection and predictive modeling—were designed to examine those two aspects of the task, i.e., time duration and pain avoidance ratings, respectively. First, through the consensus community detection analysis, we examined the community structure that changes over time, i.e., across the early, middle, and late periods (as shown in Figure 3). We then developed predictive models of pain avoidance ratings in the second main analysis (as shown in Figure 5).

Though it is still a caveat that we cannot fully dissociate the effects of time duration versus pain ratings, we could interpret the first set of results to be more about time duration, while the second set of results is more about pain ratings.

We now added a description of the implication of predictive modeling for isolating the effects of pain ratings. In addition, a discussion on the caveat of the current experimental design and relevant future direction.

Revisions to the main manuscript:

p. 25: Moreover, developing models to directly predict the pain ratings is helpful to complement the group-level analysis, because the changes in consensus community structure over the early, middle, and late periods only indirectly reflect the different levels of pain.

p. 27: This study also had some limitations. First, with the current experimental paradigm, it is difficult to dissociate the pain duration from the level of pain because the delivery of oral capsaicin commonly induces initial bursting and then a gradual decrease of pain over time. Though we aimed to model the effects of pain duration and pain avoidance ratings with our two primary analyses, i.e., consensus community detection and predictive modeling, we cannot fully dissociate the impact of time duration versus pain ratings.

“• Although the manuscript is very well-written it might benefit from an even clearer and simpler explanation of what the consensus community structure and the underlying module allegiance measure assesses.”

We thank you for the suggestion. Now we added additional (but simple) descriptions of module allegiance and consensus community detection methods.

Revisions to the main manuscript:

pp. 8-9: Here, the consensus community means the group-level representative structures of the distinct community partitions of individuals. To determine the consensus community across different individuals and times, we first obtained the module allegiance (Bassett et al., 2011) from the community assignment of each individual. Module allegiance assesses how much a pair of nodes is likely to be affiliated with the same community label, and is defined as a matrix T whose element Tij is 1 when nodes i and j are assigned to the same community and 0 when assigned to different communities. This conversion of the categorical community assignments to the continuous module allegiance values allows group-level summarization of different community structures of individuals.

p. 14: Here, high module allegiance indicates the voxels of two regions are likely to be in the same community affiliation, and vice versa.

“• The added value of the assessment of the dynamics of brain networks remains unclear. Specifically, it is unclear whether the current analysis of brain networks dynamics allows for a clearer distinction between and prediction of pain and no-pain states than other measures of static or dynamic brain activity or static measures of brain connectivity.”

The main goal (and thus, the added value) of the current study was to provide a “mechanistic” understanding of the brain processes of sustained pain, rather than the “prediction.” Even though we included the results from the predictive modeling, as in Figures 4-6, our focus was more on the interpretation of the model to quantitatively examine the functional changes in the brain, not on the maximization of the prediction performance.

Indeed, maximizing the prediction performance was the main goal of our previous study (Lee et al., 2021), in which we developed a predictive model of sustained pain based on the patterns of dynamic functional connectivity. The model showed better prediction performances compared to the current study, but it was challenging to interpret the model because of the high dimensionality of the model and its features. In addition, functional connectivity itself provides only limited insight into how functional brain networks are structured and reconfigured over time.

In this sense, the multi-layer community detection method has several advantages to achieving our goal. First, the community detection analysis allows us to summarize the complex, high-dimensional whole-brain connectivity patterns into neurobiologically interpretable subsystems. Second, the multi-layer community detection method allows us to study the temporal changes in community structure by connecting the same nodes across different time points.

Now we added a description of the rationale behind the choice of the multi-layer community detection analysis over the conventional functional connectivity methods, and the added value of our study.

Revisions to the main manuscript:

p. 3: In this study, we examined the reconfiguration of whole-brain functional networks underlying the natural fluctuation in sustained pain to provide a mechanistic understanding of the brain responses to sustained pain.

p. 7: In this study, we used this approach to examine the temporal changes of brain network structures during sustained pain, which cannot be done with conventional functional connectivity-based analyses (Lee et al., 2021).

p. 27: However, the previous model provides a limited level of mechanistic understanding because of the high dimensionality of the model and its features. In addition, functional connectivity itself provides only limited insight into how functional brain networks are structured and reconfigured over time.

Reviewer #2 (public Review):

“The Authors J-J Lee et al., investigated cortical and subcortical brain networks and their organization in communities over time during evoked tonic pain. The paper is well-written, and the findings are interesting and relevant for the field. Interestingly, other than confirming well known phenomena (e.g., segregation within the primary somatomotor cortex) the Authors identified an emerging "pain supersystem" during the initial increase of pain, in which subcortical and frontoparietal regions, usually more segregated, showed more interactions with the primary somatomotor cortex. Decrease of pain was instead associated to a reconfiguration of the networks that sees subcortical and frontoparietal regions connected with areas of the cerebellum. The main novelty of the proposed analysis, lies in the resulting high performances of the classifier, that shows how this interesting link between frontoparietal network and subcortical regions with the cerebellum, is predictive of pain decrease. In summary, the main strengths of the present manuscript are: • Inclusion of subcortical regions: most of the recent papers using the Shaefer parcellation in ~200 brain areas1, do not consider subcortical areas, ignoring possible relevant responses and behaviors of those regions. Not only the Authors smartly addressed this issue, but most of their results showed how subcortical regions played a key role in the networks reconfiguration over time during evoked sustained pain.

• Robust classification results: high accuracy obtained on training dataset (internal validation), using a leave-one-out approach, and on the available independent test dataset (external validation) of relatively large sample size (N=74).

• Clarity in the description of aim and sub-aims and exhaustive presentation of the obtained results helped by appropriate illustrations and figures (I suggest less wording in some of them).

• Availability of continuous behavioral outcome (track ball).”

We appreciate the reviewer’s summary and positive evaluations.

“Even though the results are mostly cohesive with previous literature, some of the results need to be discussed in relationship to recently published papers on the same topic as well as justifying some of the non-standard methodological procedures adding appropriate citations (or more detailed comments). The Authors do not touch upon the concept of temporal summation of pain, historically associated with tonic pain, especially when the study is finalized to better understanding brain mechanisms in chronic pain populations (chronic pain patients often exhibit increased temporal summation of pain2). I would suggest starting from the paper recently published by Cheng et al. that also shares most of the methodological pipeline3 to highlight similarities and novelties and deepen the comparison with the associated literature.”

We thank the reviewer and editor for the comment on this important topic. Temporal summation of pain indicates progressively increased sensation of pain during prolonged noxious stimulation (Price, Hu, Dubner, & Gracely, 1977), and has been suggested as a hallmark of chronic pain disorders including fibromyalgia (Cheng et al., 2022; Price et al., 2002). In a recent study by Cheng et al. (2022), the authors induced tonic pain using constantly high cuff pressure and examined whether the participants experienced increased pain in the late period compared to the early period of pain. On the contrary, in our experimental paradigm, the capsaicin liquid initially delivered into the oral cavity is being cleaned out by saliva, and thus overall pain intensity was decreasing over time, not increasing (Figure 1B). Therefore, the temporal summation of pain may occur in a limited period (e.g., the early period of the run), but it is difficult to examine its effect systematically in our study.

However, it is notable that Cheng et al.’s results overlap with our findings. For example, Cheng et al. reported the intra-network segregation within the somatomotor network and the inter-network integration between the somatomotor and other networks during the temporal summation of pressure pain in patients with fibromyalgia, which were similar to the findings we reported in Figure S9 and Figure 4. Although it is unclear whether these results reflect the temporal summation of pain, these network-level features shared across the two studies are likely to be an essential component of the sustained pain processes in the brain.

Now we added a comment on the temporal summation of pain in the main manuscript.

Revisions to the main manuscript (p. 26):

Interestingly, a recent fMRI study on the temporal summation of pain in fibromyalgia patients reported results similar to ours (Cheng et al., 2022), including the intra-network dissociation within the somatomotor network and the inter-network integration between the somatomotor and other networks during pain. Although we cannot directly examine whether the temporal summation of pain gave rise to these network-level changes due to the limitation of our experimental paradigm, these consistent findings between the two studies may suggest that our findings could be generalized to clinical conditions.

We thank the reviewer and editor for the information about this recent publication. Cheng et al. (2022) was not published at the time we wrote the manuscript, and we were surprised that Cheng et al. shares many aspects with our study, e.g., both used multilayer community detection and also reported similar findings, as described above.

However, there were some differences between the two studies as well.

First, the focus of our study was on the brain dynamics during the natural time-course of sustained pain from its initiation to remission in healthy participants, whereas the focus of Cheng et al. was on the temporal summation phenomenon of pain (TSP) and the enhanced TSP in patients with fibromyalgia patients. Because of this difference in the research focuses, our study and Cheng et al. are providing many nonoverlapping results and insights. For example, our study paid particular attention to the coping mechanisms of the brain (e.g., the network-level changes in the subcortical and frontoparietal network regions) and the brain systems that are correlated with the natural decrease of pain (e.g., the cerebellum in Figure 5). In contrast, Cheng et al. (2022) identified the brain connectivity and network features important for the increased TSP in fibromyalgia patients.

Second, our great interest was in identifying and visualizing the fine-grained spatiotemporal patterns of functional brain network changes over the period of sustained pain. To utilize fine-grained brain activity information, we conducted our main analyses at a voxel-level resolution and on the native brain space, such as in Figures 2-3 and Figures S5, S7, and S8. With this fine-grained spatiotemporal mapping, we were able to identify small, but important voxel-level dynamics.

We now cited Cheng et al. (2022) in multiple places and revised the manuscript accordingly.

Revisions to the main manuscript (p. 26):

Interestingly, a recent fMRI study on the temporal summation of pain in fibromyalgia patients reported results similar to ours (Cheng et al., 2022), including the intra-network dissociation within the somatomotor network and the inter-network integration between the somatomotor and other networks during pain. Although we cannot directly examine whether the temporal summation of pain gave rise to these network-level changes due to the limitation of our experimental paradigm, these consistent findings between the two studies may suggest that our findings could be generalized to clinical conditions.

“Here the main significant weaknesses of the study:

• The data analysis is entirely conducted on young healthy subjects. This is not a limitation per se, but the conclusion about offering new insights into understanding mechanisms at the basis of chronic pain is too far from the results. Centralization of pain is very different from summation and habituation, especially if all the subjects in the study consistently rated increased and decreased pain in the same way (it never happens in chronic pain patients). A similar pipeline has been actually applied to chronic pain patients (fibromyalgia and chronic back pain)3,4. Discussing the results of the present paper in relationship to those, could offer a more robust way to connect the Authors' results to networks behavior in pathological brains.”

We are grateful for the opportunity to discuss the clinical implication of our study. First of all, we agree with the reviewer and editor that we cannot make a definitive claim about chronic pain with the current study, and thus, we revised the last sentence of the abstract to tone down our claim.

Revisions to the main manuscript (p. 2, in the abstract):

This study provides new insights into how multiple brain systems dynamically interact to construct and modulate pain experience, advancing our mechanistic understanding of sustained pain.

However, as we noted above in E-4, some of our findings were consistent with the findings from a previous clinical study (Cheng et al., 2022), suggesting the potential to generalize our study to clinical pain conditions. In addition, we previously reported that a predictive model of sustained pain derived from healthy participants performed better at predicting the pain severity of chronic pain patients than the model derived directly from chronic pain patients (Lee et al., 2021), highlighting the advantage of the “component process approach.”

The component process approach aims to develop brain-based biomarkers for basic component processes first, which can then serve as intermediate features for the modeling of multiple clinical conditions (Woo, Chang, Lindquist, & Wager, 2017). This has been one of the core ideas of the Research Domain Criteria (RDoC) (Insel et al., 2010) and the Hierarchical Taxonomy of Psychopathology (HiTOP) (Kotov et al., 2017). If the clinical pain of a patient group is modeled as a whole, it becomes unclear what is being modeled because of the multidimensional and heterogeneous nature of clinical pain (Melzack, 1999) as well as other co-occurring health conditions (e.g., mental health issues, medication use, etc.). The component process approach, in contrast, can specify which components are being modeled and are relatively free from heterogeneity and comorbidity issues by experimentally manipulating the specific component of interest in healthy participants.

The current study was conducted on healthy young adults based on the component process approach. We used oral capsaicin to experimentally induce sustained pain, which unfolds over protracted time periods and has been suggested to reflect some of the essential features of clinical pain (Rainville, Feine, Bushnell, & Duncan, 1992; Stohler & Kowalski, 1999). Therefore, the detailed characterization of the brain processes of sustained pain will be able to serve as an intermediate feature of multiple clinical conditions in future studies.

Now we added the discussion on the clinical generalizability issue in the discussion section.

Revisions to the main manuscript:

pp. 25-26: An interesting future direction would be to examine whether the current results can be generalized to clinical pain. Experimental tonic pain has been known to share similar characteristics with clinical pain (Rainville et al., 1992; Stohler & Kowalski, 1999). In addition, in a recent study, we showed that an fMRI connectivity-based signature for capsaicin-induced orofacial tonic pain can be generalized to chronic back pain (Lee et al., 2021). Therefore, a detailed characterization of the brain responses to sustained pain has the potential to provide useful information about clinical pain.

p. 26: Interestingly, a recent fMRI study on the temporal summation of pain in fibromyalgia patients reported results similar to ours (Cheng et al., 2022), including the intra-network dissociation within the somatomotor network and the inter-network integration between the somatomotor and other networks during pain. Although we cannot directly examine whether the temporal summation of pain gave rise to these network-level changes due to the limitation of our experimental paradigm, these consistent findings between the two studies may suggest that our findings could be generalized to clinical conditions.

“Vice versa, the behavioral measure used to assess evoked pain perception (avoidance ratings), has been developed for chronic pain patients and never validated on healthy controls5. It might not be an appropriate measure considering the total absence of pain variability in the reported responses over forty-eight subjects6,7.”

We acknowledge that pain avoidance measures are not fully validated in the healthy population. Nevertheless, we used this measure in this study for the following two main reasons that outweigh the limitations.

First, a pain avoidance rating provides an integrative measure that can reflect the multi-dimensional aspects of sustained pain. One of the essential functions of pain is to avoid harmful situations and promote survival, and the avoidance motivation induced by pain is composed of not only sensory-discriminative, but also cognitive components including learning, valuation, and contexts (Melzack, 1999). According to the fear-avoidance model (Vlaeyen & Linton, 2012), if the pain-induced avoidance motivation is not resolved for a long time and is maladaptively associated with innocuous environments, chronic pain is likely to develop, suggesting the importance and clinical relevance of pain avoidance measures. In addition, our experimental design is particularly suitable for the use of avoidance rating because the oral capsaicin stimulation is accompanied by the urge to avoid the painful sensation, but it cannot immediately be resolved similar to chronic pain. Moreover, capsaicin is sometimes experienced as intense but less aversive (or even appetitive) in some cases, e.g., spicy food craver (Stevenson & Yeomans, 1993). In this case, avoidance ratings can provide a more reasonable measure of pain compared to the intensity rating.

Second, the avoidance measure provides a common scale on which we can compare different types of aversive experiences, allowing us to conduct specificity tests for a predictive model of pain. For example, a recent study successfully compared the brain representations of two types of pain and two types of aversive, but non-painful experiences (e.g., aversive auditory and visual experiences) using the same avoidance measure (Ceko, Kragel, Woo, Lopez-Sola, & Wager, 2022). These comparisons were possible because the avoidance measure provided one common scale for all the aversive experiences regardless of their types of stimuli.

To provide a better justification for the use of the avoidance measure, we now included the specificity test results of our pain predictive models. More specifically, we tested our module allegiance-based SVM and PCR models of pain on the aversive taste and aversive odor conditions (Figure S13).

Despite these advantages, the use of avoidance rating without thorough validation is a limitation of the current study, and thus future studies need to examine the psychometric properties of the avoidance rating, e.g., examining the relationship among pain intensity, unpleasantness, and avoidance measures. However, the current study showed that the predictive models derived with pain avoidance rating (Study 1) could be used to predict the pain intensity rating (Study 2). In addition, the overall time-course of pain avoidance ratings in Study 1 was similar to the time-course of pain intensity ratings in Study 2, providing some supporting evidence for the convergent validity of the pain avoidance measure.

As to the following comment, “It might not be an appropriate measure considering the total absence of pain variability in the reported responses over forty-eight subjects,” there are pieces of evidence supporting that the low between-individual variability of ratings is due to the characteristics of our experimental design, not to the fact that we used the avoidance measure. As we discussed in more detail in our response to E-1, our experimental procedure based on capsaicin liquid commonly induces the initial burst of painful sensation and the subsequent gradual relief for most of the participants (Figure 1B, left). A similar time-course pattern of ratings was observed in Study 2 (Figure 1B, right), which used the pain “intensity” rating, not the pain avoidance rating. In addition, previous studies with a similar experimental design (i.e., intra-oral capsaicin application) (Berry & Simons, 2020; Lu, Baad-Hansen, List, Zhang, & Svensson, 2013; Ngom, Dubray, Woda, & Dallel, 2001) also showed a similar time-course of pain ratings with low between-individual variability regardless of the rating types (e.g., VAS or irritation intensity), confirming that this observation is not unique to the pain avoidance rating.

Now we added descriptions on the small between-individual variability of pain ratings and the use of avoidance ratings.

Revisions to the main manuscript:

pp. 5-7: Note that the overall trend of pain ratings over time was similar across participants because of the characteristics of our experimental design, which has also been observed in the previous studies that used oral capsaicin (Berry & Simons, 2020; Lu et al., 2013; Ngom et al., 2001). However, also note that each individual’s time-course of pain ratings were not entirely the same (Figures S2 and S3).

p. 26: However, there are also differences between the characteristics of capsaicin-induced tonic pain versus clinical pain. For example, clinical pain continuously fluctuates over time in an idiosyncratic pattern (Apkarian, Krauss, Fredrickson, & Szeverenyi, 2001), whereas capsaicin-induced tonic pain showed a similar time-course pattern across the participants—i.e., increasing rapidly and then decreasing gradually (Figure 1B). This typical time-course of pain ratings has been reported in previous studies that used oral capsaicin (Berry & Simons, 2020; Lu et al., 2013; Ngom et al., 2001).

pp. 26-27: Note that Study 1 used a pain avoidance measure that is not yet fully validated in healthy participants. However, we chose to use the pain avoidance measure, which can provide integrative information on the multi-dimensional aspects of pain (Melzack, 1999; Waddell, Newton, Henderson, Somerville, & Main, 1993). It also has a clinical implication considering that the maladaptive associations of pain avoidance to innocuous environments have been suggested as a putative mechanism of transition to chronic pain (Vlaeyen & Linton, 2012). Lastly, the avoidance measure can provide a common scale across different modalities of aversive experience, allowing us to compare their distinct brain representations (Ceko et al., 2022) or test the specificity of their predictive models (Lee et al., 2021) (Figure S13). Although the psychometric properties of the pain avoidance measure should be a topic of future investigation, we expect that the pain avoidance measure would have a high level of convergent validity with pain intensity given the observed similarity between pain avoidance (Study 1) and pain intensity (Study 2) in their temporal profiles. The generalizability of our PCR model across Studies 1 and 2 also supports this speculation. However, there would also be situations in which pain avoidance is dissociated from pain intensity. For example, capsaicin can be experienced to be intense but less aversive or even appetitive in some contexts, such as cravings for spicy food (Stevenson & Yeomans, 1993). In addition, the gradual rise of avoidance ratings during the late period of the control condition in Study 1 would not be observed if the intensity measure was used. Future studies need to examine the relationship between pain avoidance and the other pain assessments and the advantage of using the pain avoidance measure.

“• The dynamic measure employed by the Authors is better described from the term "windowed functional connectivity". It is often considered a measure of dynamic functional connectivity and it gives information about fluctuations of the connectivity patterns over time. Nevertheless, the entire focus of the paper, including the title, is on dynamic networks, which inaccurately leads one to think of time-varying measures with higher temporal resolution (either updating for every acquired time point, as the Authors did in their previous publication on the same dataset4, or sliding windows involving weighting or tapering8,9). This allows one to follow network reorganization over time without averaging 2-min intervals in which several different brain mechanisms might play an important role3,10,11. In summary, the assumption of constant response throughout 2-min periods of tonic pain and the use of Pearson correlations do not mirror the idea of dynamic analysis expressed by the Authors in title and introduction. I would suggest removing "dynamic" from the title, reduce the emphasis on this concept, address possible confounds introduced by the choice of long windows and rephrase the aim of the study in terms of brain network reconfiguration over the main phases of tonic pain experience.”

Now we removed the word ‘dynamic’ from many places in the manuscript, including the title. In addition, we added a brief discussion on the reason we chose to use the long and non-overlapping windows for connectivity calculation.

Revisions to the main manuscript (p. 8):

Although the long duration of the time window without overlaps may obscure the fine-grained temporal dynamics in functional connectivity patterns, we chose to use this long time window based on previous literature (Bassett et al., 2011; Robinson, Atlas, & Wager, 2015), which also used long time windows to obtain more reliable estimates of network structures and their transitions.

“• Procedure chosen for evoking sustained pain. To the best of my knowledge, capsaicin sauce on the tongue is not a validated tonic pain procedure. In favor of this argument is the absence of inter-subject variability in the behavioral results showed in the paper, very unusual for response to painful stimulations. The procedure is well described by the Authors, and some precautions like letting the liquid drying before the start of the scan, have helped reducing confounds. Despite this, the measures in figure 1B suggest that the intensity of the painful stimulation is not constant as expected for sustained pain (probably the effect washes out with the saliva). In this case, the first six-minute interval requires particular attention because it encapsulates the real tonic pain phase, and the following ones require more appropriate labels. Ideally the Author should cite previous studies showing that tongue evoked pain elicits a very specific behavioral response (summation, habituation/decrease of pain, absence of pain perception). If those works are missing, this response need to be treated as a funding rather than an obvious point.”

We addressed this comment. Moreover, we could find previous studies that experimentally induced tonic pain through the application of capsaicin on the tongue (Berry & Simons, 2020; Boudreau, Wang, Svensson, Sessle, & Arendt-Nielsen, 2009; Green, 1991; Ngom et al., 2001), suggesting that our experimental procedure is in line with previous literature.

Reviewer #3 (Public Review ):

“In their manuscript, Lee and colleagues explore the dynamics of the functional community structure of the brain (as measured with fMRI) during sustained experimental pain and provide several potentially highly valuable insights into, and evaluate the predictive capacity of, the underlying dynamic processes. The applied methodology is novel but, at the same time, straightforward and has solid foundations. The findings are very interesting and, potentially, of high scientific impact as they may significantly push the boundaries of our understanding of the dynamic neural processes during sustained pain, with a (somewhat limited) potential for clinical translation.

However (Major Issue 1), after reading the current manuscript version, not all of my doubts have been dissolved regrading the specificity of the results to pain. Moreover (Major Issue 2), some of the results (specifically, those related to the group level analysis of community differences) do not seem to be underpinned with a proper statistical inference in the current version of the manuscript and, therefore, their presentation and discussion may not be proportional to the degree of evidence. Next to these Major Issues (detailed below), some other, minor clarifications might also be needed before publications. These are detailed below or in the private part of the review ("Recommendations for the authors").

Despite these issues, this is, in general, a high quality work with a high level of novelty and - after addressing the issues - it has a very high potential for becoming an important contribution (and a very interesting read) to the pain-research community and beyond.”

We appreciate the reviewer’s thoughtful comments. We have revised the manuscript to address the Reviewer’s major concerns, as described below.

“Major Issue 1:

The main issue with the manuscript is that it remains somewhat unclear, how specific the results are to pain.

Differences between the control resting state and the capsaicin trials might be - at least partially - driven by other factors, like:

• motion artifacts

• saliency, attention, axiety, etc.

Differences between stages over the time-course might, additionally, be driven by scanner drifts (to which the applied approach might be less sensitive, but the possibility is still there ) or other gradual processes, e.g. shifts in arousal, attention shifts, alertness, etc.

All the above factors might emerge as confounding bias in both of the predictive models.

This problem should be thoroughly discussed, and at least the following extra analyses are recommended, in order to attenuate concerns related to the overall specificity and neurobiological validity of the results:

• reporting of, and testing for motion estimates (mean, max, median framewise displacement or anything similar)

• examining whether these factors might, at least partially, drive the predictive models.

• e.g. applying the PCR model on the resting state data and verifying of the predicted timecourse is flat (no inverse U-shape, that is characteristic to all capsaicin trials).

Not using the additional sessions (bitter taste, aversive odor, phasic heat) feels like a missed opportunity, as they could also be very helpful in addressing this issue.”

We thank the reviewer for this comment on the important issue regarding the specificity of our results and the potential influences of noise. The effects of head motion and physiological confounds are particularly relevant to pain studies because pain involves substantial physiological changes and often causes head motion. To address the related concerns of specificity, we conducted additional analyses assessing the independence of our predictive models (i.e., SVM and PCR models) from head movement and physiology variables and the specificity of our models to pain versus non-painful aversive conditions (i.e., bitter taste and aversive odor) in Study 1.

First, we examined the overall changes of framewise displacement (FD) (Power, Barnes, Snyder, Schlaggar, & Petersen, 2012), heart rate (HR), and respiratory rate (RR) in the capsaicin condition (Figure S11). For the univariate comparison between the capsaicin vs. control conditions (Figure S11A), the results showed that, as expected, the capsaicin condition caused significant changes in head motion and autonomic responses. The mean FD and HR were significantly higher, and the RR was lower in the capsaicin condition compared to the control condition (FD: t47 = 5.30, P = 2.98 × 10-6; HR: t43 = 4.98, P = 1.10 × 10-5; RR: t43 = -1.91, P = 0.063, paired t-test). In addition, the increased motion and autonomic responses were more prominent in the early period of pain (Figure S11B). The 10-binned (2 mins per time-bin) FD and HR showed a decreasing trend while the RR showed an increasing trend over time in the capsaicin condition. The comparisons between the early (1-3 bins, 0-6 min) vs. late (8-10 bins, 14-20 min) periods of the capsaicin condition showed significant differences both for FD and HR (FD: t47 = 6.45, P = 8.12 × 10-8; HR: t43 = 6.52, P = 6.41 × 10-8; RR: t43 = -1.61, P = 0.11, paired t-test). These results suggest that while participants were experiencing capsaicin tonic pain, particularly during the early period, head motion and heart rate were increased, while breathing was slowed down. Note that we needed to exclude 4 participants’ data in this analysis due to technical issues with the physiological data acquisition.

Next, we examined whether the changes in head motion and physiological responses influenced our predictive model performance (Figure S12). We first regressed out the mean FD, HR, and RR (concatenated across conditions and participants as we trained the SVM model) from the predicted values of the SVM model with leave-one-subject-out cross-validation (2 conditions × 44 participants = 88) and then calculated the classification accuracy again (Figure S12A). The results showed that the SVM model showed a reduced, but still significant classification accuracy for the capsaicin versus control conditions in a forced-choice test (n = 44, accuracy = 89%, P = 1.41 × 10-7, binomial test, two-tailed). We also did the same analysis for the PCR model (10 time-bins × 44 participants = 440) and the PCR model also showed a significant prediction performance (n = 44, mean prediction-outcome correlation r = 0.20, P = 0.003, bootstrap test, two-tailed, mean squared error = 0.159 ± 0.022 [mean ± s.e.m.]) (Figure S12B). These results suggest that our SVM and PCR models capture unique variance in tonic pain above and beyond the head movement and physiological changes.

Lastly, we examined the specificity of our predictive models to pain, by testing the models on the non-painful but aversive conditions including the bitter taste (induced by quinine) and aversive odor (induced by fermented skate) conditions (Figure S13). All the model responses were obtained using leave-one-participant-out cross-validation. The results showed that the overall model responses of the SVM model for the bitter taste and aversive odor conditions were higher than those for the control condition but lower than the capsaicin condition (Figure S13A). Classification accuracies for comparing capsaicin vs. bitter taste and capsaicin vs. aversive odor were all significant (for capsaicin vs. bitter taste, accuracy = 79%, P = 6.17 × 10-5, binomial test, two-tailed, Figure S13C; for capsaicin vs. aversive odor, accuracy = 83%, P = 3.31 × 10-6, binomial test, two-tailed, Figure S13E), supporting the specificity of our SVM model of pain. Similarly, the model responses of the PCR model for the bitter taste and aversive odor conditions were lower than the capsaicin condition, and their temporal trajectories were less steep and fluctuating compared to the capsaicin condition (Figure S13B). The time-course of the model responses for the control condition was flatter than all other conditions and did not show the inverted U-shape. Furthermore, the model responses of the bitter taste and aversive odor conditions did not show the significant correlations with the actual avoidance ratings (bitter taste: mean prediction-outcome correlation r = 0.05, P = 0.41, bootstrap test, two-tailed, mean squared error = 0.036 ± 0.006 [mean ± s.e.m.], Figure S13D; aversive odor: mean prediction-outcome correlation r = 0.12, P = 0.06, bootstrap test, two-tailed, mean squared error = 0.044 ± 0.004 [mean ± s.e.m.], Figure S13F), suggesting the specificity of PCR model to pain.

Overall, we have provided evidence that our models can predict pain ratings above and beyond the head motion and physiological changes and that the models are more responsive to pain compared to non-painful aversive conditions.

Now we added descriptions on the specificity tests to the main manuscript and also to the Supplementary Information.

Revisions to the main manuscript (p. 20):

Specificity of the module allegiance-based predictive models To examine whether the predictive models were specific to pain and the prediction performances were not influenced by confounding variables such as head motion and physiological changes, we conducted additional analyses as shown in Figures S11-13. The SVM and PCR models showed significant prediction performances even after controlling for head motion (i.e., framewise displacement) and physiological responses (i.e., heart rate and respiratory rate) (Figures S11 and S12) and did not respond to the non-painful but aversive conditions including the bitter taste and aversive odor conditions (Figure S13), supporting the specificity of our predictive to pain. For details, please see Supplementary Results.

Revisions to the Supplementary Information (pp. 2-4):

Specificity analysis (Figures S11-13) To examine whether the predictive models (i.e., SVM and PCR models) were specific to pain and not influenced by confounding noises, we conducted additional specificity analysis assessing the independence of the models from head movement and physiology variables and specificity of our models to pain versus non-painful aversive conditions (i.e., bitter taste and aversive odor) in Study 1. First, we examined the overall changes of framewise displacement (FD) (Power et al., 2012), heart rate (HR), and respiratory rate (RR) in sustained pain (Figure S11). For the univariate comparison between capsaicin vs. control conditions (Figure S11A), the results showed that, as expected, capsaicin condition caused significant changes in motion and autonomic responses. The mean FD and HR were significantly higher, and the RR was lower in the capsaicin condition compared to the control condition (FD: t47 = 5.30, P = 2.98 × 10-6; HR: t43 = 4.98, P = 1.10 × 10-5; RR: t43 = -1.91, P = 0.063, paired t-test). For the temporal changes of movement and physiology variables (Figure S11B), the results showed that the increased motion and autonomic responses are more prominent in the early period of pain. The 10-binned (2 mins per time-chunk) FD and HR showed decreasing trend while the RR showed increasing trend over time in capsaicin condition. Additional univariate comparisons between early (1-3 bins, 0-6 min) vs. late (8-10 bins, 14-20 min) period of capsaicin condition showed that differences were significant for FD and HR (FD: t47 = 6.45, P = 8.12 × 10-8; HR: t43 = 6.52, P = 6.41 × 10-8; RR: t43 = -1.61, P = 0.11, paired t-test). This suggests that while participants were experiencing tonic pain, particularly in the early period, motion and heart rate was increased but breathing was slowed. Note that we needed to exclude 4 participants’ data due to technical issues with physiological data acquisition. Next, we examined whether the head movement and physiological responses are the main driver of our predictive models (Figure S12). For all the original signature responses from SVM model (2 conditions × 44 participants = 88), we regressed out the mean FD, HR, and RR (concatenated across conditions and participants as the SVM model was trained) and calculated the classification accuracy (Figure S12A). Although the signature responses were controlled for movement and physiology variables, the SVM model still showed a high classification accuracy for the capsaicin versus control conditions in a forced-choice test (n = 44, accuracy = 89%, P = 1.41 × 10-7, binomial test, two-tailed). Similarly, for all the original signature responses from PCR model (10 time-bins × 44 participants = 440), we regressed out the 10-binned FD, HR, and RR (concatenated across time-bins and participants as the PCR model was trained) and calculated the within-individual prediction-outcome correlation (Figure S12B). Again, the PCR model showed a significantly high predictive performance (n = 44, mean prediction-outcome correlation r = 0.20, P = 0.003, bootstrap test, two-tailed, mean squared error = 0.159 ± 0.022 [mean ± s.e.m.]) while controlling for movement and physiology variables. These results suggest that our SVM and PCR models captures unique variance in tonic pain above and beyond the head movement and physiological changes. Lastly, we examined the specificity of our predictive models to pain, by testing the models onto the non-painful but tonic aversive conditions including bitter taste (induced by quinine) and aversive odor (induced by fermented skate) (Figure S13). All the signature responses were obtained using leave-one-participant-out cross-validation. The results showed that the overall signature responses of SVM model for bitter taste and aversive odor conditions were higher than those for control conditions, but lower than capsaicin condition (Figure S13A). Classification accuracy between capsaicin vs. bitter taste and vs. aversive odor were all significantly high (capsaicin vs. bitter taste: accuracy = 79%, P = 6.17 × 10-5, binomial test, two-tailed, Figure S13C; capsaicin vs. aversive odor: accuracy = 83%, P = 3.31 × 10-6, binomial test, two-tailed, Figure S13E), suggesting the specificity of SVM model to pain. Similarly, the temporal trajectories of the signature responses of PCR model for bitter taste and aversive odor conditions were not overlapping with that of the capsaicin condition (Figure S13B). Furthermore, the signature responses of bitter taste and aversive odor conditions do not have significant relationship with the actual avoidance ratings (bitter taste: mean prediction-outcome correlation r = 0.05, P = 0.41, bootstrap test, two-tailed, mean squared error = 0.036 ± 0.006 [mean ± s.e.m.], Figure S13D; aversive odor: mean prediction-outcome correlation r = 0.12, P = 0.06, bootstrap test, two-tailed, mean squared error = 0.044 ± 0.004 [mean ± s.e.m.], Figure S13F), suggesting the specificity of PCR model to pain. Overall, we have provided evidence that the module allegiance-based models can predict pain ratings above and beyond the movement and physiological changes, and are more responsive to pain compared to non-painful aversive conditions, which suggest the specificity of our results to pain.

“Major Issue 2:

Another important issue with the manuscript is the (apparent) lack of statistical inference when analyzing the differences in the group-level consensus community structures (both when comparing capsaicin to control and when analysing changes over the time-course of the capsaicin-challenge).

Although I agree that the observed changes seem biologically plausible and fit very well to previous results, without proper statistical inference we can't determine, how likely such differences are to emerge just by chance.

This makes all results on Figs. 2 and 3, and points 1, 4 and 5 in the discussion partially or fully speculative or weakly underpinned, comprising a large proportion of the current version of the manuscript.

Let me note, that this issue only affects part of the results and the remaining - more solid - results may already provide a substantial scientific contribution (which might already be sufficient to be eligible for publication in eLife, in my opinion).

Therefore I see two main ways of handling Major Issue 2:

• enhancing (or clarifying potential misunderstandings regarding) the methodology (see my concrete, and hopefully feasible, suggestions in the "private part" of the review),

• de-weighting the presentation and the discussion of the related results.

I believe there are many ways to test the significance of these differences. I highlight two possible, permutation testing-based ideas.

Idea 1: permuting the labels ctr-capsaicin, or early-mid-late, repeating the analysis, constructing the proper null distribution of e.g. the community size changes and obtain the p-values. Idea 2: "trace back" communities to the individual level and do (nonparametric) statistical inference there.”

We appreciate this important comment. We did not conduct statistical inference when comparing the group-level consensus community affiliations of the different conditions (Figure 2) or different phases (Figure 3) because of the difficulty in matching the community affiliation values of the networks to be compared.

For example, let us assume that the 800 out of 1,000 voxels of community #1 and 1,000 out of 4,000 voxels of community #2 in the control condition are commonly affiliated with the same community #3 in the capsaicin condition. To compare the community affiliation between two conditions, we should first match the community label of the capsaicin condition (i.e., #3) to that of the control condition (i.e., #1 or #2), and here a dilemma occurs; if we prioritize the proportion of the overlapping voxels for the matching, the common community should be labeled as #1, whereas if we prioritize the number of the overlapping voxels for the matching, the label of the common community should be #2. Although both choices look reasonable, none of them can be a perfect solution.

As the example above, it is impossible to exactly match the community affiliation of the different networks. We must choose an imperfect criterion for the matching procedure, which essentially affects the comparison of network structure. This was the main reason that we limited our results of Figures 2-3 to a qualitative description based on visual inspection. Moreover, the group-level consensus community structures in Figures 2-3 are not a simple group statistic like sample mean; they were obtained from multiple steps of analyses including permutation-based thresholding and unsupervised clustering, which could further complicate the interpretation of statistical tests.

Alternatively, there is a slightly different but more rigorous approach to the comparisons of the community structures, which is the Phi-test (Alexander-Bloch et al., 2012; Lerman-Sinkoff & Barch, 2016). Instead of direct use of the community labels, this method converts the community label of each voxel into a list of module allegiance values between the seed voxel and all the voxels of the brain (i.e., 1 if the seed and target voxels have the same community label and 0 otherwise). This allows quantitative comparisons of voxel-level community profiles between different conditions without an arbitrarily matching of the community labels. We adopted this Phi-test for our analyses to examine whether the regional community affiliation pattern is significantly different between (i) the capsaicin vs. control conditions and (ii) the early vs. late periods of pain (Figure S6), which correspond to the main findings of the Figures 2 and 3 in our manuscript, respectively.

More specifically, to compare the group-level consensus community structures between the capsaicin vs. control conditions and the early vs. late periods, we first obtained a seed-based module allegiance map for each voxel (i.e., using each voxel as a seed). Then, we calculated a correlation coefficient of the module allegiance values between two different conditions for each voxel. This correlation coefficient can serve as an estimate of the voxel-level similarity of the consensus community profile. Because module allegiance is a binary variable, these correlation values are Phi coefficients. A small Phi coefficient means that the spatial pattern of brain regions that have the same community affiliation with the given voxel are different between the two conditions. For example, if a voxel is connected to the somatomotor-dominant community during the capsaicin condition and the default-mode-dominant community during the control condition, the brain regions that have the same community label with the voxel will be very different, and thus the Phi coefficient will become small. Moreover, the Phi coefficient can be small even if a voxel is affiliated as the same (matched) community label for both conditions, when the spatial patterns of the same community is different between conditions.

To calculate the statistical significance of the Phi coefficient, we conducted permutation tests, in which we randomly shuffled the condition labels in each participant and obtained the group-level consensus community structure for each shuffled condition. Then, we calculated the voxel-level correlations of the module allegiance values between the two shuffled conditions. We repeated this procedure 1,000 times to generate the null distribution of the Phi coefficients, and calculated the proportion of null samples that have a smaller Phi coefficient (i.e., a more dis-similar regional community structure) than the non-shuffled original data.

Results showed that there are multiple voxels with statistical significance (permutation tests with 1,000 iterations, one-tailed) in the area where the community affiliations of the two contrasting conditions were different (Figure S6). For example, the frontoparietal and subcortical regions for the capsaicin vs. control (c.f., Figure 2), and the frontoparietal, subcortical, brainstem, and cerebellar regions for the early vs. late period of pain (c.f., Figure 3) contain voxels that survived after thresholding with FDR-corrected q < 0.05, suggesting the robustness of our main results.

Particularly, the somatomotor and insular cortices showed statistical significance in the permutation test, and this may reflect the large changes in other areas that are connecting to the somatomotor and insular cortices across different conditions. The statistical significance was also observed in the visual cortex, which was unexpected. We interpret that the spatial distribution of the visual network community is too stable across conditions, and thus the null distribution from permutation formed a very narrow distribution of Phi coefficients. Therefore, a small change in the community structure could achieve statistical significance.

Now we added descriptions on the permutation tests.

Revisions to the main manuscript:

p. 9: Permutation tests confirmed that the community assignment in the frontoparietal and subcortical regions showed significant changes between the capsaicin versus control conditions (Figure S6A).

p. 13: Permutation tests further confirmed that the community assignment in the frontoparietal, subcortical, and brainstem regions showed significant changes between the early versus late period of pain (Figure S6B).

pp. 36-37: Permutation tests for regional differences in community structures. To test the statistical significance of the voxel-level difference of consensus community structures (Figures 2 and 3), we performed the following Phi-test (Alexander-Bloch et al., 2012; Lerman-Sinkoff & Barch, 2016). First, for each given voxel, we compared the community label of the voxel to the community label of all the voxels, generating a list of voxel-seed module allegiance values that allow quantitative comparison of voxel-level community profile (e.g., [1, 0, 1, 1, 0, 0, ...], whose element is equal to 1 if the seed and target voxels were assigned to the same community and 0 otherwise). Next, a correlation coefficient was calculated between the module allegiance values of the two different brain community structures (i.e., capsaicin versus control, and early versus late). This correlation coefficient is an estimate of the regional similarity of community profiles (here, the correlation coefficient is Phi coefficient because module allegiance is a binary variable). To estimate the statistical significance of the Phi coefficient, we performed permutation tests, in which we randomly shuffled the labels and then obtained the group-level consensus community structures from the shuffled data. Then, the Phi coefficient between the module allegiance values of the two shuffled consensus community structures was calculated. We repeated this procedure 1,000 times to generate the null distribution of the Phi coefficient for each voxel. Lastly, we examined the probability to observe a smaller Phi coefficient (i.e., a more dissimilar community profile) than the one from the non-shuffled original data, which corresponds to the P-value of the permutation test. All the P-values were one-tailed as the hypothesis of this permutation test is unidirectional.

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Reply to the reviewers

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

Avar et al report on the development of a high-throughput method to screen modifiers of prion replication in cell lines using a genome-wide siRNA library. They identified a number of hits and further studied one candidate, the ribonucleoprotein Hnrnpk. The authors convincingly show the interest of their method. However, the claims that the ribonucleoprotein Hnrnpk impact prion propagation need to be more quantitatively and statistically substantiated.

1. * A large part of the manuscript is dedicated to the validation of the high-throughput assay (called QUIPPER). QUIPPER is made in 384-plates and provides great technological improvement. It works with different prion-permissive cell lines and different prion strains. QUIPPER is an antibody-FRET-based assay that detects a specific population of PrPSc that resists phospholipase C (PIPLC) treatment. Historically, PIPLC has been shown to cleave cell surface PrPC while preserving PrPSc (which is endocytic or inaccessible). I would recommend that the authors quantify the proportion of PIPLC-resistant PrPSc (PrPPIPLC) versus total PrPSc in their different models. First, PrPPIPLC proportion may be cell and strain dependent. Second and most importantly, as siRNA effects are studied using PrPPIPLC as readout, it is crucial to know if this form is a bona fide surrogate of PrPSc and infectivity or only a specific, subcellular, potentially minor form of PrPSc. This is particularly important as the effects of Hnrnpk knock-down in QUIPPER and western blot sounds discordant; in QUIPPER, the effects are strong (> 5-fold) while by western blot, the effects are much more modest (We addressed this issue in several ways; firstly, we quantified the proportion of PIPLC-resistant PrP (PrPPLC) versus PrPSc in two different models (Fig. 1B and D). Secondly, we directly compared residual infectivity of cells treated with PK or PIPLC (Figure 1C), using the standard scrapie cell assay. The results show that infectivity is retained upon PIPLC treatment. In addition, we assessed the 161 hits obtained via QUIPPER using PrPSc as a readout (Fig. 3B).

To provide further data on the robustness of our PIPLC-based readout, we have performed western blotting of infected and uninfected cells upon PIPLC treatment and assessed the band patterns following PIPLC administration. This Figure is now incorporated in the manuscript as Supp. Fig. 1C and demonstrates that upon PIPLC digestion of NBH and RML infected CAD5 and GT-1/7 cells, PrP is barely detectable in the non-infected cells, while it is in the prion infected ones. The blots also show that the PIPLC-resistant PrP (PrPPLC) is resistant to PK digestion. These new data, together with those provided in Fig. 1B and Figure 1C, show that PrPPLC is equivalent to PrPSc in terms of PK resistance and infectivity.

The reviewer pointed out a discordance between Western Blotting and QUIPPER. Although it is not clearly stated, we think the reviewer may be suggesting a discordance based on Fig. 3D. We would like to point out that Fig. 3D does not report fold changes as the reviewer is suggesting, but Z-scores, measured by standard deviations from the mean, not allowing to infer fold-changes. We quantified the effect of NT and HNRNPK targeting siRNAs on prion levels (Fig. 4A) and saw a three-fold change. We believe that the quantifications provided in the new version of the manuscript alleviate the concerns regarding any discordance.

Technically, this is quite easy as it necessitates, after PIPLC treatment, the quantification of PrPSc in the supernatant versus PrPSc in the cell pellet. In Fig. 1C, the authors show that PrPPIPLC is infectious in a cell-scrapie assay. Using this approach, they could also quantify the infectivity of these species relative to the total infectivity content.

We addressed this in Supplementary Fig. 1C as depicted above. Supplementary Fig. 1C shows the alikeness of the PrP species measured via the QUIPPER vs. the canonical PK digestion: upon digestion with PIPLC following a PK treatment, we detect PrPSc. Therefore, the experiment demonstrates that PrPPLC is alike in nature to PrPSc. The difference between the PK digested (lanes 3&4) vs PIPLC treated then PK digested lanes (lanes 7&8) is the PrPSc that is released into the media following PIPLC digestion.

• *

• The authors identified a list of prion modifiers candidate. Surprisingly, the authors did not perform a pathways analysis to identify potential pathways that could impact prion propagation.*

Despite extensive efforts, there were no pathways that were enriched in our 40 hits, which is mentioned in the discussion part of the manuscript. Two analyses (for the 161 candidates and 40 hits) are now added to Supplementary Fig. 3C and pasted below.

• *

• The authors then studied in more details one hit, the ribonucleoprotein Hnrnpk. They studied the impact of Hnrnpk knock-down on PrPC and PrPres levels in different cell lines. These data (Fig 4 and Fig S4) lack quantitative (on a higher number of wells) and statistical analyses. The western blot that are shown suggest that PrPC levels are slightly increased by the siRNA and that the increase in PrPres levels is modest, barely significant given the western blot method. Same comment after PSA treatment, at least in PG127-infected hovS cells.*

We performed a quantification on the western blots for all figures mentioned by the reviewers throughout the manuscript. These are incorporated to the manuscript for the figures: Fig. 4A, Fig. 4B, Supplementary Fig. 4A, Supplementary Fig. 4C, Supplementary Fig. 4D, Supplementary Fig. 4F, Supplementary Fig. 4G.

Additionally, statistical analyses have been incorporated into the manuscript in these figures: Fig. 4C, Fig. 4D, Fig. 4E, Fig, 4F, Fig, 4G, Fig, 4H, Supplementary Fig. 4F. The analyses and the quantitative data demonstrate the effect of Hnrnpk downregulation and PSA treatment on prion levels to be significant. Moreover, we also addressed the regulation of prions via HNRNPK using vacuoles as a read-out as well as with a different mode of regulating HNRNPK expression using shRNAs. All these results, point to HNRNPK as a true modulator of PrPSc.

In Figure 4A and B, the use of POM1 and/or POM2 to detect PrPC / PrPres is confusing. POM2 is supposed to detect mostly full-length PrPC (Fig 4A top panel), but more than 3 glycoforms are detected. In Fig 4B, POM1 is used for PrPC but because it has a central epitope, it detects both PrPC and PrPSc.

Both antibodies are able to recognize both PrPC and PrPSc as it has been shown in many publications from the Aguzzi lab as well as other labs in the field. https://pubmed.ncbi.nlm.nih.gov/19060956/

Note also in Fig 4B, that DMSO alone seems to impact PrPC levels in PG127-infected hovS cells. This advocates again for a more quantitative analysis.

We have quantified the western blots using the DMSO control as standard value. As DMSO was used to dilute PSA, this should take into account potential effects coming from DMSO (Fig. 4D, Fig. 4F, Fig. 4H and Supplementary Fig. 4F).

• Psammaplysene A (PSA) is a pharmacological Hnrnpk binder. The authors used this molecule to further demonstrate that Hnrnpk is involved in prion propagation. I disagree with the author's conclusion that "PSA effect does seem to be limited when HNRNPK shRNAs are applied". In Fig S4D, 1µM PSA seems do decrease PrPres levels at similar levels whether the shRNA is applied or not. Again quantification and statistical analyses from several independent experiments would help supporting the authors conclusions.*

We assessed this point carefully by quantification of the western blots (Fig. 4H) and providing statistical data (Student’s t-test) from three experiments. As we see a threefold lower decrease of prions with and without Hnrnpk regulation when PSA is present, we concluded that the effect we see from PSA should be arising through Hnrnpk. However, we cannot conclusively delineate the effect of PSA, because Hnrnpk ablation is not possible due to essentiality of Hnnrpk. This has now been added to the discussion portion of our manuscript.

• The authors finally tested PSA on organotypic brain slices (in that case, they provide statistical results) and on flies infected with ovine PG137 prions. PSA administration significantly reduced the locomotor deficits prion-infected flies. The authors quantified the effects of PSA on prion accumulation in flies. Because the overall levels were not detectable by immunoblot, they used a cell-free assay termed RT-QuIC to address prion seeding activity in fly heads. I have specific comments about these experiments:
• Maybe I missed it, but I could not find which recombinant PrP is used in RT-QuIC assay.*

This information is provided in the M&M section of the manuscript at hand. The relevant section on P25 reads, where HaPrP23-231 refers to hamster PrP:

The reaction buffer of the RT-QuIC consisted of 1 mM EDTA (Life Technologies), 10 μM thioflavin T, 170 mM NaCl, and 1× PBS (incl. 130 mM NaCl) and HaPrP23-231 filtered using 100-kD centrifugal filters (Pall Nanosep OD100C34) at a concentration of 0.1 mg/ml.

In addition, we added this information to the main text as well.

- This is important as recombinant PrP self-polymerize after a period of time and here the authors have left the RT-QuIC assay running for unusually long period of times (RT-QuIC are stopped after 24h-48h).

For prions, long RT-QuIC experiments are often performed (also see: https://pubmed.ncbi.nlm.nih.gov/32598380/, https://journals.asm.org/doi/10.1128/mBio.02451-14, https://www.nature.com/articles/s41598-021-84527-9, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3458796/ and others).

In addition, this is controlled for in all experiments performed in the lab, as the prion-negative sample containing the same RT-QuIC substrate does not become positive after the entire duration of the assay (Fig. 5D).

- Instead of titrating prion seeding activity by endpoint titration, the authors quantified PSA activity by measuring the effect on another parameter of the RT-QuIC, the length of the lag phase before the conversion reaction is visible. While this is an interesting criterion, reduction of seeding activity must be shown to unequivocally demonstrate that PSA has delayed prion pathogenesis in flies.

Based on the data presented in the manuscript, we assessed prion pathogenesis in flies using a well-established climbing assay, demonstrating that treatment with PSA significantly improves locomotor behavior, which has been shown to be directly linked to prion levels and is known to have even greater sensitivity then the traditional mouse bioassay (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5998032/, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6113635/, https://link.springer.com/article/10.1007/s00441-022-03586-0).. The RT-QuIC represented here represents itself as a secondary read-out to the climbing assay, for which Lag-time quantification is used routinely (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3893511/, https://www.nature.com/articles/s41598-017-10922-w, https://journals.asm.org/doi/10.1128/mBio.02451-14, https://www.nature.com/articles/s41598-021-87295-8). Our results effectively highlight the overlap between the complementary read-outs.

- Can the authors exclude any interfering effect of PSA on the RT-QuIC reaction, given the amount of material used to seed the reaction (1:20 diluted head homogenates)?

We do not know how much PSA has reached the Drosophila brain, therefore, the experiment suggested by the reviewer cannot be tied to a 1:20 dilution. However, the concern of the reviewer is valid, and we therefore performed a spiking experiment of a prion positive sample using 1uM PSA (the highest amount used to treat cells, for which we saw a strong prion-reducing effect). We did not see an interference in the RT-QuIC signal due to PSA in the reaction. This has been incorporated into Figure 5D.

• could the authors comment on the fact that HNRNPK knock-out is not possible and that their siRNA and shRNA are not affecting the cell viability?*

To select hits during the screen process, we apply a viability filter, excluding siRNAs that reduce viability by more than 50% when compared to the non-targeting control siRNA (Supplementary Fig. 1F). For GT-1/7 cells we do not see any effect on viability of siRNA treatment after 96h. However, as downregulation of HNRNPK worsens the cytopathological vacuolation in the hovS model, as shown in Supp. Fig 4A, we do see an effect on cell fitness using both siRNA as well as shRNA. In addition, as knocking down HNRNPK will not lead to its complete loss, the remaining levels might be enough to sustain viability. Moreover, the longest knockdown experiment we performed is 7 days, we cannot exclude that longer exposure would have an impact on viability, but this question is not in the scope of the paper.

• In the discussion the authors do not discuss how Hnrnpk could impact prion propagation. This may deserve a comment as this protein is present in the nucleus. As PrPC has been also identified in this compartment, can this specific form be involved in prion pathogenesis?*

We additionally elaborated on potential ways of how Hnrnpk might impact prion propagation in the discussion, which includes potential nuclear PrPSc as well as with regards to our data obtained from the sequencing efforts shown in Fig. 4I. In addition, we investigated some functional targets of Hnrnpk how they are affected by PSA, which is now added to Supp. Fig 4G.

Reviewer #1 (Significance (Required)):

The QUIPPER method is a great conceptual and technological approach that could be applied to genome-wide analyses and screening for therapeutic molecules.

* The study will interest a general audience interested in neurodegenerative diseases linked to protein misfolding. There are commonalities in pathways and modifiers of the conversion. Further PrP has emerged as a receptor for alpha-synuclein (Parkinson disease) and A-beta peptides (Alzheimer's disease).

Expertise key words: prion diseases - prion pathogenesis in cell models*

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

Prions are protein-based infectious agents that underlie neurodegenerative disease. For prion diseases (e.g., mad cow disease), the infectious agent is the cellular prion protein (PrPc). It exists in a normal conformation and carries out its normal cellular function. However, when it becomes misfolded and aggregates it can adopt an altered conformation, referred to as the prion conformation, or PrPSc. PrPSc aggregates can template the conversion of other PrPc molecules into the PrPSc form. In this way the prions can propagate from one cell to the next and throughout an organism. Prion diseases are truly devastating and identifying ways of stopping prion propagation is of great interest. In this manuscript by Aguzzi and colleagues, the authors designed a way to screen for prion propagation modifiers in mammalian cells. They built a highly sensitive readout of PrPSc propagation and adapted it to a 384-well plate format in adherent cells. They then used this to perform a genomewide siRNA screen, looking for genes that increased or decreased PrPSc propagation when knocked down.

* They identified nearly 1,200 modulators of prion propagation and then subjected them to various validations and filtering to focus on only those hits that affected PrPSc but not PrPc (though hits that affect levels of PrPc could certainly be interesting). All this led to 40 genes (20 that increased and 20 that decreased prion propagation.*

* Among these 40, the authors focused on one hit, hnRNPK, an essential RNA-binding protein with diverse cellular functions. They provide evidence that reducing levels of hnRNPK leads to increase prion levels.*

* They next move to a marine compound called Psammaplysene A (PSA), which had previously been shown to have some neuroprotective properties and to be able to bind to hnRNPK. Because of the latter observation, the authors test if PSA can affect prion levels. They show that indeed treatment of their cell line prion infection model, or an organotypic slice model, or a fly model with PSA is sufficient to decrease prion levels.*

* The authors propose that PSA works to reduce prion levels by increasing the activity of hnRNPK and that this also implies a role of RNA (because hnRNPK is an RNA-binding protein) in prion propagation. * In a nutshell, in my opinion the design and execution of this genomewide screen is ingenious and has yielded a treasure trove of potential prion modifiers. The ability to distinguish between modifiers of Prpc and PrpSc is super powerful. However, the follow-up and focus on hnRNPK and its connections (which seem tenuous) to the marine compound PSA are incomplete and raise more questions than answers. In its present form, it is hard to assess the potential significance of hnRNPK in prion propagation. I have some comments and suggestions for the authors to consider.

* 1.To my eye, Fig. 4A looks like Hnrnpk siRNA leads to slightly increased levels of PrPc (detected with POM2 antibody) and this could explain the increase in PrPSc levels. Can the authors assess Prnp RNA levels and the effects of their siRNAs on Prnp expression? It would also be useful to provide quantification of immunoblots if possible.*

We quantified the western blots as mentioned in our response to reviewer 1. The quantifications are now provided for figures: Fig. 4A and Supplementary Fig. 4A, showing that the increase in prion levels is much stronger than that of PrPC. These confirm the results from the screen as seen in Fig. 3D. In addition, we would again like to point out that the use of shRNAs to knockdown HNRNPK did not yield the increase in PrPC levels aforementioned, as evident by Supplementary Fig. 4D which demonstrates a decrease of PrPC, despite increasing PrPSc levels. Moreover, we show quantification of RNA levels upon downregulation of Hnrnpk and with PSA, which show that downregulation of Hnrnpk via siRNAs indeed increases Prnp mRNA levels and that PSA does not change RNA levels of neither Hnrnpk nor Prnp (Fig. 4C).

• In Supplemental Fig. 4B it also looks like knocking down Hnrnpk results in decreased PrPc levels in this experiment and its not clear how robust the increase in PrPSc levels are. Quantification of these experiments, if possible, would be helpful.*

Please see response above. We now provide quantification to all western blots.

• The authors treat with PSA, which is supposed to bind to Hnrnpk. They state that this treatment does not affect PrPc levels but to my eye Supplemental Fig. 4C looks like highest doses of PSA cause a decrease in PrPc levels. Quantification of the immunoblots would also be useful here.*

Please see response above. We now provide quantification to all western blots and added a sentence to the manuscript.

• The authors use Hnrnpk knockdown along with PSA to test if the effects of PSA depend on Hnrnpk. They see PSA decreases PrPSc levels and that this is, to my eye, only slightly attenuated by Hnrnpk reduction. I interpret these results slightly different than the authors. To me, it seems that this result indicates that PSA's effects are (mostly) independent of Hnrnpk.*

Addressed in point 4 from reviewer one.

• In the original paper identifying PSA and hnRNPK physical interaction, RNA-binding was important. In the authors' assays, does Hnrnpk's effect on prions depend on RNA-binding? Specific mutations to the RNA-binding domains can be made to assess this.*

This is a very interesting point. We did try to obtain data to support this claim, however, due to the essentiality as well as tight control of Hnrnpk expression, we were not able to express different forms of Hnrnpk and acquire conclusive data. Therefore, it is currently being pursued how Hnrnpk might affect prion propagation in the scope of another publication.

• The genetic interaction in the vacuolation phenotype between Prnp and Hnrnpk that the authors report is very interesting (Supplemental Fig. 4A). It seems like this system and phenotype could be useful for the authors in exploring mechanisms by which HnrnpK is functioning.*

• *

We absolutely agree to the reviewer’s comment. As mentioned above a second publication is under way to investigate the mechanisms of Hnrnpk’s antiprion function, which is not in the scope of this study.

• The authors propose that PSA increases activity of Hnrnpk but does it change any Hnrnpk RNA targets from their RNA sequencing? Some functional readout of Hnrnpk function would be useful here to test this hypothesis.*

Although we do suspect RNA binding has an important role in the anti-prion function of Hnrnpk, we cannot exclude other modalities which Hnrnpk might be function through, such as DNA binding and protein-protein interactions. Therefore, to answer this question, a considerable effort that explores each of the potential of these modalities with regards to the anti-prion function of Hnrnpk would be needed. This extensive effort, however, is out of the scope of the manuscript at hand. However, we investigated the effect of PSA on some known functional targets of Hnrnpk (as suggested by the reviewer) from our sequencing efforts and added this analysis as Supplementary Fig. 4H to the manuscript. These results suggest that PSA leads to an increase of the expression of DNA targets of Hnrnpk, potentially suggesting a modality of action. Moreover, we amended the discussion with regards to potential pathways that might be yielding the effect seen as evidenced by the RNAseq data.

• In the Introduction, the authors mention two yeast papers in introducing the concept of using unicellular model organisms to perform modifier screens. The first paper (Outeiro and Lindquist, 2003) is a classic but does not contain a yeast screen. The other one does include a loss of function screen in yeast (for polyQ toxicity modifiers) but those results seems to be due to loss of the [RNQ+] prion from certain deletion strains instead of from specific roles of modifier genes, so that paper might not be the best exemplar of yeast modifier screens.*

We sincerely thank the reviewer for their careful readthrough of the manuscript, the portion that refers to the manuscripts as screens was amended and two new citations for appropriate yeast screens were added to the manuscript.

• The authors asked if any of their hits from their screen had human genetics connections to neurodegeneration. They mention one of their hits Dock3 right after saying that no hit reached statistical significance after multiple testing corrections. This seems a bit misleading since any time one makes a list of anything there will always be, by definition, one at the top of the list.*

We amended the wording to improve clarity of the manuscript.

• The authors perform RNA sequencing on prion infected cells that either had Hnrnpk siRNA or PSA and since these two treatments had opposite effects they looked for genes that went in the corresponding directions. They didn't find anything significant when looking for genes downregulated by Hnrnpk siRNA and upregulated by PSA. They did find glucose metabolism genes when looking in the opposite direction. The significance of this finding is unclear and the authors do not expand on it.*

Addressed in point 7 of reviewers 1 and 2, we expanded the discussion portion of the manuscript with regards to these results.

• To me, the data with PSA seem more robust than the Hnrnpk data and it seems that the authors are trying to perhaps over-fit them together. It is possible that PSA affects prion levels independent of Hnrnpk function. This would not dampen my enthusiasm at all for this finding and could be of interest to those in the prion field, in which the search for anti-prion compounds is of great interest.*

Upon statistical analysis of the result in Fig 4H, we see a three-fold decrease of PSA activity upon HNRNPK downregulation, suggesting PSA activity might be linked to HNRNPK. However, the reviewers point is well taken and we emphasized the value of understanding the function of PSA or mimicry of its effect as potential therapy in the future.

***Cross-commenting:**

All three reviewers seem to appreciate the novelty and impact of the new QUIPPER method the authors have developed to discover modifiers of prion propagation. All three reviewers also seem to be somewhat less convinced by the connection to hnRNPK, including how the compound PSA's anti-prion effects involve hnRNPK (or not).*

* In my opinion, this manuscript presents important and novel work and a really ingenious new method to study prion propagation, which will be broadly useful to the prion field. I feel that the hnRNPK data could be strengthened, especially with more quantitative analyses. The PSA treatment data are compelling but it seems that the effects might be independent of hnRNPK and that the authors are trying to force a connection which might not be there.*

* Reviewer #2 (Significance (Required)):*

* *** 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 have expertise in neurodegenerative disease, protein misfolding, yeast modifier screens, CRISPR modifier screens in human cells, and RNA-binding proteins. I have general knowledge about prions, including PrP, but I am not a prion expert.*

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

The authors conducted an arrayed RNAi-based genome-wide high-throughput screening of all protein-coding modifier genes that affect prion propagation in cultured cells (murine and human cell lines) using a novel quantitative high throughput QUIPPER assay that they developed. They identified 1191 genes, of which 40 selectively affect PrPSc. Half of the 40 genes seem to inhibit PrPSc (limiter) whereas the other half do the opposite (stabilizers). One of the strong limiters is Hnrnpk, is an essential small heterogeneous nuclear ribonucleoprotein that has been implicated in a few protein misfolding diseases. The biological relevance of the findings is demonstrated by the detection of previously reported modifier genes as well as thorough verification of Hnrnpk as an effective prion limiter that seems to be independent of the two prion strains or host species (mouse and human cell lines as well as Drosophila).

* The manuscript is very well written, the approach is novel, very well verified, and effective, the data are solid, and the main conclusions convincing.*

* Two issues need to be discussed.*

* Major comments:*

* First, some genes encoding proteins involved in PrP processing, such as ADAM10 and ADAM8, are known to affect PrPC levels, but they are not among the modifier genes identified. Based on Table 2, ADAM8 expression is very low in the GT-1/7 cells. This points to one of the caveats of the RNAi screening approach in that potential roles of low expressing genes in the cell lines used could be missed. Although it is beyond the scope of this manuscript, it would be helpful to add discussions on complimentary screening enhancing gene expression and the use of more cell lines that will allow identification of more modifiers.*

We thank the reviewer for their concern. The point regarding the screen being less sensitive for genes that are low-expressed in the cell line in question is valid. Upon advancing of the CRISPR-based technologies and the improvement of these technologies to be used in combination with prions, we see their value. We added a sentence to the discussion, talking about gene activation as a future alternative to perform a complimentary screen.

Second, the statement that PSA's anti-prion effect potentially arises through enhancing the activity of HNRNPK makes sense, but it is also possible that PSA can directly inhibit prion replication as well. It would be helpful to calculate the percentage of reduction in PrPSc by PSA treatment and the percentages compared between shNT and shHNRNK cells.

We thank the reviewer for the careful read through of the manuscript. The point was addressed for reviewer 1 point 4. In addition, if PSA is added to the RT-QuIC, it does not prevent aggregate formation, indicating that PSA is unlikely to directly inhibit prion replication, but rather depends on a cellular host-intrinsic molecule for its activity. However, we also elaborate more on the possibility of potential other mechanisms for Hnrnpk and PSA’s function on regulating prion levels in the discussion section of our manuscript.

Minor comments:

* First, Figure 1C shows that the relative intensity for RML CAD5 cell lysate infected cells is less than with PIPLC treated or PK treated, which seems to be the opposite of what is expected, because PIPLC or PK treatment should not increase infectivity. Please explain.*

We agree with the reviewer that the results were surprising. For the practicality of the screen, we wanted to show that the treatment does not eliminate the infectious species, which we were able to demonstrate. However, the increase of infectivity could stem from many different factors, e.g. the amount of duration of PK treatment might not harm but instead rather expose the infectious species, or PIPLC might remove cell surface molecules that could prevent infection of cells. However, as there are a plethora of possible scenarios and it was not relevant for the study at hand, we did not go into further detail.

Second, in Fig S1 e, the labels are too small to read. In Fig 3D, it would be easier to match the stabilizer or limiter genes with the corresponding Z score dots if the genes with a negative Z scores are labelled on the left side while genes with positive Z scores be labelled on the right side.

We amended the figures as per the reviewer’s suggestion.

Third, The following sentence on page 11 is confusing: "20 out of these 40 candidates reduce prion propagation upon silencing, and 20 candidates enhanced prion propagation, and henceforward are called stabilizers or limiters, respectively (Fig. 3D-E, Supplementary Table 1)." Did the author mean to say "....and 20 candidates enhanced prion propagation upon silencing, and hence..."?

We reworded the sentence according to the reviewer’s comment.

* Fourth, In the subheading "Hnrnpk expression limits of prion propagation in mouse and human cells", "of" should be deleted.*

We addressed this in the main manuscript file.

***Cross-commenting:**

I agree with Reviewer #2's assessment that more quantification will be helpful and the link between the effect of PSA treatment and hnRNPK can be strengthened. I want to stress that the knockdown data clearly shows the involvement of hnRNPK as a prion limiter in cultured cells. The question on PSA does affect the interpretation of the ex vivo and in vivo data.*

* The blot in Fig. S4c seems to show some decrease in PrPC levels in NBH-treated GT-1/7 cells. This blot needs to be quantified to confirm whether the PrPC level is changed by PSA treatments. Whether PSA directly inhibits prion replication can be relatively easily assessed in RT-QuIC reactions. Alternative to the use of PSA, RNAi-mediated hnRNPK knockdown can also be done on cultured tissue slices or in brain, but this will require a lot more time and efforts and may be too much to ask for in this manuscript.*

Quantifications for blots were added throughout the manuscript and the text was amended accordingly, and all the points mentioned have been addressed throughout this response letter.

Reviewer #3 (Significance (Required)):

* The findings are novel and very significant. They identified a large number of modifier genes, and established a solid foundation for future studies on prion modifier genes to study prion replication and pathogenesis and for novel therapies against prions and potentially some other protein misfolding diseases. HNRNPK seems to be good target for therapeutic intervention and PSA may be a good candidate for prion treatment. The novel QUIPPER assay can be used to screen for anti-prion compounds and potentially adapted to study other misfolding proteins associated with cells.*

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

All the Reviewer’s comments are reproduced below, with our responses interspersed in [[brackets]]. Citations from the revised manuscript are included in “quotation marks”. The website accepts input only as plain text. Consequently, we had to transform the mathematical expressions into plain text. We apologize for the reduced readability.

Reviewer #1

1) The authors state that: "the conductance density mediated by the expression of the mutant was 2.5 times smaller than the wild type, although we transfected the same amount of plasmid DNA (Fig. 2E). Assuming that protein expression is independent of the mutation, the observation suggested that the unitary proton flux ratio RC of wild type to mutant channel was equal to 2.5" (lines 82‐85).

Macroscopic conductance (G) depends on channel number (N), microscopic or unitary conductance (γ), and open probability (PO) by G=N γ PO. The authors assume that the level of WT and D174A mutant protein expression on plasma membrane, which determines N, are equal; however, this critical assumption does not appear to have been tested.

The fact that conductance density (nS/pF) is plotted in Fig. 2E does not alter this caveat because this procedure normalizes the data only for cell surface area (i.e., size). The authors' conclude that "The conductance density relationship (Fig. 2E) compares the maximal conduction of both constructs; this is the fully open channel (open probability ≈ 1)"(lines 87‐88). However, neither raw currents nor G‐V data are shown. Typically, currents measured at large, near‐saturating PO are used to compare the relative conductances of WT and mutant ion channels. The currents shown in Fig. 2A and 2B exhibit prominent 'droop' at even modest depolarizing potentials (+10 mV for D174A and +30 mV for WT), indicating that the proton gradient has been substantially perturbed by the flow of ge depolarizing voltages needed to drive channels to near‐maximal PO. Furthermore, there is no evidence that maximal PO itself is also not different in WT and D174A channels. Indeed, maximal PO for native Hv1 channels measured using variance analysis is reported by significantly smaller than 1.0, and assuming that PO = 1.0 for either WT or D174A is therefore not well supported. Maximal could be altered by the D174A mutation, which has a clear and strong effect on channel gating evidenced by the large (‐70 mV) negative shift in threshold potential reported both here and previously in the literature. Effects of mutations on maximal PO due to altered gating behavior could be separate and distinct from any change in plasma membrane channel number (N). 3 Lastly, because D174A channels have a much higher PO than WT at 0 mV, the mutant will necessarily conduct inward proton currents at the physiological resting membrane potential (RMP) in tsa‐201 cells (perhaps ‐30 mV?). Inwardly directed proton currents will therefore cause intracellular acidification under resting conditions.

The constitutive acid load in cells expressing D174A, but not WT, is likely to have a variety of physiological consequences, including decreased protein expression or plasma membrane targeting of D174A. There is evidence that another constitutively open Hv1 mutant (R205H) also generates smaller currents macroscopic conductance than WT, and this phenomenon is likely to result from decreased cell surface expression. To conclude that the microscopic conductances of WT and D174A are unequal, the authors must demonstrate that N is not different. The authors' conclusion that D174A "conducts protons at a lower rate" (line 89) is therefore not well supported by the experimental data.

[[

We toned down our conclusions from the experiments to accommodate the reviewer's criticism: (page 4): " Consequently, the mutant channel is nearly fully open (Fig. 2D), readily seen when the membrane potential is 0 mV and external voltage is absent. The high open probability of the D174 mutant under symmetrical pH conditions is readily seen in the tail current amplitude reaching a quasi-saturation (Fig. 2A). The resulting outward currents have a higher amplitude in the wild-type (Fig. 2A+B). Interestingly, the conductance density mediated by the expression of the mutant was 2.5 times smaller than the wild type, although we transfected the same amount of plasmid DNA (Fig. 2E). Our observation suggests a reduced flux through the mutant if we assume that protein abundance in the plasma membrane is independent of the mutation."]]

2) The authors indirectly measure apparent proton flux rates (λD) in LUVs containing WT and D174A mutant Hv1 channels using a fluorescence‐based approach, and conclude that λD is 2.4 times smaller for D174A than WT. However, the method for estimating λD is not performed under voltage clamp, and the driving force for proton current is neither known nor measured.

[[

The reviewer is mistaken. The method for estimating λD is performed under voltage clamp, and the driving force for proton current is known.

Page 6: “To obtain λD, we encapsulated c_k^i=150 mM KCl in the HV1 containing large unilamellar vesicles (LUVs) and exposed these vesicles to a buffer with a K+ concentration c_k^o= 3 mM. The addition of valinomycin facilitated K+ efflux, thereby inducing a membrane potential, ψ. ψ constituted the driving force for H+ uptake. It can be calculated according to the Goldman equation:

ψ = -RT/F ln ((c_k^i+(P_H/P_K ) c_H^i)/(c_k^o+(P_H/P_K ) c_H^o ))

(1)

The ratio of the HV1 mediated proton permeability P_H to the valinomycin-mediated potassium permeability P_K is always smaller than 0.04. We base our conclusion on the observation that the CCCP-mediated proton permeability represents an upper limit for P_H since CCCP always induces a faster vesicular proton uptake than HV1 (Fig. 3). Accordingly, the maximum value of P_H/P_K can be estimated as the ratio of valinomycin to CCCP conductivities. The respective values are equal to 1.6 10-3 Ω-1 cm2 [1] and 4 10-6 Ω-1 cm-2 [2]. At pH 7.5, we find c_H^o=10^(-7.5) M, i.e., c_k^o ≫ (P_H/P_K )c_H^o. Similarily, c_k^I ≫ (P_H/P_K ) c_H^i for a broad range of intravesicular pH. With these simplifications, Eq. 1 transforms into the Nernst equation yielding:

ψ = -RT/F ln (c_k^i)/(c_k^o )=-100 mV

(2)

ψ of such size may decrease intravesicular pH by nearly two units. Such acidification does not violate c_k^i ≫ (P_H/P_K ) c_H^i so that ψ remains constant throughout the experiment. That is, the vesicle experiments proceed under voltage clamp conditions. The simple explanation is that, due to the small proton concentration and the limited buffer capacity, the K+ conductance exceeds H+ conductance under all conditions. The conclusion is in line with simulations (32), confirming that the membrane potential is driven very near the Nernst potential for K+.”]]

The authors state that "Transmembrane voltage constituted the driving force for proton uptake into LUVs (Figure M). It resulted from facilitated K+ efflux out of the vesicles (30)", (lines 261‐262), but this voltage is unknown and not likely to equal the Nernst equilibrium potential for K+ once Hv1 channels begin to open.

[[

The reviewer is mistaken. The voltage is known (see the equations above). The opening of the HV1 channels does not alter the potential because c_k^o ≫ (P_H/P_K ) c_H^o and c_k^i ≫ (P_H/P_K ) c_H^i for a broad range of intravesicular pH (see above).]]

Once Hv1 channels begin to open, intra‐lumenal pH (pHi) will necessarily occur during the experiment. Such changes are likely exacerbated by a) the low proton buffering capacity of the system (5 mM HEPES) and b) the absence of any counter‐charge pathway to balance the effect of proton charge movement on the membrane potential.

[[

Vesicle acidification occurs. It signifies the presence of functional proton channels. Nevertheless, the membrane potential does not change (see Equation 1 above). The statement b) is not correct because the outward K+ movement counters the inward-directed proton charge movement.]]

Given the small volume of LUVs, even a relatively modest difference in either membrane potential or pHi could substantially alter the driving force for proton movement. Together, these factors are highly likely to result in a rapid and potentially large change in the driving force for proton flux.

[[

As outlined above, membrane potential stays invariant. Vesicle acidification changes the driving force for proton flux. The steady state is reached when the electrochemical potentials for protons on the two sides of the membrane are equal to each other.]]

Driving force changes may also be different for WT and D174A because their relative PO may be different under the experimental conditions used here. Because D174A activates at much more negative voltages, it is likely to open more quickly and to a higher PO than WT at early times after depolarization is initiated by addition of valinomycin (Fig. 3A). This fact will likely result in a larger initial inward current being carried by D174A than WT channels. The result would be a more rapid acidification of LUVs by D174A.

[[

The reviewer is mistaken. Assuming a transport rate of 20,000 potassium ions per second (G. Stark, B. Ketterer, R. Benz and P. Läuger; Biophys. J. 1971 Vol. 11 Pages 981-981) and a membrane capacity of 1 μF cm-2, it takes valinomycin about 10 ms to drive the vesicular potential to near Nernst values. Activation of the proton channel is at least 10 times slower. Thus, both mutant channel and wild type channel may open at roughly the same instant. The driving force is sufficient to open both channels to the same probability.]]

The experimental data in Fig. 3A are consistent with the expectation that the proton gradient and driving force more rapidly approach equilibrium for D174A than WT channels: the apparent rate of AMCA fluorescence change is slower in D174A. Although the authors correctly interpret the experimental data to mean that the apparent λD is slower for D174A, they do not rule out the artifactual explanation for the measured differences. Indeed, the observation in Fig. 3A that AMCA fluorescence change eventually reaches a plateau and is not affected by CCCP means that the proton gradient has become exhausted during the experiment, and directly demonstrates that the proton driving force is uncontrolled under the current experimental conditions.

[[

The reviewer's interpretation of our results is flawed. Instead of becoming exhausted, the proton gradient builds up during the experiment. Initially, extravesicular and intravesicular pH values are equal to each other. Valinomycin-mediated K+ efflux results in a membrane potential that drives Hv1-mediated H+ influx.

Page 8: “The number NC of reconstituted HV1 dimers per vesicle determines the acidification rate λ, i.e., the time that elapses before reaching the steady state. The final intraluminal pH is independent of NC. Similarly, CCCP addition in the steady state does not change the intraluminal pH of HV1-containing vesicles. But CCCP will affect the intraluminal pH of vesicles deprived of HV1 since H+ background permeability is too small to allow vesicle acidification within the time allotted for the experiment. Consequently, only HV1-free vesicles will acidify upon CCCP addition. That is, CCCP addition allows estimating the fraction of vesicles deprived of HV1.”]]

In contrast to the authors' statement that "Our experiments with the purified and reconstituted channels corroborated the conclusion (Fig. 3A)", (lines 92‐93) it is not clear that unitary proton flux rates/unitary conductances are actually different in WT and D174A.

[[

The reviewer is mistaken. Since we measured under voltage clamp conditions, ensured rapid installment of the membrane potential, and selected a potential large enough to allow for the same open probability of wild-type and mutant channels, the measured transport rates, λ, are valid. Moreover, we determined the number of HV1 channels per vesicle and thus calculated the transport rate of an individual channel, λD. Since λD is different for WT and D174A, the unitary proton flux rates/unitary conductances are actually different in the wild type and mutant.]]

3) The presumed differences in unitary conductances (i.e., 'transport rate') between WT and D174A are used to estimate Arrhenius activation energies (Ea): ("The difference in measures transport rates allows a rough estimation of the Arrhenius 128 activation energy Ea for HV1‐mediated proton flow. It amounts to 40 kJ/mol for the wild type and 23 kJ for the mutant. Thus, Ea exceeds the corresponding 15 kJ/mol barrier measured for gramicidin A (32, 33)", (lines 128‐130). The method for determining Ea in the current work is not well‐described. In Ref. 32, the authors estimate Arrhenius activation energy (Ea = 20 kJ/mol) for gramicidin D (not gramicidin A) from the slope of a line fit to measurements of currents at various temperatures. Here, the authors measure AMCA fluorescence decay rates at 4 °C and 23 °C and observe a similar temperature‐dependent difference in WT and D174A (Fig. S2). Given that the data indicate that WT and D174A are similarly temperature‐dependent, it is unclear how the authors arrive at different Ea values. The authors' conclusion that "The increment in Ea suggests that the transport mechanism may be different from a pure Grotthuss type, where the proton uses an uninterrupted water wire to cross the membrane", (lines 131‐133) therefore does not appear to be well‐supported.

[[

We removed both the calculation and discussion of activation energies. Knowledge and discussion of activation energies distract from the scope of the manuscript. We show the experiments at different temperatures solely to demonstrate that Hv1 and D174A facilitate proton transport at a decreased temperature where the background conductivity of the lipid bilayer to water is small.]]

4) The authors report no difference in water permeability in WT vs. D174A (Fig. 5 and S1) and interpret the results to mean that proton currents are not associated with measurable bulk water flow. A similar conclusion was reached for native Hv1 channels using deuterium substitution (DeCoursey & Cherny, 1997).

[[

The comment of the reviewer is misleading:

• Equal water permeabilities of WT and D174A would not exclude an association between proton currents and water flow. Accordingly, our manuscript does not contain the stipulated interpretation.
• DeCoursey & Cherny (1997) did not evaluate bulk water flow through proton channels. They compared D+ and H+ currents across the plasma membrane of rat alveolar epithelial cells. Page 2: “Comparing deuterium ion and proton currents through the plasma membrane of rat alveolar epithelial cells, DeCoursey & Cherny (22) found an isotope effect exceeding that for hydrogen bond cleavage in bulk water. It suggested the involvement of an amino acid side chain in proton conduction (22). Alternatively, altered properties of confined water could have been responsible for the higher isotope effect.”]]

However, the absence of bulk water flow does not itself rule out the possibility that 'trapped' waters within the Hv1 pore do not themselves carry the measured proton current. If intra‐pore water molecules are tethered by hydrogen bonds with protein atoms, they may not move when Hv1 channels open.

[[

The reviewer’s comment contains one misinterpretation and one unfounded statement:

1. We never stated that 'trapped' waters within the Hv1 pore do not themselves carry the measured proton current. On the contrary, we envisioned the trapped waters delivering the protons to one or more titratable amino acid side chains and accepting the protons from them.
2. The reviewer’s view that intra‐pore water molecules tethered by hydrogen bonds with protein atoms may not move when Hv1 channels open is a misconception. Page 12 bottom: “The contrasting opinion that instead of a channel obstruction hydrogen bonds may immobilize the pore water (19) is not convincing. First, the lifetime of a hydrogen bond is in the ps range while HV1’s mean open time exceeds 100 ms (41). Thus, hydrogen bonds may break more than 1011 times during the open state, rendering them unfit for tethering intraluminal water molecules. Second, the effect of hydrogen bonds between water molecules and pore residues is limited to decreased water mobility in narrow channels (23). Their number, NH, allows for predicting pf (26). Specifically, every H-bond donating or receiving pore-lining residue contributes an average increment ΔΔG╪ of 0.1 kcal/mol to the Gibbs free energy of activation ΔG╪ (24). Equation (1) allows the calculation of ΔG╪:

ΔG╪= N_H ΔΔG╪ + ΔΔG╪_i (13)

where ΔΔG╪_i = 2 kcal/mol (24). Since N_H = 6 (Fig. S1) in the open HV1 conformation, Eq. 1 predicts ΔG╪ = 2.6 kcal/mol. Eq. (2) allows calculating HV1’s pf from this value (42):

p_f = v_0 v_w exp(-ΔG╪/RT) (14)

where vw = 3 × 10−23 cm3 is the volume of one water molecule and ν0 is the universal attempt frequency, ν0 = kB∙T/h ≈ 6.2 × 1012 s−1 at room temperature (kB is Boltzmann’s and h is Planck’s constant).”]]

Proton transfer through a hydrogen‐bonded network of waters requires only that the electronic structure of the network be rearranged during proton transfer; water is not required. As in the previous study (DeCoursey & Cherny, 1997), the lack of water flux reported here demonstrates seems to reinforce the notion that H+ moves separately from its waters of hydration (i.e., hydronium, H3O+, is not the permeant species) and does not necessarily imply information about the mechanism of proton transfer (i.e., side chain ionization vs. Grotthuss‐type transfer in a water‐wire).

[[

The reviewer is mixing two unrelated issues. Of course, proton transport may be separated from mass transfer. Yet, charge transfer may or may not include one or several titratable amino acid side chains. If proton side chain ionization is not involved in proton transfer, a water wire must exist that connects the aqueous solutions on both sides of the membrane. In this case, an osmotic gradient will drive water molecules through the open channel. Since we did not observe such water flux, we conclude that the water wire is interrupted by at least one side chain. Thus, our experiments imply information about the mechanism of proton transfer.]]

The authors state that: 1) "every H‐bond donating or receiving pore‐lining residue would have contributed an increment ΔΔ𝐺‡ of 0.1 kcal/mol to the Gibbs free energy of activation Δ𝐺‡ (25)" (lines 145‐147), and 2) calculating NH from this Δ𝐺‡ allows estimation of the channel's unitary water permeability (Eqn. 2). Although hydrogen bonding patterns will undoubtedly alter the free energy for channel activation, this is not the same free energy change as that for proton transfer.

[[

The reviewer's remark is in line with the previous and the current versions of our manuscript.]]

Hv1 gating involves conformational changes that are both voltage and Δ pH-dependent, and the D174A mutation is known to alter the voltage dependence of gating (Fig. 2 and previous studies). The effect of D174A on Hv1 unitary conductance, however, is speculated but not unambiguous (see above).

[[

Our experiments unambiguously demonstrate the effect of D174A on Hv1 unitary conductance. The interpretation of the experiments is straightforward – there is no speculation involved. The contrasting opinion of the reviewer rests on his misinterpretations of (i) our measurements of proton transport rate λD for wild-type and mutant (see above) and the CCCP-effect (see above).]]

In the absence of definitive experimental data showing differences in the unitary conductance of WT vs. D174A, the authors' assumption that water permeability would be strongly temperature‐dependent (lines 154‐160) seems premature and their ensuing conclusion tenuous: "pore residues interrupt the HV1 spanning water wire, trapping the water molecules inside the HV1 channel. In contrast to water, protons cross the pore by hopping from one acidic residue to another through one or more bridging water molecules (Fig. 6)" (lines 161‐164).

[[

The reviewer chooses to misinterpret our lines. We did not assert that water permeability through the Hv1 channel would be strongly temperature‐dependent. We referred to the well-known fact that there is a strong temperature dependence of lipid bilayer water permeability - in contrast to the tiny effect of temperature on the water permeation across aqueous channels.

Page 11, bottom: “Considering the stark dependence of the activation energy for background water flow across lipid bilayers (24), we repeated the experiments at a decreased temperature of 4°C. Thanks to the low background water permeability at 4°C, even tiny contributions of HV1 to Pf should be detectable. Yet, the channels did not contribute to the water flow through the vesicular membrane even though channel water permeability but weakly depends on temperature (24).”]]

Furthermore, the authors calculate the number of hydrogen bonds (NH) that pore waters could form with pore lining residues based on an X‐ray structure of a chimeric proton channel protein (pdb: 3WKV) that is: a) manifests discontinuous transmembrane water density and is known to represent a non‐conductive conformation, b) contains residues from Ci‐VSP in the critical S2‐S3 linker that form part of the proton transfer pathway, and c) exhibits structural features (i.e., highly conserved ionizable residues such as D185 and R205, which like D174 are reported to dramatically alter Hv1 gating, are packed into a solvent‐free crevice) that are inconsistent with physiological function. Given that all Hv1 ionizable mutant combinations tested so far (the sole exception of D112V ‐ other nonionizable substitutions at D112 are tolerated) remain functional (Musset, Smith et al., 2011, Ramsey, Mokrab et al., 2010), the identities of water‐interacting residues speculative.

[[

We substituted the X‐ray structure of the chimeric proton channel protein for the AlphaFold structure. We now provide views of the open and closed conformations in the Supplement based on the homology structure (13). Microsecond-long molecular dynamics simulations have optimized the latter.

The experimental observation of mutants’ functionality (with the sole exception of D112V) supports our view that proton transfer occurs through a hydrogen‐bonded network of waters that is only once (at D112) interrupted by an amino acid side chain. The nature of the amino acids interacting with the proton transferring water molecules is of little importance.]]

Interpreting differences in the calculated NH based on pdb: 3WKV therefore seems unlikely to reveal fundamentally important insights into Hv1 function. The author's conclusion that "The observation rules out the formation of an uninterrupted water chain spanning the open channel from the aqueous solution at one side of the membrane to the other. NH would have governed water mobility if such a water wire had formed (24)", (lines 143‐145) therefore does not appear to be strongly supported.

[[

We did not base our conclusion of an obstructed water pathway on the analysis of structural models. In contrast, the conclusion is the result of our experiments. The structural models permitted the prediction of the expected water permeability. Depending on the model and the channel conformation, we find NH values between six and 16. All of these NH values translate into water permeabilities exceeding gramicidin’s water permeability. Thus, we would have been able to detect the water flux through an unobstructed proton channel.]]

Reviewer #2:

Summary: Voltage‐gated proton channels are peculiar members of the voltage‐gated ion channel family due to their absence of canonical pore. Instead, protons permeate through their voltage‐sensing domain. The mechanisms of proton permeation in Hv1 channels are still unclear, with currently two competing hypotheses: (i) hopping through titrable residues within the protein; or (ii) via Grotthuss mechanism involving proton jumping through a continuous water wire. So far, these hypotheses were only tackled by computation. The authors therefore aimed to experimentally test the two hypotheses. To do so, the authors measured the transport rates of protons and water through wild‐type and mutant D174A Hv1 reconstituted in lipid vesicles. Overall, the presented data are convincing and support their conclusion that proton conduction through the channel is not solely mediated by water transport. However, there are several aspects of the paper that I did not understand and would require clarification.

[[

We thank the reviewer for the positive evaluation.]]

Major comments: My major concern is about the relevance of using the D174A mutant. The authors explain at the beginning of the paper that Hv1‐D174A is open at 0 mV, which allows measuring proton flux in systems in which voltage cannot be controlled. However, it seems from the proton flux experiments that wild‐type Hv1 can conduct protons perfectly well in the used experimental paradigm. So why test a mutant? It is actually not clear why wild‐type Hv1 can conduct protons in the proton conduction assay.

[[

We introduced the D174A mutation to measure water flux in a setting where the membrane potential is zero. We only performed the proton flux measurements to show that our reconstituted HV1 channels are functional. HV1 can conduct protons because we establish a transmembrane potential in the proton conduction assay. That is, only initially, extravesicular and intravesicular pH values are equal. Valinomycin addition results in a K+ efflux that, in turn, generates a membrane potential. This potential drives the HV1-mediated H+ influx.]]

The authors should clearly state the trans‐membrane potential created by the K+ gradient across the vesicle, as well as the pH inside and outside the vesicle, and related these conditions to their electrophysiology data to give us an idea of the open probability of wild‐type Hv1 in the conditions used in the proton conduction assays. This is critical to be able to compare the relative rates of proton transport between the wild‐type and the mutant.

[[Page 6, bottom:

" ...we encapsulated c_k^i=150 mM KCl in the HV1 containing large unilamellar vesicles (LUVs) and exposed these vesicles to a buffer with a K+ concentration c_k^o= 3 mM. The addition of valinomycin facilitated K+ efflux, thereby inducing a membrane potential, ψ. ψ constituted the driving force for H+ uptake. It can be calculated according to the Goldman equation:

ψ = -RT/F ln ((c_k^i+(P_H/P_K ) c_H^i)/(c_k^o+(P_H/P_K ) c_H^o ))

(1)

The ratio of the HV1 mediated proton permeability P_H to the valinomycin-mediated potassium permeability P_K is always smaller than 0.04. We base our conclusion on the observation that the CCCP-mediated proton permeability represents an upper limit for P_H since CCCP always induces a faster vesicular proton uptake than HV1 (Fig. 3). Accordingly, the maximum value of P_H/P_K can be estimated as the ratio of valinomycin to CCCP conductivities. The respective values are equal to 1.6 10-3 Ω-1 cm2 [1] and 4 10-6 Ω-1 cm-2 [2]. At pH 7.5, we find c_H^o=10^(-7.5) M, i.e., c_k^o ≫ (P_H/P_K )c_H^o. Similarily, c_k^I ≫ (P_H/P_K ) c_H^i for a broad range of intravesicular pH. With these simplifications, Eq. 1 transforms into the Nernst equation yielding:

ψ = -RT/F ln (c_k^i)/(c_k^o )=-100 mV

(2)

ψ of such size may decrease intravesicular pH by nearly two units.

Such acidification does not violate so that remains constant throughout the experiment. That is, the vesicle experiments proceed under voltage clamp conditions. The simple explanation is that, due to the small proton concentration and the limited buffer capacity, the K+ conductance exceeds H+ conductance under all conditions. The conclusion is in line with simulations (32), confirming that the membrane potential is driven very near the Nernst potential for K+.”]]

Similarly, the buffers and pH used for the water transport assay are not explicitly mentioned. Are they the same as for the proton transport assay or are the buffers inside and outside the vesicle symmetrical?

[[

We added the information about buffers and pH used to the legend. Except for 150 mM sucrose, the internal and external solutions were identical: 150 mM KCl, 5 mM HEPES (pH 7.5), and 0.5 mM EGTA.]]

Finally, in the introduction the authors base their assumptions about water transport on an X‐ray structure of Hv1 in a closed conformation (3WKV). I do not think it is relevant to study permeation, which in theory should only happen in an open state. If the authors want to make assumptions about the number of hydrogen bonds in the pore and how many water molecules are in the pore (and I don't think they need to do it), they should rather base their assumptions on the computational models of Hv1 open state.

[[

We thank the reviewer for the advice. We added a figure to the Supplement. It shows Hv1 models from long-timescale molecular dynamics simulations (Geragotelis et al, Proc Natl Acad Sci U S A 2020 Vol. 117 Issue 24 Pages 13490-13498). The open structure reveals NH=6. We used this value for our calculations.]]

Minor comments:

1) Figure 6: the authors should precise that the model of proton conduction through Hv1 is just an assumption. The structural features of Hv1 open state are indeed unknown.

[[We modified the figure based on the simulation results of Geragotelis et al. We indicated in the legend that the scheme is based on HV1 homology models.]]

2) Page 9, lines 170‐171 "Drastically prolonged tail current kinetics might reflect a decreased voltage‐dependence of the deactivation in the D174 mutant". Or rather the prolonged kinetics reflect the stabilization of the open state by the mutation (as stated by the authors just after).

[[Page 14:

“Drastically prolonged tail current kinetics might reflect (i) a decreased voltage dependence of the deactivation in the D174A mutant or (ii) a stabilized open state (14).”]]

3) Supplementary figures are displayed in an odd fashion. Figure S3 should be placed before Figures S1 and S2.

[[We added two more Supplementary Figures and displayed them in the order of text mentionings.]]

4) In Figure 2, displaying the current trace corresponding to the 0 mV voltage step would improve readability of the figure, by showing that Hv1‐D174A mutants conduct protons at 0 mV and not wt Hv1.

[[

We show the current trace corresponding to the 0 mV voltage step for the D174A mutant in panel A and the trace for the wild-type in panel B of Fig. 2.]]

5) Figure 2 legend "Pronounced inward H+ currents activate negatively to the reversal potential (here ‐70 mV)". I think the authors mean "Here 0 mV", ‐70 mV is the threshold potential. Panel (c), I guess the EH vs Vrev plot is for D174A mutants but it is not mentioned in the legend

[[

We corrected the legend. “Pronounced inward H+ currents activate negatively (here – 70 mV) to reversal potential (here – 8 mV), indicating a high open probability of the D174A mutant at 0 mV.” And “Comparison of calculated Nernst potential for protons (EH) and measured reversal potential (Vrev) for the D174A mutant.”]]

6) Page 4, line 89: the fact that D174A conducts protons at a lower rate is, at this point, based on a lot on assumption. I would just correct the last sentence by saying "Thus, D174A, while opening with less depolarization, seems to conduct protons at a lower rate"

[[We toned down our statement and inserted a phrase very close to the one suggested.

Page 5: “Our observation suggests a reduced flux through the mutant if we assume that the protein expression level is independent of the mutation.”]]

7) Page 6, line 107. The word "therefore" is not necessary

[[ok]]

8) Page 7, line 128: "of" in "measures of transport" is missing

[[We deleted the paragraph.]]

9) Page 12, lines 261‐262: "Figure M" ??

[[“Inset of Figure 3A”]]

CROSS‐CONSULTATION COMMENTS I agree with the two other reviewer's comments. I think our reviews more or less raise the same weaknesses in the study.

Significance

This paper addresses a single question with a clearly defined experimental paradigm. Once the issues addressed, the paper should bring important significance to the field of voltage‐gated ion channels since the nature of proton conduction in Hv1 was not known. It could help explain ion conduction in some channelopathies involving ion conduction through the voltage‐sensing domain. The audience is mainly the voltage‐gated ion channel community, as well as the community of membrane permeation mechanisms My field of expertise is in ion channel structure‐function and pharmacology. I have little expertise in the described proton and water flow assays. Therefore I do not have sufficient expertise to evaluate the detailed experimental protocol that led to the measurements.

Reviewer #3:

Summary: This study addresses a fundamental question about the mechanism of proton conduction in the voltage gated proton channel Hv1 i.e., whether protons hop through an uninterrupted water wire, or move by other means involving titratable channel residues. The authors argue that an uninterrupted water wire entails a certain rate of water movement through the open channel, which they estimate to be around 10‐12 cm3s‐1 based on a structural model of Hv1 and previous work on other channels. They then measure water permeability of LUVs containing a purified Hv1 mutant expected to be open at 0 mV via light scattering, and proton flux using a pH sensitive fluorescent dye. They calculate a water permeability much lower than predicted and conclude that the water in the conduction pathway does not form an uninterrupted water wire. The manuscript is written clearly, and the experimental measurements are convincing.

[[We thank the reviewer for the positive evaluation.]]

There are nonetheless some ambiguities in the way the formation of water wires is discussed.

Major comments: A protein like Hv1 is larger and more complex than small peptides like gramicidin. In this context, transient water wires, frequently interrupted by titratable residues, or by steric hindrance from hydrophobic sidechains etc. are likely. Can the authors provide an estimate for the maximum frequency and lifetime of uninterrupted proton wires compatible with their measurements? This would be helpful to evaluate whether short‐lived uninterrupted water wires could contribute significantly to proton conduction or not. Trapping usually implies restricted movement. So, for how long do water molecules need to stay inside the channel in order to be considered trapped? Are the water molecules really trapped or simply forming broken wires?

[[Page 13, bottom:

“The question arises whether the obstacle in the water pathway is permanent. HV1’s titratable residues or steric hindrance from fluctuating sidechains may frequently interrupt otherwise intact water wires. Yet, our calculations (Eqs. 7 – 11) show that proton diffusion from the bulk solution to the pore mouth is the transport limiting step. Undoubtedly, transient closure would have caused a detectable pore resistance because part of the protons arriving at the pore mouth could not enter the pore. If the pore was closed longer than one ps, an arriving H+ may diffuse out of the capture zone and vanish into the bulk:

t_c=(r_0^2)/6D = 10^(-16)/(6 × 8.65 × 10^(-5) ) s = 2 × 10^(-13) s

(16)

where tc denotes the time a proton requires to diffuse a distance equal to the capture radius r0. Since transient closures would give rise to experimentally undetected pore resistance, they must be ruled out. The observation agrees well with noise experiments, where Lorentzian time constants, albeit smaller than the time constants for H+ current activation but larger than 0.1 s were observed (41).

We provided the calculations showing the diffusion limitations on page 9:

“…we show that the transport limiting step is H+ diffusion to the pore (access resistance) and not transport through the pore. Therefore, we first calculate the maximum current Imax permitted by diffusion for a constantly open pore (35):

I_max=2π F r_o D_H c_H

(7)

where F, r0, DH, and cH are Faraday's constant, the capture radius, the H+ diffusion constant, and the H+ concentration, respectively. The only unknown parameter is r0. Taking the gA estimate r0 = 0.87 Å (36), disregarding buffer effects and assuming DH = 8.65×105 cm2s-1, we find:

I_max=2π (9.6 ×10^4 As)/mol × 0.87 × 10^(-8) cm × 8.65 x 10^(-5) (cm^2 s^(-1) × 4 × 10^(-7.5) mol)/(1000 cm^3 )

(8)

I_max=5.6 × 10^(-17) A

(9)

Eq. 8 considers that the approximately 25 % charged lipids in the bilayer induce an increase in surface proton concentration, i.e. it accounts for a surface potential of roughly -40 mV in 150 mM salt. The maximal unitary rate would then be equal to:

q_max = 5.6 × 10^(-17) C/s/1.6 × 10^(-19) C =348 s^(-1)

(10)

Here we used the r0 value determined for gA (36). Acidic moieties at the entrance of HV1 and proton surface migration along the lipid bilayer could serve to increase that value (37, 38). The observation suggests transport limitations by poor proton availability. Calculation of the channel resistance, Rch (35), confirms the hypothesis:

R_ch = R_pore+R_access =[l_ch+(π a_ch)/2] ρ/(π a_ch^2 )

(11)

where R_pore is the resistance of the pore proper and R_access is the access resistance. Assuming a channel radius, a_ch, of 0.15 nm, a length, l_ch of 4 nm and solution resistivity (H+ as the sole conducted ion at bulk pH of 7.5 and a surface potential of -40 mV), ρ, of 2×105 Ω cm, we find R_ch = 4×1013 Ω. Thus, the resulting current, Iρ, that we may expect for the vesicular membrane potential of 100 mV is equal to 3×10-15 A. Accordingly, Iρ exceeds Imax by more than one order of magnitude. Consequently, we may safely conclude that HV1 conductance is limited by proton availability under our conditions. ”]]

The main conclusion of the paper rests on the negative results from the water permeability assay of Fig. 5. It is recommended to include a positive control (e.g., with gramicidin A), run under the same conditions and similar number of channels per LUV, to show how the results should look like in case of significant water permeability.

[[We included the gramicidin measurements (Fig. 6) as requested.]]

Figure 6 show a simplified scheme of proton transport with trapped water molecules in Hv1. Panel A represents a resting state (nonconductive); panel B represents an open state (conductive), favored by the D174A mutation. So, what makes B conductive and A nonconductive? Is it the presence of two salt bridges in B vs. three salt bridges in A? This should be clarified.

[[

We modified the figure based on the simulation results of Geragotelis et al. We indicate with arrows the parts of the channel where the proton is free to move and crosses the sites with insurmountable energy barriers.

Legend to the figure (now Fig. 8): “In the region of the selectivity filter adjacent to D112, the channel is too narrow to let water molecules pass (see also Fig. S1). Yet, the proton may bypass the electrostatic barrier of the open channel at D112 (18), i.e., jump between the two neighboring water molecules. Removal of D174 shifts the voltage sensitivity so that most channels are already open at a transmembrane potential of 0 mV. B) The closed channel. It neither allows water nor proton transport. In its new location, D112 provides an insurmountable electrostatic barrier to proton passage.”]]

Minor comments: The interpretation of Fig. 2E strongly depends on the assumption that the D174A mutation does not alter membrane trafficking. It is recommended to check the validity of this assumption, e.g., by colocalization with a plasma membrane marker. Images of SDS‐PAGE results for the studied Hv1 proteins should be provided to show preparation purity.

[[

We toned down the interpretation of Fig. 2E. As it stands now, Fig. 2 shows that the mutant (i) is functional and (ii) has a high open probability at 0 mV. These conclusions are independent on membrane trafficking. We included images of SDS page results for the studied HV1 proteins in the Supplement.]]

CROSS‐CONSULTATION COMMENTS I agree with the comments from the other two reviewers. My major point is that refuting major water permeability in Hv1 is not the same thing as refuting that protons can be conducted by transient water wires, unless it is proved that the transient water wires cannot sustain enough proton movement to account for the single channel conductance. Reviewer #3 (Significance (Required)): The Hv1 channel plays important roles in the human body, including the immune, respiratory, and reproductive systems. Despite recent advances in understanding the mechanism of proton conduction by Hv1, whether or not protons hop within a continuous water wire in the open channel is a subject of debate (DeCoursey J. Physiol. 2017, Bennett & Ramsey J. Physiol. 2017). This work provides important insights on the debate by refuting the existence of a water wire that can sustain large water permeability. The findings reported here will be of interest to ion channel biophysicist like this reviewer, but also to biologists studying cellular pH homeostasis and the pathophysiology of Hv1.

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

August 17, 2022

RE: Review Commons Refereed Preprint #RC-2022-01442

Dear Editor of the EMBO Journal,

Please find our updated manuscript and response to the reviewers’ comments. We appreciate the effort that the reviewers have put into the evaluation of our manuscript.

We are happy with the potential importance the reviewers realise in the study:

Reviewer 1: The finding that ubiquitination occurs inside mitochondria would be an important conceptual advance, which would open new perspectives both for ubiquitination and mitochondrial biology

Reviewer 2: This work would represent a significant/exceptional discovery if supported by compelling data.

Reviewer 3: the results are interesting and very important, as mentioned in the major comments section…

With regard to the major comments raised by the reviewers, you will find below our specific response point by point with explanations and suggested novel experiments (highlighted in yellow). In summary we suggest the following actions to fully support our model:

• We will perform a-complementation with ubiquitin (lacking the GG motif) fused at its C-terminus to the short fragment of b-galactosidase (a). Blue colonies with ωm will indicate import.
• As shown in Figure S2, now added to the manuscript, we show detection of ubiquitinated proteins and mono ubiquitin in extracts of mitochondria pre-treated with trypsin.
• A bio-archives address of our other manuscript will be provided.
• The use of a-complementation for protein localization was developed by us 15 years ago and since then has been used by us and other groups verifying its use as a screening tool. One point is clear, ωm or ωc do not leak into other subcellular compartments. Nevertheless, in the research of specific genes validation is important. Yes!!! ωm and ωc are exclusively located in mitochondria or the cytosol respectively.
• We will highly purify mitochondria on gradients and treat them with protease.
• We cannot be sure that we will be able to detect a protein with ubiquitin modifying activity which functions solely on certain proteins in mitochondria, so publication cannot rely on this.
• Repeat mass spectrometry with careful editing will be undertaken as suggested by the reviewer.
• We will attempt to perform protease protection assays in the presence of specific detergents.

Before tackling the very tough revision, we would like to know if EMBO Journal would positively consider acceptance of our manuscript based on the review and planned revision.

Prof. Ophry Pines Microbiology & Molecular Genetics Hebrew University of Jerusalem Jerusalem 91220 Israel

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

Summary:

In this manuscript, Zhang et al. investigate whether ubiquitination occurs inside mitochondria of the budding yeast S. cerevisiae. They first observe thanks to a sensitive complementation assay that several components of the yeast ubiquitination (and deubiquitination) machinery can localize inside mitochondria. To be able to specifically probe ubiquitin conjugates assembled inside mitochondria they fused HA-tagged ubiquitin to a mitochondrial targeting sequence. Using this construct, they demonstrate that ubiquitin conjugates can be assembled in mitochondria. A series of elegant experiments demonstrates that the pattern of ubiquitin conjugates depends on the mitochondrial localization and the activity of the ubiquitin conjugating enzyme Rad6. Altogether, these results convincingly demonstrate that ubiquitination can occur inside yeast mitochondria when ubiquitin is intentionally targeted inside this organelle. It however remains unclear whether mitochondrial ubiquitination occurs in endogenous conditions (without targeting ubiquitin into this compartment) and whether it affects mitochondrial functions.

Response: Regarding the question whether mitochondrial ubiquitination occurs in endogenous conditions, we feel that this is obvious based on our results. We detect numerous ubiquitination related enzymes (E1, E2, E3, DUB) eclipsed in mitochondria but none of the proteasome subunits. As pointed out by the reviewer “these results convincingly demonstrate that ubiquitination can occur inside yeast mitochondria”. With that said, additional data will be incorporated into the manuscript as suggested by the reviewer and can be seen below.

Major comments:

1) The materials and methods section is lacking important information (western blot protocol, details of antibodies, strains, plasmids...). It is thus difficult to evaluate how several experiments were performed and how their design (e.g. the promoters chosen to express tagged proteins) could impact the interpretation of the results. This is a major issue that needs to be corrected. The main text should also explicitly indicate whether tagged proteins used in the alpha-complementation assay are overexpressed or not.

Response: The materials and methods section will be updated accordingly.

2) Despite the previous comment, the data presented in the manuscript convincingly demonstrate that multiple components of the ubiquitination machinery can localize within mitochondria and that ubiquitin conjugates can be assembled in mitochondria when ubiquitin is modified to be intentionally targeted into this compartment. However, little data is shown to support the hypothesis that ubiquitin conjugates can be assembled in mitochondria when ubiquitin is not fused to a mitochondrial targeting sequence. Thus, in my opinion, the evidences presented in the current manuscript are not sufficient to conclude that ubiquitin conjugates are assembled in mitochondria in endogenous conditions (as this is done implicitly). Additional evidences are needed to draw this conclusion (see some experimental suggestions hereafter). Without further evidences, the speculative aspects of the claim that "ubiquitination occurs in the mitochondrial matrix" should be discussed explicitly.

Response: See the discussion above why we are confident that ubiquitination occurs in mitochondria. Our major problem with ubiquitin and the ubiquitination enzymes is that they are eclipsed in mitochondria. We propose as suggested by the reviewer (item 4 of his review) to perform a-complementation with ubiquitin fused at its C-terminus to the short fragment of b-galactosidase (a). Blue colonies with ωm will indicate import.

3) The authors used a mass spectrometry approach to identify mitochondrial ubiquitination substrates. However, they have not yet succeeded in identifying a substrate whose modification is specifically regulated by a given component of the mitochondrial ubiquitination machinery. They have also not identified a phenotype or process impacted by mitochondrial ubiquitination. Thus, at this stage, the biological consequences of mitochondrial ubiquitination remain elusive.

__Response: __We have not identified a substrate whose modification is dependent on a given component of the mitochondrial ubiquitination machinery, even though we have tried. Again, the problem is low levels of these proteins eclipsed in mitochondria. Even when we do find a protein that is ubiquitinated (e.g. Aco1) its ubiquitination is not exclusively dependent on Rad6. Thus, different ubiquitin enzymes may have the same substrates.

4) The authors have not directly investigated whether ubiquitin itself (without a mitochondrial targeting sequence) localizes in mitochondria. I encourage them to address this question since it would provide an important piece of evidence suggesting that mitochondrial ubiquitination can occur in endogenous conditions. This could be done using the alpha-complementation assay and the results could be presented within Figure 1. Ideally this experiment should be performed without overexpressing ubiquitin. Note that if the authors decide to use a C-terminally tagged form of ubiquitin for this experiment, the GG motif of ubiquitin should be mutated to avoid cleavage of the alpha tag by cellular DUBs. This form of ubiquitin will not be conjugatable, but this is not an issue for this experiment since its aim is to determine whether ubiquitin can be targeted to mitochondria, not to probe conjugates.

Response: We will perform experiments as suggested by the reviewer including ubiquitin fused at its C-terminus to the short fragment of b-galactosidase (a), see item 2. We have previously made a PreSu9-Ubi lacking a GG motif but now will look at a different combination of this and other constructs.

5) In the top panels of Figure 2 and S1, free ubiquitin is well detectable in the total and cytosolic fractions. It is however not clear to me whether it is also detectable in the concentrated mitochondrial fraction. If yes and if it would be resistant to trypsin digestion, it would provide additional evidence that endogenous ubiquitin can be targeted to the mitochondrial matrix (see previous comment).

Response: See Item 6.

6) The data shown in the top panel of Figure 2 and S1 also suggest that free ubiquitin is less concentrated in mitochondria than in the cytosol (since it is more difficult to detect in the concentrated mitochondrial fraction than in the cytosolic fraction, see previous comment). It is thus possible that the use of preSu9-HA-Ubi (or preFum1-HA-Ubi) lead to an artificially high intra-mitochondrial concentration of free ubiquitin. As the concentration of free ubiquitin is known to impact ubiquitination processes, I encourage the authors to compare the relative levels of free ubiquitin present in the mitochondrial fraction prepared from WT and preSu9-HA-Ubi (or preFum1-HA-Ubi) expressing cells. If free ubiquitin is detectable in mitochondrial fractions and resistant to trypsin (see previous comment), this could be done by repeating the experiment shown in Figure 3B and probing the blot with an antibody that recognizes free ubiquitin.

Response to 5 and 6: Detection of ubiquitin in mitochondria is extremely difficult even when mitochondria are 15-fold concentrated versus the cytosol and when HA-Ubi is overexpressed. Thus, ubiquitin is eclipsed in mitochondria. Nevertheless, as shown in the Figure below which was not part of the submitted manuscript yet was performed in parallel to experiments done early on, shows detection of very weak bands of free ubiquitin in extracts of mitochondria pre-treated with trypsin.

Endogenous ubiquitination pattern in mitochondria of _Δrad6 _cells is restored to normal by Rad6-α. __WT or Δrad6 cells containing a Rad6-α construct or an empty plasmid were subjected to subcellular fractionation. Mitochondrial fractions with or without trypsin treatment, were probed for ubiquitin by WB. Aco1 is a matrix mitochondrial protein, and Tom70 is a mitochondrial outer membrane protein (MOM) facing the cytosol.

7) I strongly encourage the authors to provide more data indicating that "ubiquitination occurs in mitochondria" by performing experiments that do not rely on the use of the preSu9-HA-Ubi or other forms of ubiquitin that are intentionally targeted to mitochondria. For instance, they could analyse the pattern of HA-Ubi conjugates of trypsin digested mitochondrial fractions prepared from wt, rad6-delta, and rad6-delta complemented with preSu9-Rad6-alpha-SL17. Note that if trypsin digested mitochondrial fractions are too contaminated by ubiquitinated proteins present outside mitochondria to perform this experiment, the authors may use the unspecific DUB Usp2 as an alternative protease to strip ubiquitinated proteins from the mitochondria periphery.

Response: Concentrated mitochondrial extracts from WT and Δrad6 cells untreated or treated with trypsin were probed with anti-ubiquitin antibodies (Figure above). A very weak band corresponding to free ubiquitin can be detected in extracts of mitochondria treated with trypsin but these are very weak and are on the limit of detection.

Minor comments:

1) Overall, the manuscript is well organized and easy to follow. The text is clearly written; the figures are well annotated.

2) The authors should provide full images of all the blots with anti-ubiquitin and anti-HA antibodies so that one can see the bands corresponding to free ubiquitin (or free HA-Ubi). For instance, in Figure 3B, it is not possible to see the presence (or absence) of the band corresponding to free HA-Ubi because the very bottom of the image is cut.

3) The authors should indicate whether the MTS of Su9 (and Fum1) are expected to be cleaved after import of preSu9-HA-Ubi (and preFum1-HA-Ubi) in mitochondria. They should also label on the corresponding immunoblots the presence (or absence) of the band corresponding to the free preSu9-HA-Ubi (and preFum1-HA-Ubi) (or HA-Ubi if the MTS is expected to be cleaved from these constructs).

4) In Figure 3B, the ubiquitin conjugates produced with preSu9-HA-Ubi and preFum1-HA-Ubi have different migration patterns. I think this should be explicitly mentioned and discussed. Could it be due to the presence of lysine residues in the Su9 or Fum1 MTS that could lead to the assembly of artificial ubiquitin chains?

5) The authors indicate that "endogenous Rad6 [...] is expressed at very low levels and can hardly be detected in the mitochondrial fraction by WB (Figure S5)". I did not manage to observe the band corresponding to endogenous Rad6 in the mitochondrial fraction in the pdf. The authors should provide a more contrasted or better quality image.

CROSS-CONSULTATION COMMENTS I agree with reviewer 2 that proper validation of the complementation assay is crucial for this manuscript. I was myself wondering whether it uses endogenously tagged proteins or whether it is based on an overexpression system. I imagine this information will be detailed in the manuscript in preparation mentioned by the authors. I am therefore wondering whether it would be possible to ask the authors to provide the draft of this manuscript (or at least the validation part).

Response: A bio-archives address of our other manuscript will be provided upon resubmission. See other issues referred to the response Reviewer 2.

I agree with most comments of reviewer 3. Regarding the hypothesis that preSu9-HA-Ubi could form aggregates on the cytosolic surface of the mitochondria, I think that the results presented on Figure 7B rather argue against it (since they indicate that Rad6 localized inside mitochondria can restore the pattern of ubiquitin conjugates). That's why (in my opinion) the major question the author now need to adress is whether intra-mitochondrial ubiquitination occurs in endogenous conditions (ie without forcing ubiquitin into this compartment and without E2 or E3 overexpression).

Response: See response to the other reviewers

Reviewer #1 (Significance (Required)):

The finding that ubiquitination occurs inside mitochondria would be an important conceptual advance, which would open new perspectives both for ubiquitination and mitochondrial biology research. However, the significance of the current manuscript is limited because the presented evidences heavily rely on the use of artificial conditions (ubiquitin tagged with a mitochondrial-targeting sequence) that may trigger irrelevant ubiquitination events. The significance would be much higher if the authors would provide further evidences indicating that intra-mitochondrial ubiquitination occurs in endogenous conditions and/or if they had identified a mitochondrial process specifically impacted by mitochondrial ubiquitination.

Expertise of the reviewer: Ubiquitination, Yeast biology, protein-protein interactions. No specific expertise in mitochondrial biology

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

In the manuscript by Yu et al., the authors test the concept that certain proteins are unevenly distributed within distinct cell compartments. Due to this localization discrepancy, protein detection in some subcellular compartments can be "eclipsed" by a predominant subset of specific protein localizing in another cell compartment their actual distribution. Therefore, tiny amounts of physiologically relevant proteins could be biologically relevant. Still, their function in some locations can be overlooked (or eclipsed) because of the high expression level of the same protein in another subcellular compartment(s). Although, this concept is not particularly novel. For example, it is already known that many different proteins can localize to distinct cellular locations (e.g., permanent mitochondrial and peroxisomal localization of many proteins or transient localization of particular proteins to separate cell compartments). The authors apply a yeast system and an α-complementation assay to test further the role of such eclipsed proteins in mitochondrial biology. Specifically, they focus on the ubiquitin (Ub, or as abbreviated incorrectly in this manuscript; Ubi) conjugation pathway, components of which have never been convincingly shown to localize inside the mitochondria. This work proposes that certain ubiquitination events can occur inside yeast mitochondria. This work would represent a significant/exceptional discovery if supported by compelling data. However, the major problem with this work is that the conclusions are based on the ectopic expression of distinct proteins. This approach is not failproof in precise protein expression/delivery to the specific subcellular locations and is likely to result in a non-specific localization. Thus, the problem of eclipsed proteins is addressed by the methodology that may lead to the artificial generation of eclipsed overexpressed proteins. A more effective approach would be if the authors found a way to study this issue with endogenous proteins. The need for overexpression of mitochondria-targeted ubiquitin makes it challenging to reconcile the physiological role of these fundings. In addition, some critical technical issues and omissions further reduce the potential impact of this work (see Specific comments below). For example, strong evidence of mitochondria fraction purity and additional evidence that all the essential constructs used in this work are not misdirected to a different compartment are needed.

Response: “Although, this concept is not particularly novel” is a very disappointing remark by the reviewer!! While dual targeting of proteins has been known for many, many years, how widespread the phenomenon was unknown and thought to be negligible. We are leaders for the last 30 years in the field of dual targeting and distribution and in particular distribution of single translation products. We coined the terms “echoforms” and “eclipsed distribution” and developed methods to detect and screen for dual targeting. The concept of eclipsed distribution and in particular eclipsed targeting to mitochondria is very new, and is leading to a novel perception of the mitochondrial proteome (see MS submission). While the reviewer appears to be an expert on ubiquitination, we are experts on dual targeting.

• Ub was abbreviated incorrectly in this manuscript, Ubi. __Response: __This will be corrected.

Other comments will be referred to in the response to Specific comments.

Specific comments 1. The authors should demonstrate beyond doubt that the ω components of their assay (ω-C, which supposedly stays in the cytosol-ONLY and the ω-M component, which seemingly remains in the mitochondria-ONLY) are in the compartment that the authors claim. These two proteins are transfected into yeast cells and overexpressed. Therefore it is possible that they leak to other, not intended, subcellular compartments. The authors assume that ω-M and ω-C are exclusively located either in the mitochondria or the cytosol. However, this should be shown as validation of the assay. The indicated reference from 2005 (Ref.13) and others are irrelevant since assays have variations and are often researcher/lab dependent. This validation is very important since a misallocation of the overexpressed ω-M or ω-C, leaking into other subcellular compartments, may cause misdetection of the α-constructs.

Response: The use of a-complementation for protein localization was developed by us 15 years ago and since then has been used by us and other groups verifying its use as a screening tool. One point is clear, ωm or ωc do not leak into other subcellular compartments. Nevertheless, in the research of specific genes validation is important. Yes!!! ωm and ωc are exclusively located in mitochondria or the cytosol respectively.

It is not surprising that Ub conjugates are detected in mitochondrial fractions. It could be due to ubiquitination of the OMM (coming from the cytosol) or perhaps since the subcellular fractions were not pure mitochondria free from contamination (the likely culprit could be the ER). The mitochondrial fractions in this work were obtained by 10,000 g separation between cytosolic and mitochondrial crude fractions. Indeed, these 10,000 g crude fractions are highly impure with membranes from other compartments (i.e., microsomes, lysosomes, and so on). Therefore, more sophisticated purification methods should be used. In addition, the authors should also test these fractions for non-mitochondrial proteins from other membrane organelles.

Response: We agree with the reviewer and therefore will take the following approaches:

1. i) We will treat isolated mitochondria with protease in order to remove adhering proteins and digest OMM proteins…… see attached figure.
2. ii) We will highly purify mitochondria on gradients and this will be straight forward since we are now employing such methods in other projects in the lab. iii) Matrix protein enrichment (by mass spec) is associated with IP for preSu9-HA-Ub conjugates which is three-fold higher than for HA-Ub. In any case the fact that we identify conjugates of proteins not known to be mitochondrial, strongly supports our thesis.

Figure 2. Coomassie blue staining does not show any signal in the "M" fraction. It can be interpreted that the authors do not get any mitochondria there, and therefore the lack of Ub signal is due to the absence of the protein in the samples. Using the same amount of protein from each fraction would probably reduce the necessity of 15x enrichment.

Response: The Coomassie blue staining does show a signal in the "M" fraction which is weak yet when a 15x enrichment is run, the protein level by Coomassie blue staining is similar to the cytosolic fraction.

Figure 3. It is puzzling why the HA-UBQ presence is so strong in the crude mitochondrial fraction, but the preSu9-HA-Ub signal (mito-matrix) is comparatively weak. These data suggest that the crude mito-fraction could be highly contaminated with OTHER membranes. On the other hand, the preSu9-HA-UBQ signal is no more than 1-5% of the total mitochondrial signal. The high enrichment of the HA-Ubi in both cytosols and the mitochondria could indicate the OMM ubiquitination or (again) contamination by other compartments. The constructs with MTS are detected in the mitochondria. However, the localization of tagged MTS-Ubi in a non-targeted compartment (e.g., cytosol) should be excluded by additional exposure times. Because the manuscript talks about eclipsed proteins, this is important.

Response: The HA-Ub is strong in the mitochondrial fraction, in the absence of trypsin, but is very weak in the presence of the protease indicating that most of the ubiquitinated proteins are externally attached to mitochondria. In contrast, PreSu9-HA-Ub is imported into the mitochondrial matrix and is protected from trypsin. This manuscript refers to “eclipsed in mitochondria” (not the cytosol) and this is true for ubiquitination enzymes as well as for ubiquitin.

Figure 3C-E. These data indeed suggest that the Ub-conjugates could be formed inside the mitochondria. However, the above-discussed possibility that other than mitochondria compartments co-sediment in the 10,000g fractions makes the data interpretation highly challenging.

__Response: __We will highly purify mitochondria on gradients and this will be straight forward since we are now employing such methods in other projects in the lab.

Figure 4. Unsurprisingly, mitochondrial targeting of Ub leads to detecting some co-immunoprecipitating mitochondrial proteins. However, these data do not support the notion that Ub conjugation machinery acts inside the mitochondria and that the target proteins are indeed conjugated with Ub (the interaction with Ub is not equal to being conjugated). At the minimum, the authors should provide a validation that some of the detected mitochondrial matrix proteins are indeed ubiquitinated. To this end, purified mitochondria could be used for the candidate protein IP under denaturing conditions and then blotted for the candidate protein and Ub.

__Response: __As shown in Table S2 and figure S7, forms of Ilv5, a mitochondrial protein, are ubiquitinated in WT and Drad6 cells. These modified forms of Ilv5 can be eluted from mitochondrial extracts of WT and Drad6 cells. However, the ubiquitination of ilv5 is not dependent or effected by the Drad6 mutation. We cannot be sure that we will be able to detect a protein with ubiquitin modifying activity which functions solely on certain proteins in mitochondria.

Figure 5. The knock-out of the E2 Rad6 causes a change in the mitochondria ubiquitination pattern. This is an interesting observation, but again it does not prove that the change in the mitochondrial ubiquitination is due to the activity of Rad6 inside of the mitochondria, as opposed to ubiquitination of the OMM proteins or contaminating fractions. One also wonders why overexpression of mitochondria-targeted Ub would be necessary to detect the ubiquitination if this process was physiologically relevant, especially given that detecting endogenous Ub is not challenging. Furthermore, the apparent increase in ubiquitination in E2 mutant cells (Fig. 5) should also be addressed in more detail. Finally, data from one WB is shown, and quantification of several independent experiments should also be provided.

__Response: __We show in the MS that RAD6 is exclusively targeted to mitochondria (Su9MTS) while unimported molecules are degraded (SL17; degron). This hybrid Rad6 can restore the WT ubiquitin pattern, while a rad6 active site mutant cannot.

Figure 6. Can the authors provide Western blot data showing the expression of Rad6? Furthermore, quantifying these rescue experiments is necessary to make this conclusion more solid.

Response: Even though we did not succeed in making good Rad6 antisera, we can clearly detect Rad6-a fusion proteins (Figure 7B).

Figure 7. The authors found that preSu9-Rad6-α have problems being imported into the mitochondria matrix; therefore, they rebuild it as a preSu9-Rad6-α-SL17 protein. SL17 is a degron that targets the cytosolic protein (not imported into the mitochondria) to the proteasome and degraded (Figs. 7A-B-C). These issues could be a red flag for the rest of the manuscript, suggesting that other constructs (that were not critically evaluated for their localization in this work) could leak to different cellular compartments.

Response: The wording used by the reviewer is particularly disturbing since current understanding in cell biology of eukaryotic cells does not accept “leaking” of proteins to different cellular compartments. One wouldn’t want DNAses, RNAses, Proteases etc leaking from one compartment to another. The localization of proteins to different cellular compartments involves very precise signals on the proteins, and specific cellular components, such as translocases, are required to target proteins to their exact destination. This is true for Rad6; it contains an MTS like sequence which when removed blocks import of the protein into mitochondria. Rad6 according to our analysis is an eclipsed dual targeted protein, so it no surprise that it is in two compartments and the trick with the SL17 degron solves the problem.

The manuscript needs to be carefully edited, some references are in the not correct format, and there are issues with figure labels.

Response: Careful editing will be undertaken as suggested by the reviewer.

CROSS-CONSULTATION COMMENTS I agree with a great summary by reviewer 1. This discovery should be validated by top-quality data.

Reviewer #2 (Significance (Required)):

In the manuscript by Yu et al., the authors test the concept that certain proteins are unevenly distributed within distinct cell compartments. Due to this localization discrepancy, protein detection in some subcellular compartments can be "eclipsed" by a predominant subset of specific protein localizing in another cell compartment their actual distribution. Therefore, tiny amounts of physiologically relevant proteins could be biologically relevant. Still, their function in some locations can be overlooked (or eclipsed) because of the high expression level of the same protein in another subcellular compartment(s). Although, this concept is not particularly novel. For example, it is already known that many different proteins can localize to distinct cellular locations (e.g., permanent mitochondrial and peroxisomal localization of many proteins or transient localization of particular proteins to separate cell compartments). The authors apply a yeast system and an α-complementation assay to test further the role of such eclipsed proteins in mitochondrial biology. Specifically, they focus on the ubiquitin (Ub, or as abbreviated incorrectly in this manuscript; Ubi) conjugation pathway, components of which have never been convincingly shown to localize inside the mitochondria. This work proposes that certain ubiquitination events can occur inside yeast mitochondria. This work would represent a significant/exceptional discovery if supported by compelling data. However, the major problem with this work is that the conclusions are based on the ectopic expression of distinct proteins. This approach is not failproof in precise protein expression/delivery to the specific subcellular locations and is likely to result in a non-specific localization. Thus, the problem of eclipsed proteins is addressed by the methodology that may lead to the artificial generation of eclipsed overexpressed proteins. A more effective approach would be if the authors found a way to study this issue with endogenous proteins. The need for overexpression of mitochondria-targeted ubiquitin makes it challenging to reconcile the physiological role of these fundings. In addition, some critical technical issues and omissions further reduce the potential impact of this work (see Specific comments above). For example, strong evidence of mitochondria fraction purity and additional evidence that all the essential constructs used in this work are not misdirected to a different compartment are needed.

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

Summary: In this study, the authors detected a set of components of a ubiquitination system in the mitochondrial matrix in budding yeast using the subcellular compartment-dependent α-complementation assay. The authors detected the conjugates of mitochondrial targeting signal sequence-directed HA-Ub (preSu9-HA-Ub) in the mitochondrial matrix. The immunoprecipitates of the preSu9-HA-Ubi conjugates were highly enriched for the mitochondrial matrix proteins. Subsequently, the authors focused on the Rad6 E2 ubiquitin conjugating enzyme in the mitochondrial matrix and evaluated its inactivation-altered ubiquitination pattern in the organelle. The authors conclude that ubiquitination occurs in the mitochondrial matrix because of the eclipsed targeted components of the ubiquitination machinery.

Major comments: The authors argued that the proteins that were modified with preSu9-HA-Ubi, which was forced to be imported into the mitochondria, are present in the mitochondrial matrix, because these species are resistant to trypsin digestion. However, it was possible that they formed severe aggregates on the cytosolic surface of the mitochondria, and hence, were resistant to the proteinase. In other words, a small amount of proteins that were not imported into the mitochondria could be deposited on the cytosolic surface of the mitochondria, where they were modified with preSu9-HA-Ubi by cytosolic Rad6. To confirm if the preSu9-HA-Ubi-modified proteins were really present in the mitochondrial matrix, they should perform the protease protection assay in the presence of an appropriate detergent (Figure 3D). In addition, subcellular fractionation of the organelle by density gradient centrifugation, indirect immunofluorescence microscopic analysis of the preSu9-HA-Ubi conjugates, and/or experiments on the in vitro import of preSu9-HA-Ubi and Rad6 into the mitochondria would strongly support the authors conclusion. Other experiments that might support the authors conclusion would be to test whether the band pattern for the preSu9-HA-Ubi conjugates changes when the mitochondrial import is impaired.

Response: We will attempt to perform 1) Protease protection assay in the presence of a detergent (Figure 3D). 2) Subcellular fractionation of the organelle by density gradient centrifugation. 3) In vitro import of Rad6 into the mitochondria.

Minor comments: In Figure 3B, the molecular weight distributions of the preSu9-HA-Ubi conjugates and those of the preFum-HA-Ubi conjugates are different. Is there any reason for this difference?

In Figure 3E, the position of "-" (MG132) for lane 1 is not correct.

In Figure 6A: The band pattern for preSu9-HA-Ubi (lane 13) in the rad6-delta cells expressing Ubc8-alpha is different from that of the wild-type cells expressing Ubc8-alpha (lane 12) as well as that obtained from the rad6-delta cells harboring empty plasmids (lane 9). Is there any explanation for this observation?

In Figure 7B and S6: The level of preSu9-Rad6-alpha-SL17 in the rad6-delta cells is always lower than that in the wild-type cells (compare lanes 13 and 10 in Figure 7B, and lanes 13 and 12 in Figure S6). Is there any explanation for this observation? The protease protection assay (with detergent control) is needed to fully confirm that preSu9-Rad6-alpha-SL17 is present in the mitochondria.

In Figure S7, the authors presented the matrix proteins, Ilv5 and Aco1, detected in the preSu9-HA-Ubi IPed samples and described this observation in the main text. However, the authors also showed the blots for Idh1 and Fum1, which were also pulled down with preSu9-HA-Ubi from the WT cells more than from the rad6-delta cells. Is this correct? If so, please elucidate this observation in the main text.

Figure 8D and 8E are not cited in the main text. Although there are no explanations for these figures in the main text, it looks like Rad6-deltaN11-alpha resides in the mitochondrial fraction. However, the alpha-complementation assay suggests that it resides in the cytosol. Please explain this discrepancy.

First page of the discussion section, item 6): E2 Rad6, but not E3 Rad6?

Figure S7: HA-Ub (cytosolic form) control is needed in addition to the empty vector control.

Figure S7, left panel: There is an unnecessary line break in "Hsp60" and "Ilv5."

Figure S7, right panel: There is an unnecessary line break in "Hsp60."

CROSS-CONSULTATION COMMENTS I agree with comments of reviewer 1 and 2. -Validation of the complementation assay. -I also think that it is important to address whether intra-mitochondrial ubiquitination can be observed with endogenous level of ubiquitin. If even a small amount of preSu9-HA-Ub is mistargeted to the cytosol, proteins at the cytosolic side of mitochondrial outer membrane could be ubiquitinated and detected in the mitochondrial fraction. -Preparation of mitochondria with more sophisticated purification methods (i.e. high resolution density gradient) would be needed to separate mitochondria from ER and other organelles. -More information is needed in the materials and methods section.

Reviewer #3 (Significance (Required)): Significance Although the results are interesting and very important, as mentioned in the major comments section, additional experiments are needed to support their model. However, researchers working on the mitochondrial biology and ubiquitin systems might be interested in and influenced by the reported findings.

Author Response

Reviewer #1 (Public Review):

The authors report a public browser in which users can easily investigate associations between PGSs for a wide range of traits, and a large set of metabolites measured by the Nightingale platform in UKBB. This browser can potentially be used for identifying novel biomarkers for disease traits or, alternatively, for identifying novel causal pathways for traits of interest.

Overall I have no major technical concerns about the study, but I would encourage the authors to revisit whether they can find a more compelling example that can better showcase the work that they have done. I understand that this is partly a resource paper but I think the resource itself can have more impact if the paper provides a clearer use-case for how it can drive novel biological insight.

Many thanks for your comments. We have undertaken a new application of bi-directional Mendelian randomization to demonstrate how users may use this approach to disentangle whether associations in our atlas likely reflect either causes or consequences of PGS traits/diseases. This example is described on page 9:

‘For example, we applied Mendelian randomization (MR) to further evaluate associations highlighted in our atlas with triglyceride-rich very low density lipoprotein (VLDL) particles. For instance, both VLDL particle average diameter size and concentration were associated with the PGS for body mass index (BMI) (Beta=0.04, 95% CI=0.033 to 0.046, P<1x10-300 & Beta=0.012, 95% CI=0.006 to 0.019, P=2.7x104 respectively) and coronary heart disease (CHD) (Beta=0.026, 95% CI=0.019 to 0.032, P<1x10-300 & Beta=0.035, 95% CI=0.028 to 0.042, P<1x10-300 respectively). Conducting bi-directional MR suggested that the associations with average diameter of VLDL particles are likely attributed to a consequence of BMI and CHD liability as opposed to the size of VLDL particles having a causal influence on these outcomes (Supplementary Table 6). In contrast, MR analyses suggested that the concentration of VLDL particles increases risk of CHD (Beta=1.28 per 1-SD change in VLDL particle concentration, 95% CI=1.25 to 1.65, P=2.8x10-7) which may explain associations between the CHD PGS and this metabolic trait within our atlas.’

and discussed in the discussion on page 21:

‘We likewise conducted bi-directional MR to demonstrate that associations between the CHD PGS and VLDL particle size likely reflect an effect of CHD liability on this metabolic trait. In contrast, the association between the CHD PGS and VLDL concentrations are likely attributed to the causal influence of this metabolic trait on CHD risk, suggesting that it is the concentration of these triglyceride-rich particles that are important in terms of the aetiology of CHD risk as opposed to their actual size. We envisage that findings from our atlas, as well as other ongoing efforts which leverage the large-scale NMR data within UKB, should facilitate further granular insight into lipoprotein lipid biology.’

PGS construction: It's unclear how well the PGS work. Should the reader prefer the stringent or lenient PGS? Perhaps there could be some validation with traits that have decent sample sizes in UKBB. Was there any filtering to remove traits with few GWS hits, low sample sizes, or low SNP heritability as these are unlikely to produce useful PGSs?

An example of validation was previously included for the chronic kidney disease PGS and its association with circulating creatinine, although this has now been removed due to the feedback you provided in your comments below. However, we have now provided the weights for all of the PGS included in our web atlas should users want to use these scores for prediction purposes (page 7):

‘The specific weights for clumped variants used in all PGS can be found at https://tinyurl.com/PGSweights.’

On page 8 we have mentioned that in this work we have used a more lenient threshold to facilitate endeavours in a ‘reverse gear Mendelian randomization’ framework. However, the option to use the more stringent threshold remains an option for users interested in this as an alternative:

‘In this paper, we have discussed findings using PGS that were derived using the more lenient criteria (i.e., P<0.05 & r2<0.1), although all findings based on both thresholds can be found in the web atlas.’

‘Specifically, we believe our findings can facilitate a ‘reverse gear Mendelian randomization’ approach to disentangle whether associations likely reflect metabolic traits acting as a cause or consequence of disease risk (Holmes and Davey Smith, 2019) as illustrated using triglyceride-rich very low density lipoprotein (VLDL) particles in the next section.’

We have not filtering based on other criteria such as the number as SNPs given that certain scores, despite only been constructed using few SNPs, may still provide useful to users. For example, our score for ‘Drinks per day’ based on the more stringent threshold (i.e. P<5x10-8) consists of only 6 SNPs. However, one of these is rs1229984, a missense variant located at the alcohol dehydrogenase ADH1B gene region and known to be a strong predictor of alcohol use (e.g. https://pubmed.ncbi.nlm.nih.gov/31745073/).

Reviewer #2 (Public Review):

The authors set out to create an atlas of associations between phenome-wide polygenic scores and circulating lipids, fatty acids, and metabolites. To do so, they utilize GWAS from 129 traits available in the OpenGWAS database to derive polygenic (risk) scores (PGS) along with the recently released NMR metabolomics data containing 249 biomarkers (and ratios) in ~120,000 UK Biobank participants. The authors create a publicly available web portal containing PGS to NMR biomarker associations:

http://mrcieu.mrsoftware.org/metabolites_PGS_atlas/.

The strength of this study is in the comprehensive nature of the atlas, containing associations for 129 traits phenome-wide, the large sample size of the UK Biobank NMR data, and the use of PGS for prioritising molecular traits for follow-up experiments, which is an emerging area of interest (International Common Disease Alliance, 2020; Ritchie et al., 2021a). To our knowledge this study is the first to explore this for circulating metabolites.

In its current form the atlas has several limitations, which should be straightforward to address. Notably, results in the current atlas may be confounded by (1) technical variation in the NMR data (Ritchie et al., 2021b), and (2) major biological determinants of biomarker concentrations, including body mass index, fasting time, and statin usage.

Firstly, thank you for the suggestion to use your ‘ukbnmr’ R package to help remove technical variations from the UK Biobank NMR metabolites data. We have applied it to remove outliers and variation in the individual data due to (1) the duration between sample preparation and sample measurement, (2) position of samples on shipment plates, (3) different equipment (spectrometers) used. This meant that we needed to re-run our entire analysis pipeline for this project from scratch to the updated dataset. Results do not appear to have drastically changed, although nonetheless we have updated results from all downstream analyses in our online web atlas using this updated dataset provided by ‘ukbnmr’.

Secondly, the reviewer is correct that biological factors, such as body mass index (BMI) and statin usage, are indeed strongly correlated with metabolites levels. However, we are not able to adjust for such biological factors directly in our analyses, given that they are potential colliders in the causal relationship between diseases/traits and metabolites. Statin usage may be caused by both the high genetic liability to coronary artery disease as well as abnormal lipoprotein lipid levels. Likewise, obesity (and changes in BMI) may result from a high genetic predisposition to cardiometabolic disorders and disrupted metabolism. Thus, adjusting for statin usage and BMI will induce collider bias (https://jamanetwork.com/journals/jama/fullarticle/2790247), which creates spurious associations between the disease/trait PGS and metabolites.

To better illustrate this issue, we have added additional text on page 14 to justify this study design decision as well as added a new figure (Figure 3) to help demonstrate this clearly to the readers. Fasting time on the other hand we believe is unlikely to act as a collider and was adjusted as a covariate in all linear regression models in this work. This is mentioned on page 25.

…Further, association results for two (of the 129) PGSs, systolic blood pressure (SBP) and diastolic blood pressure (DBP), are invalid (vastly inflated) as the GWASs used to construct these PGSs included UK Biobank samples.

Many thanks for your suggestion. We have now removed the SBP and DBP PGS from our atlas due to overlapping samples in UKB. Furthermore, our colleagues at the University of Bristol have notified us that the Glioma GWAS data obtained from the OpenGWAS platform was uploaded with incorrect effect alleles. This PGS has also been subsequently removed from the atlas. Additionally, we removed the Alzheimer’s disease (without APOE) PGS because the pleiotropic effect of lipid associated genes is now systematically examined using lipid gene excluded PGS.

To demonstrate how one might use these PGS to NMR biomarker associations to prioritise (or deprioritise) findings for follow-up, the authors select a biomarker of interest, glycoprotein acetyls (GlycA), to perform bi-directional Mendelian randomization to orient the direction of causal effects between GlycA and traits of associated PGS. However, the conclusions of this analysis are hampered by the heterogeneous nature of the GlycA biomarker, which captures the levels of five proteins in circulation (Otvos et al., 2015; Ritchie et al., 2019), making it a difficult target to appropriately instrument for Mendelian randomization analysis. This, however, does not detract from the broader point the authors make: that PGS can help prioritize molecular traits for experimental follow-up.

We have now conducted further sensitivity analyses to evaluate the genetically predicted effects of each of the five proteins in the reference you have provided. This is discussed on page 11:

‘We also conducted further sensitivity analyses given that the NMR signal of GlycA is a composite signal contributed by the glycan N-acetylglucosamine residues on five acute-phase proteins, including alpha1-acid glycoprotein, haptoglobin, alpha1-antitrypsin, alpha1-antichymotrypsin, and transferrin (Otvos et al., 2015). Using cis-acting plasma protein (where possible) and expression quantitative trait loci (pQTLs and eQTLs) as instrumental variables for these proteins (Supplementary Table 12) did not provide convincing evidence that they play a role in disease risk for associations between PGS and GlycA (Supplementary Table 13). The only effect estimate robust to multiple testing was found for higher genetically predicted alpha1-antitrypsin levels on gamma glutamyl transferase (GGT) levels (Beta=0.05 SD change in GGT per 1 SD increase in protein levels, 95% CI=0.03 to 0.07, FDR=3.6x10-3), although this was not replicated when using estimates of genetic associations with GGT levels from a larger GWAS conducted in the UK Biobank data (Beta=1.6x10-3, 95% CI=-6.9 x10-3 to 0.01, P=0.71). For details of pleiotropy robust analysis and replication results see Supplementary Table 14.’

There are also several important limitations to the study which cannot be addressed, which the authors discuss appropriately in the paper. First, the NMR data does not provide a comprehensive view of the metabolome - it is heavily focused on lipids and fatty acids. Many small metabolites in circulation cannot be measured by NMR spectroscopy, and further insights must wait for data from molecular profiling efforts planned or underway in UK Biobank (e.g. mass spectrometry). Second, the authors restricted analysis to participants of European ancestries. This a pragmatic analysis choice given (1) the PGSs were derived from GWAS performed in European ancestries, (2) PGS associations are particularly susceptible to confounding from genetic stratification and differences in environment, and (3) the very small sample sizes for which NMR data is currently available in UK Biobank participants. Finally, although a large sample size, UK Biobank is not a random sample of the population: healthy adults are over-represented, meaning PGS to metabolite associations may be different in disease cases or less healthy individuals.

Overall this study has strong potential, with straightforward to address limitations, and the resulting atlas will provide a useful characterisation of the relationships between NMR biomarkers and polygenic predisposition to various traits and diseases, which can be used by domain experts to prioritise biomarkers or traits for experimental follow-up.

Reviewer #3 (Public Review):

Fang et al. created an atlas for associations between the genetic liability of common risk factors or complex disorders and the abundance of small molecules as well as the characteristics of major apolipoproteins in blood. The whole study is well executed, and the statistical framework is sound. A clear strength of the study is the large array of common risk factors and disease analyzed by means of polygenic risk scores (PGS). Further, the development of an open access platform with appealing graphical display of study results is another strength of the work. Such a reference catalog can help to identify novel biomarkers for diseases and possible causative mechanisms. The authors further show, how such a systematic investigation can also help to distinguish cause from causation. For example, an inflammatory molecule readily measured by the NMR platform and strongly associated in observational studies, is likely to be a consequence rather than a cause for common complex diseases.

However, in its current form, the study suffers from some weakness that would need to be addressed to improve the applicability of the 'atlas'. This includes a distinction of locus-specific versus real polygenic effects, that is, to what extent are findings for a PGS driven by strong single genetic variants that have been shown to have dramatic impact on small molecule concentrations in blood.

Thank you for your suggestions to help refine our work. In line with this comment, we have repeated all analyses 1) after applying the ‘ukbnmr’ R package as recommending by reviewer #2 to remove technical variations and outliers and 2) conducted sensitivity analyses to remove an established list of lipid gene loci from PGS construction. Full results can be interrogated in the web atlas to evaluate whether PGS association may be driven by locus-specific effects at these regions, which may be particularly informative given the representation of lipoprotein lipid metabolites on the NMR panel. Findings are reported on page 19:

‘The polygenic nature of complex traits means that the inclusion of highly weighted pleiotropic genetic variants in PGS may introduce bias into genetic associations within our atlas. To provide insight into this issue, we constructed PGS excluding variants within the regions of the genome which encode the genes for 14 major regulators of NMR lipoprotein lipids signals which captured 75% of the gene-metabolite associations in the Finnish Metabolic Syndrome In Men (METSIM) cohort (Gallois et al., 2019). For details of these genes see Supplementary Table 5).

For PGS with these lipid loci excluded, anthropometric traits such as waist-to-hip ratio (N=209), waist circumference (N=206) and body mass index (N=205) still provided strong evidence of association with the majority of metabolic measurements on the NMR panel based on multiple testing corrections. Elsewhere however, the Alzheimer’s disease PGS, which was associated with 60 metabolic traits robust to P<0.05/19 in the initial analysis including these lipid loci (Supplementary Table 17), provided no convincing evidence of association with the 249 circulating metabolites after excluding the lipid loci based on the same multiple testing threshold (Supplementary Table 18). Further inspection suggested that the likely explanation for this attenuation of evidence were due to variants located within the APOE locus which are recognised to exert their influence on phenotypic traits via horizontally pleiotropic pathways (Ferguson et al., 2020).’

…Further, it is unclear how much NMR spectroscopy adds over and above established clinical biomarkers, such as LDL-cholesterol or total triglycerides. This is in particular important, since the authors do not adequately distinguish between small molecules, such as amino acids, and characteristics of lipoprotein particles, e.g., the cholesterol content of VLDL, LDL or HDL particles, the latter presenting the vast majority of measures provided by the NMR platform. Finally, the study would benefit from more intriguing or novel examples, how such an atlas could help to identify novel biomarkers or potential causal metabolites, or lipoprotein measures other than the long-established markers named in the manuscript, such as creatinine or lipoproteins.

To address these comments, we have added a new example focusing on the granular measures of VLDL particles provided by the NMR data (on top of the examples listed at the start of the response to reviewer document), which as the review points out is one of its strengths of the measures generated by this platform over long-established biomarkers (page 21):

‘We likewise conducted bi-directional MR to demonstrate that associations between the CHD PGS and VLDL particle size likely reflect an effect of CHD liability on this metabolic trait. In contrast, the association between the CHD PGS and VLDL concentrations are likely attributed to the causal influence of this metabolic trait on CHD risk, suggesting that it is the concentration of these triglyceride-rich particles that are important in terms of the aetiology of CHD risk as opposed to their actual size. We envisage that findings from our atlas, as well as other ongoing efforts which leverage the large-scale NMR data within UKB, should facilitate further granular insight into lipoprotein lipid biology.’

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Reply to the reviewers

Reviewer #1:

1) The authors could consider qualifying the observations as preliminary as no

mechanistic data or longer-term pathophysiology is investigated. Indeed, the latter is well

beyond the current scope and may require generation of cell-type specific STING ki mice.

• *

Thank you for the comment. We have qualified our observations as preliminary (line 662).

Indeed, generating cell-type specific STING ki mice is part of our future plans.

2) The authors consistently write "NF-kB/inflammasomes" - these two pathways (although

related) are quite distinct and should not be lumped together in such a way.

• *

Thank you for this important note, we now corrected the text (for example see section headings in the Results section, lines 338 and 415).

3) Line 79: "NRLP3" should be corrected to NLRP3.

• *

Line 210: age of "adult mice" in weeks should be state in the text and figure legend.

Thank you, corrected.

4) Line 262: In Figure 3B and D the images look very different and there is no indication of

what a positive inclusion is? This should be indicated on the image.

• *

Thank you for the suggestion. We replaced the corresponding panels with new images, where we show the nuclei with blue, and the Thioflavin S staining with magenta pseudo-color (current Figure 3E). We marked the outline of Thioflavin S positive cells with yellow. An inset showing the magnification of some neurons with inclusions is also presented.

5) Line 280: The data of Ifi44 should also be mentioned in the text.

• *

Thank you. We performed new experiments to show the gene expression changes in the

striatum and in the substantia nigra, therefore majority of the gene expression data from the cortex has been moved to the supplementary material (supplemental Figures 3 and 5), and is not discussed in detailed in the current manuscript.

6) Line 290: Figure 4, Examining IL-1B and Caspase-1 transcripts is not a readout of

inflammasome activation. pro-IL1B is upregulated in response to NFkB activity. Inflammasome

activation is commonly examined in other methods e.g. via ASC puncta formation (imaging

based), active IL-1B secretion (ELISA), Caspase-1 and IL-1B cleavage via western blot

Thank you for this suggestion, we performed new experiments and added the data as Figure 6.

• We performed Western blot analysis to detect IL-1β cleavage and NLRP3 proteins from the striatum (Figure 6C-E). 2) We quantified the number of ASC puncta within microglia and astroglia from striatal sections (Figure 6F-I). 3). 3) We also measured the protein levels of several additional immune mediators in the striatum of STING ki and KO animals (supplemental Figure 6, summary heatmap is on Figure 7A). 7) Line 310: The NF-kB subunit examined should be stated (p65?). Furthermore, IRF3

translocation might be a better readout for STING activation.

• *

We indeed detected the p65 subunit of NF-kB (antibody is listed in the supplemental Table), and now it is also indicated in the text (line 366). We also performed subcellular fractionation and quantified IRF3 in the nuclear and cytoplasmic fractions. The data is now added on Figure 6A, B.

8) Discussion: Given the findings here suggest a strong role for NF-kB, a short discussion

of IFN vs non-IFN responses from STING should be included. There have been a number of

seminal papers demonstrating the importance of non-IFN STING responses of late as well as

much evidence from SAVI mice to suggest some non-IFN driven pathologies.

• *

Thank you for the suggestion. The data on inflammasomes were given a separate section in the results (from line 395). In the discussion, from line 535 we discuss the IFN dependent response and from line 548 we discuss the non-IFN driven pathways.

9) Discussion: Is there any evidence from the human SAVI patients of neuroinflammation

etc. This should be mentioned either way in the discussion

• *

Thank you for this comment. The manifestation of neurological symptoms is not a core feature of the human SAVI disease. Some patients suffer from various neurological symptoms e.g. calcification of basal ganglia, spastic diplegia and episodes of seizure (Fremond et al., 2021). We inserted a short text in discussion (lines 532-534).

10) Discussion: There is a large body of work demonstrating STING-induced cell death in

numerous cell types. Despite this it is not mentioned nor discussed but should be. It could

represent how dopaminergic neurons are lost in the STING ki mice.

• *

Thank you for pointing out the gap in our discussion. We added additional text in lines 604-618.

11) The resolution/quality of some of the imaging is not great but this may be due to PDF

Compression

Thank you, we upload the figures with higher resolution.

Reviewer #2:

1) The authors base their conclusions (line 215-216) on the neuroinflammatory status of

their mice strongly on an assessment of the Iba1 and GFAP-positive area fraction. Increase of

Iba1 and GFAP areas does not necessarily correlate with an increased cytokine production and

release by the cells. Therefore, in addition the measurement of cytokine mRNAs it would be

necessary to measure cytokines also on protein level (see also #4 and #5).

• *

Thank you for this suggestion, we measured the protein levels of several immune mediators with LEGENDplex™ assay from the striatum, and the new data are included as Figure 7A and supplemental Figure 6.

2) In the same context: Is the increase of Iba1 and GFAP- covered area due to increased

proliferation of microglia and astrocytes or due to increased expression of these markers in

activated glia? How is the number of Iba1/GFAP-positive cells affected?

• *

We quantified the number of glia cells in the striatum and in the substantia nigra of adult STING WT and STING ki mice, and, parallel with higher immunoreactivity for the corresponding markers, we detected increased number of cells as well. The quantifications are now included in supplemental Figure 1.

3) Nowadays we know that microglia and astrocytes can exist in a variety of activated

states which can be either beneficial or detrimental. An analysis of disease-associated

microglial markers (Keren-Shaul et al. 2017) would give a good picture of the state microglia are

in.

• *

Thank you for the suggestion. In addition to the panel of immune modulators at the protein level (supplemental Figure 6), we performed qPCR analysis of additional “M1” marker (Nos2) and additional “M2” markers (Il4, Fizz2, Ym1) (Gong et al., 2019). The data is included in Figure 7A and shown in supplemental figure 6. The findings are described from line 431.

4) It also would be of interest to determine which cell type is responsible for the observed

neurodegeneration. Which cytokines are released by microglia or astrocytes upon STING

activation? Even in vitro experiments would help here to get a more profound understanding.

• *

We agree with the suggestion, however, the further in vitro experiments are beyond the scopes of this study and will be the basis of a future project.

5) In line 273 the authors describe that STING is known to activate NFkB and the

inflammasome. As proof that this is also occurring in their mouse, they perform qPCR analysis

of whole brain IL-1b, TNF-a and Casp1 expression. While this analysis indicates that there is

indeed an increased mRNA production of proinflammatory cytokines in the brains of STING ki

mice, it does not give any indication whether the inflammasome is active or not. The inflammasome is a protein complex largely regulated on protein level. Meaning an assessment

of the cleavage of Caspase 1 on protein level or the presence of cleaved IL-1b in comparison to

uncleaved Pro-IL-1b by Western Blot as well as a staining for the number of inflammasomes

would be required to draw these conclusions.

• *

Thank you for the suggestion. We performed additional experiments: 1) Western blot to detect pro-IL1b and IL1b and NLRP3 proteins from the striatum (Figure 6C-E), and 2) we quantified the number of ASC puncta within microglia and astroglia from striatal sections (Figure 6F-I).

6) To conclude that NFkb/inflammasome pathway is the most active/crucial in astrocytes

(line 354) a staining for ASC inflammasomes would be of importance, especially as astrocytes

normally do not express NLRP3.

• *

Thank you for this comment. We stained brain sections for ASC specks and for microglia (Iba1) and astroglia (GFAP) markers (Figure 6F-I). Although amount of ASC specks in astroglia was lower than in microglia, we found still a substantial amount of ASC specks in astroglia in the brains of STING ki animals.

7) As already shown for ALS (Yu et al., 2020) and Parkin KO (Sliter et al. 2018), the authors want to

further assess the relevance of the STING pathway to PD (line 27-28). Therefore, an in-depth analysis of

key PD hallmarks beyond phosphorylated a-synuclein, loss the other was parkin/PINK related (so TDP

deleted) of TH-stained neurons and dopamine reduction is needed. In the discussion the authors

hypothesize that autophagy (line 467) may be linked to the observed phenotype. Therefore,

assessment of autophagy/mitophagy as well as mitochondrial dysfunction and mtDNA should

be analysed. In the same line of thought it would be important to know if and how the observed

dopamine reduction effects mouse behaviour, thus mice should be subjected to the Rotarod or

pole or beam walk test.

• *

Thank you for these suggestions. In the work by Yu et al. and Sliter et al., the STING pathway was shown to mediate neurodegeneration resulting from TDP-43 pathology and mitochondrial damage. Our work is complementary by investigating the effects of constitutive activation of STING. We have therefore focused on the signaling pathways downstream of STING. As mentioned above, the most important next step will be to separate the contributions of neuronal and glial cells by generating cell type specific STING activation. Of course, it will be interesting to see at a later time point whether STING activation feeds back. We also speculate that STING activation may also cause TDP-43 pathology. Yet, this will be part of a future study. To acknowledge that the pathology is not specific to alpha-synuclein, we added a short statement from line 634.

With respect to the comprehensive analysis of the PD phenotype, our work includes the

classical parameters of TH neuron number, TH fiber density, dopamine concentration and

synuclein pathology. With respect to mouse behavior, we note that the STING ki mice have severe inflammation in the lung, kidney and other (peripheral) organs, reduced body weight and reduced lifespan (Luksch et al., 2019; Motwani et al., 2019; Siedel et al., 2020). Motor deficits cannot be attributed to dopamine neuron degeneration and for this reason were not included (stated in the Discussion, lines 624-625). In order to expand the description of the PD phenotype we now included measurements of cytosolic reactive oxygen species, mitochondrial oxygen species and nitric oxide, which result from inflammation and are known to affect dopaminergic neurons (new Figure 8).

Reviewer #3:

1) The method for quantification of TH-positive cells is not sufficient. They just described

how they stained every fifth sections but did not mention how they count. This is a critical point

and they should carefully provide information more than just referring their previous paper.

Counting of dopaminergic neurons and quantification of fibers was described in a dedicated section of the methods. This section has now been expanded (from line 154).

2) It is not persuasive that they did not investigate local inflammation in SN. They

presented increased microglia and astrocytes in the striatum but not analyzed these cells in SN

• *

Indeed, we measured neuroinflammation in the substantia nigra as well, however, although increased in STING ki mice, it was less pronounced than neuroinflammation in the striatum. We now include the quantification of area fraction as well as cell number counting of microglia and astroglia in the substantia nigra of STING WT and STING ki animals (supplemental Figure 1), and also the expression of inflammatory mediators in Figure 4.

3) In Figure 3, they analyzed alpha-synuclein phosphorylation and beta-sheet structure in

the striatum. This is funny from the aspect of Parkinson's disease, which dominantly affects SN.

They should perform similar experiments with SN samples. In a different aspect, the aggregates

detected by Thio S may not be alpha-synuclein and could be tau, TDP43 or other substances.

Phospho-synuclein of course does not mean aggregation, so they can consider electron

microscopy.

• *

We agree with the reviewer. To complement our data, we therefore performed solubility assay both from the striatum and from the substantia nigra to quantify the ratio of alpha-synuclein in the Triton X-100 soluble and insoluble fractions (Figure 3C, D) as previously (Szego et al., 2022; Szegő et al., 2019). Additionally, we quantified phosphorylated alpha-synuclein from the substantia nigra as well Figure 3A,B).

We also agree with the reviewer that the presence of Thioflavin S-positive inclusions may also contain other, beta-sheet forming proteins and noted this from line 634.

4) Figure 5, pSTAT3 increased in Iba1-negative cells, which seem neurons from the size of

nuclei. First, the authors should investigate the identity of pSTAT3-positive cells with GFAP and

MAP2. If pSTAT3 is actually increased in neurons, what does it mean in the pathology? For

instance, in viral infection, STAT3 activation triggers suicide of neurons to prevent further

proliferation of viral particles in neurons. Is it homologous or other function?

• *

Thank you for this suggestion. The brain sections were stained for Iba1 and GFAP. pSTAT3 nuclear staining indeed increased in non-glia cells, based on the morphology, we think in neurons. However, detailed characterization of the signal is out of the scopes of this (preliminary) study.

5) In Figure 6 and overall, cell types in which the activation of three signaling pathways,

were mixed up and hard to understand the actual situation in the brain.

• *

In our model, STING is activated in all cells. Consequently, we cannot determine the origin of immune mediators found elevated in the STING ki mice. This will require cell type specific STING activation. In order to react to the reviewer’s comment and be clearer, we have added more details about the brain region and age of mice used for each analysis also in the figures.

6) In the method section, the original paper for generation of heterozygous STING N153S

KI mice should be Warner et al, JEM 2017.

• *

We used a STING N153S ki mouse strain that was independently generated in the Technical University Dresden (Luksch et al., 2019).

7) NF-κB stains seem located in cytoplasm in Figure 5B.

• *

We agree: especially in the young STING ki mice, cytoplasmic NF-kB staining is increased

compared to STING WT mice. To quantify nuclear translocation, however, we counted the

number of those cells where NF-kB signal was overlapping with the nuclear Hoechst staining.

8) In Figure 4 and 6, why the authors evaluate gene expressions in frontal cortex instead of

SN or striatum.

• *

As noted in several comments, we show here that the STING-induced pathology involves

dopaminergic neurons, but believe that it is not specific for the dopaminergic system given that STING-ki is ubiquitously expressed. For practical reasons, we have used cortical samples for the expression analysis. For consistency, we now performed additional qPCR measurement from the striatum and from the substantia nigra and included them as new Figure 4 and supplemental Figure 6N-Q. The previous data from the cortex was moved to the supplemental Figures 3 and 5. Additionally, we measured the levels of several inflammatory modulators from the striatum of STING ki and KO animals (Figure 7A and supplemental figure 6A-M).

9) In some groups (Sting-ki;ifnar1-/- in Fig 6C, 6E), the values were separated to two

groups, which makes readers to doubt on soundness of their genotyping.

• *

Our genotyping protocol is highly standardized, and the genotype of the animals were correctly assigned. Here we provide an example of gel images showing the products after PCR reactions for the STING N153S allele (Figure 1a), STING WT allele (Figure 1b), Ifnara WT allele (Figure 1c) and lack of Ifnara allele (Figure 1d) of the same animals. We note that a bimodal distribution of phenotypes is often observed in Ifnar-/- mice.

Author Response

Reviewer #2 (Public Review):

This work will be of potential interest to biologists studying aging. While transposable elements have been reported to have higher expression as organisms age, it was previously unclear if their expression can exacerbate aging phenotypes or if they are a byproduct of aging. The authors present evidence in this manuscript that artificially increasing transposable element expression during the whole Drosophila life cycle can worsen aging phenotypes.

Strengths

The authors provide direct evidence that expression of their gypsy construct across the whole life of animals decreases fly lifespan (Figure 4), and that this outcome is dependent on reverse transcriptase (Figure 6).

Monitoring TE mobilization can be difficult in general and is often expensive when using a sequencing approach. The authors accurately monitor gypsy mobilization from their ectopic copy through qPCR and sequencing.

Weaknesses

Experiment design, data interpretation, and story structure:

The current model proposes that TE increases activity in aged animals and potentially contributes to the aging process. However, this paper artificially drives gypsy activation throughout the whole fly life cycle. Under this design, TE may already bring deleterious effects from early developmental stages or young age, thus ultimately shortening their life cycle. To truly test the function of TE during the aging process, the authors need to temporally control gypsy expression and only express their construct in aged animals.

Figure 1: I am not sure I got any convincing messages from this figure. First, flies at 30 days of age should not be considered as old. Second, the authors try to claim that TE expression increased with aged FOXO mutants. However, there is no data to show the comparison between aged wild-type and FOXO mutants (panel e is young wt vs young FOXO null). Meanwhile, Figure 1 has nothing to do with Gypsy. How could this figure fit into the story?

It is clear that we did not do a good job explaining this section. First, we did not mean to imply that the 30-day flies are old. They are simply older than the 5-day flies. The 30-day timepoint was chosen to match previous experiments and data sets in the literature. It was also chosen to minimize any survivor bias that could occur by doing the assay in very old flies. We have clarified this in the text and figures.

Second, it is the number of transposons that show an increase in expression in the dFOXO null animals that we mean to highlight (18 for dFOXO vs only 2 for wDAH). Panel e is meant to illustrate that the transposon landscapes, even in young flies differ by genotype making a direct transposon to transposon comparison impossible. We have added text to clarify these points.

Third, we also do not mean to imply that anything here is specific for gypsy. The work going forward in the paper uses gypsy as a tool because it is one of the better understood retrotransposons, there existed a validated active clone of the transposon and it has already been implicated in aging in the fly. We took gypsy as a model retrotransposon. We have added text to clarify here.

Figure 3: While the data presented in this Figure is sound, it is unclear how this data fits into the overall narrative that transposon activity drives aging.

Figure 3 is a continuation of the characterization of our ectopic gypsy. We wanted to rule out that there is a “hotspot” of insertion that would account for any phenotypes we observe. We find no hotspot in the males we use for analysis suggesting it is the act of transposition, not a specific target gene that is important. We have added to the text to clarify the motivation for these experiments.

Figure 5: It is interesting to see the copies of gypsy are not increased after 5 days. Does gypsy still mobilize after this young age? If yes, the authors should observe increased gypsy gDNA in later time points, unless the cells having gypsy new insertions keep dying. The authors should specifically check tissues with low cell turnover (such as brain) or high cell turnover (such as gut).

Reviewer 2 makes a great observation. In fact, using primer pairs that specifically detect the ectopic gypsy, we consistently see insertion numbers go down in very old animals (figure 5a&b). With our current understanding of retrotransposition, we should not be able to see loss of insertions unless the host cells are being lost from the analysis. We favor the idea that the reviewer suggests; that the cells that have high levels of insertion are dying and disappearing from the analysis. We think this is also reflected in the bias for intergenic or intronic sequences in our insertion mapping of figure 3. In an attempt to address this question we did measure insertions in heads versus bodies. In male flies aged 14 days there was no difference in the average number of insertions (although the variability was greater in heads). This data is reported in Supplemental Figure 6a.

Figure 8: Using Ubiquitin GAL4 to drive both gypsy and FOXO expression could dilute the expression of each individual gene. Thus, it is possible the rescue effect seen by expressing FOXO in addition to gypsy may just be due to lower gypsy expression. Including qPCR data showing gypsy expression levels in Ubi>gypsy, UAS FOXO flies compared to Ubi>gypsy flies would be helpful.

We included this data in Figure 2b and 8c. Unfortunately, we did not clearly direct the reader to compare the values. Comparing Figure 2b with Figure 8c shows the RNA level of the ectopic gypsy is comparable in both cases. Perhaps even slightly higher in the UAS-FOXO case. We have added a sentence to make this clear.

It is unclear if FOXO can rescue TE-specific aging phenotypes. While it appears that FOXO overexpression rescues the decrease in lifespan caused by gypsy expression, the authors did not test if FOXO overexpression could rescue the effects of gypsy in the paraquat resistance assays or rhythmicity experiments.

We include in this revision data showing dFOXO overexpression rescues the paraquat resistance and lowers the levels of overall insertions in the animals.

Author Response

Reviewer #1 (Public Review):

The authors of the paper provide new evidence of how prefrontal cortex of mutant mice used as a disease model of schizophrenia differs from wild type littermates. By analyzing local network dynamics at the level of specific cell type, authors shed new light on the circuit mechanisms that underlie changes in network dynamics in these mice.

The claims in the submitted manuscript are supported by the data. I have a few comments and questions that need to be clarified.

We thank the reviewer for highlighting the novelty of our work and its relevance (…shed new light on the circuit mechanisms that underlie changes in network dynamics in these mice…) for the field and the validity of our data (….claims in the submitted manuscript are supported by the data).

1) Average firing rates

Authors claim that they saw a significant reduction in interneuron firing rates in Disc1 mutant mice compared to control mice Fig.1c. However, the difference could be general and not interneuron specific. Due to the high firing rates of interneurons, the statistical test will work better on interneurons than on pyramidal cells as pyramidal cells average firing rates are lower. What I suggest to do is to take interneuron cells that fire at a lower rate (lower 33% for example ) and compare the control and Disc1 groups. Also I would suggest to take pyramidal cells that have higher firing rates (upper 33% for example) and compare firing rates across the same groups. One would like to see if these differences are not due to changes in firing rates per se.

We thank the reviewer for pointing out this important aspect. In our original analysis, we did not take into account that additional differences in the PYR population might be present but ‘masked’ by the overall lower firing rate of that neuronal population. As suggested by the reviewer, we separately considered the firing rate of the ‘top 33%” of the PYR population, which did not significantly differ between genotypes (p=0.958, n=209 control and 245 Disc1 PYRs, Welch’s test). As suggested, we moreover considered the ‘bottom 33%’ of INT firing rates, for which the significantly lower rates of Disc1-mutant INTs remained (control: 4.2 ± 0.6 Hz vs. Disc1: 1.8 ± 0.2, n=26 and 34 neurons, p=0.013, Mann-Whitney U-test). Since only few INTs were recorded per session in some cases (ranges: Disc1: 2-12/session; control: 2-19/session), we performed this analysis on the basis of individual cells (see also our reassessment of the main statistical comparisons in response to #1 by reviewer 2 and #4 by reviewer 3). These data are now reported in the new Fig. 1 – figure supplement 3 and referred to in line 76 ff. (line 72 ff. without tracked changes) of the revised manuscript.

2) Optogenetic tagging

Authors indicate that light triggered and spontaneous spike waveform are similar Fig.1d. This is nice, but would be better to see all the tagged neurons. I would suggest showing all optically tagged neurons spike features. Authors can impose with a different color spike features of tagged neurons in Fig.1a. I suspect that since all PVI are narrow spiking and they must fall into the area of blue colored cells in Fig.1a.

Following the reviewers suggestions, we included the average waveforms with and without light for all opto-tagged PVIs in the revised Fig. 1f. Moreover, we included the kinetic features of opto-tagged PVIs in Fig. 1a (red dots), and separately for control and Disc1-mutant mice in the new Figure 1-figure supplement 2. As predicted by the reviewer, the PVIs indeed cluster with the other putative INTs. We would moreover like to point to our new analysis in response to #2 of reviewer 2 addressing the spike kinetics of optotagged PVIs versus untagged putative INTs, which are similar in their trough-to-peak duration and asymmetry index. These data are shown in the novel Fig. 1 – figure supplement 2.

3) It was not clear why authors assessed only firing rates in last 25ms (line 348-349). If they have a clear justification for this they should provide it. But why not use the latency of the first spike also as an additional metric. A well tagged cell will respond to light pulse with short latency (within 5 ms). My concern is that non PVI cells may increase firing rate after 25ms of stimulation of PVI cells due to disinhibition.

Despite the latency to the first spike being frequently used as a method to detect ChR2-positive neurons, the laser stimulation produced significant photoartefacts in our hands. We were therefore concerned that spikes that happen shortly after the onset of the light pulse might be missed, and hence the latency to the first spike might be misinterpreted. Selecting a later time point in the stimulation interval allowed us to assess the firing rate during light application without the interference by artefacts. Nevertheless, we fully agree with the reviewer’s concern that ChR2-negative non-PVIs might increase their rate due to disinhibition, and that these neurons might thus be falsely classified as PVIs. However, we are confident that that is not the case. First, optotagged PVIs cluster well within the population of electrophysiologically identified INTs (see our response to your first remark on ‘optogenetic tagging’) and were indistinguishable from this population in terms of spike kinetics (see our response to #2 of reviewer 2 and the new Fig. 1 – figure supplement 2), suggesting that no disinhibited PYRs were included in the optotagged sample of cells. Second, we performed an additional analysis to address the time course of firing rate changes in optotagged PVIs. We computed smoothed spike trains (convolved with a 5 ms SD Gaussian kernel), and extracted the average firing rate of each optogenetically identified PVI centered on the onset of the light pulses. This analysis revealed a rapid increase in firing rate upon light delivery, arguing against disinhibitory network effects. These new data are now shown in the new Fig. 1 – figure supplement 5 and reported in line 89 (85 without tracked changes) of the revised manuscript.

4) Spike cross-correlations

The authors show that spike transmission probability from PYR to PVI is reduced in Disc1 mice compared to the controls Fig.2d and Fig.2e, but what happens to PVI to PYR spike transmission probability? Is it different in those groups? Answering this question is important since the authors discuss this topic in line 185-193.

Inhibitory synaptic interactions are indeed detectable by spike-train cross-correlation. However, we find these to be harder to quantitatively interpret than excitatory connections. Those interactions are not visible as spike transmission but rather as a reduction in spike transmission. Reliable estimates of the reduction in spike rate of postsynaptic PYRs require very large spike numbers of postsynaptic neurons that need to be sampled. For instance, Senzai et al., 2019 (Neuron 101: 500-513.e5) identified inhibitory interactions in continuous recordings lasting up to 68 h. Since we did not explicitly design our experiments to investigate inhibitory interactions, our recordings were substantially shorter than the required length. Using the method of Senzai et al., 2019 to identify inhibitory interactions, we detected only 5 INT-INT interactions (in the pooled Disc1-mutant and control data set). This low number does not allow the quantification of potentially reduced spike transmission. Thus, attempts to quantify inhibitory interactions properly would require a substantial amount of additional long-duration recordings. While the point raised by the reviewer is highly relevant and should be investigated in future, we think that given the extensive amount of experimentation needed to address this question, it is beyond the scope of the current manuscript.

5) Authors could try to link oscillations with spike transmission probabilities. On line 180 authors discuss that lower synchrony between PVI might be responsible for observed reduction in gamma power in Disc1 mutant mice. With the available data authors could test this hypothesis. They can look at spike cross correlations in their pool of INT and PVI (if they have pairs of PVI recorded in the same session) population.

We thank the reviewer for this excellent suggestion! We computed the cross-correlations for all simultaneously recorded putative INTs and quantified the baseline-subtracted mean cross-correlation within 10 ms around zero time lag. This analysis revealed weaker cross-correlation in Disc1-mutant mice (p=0.026, Mann-Whitney U test, tested on averages from n=7 control and Disc1 mice with at least 2 INTs recorded simultaneously), suggestive of reduced synchronization of putative INTs at short time lags. These new data are now included in the new Fig. 4 and reported in line 201 ff. (185 ff. without tracked changes) of the revised manuscript.

6) An alternative way to link oscillations with lower spike transmission probabilities in PYR-PVI pairs is to use synchrony triggered LFP analysis. One could take all time points when PVI and PYR cells fired acausal spikes within 2ms window and look at the LFP around this time point. Than take the average of the synchrony-triggered LFP and look at the power spectrum.

The proposal to link spike transmission with LFP power is indeed intriguing. As suggested by the reviewer, we extracted the 60-90 Hz-filtered LFPs triggered by INT spikes that followed a spike in a presynaptic PYR by <2 ms and measured the average gamma amplitude in a window of 20 ms around the INT spike. This analysis revealed comparable gamma amplitudes in Disc1 compared to control pairs. This finding suggests that local PYR-INT loops are still capable to produce gamma oscillations, and that the gamma oscillation defect of Disc1 mice is likely not caused by such a local defect. To investigate the relationship between INT spike timing and gamma oscillations more generally, we further extracted gamma amplitudes of spike-triggered LFPs using all available spikes of the INTs. Moreover, we compared the data to gamma amplitudes measured at randomly selected time points. ANOVA analysis followed by Tukey tests performed on the level of mouse averages indicated that while INT spiking-associated gamma amplitudes were significantly larger than those depicted from random time points in wild type mice (p=0.001). However, the same was not true for Disc1-mutant mice (p=0.591). Furthermore, this analysis revealed significantly reduced spike-triggered high gamma amplitudes in Disc1-mutant compared to control mice (p=0.011). While these results argue against a driving role of local connection alterations in gamma defects, they generally confirm the impaired synchrony of INT spiking relative to gamma oscillation that we observed in our analysis of phase coupling. These data are now shown in the new Fig. 4, which summarizes all new analyses regarding gamma oscillations and phase-coupling, and in figure 4 – figure supplement 2. The new results are described in the main text of the revised manuscript in line 188 ff. (172 ff. without tracked changes).

Considering the reduced short time scale synchronization of INTs (see our new results towards the reviewer’s #5) and reduced gamma amplitude of INT spike-triggered LFPs, it is possible that impaired synchronization among prefrontal INTs might contribute to the observed reduction in gamma power of Disc1-mutant mice (thereby, essentially, reflecting impaired INT gamma (ING)). Additionally, reduced long-range excitatory drive maintaining local gamma oscillations might be a contributing factor. For example, recent work showed that high gamma oscillations in the mPFC occur synchronized with gamma oscillations in the olfactory bulb (Karalis & Sirota, 2022, Nat Commun 13:467). It remains to be investigated whether local INTs are rhythmically driven by input from the olfactory bulb (in a multi-synaptic pathway including olfactory cortex) and to what extent that drive maintaining afferent gamma might be altered in Disc1-mutant mice. While the current data set does not allow a systematic evaluation of these possibilities, they should be further explored in future experiments.

7) Cell assembly analysis

The authors used 10ms for testing synchronization among pairs of PYR neurons in Fig.4a but 25ms for analysis of assembly dynamics. I think the authors justified why they used 25ms bin size, but it was not clear why they used 10ms? Could the authors clarify the reasons behind this decision?

The synchronization analysis was originally applied to PYRs converging on a common postsynaptic INT. English et al. (Neuron 95:505-520, 2017) systematically tested the effect of presynaptic cooperativity on spike transmission in the hippocampus (their Fig. 5). Their analysis revealed a maximum in cooperativity at ~10 ms. To maximize the sensitivity of our approach, we thus focused on 10 ms for this analysis. However, we agree that using the same time window as for assembly extraction is a reasonable proposal, in particular since we find no difference in the synchronization of identified presynaptic PYRs (Fig. 3e of the revised manuscript). Thus, we have recomputed cross-correlations using a 25 ms bin size. To further improve the analysis, we restricted it to neurons with at least 1000 spikes and simplified the quantification of excess spiking by using the ‘coinicident_spikes’ function of the Python package neuronpy.utils.spiketrain. Excess synchrony is now estimated by quantifying the number of coincident spikes between a reference and a comparison spike train detected in a 25 ms time window normalized by the firing rate expected by chance (2*frequency of comparison train * synchrony window * number of the reference train).

By using this improved analysis with a 25 ms time window, we could replicate our original finding of enhanced synchronization of PYR spiking. However, when we averaged the data on the basis of individual mice as suggested in #1 of reviewer 2 and #4 of reviewer 3, we could not observe this effect (irrespective of whether we used the new, coincident spikes-based analysis or the original excess synchrony analysis at either 10 or 25 ms synchrony window). This result is now stated in line 215 ff. (199 ff. without tracked changes) of the revised manuscript.

Reviewer #2 (Public Review):

This is an interesting paper, in which the authors assessed spiking and network deficits in a well-established mouse model of schizophrenia. This mouse model carries a genetic deletion of the Disrupted-in-schizophrenia-1 (Disc1) gene, which is highly penetrant in the human condition. The authors combined behavioral analyses with state-of-the-art electrophysiological recordings in vivo, coupled to optogenetic tagging, to study a subnetwork formed by a major inhibitory neuron subclass (the parvalbumin (PV)-expressing interneuron) and principal excitatory pyramidal neurons in the medial prefrontal cortex. This work indicates reduced firing rates of PV cells in Disc1-KO mice, likely due to reduced coupling with pyramidal neurons, leading to alterations in local network activity. Indeed, the authors found that Disc-KO mice exhibited reduced levels of gamma oscillations and somewhat hypersynchronous networks.

Taking advantage of novel techniques and analytical strategies, the manuscript provides rich, novel insight into the neurobiology of a mouse model of this severe psychiatric condition. The data is of high quality, the findings interesting and the manuscript is well written.

Overall, the results support the authors' conclusions, although some additional analyses are necessary to corroborate their interpretations.

Although the paper does not give information on how PV cell dysfunctions are engaged during cognitive tasks, this study can be considered as an important first step in advancing our knowledge on the basic dysfunctions of cortical networks in this model of schizophrenia

We thank the reviewer for praising the ‘high quality’ of our work, and the ‘rich, novel insights’ on the neurobiology of a mouse model of a psychiatric disorder.

1) The major findings stem from the analysis of the spiking activity of individual neurons recorded using either silicon probes or arrays of tetrodes. Both techniques allow simultaneous recording of many neurons from a single animal; therefore, from a statistical point of view neurons recorded from one animal are pseudo replicas and cannot be considered as independent measurements. Throughout the manuscript, the authors perform two-sample tests on the pooled data from all recorded neurons to compare differences between genotypes; therefore, artifactually increasing the power of statistical tests. Comparisons between genotypes should be performed using each mouse as an independent measurement.

To be able to compare the data on the basis of mouse averages, we performed additional recordings, which resulted in a final data set of 9 Disc1 and 7 control mice. We recomputed the main results of this study based on mouse averages. First, consistent with our original cell-by-cell analysis, we found significantly reduced firing rates of putative INTs but not of PYRs (line 72 (69 without tracked changes)). Moreover, we confirmed our results on decreased spike transmission probability at PYR-INT connections (line 121 (107 without tracked changes)), decreased spike transmission in the resonance window (line 163 (147 without tracked changes)), reduced high gamma power (line 173 ff. (157 ff. without tracked changes)), lower phase-coupling of INT spikes to high gamma oscillations (line 178 (162 without tracked changes)), and reduced strength of assembly activations in Disc1 compared to control mice (line 229 ff. (211 ff. without tracked changes)). Similarly, we performed new analysis on INT-INT synchronization and INT spike-triggered gamma amplitudes (as requested by reviewer 1 #5 & 6), which showed significant effects on the level of mouse averages (line 188 ff. (line 172 without tracked changes)). Second, our original finding on significant differences in the synchronization of individual PYR-PYR pairs could not be reproduced on the level of individual mice. This is reported in line 215 (199 without tracked changes) of the revised manuscript. Finally, the analyses based on optogentically identified PVIs did not allow comparison by mouse averages due to the low number of experiments (n=3 mice each). Given that the vast majority of our conclusions is based on electrophysiologically identified INTs, with optogenetic identification experiments being only confirmatory in nature, and that performing additional experiments for optogentic identification of PVIs would be very laborious, we report the results of these analyses as comparisons between neurons or connected pairs. This is clearly stated at the respective sections throughout the revised manuscript. We hope that the reviewer can agree with our decision.

2) The superficial layers of the mPFC are difficult to reach with a vertical approach of the probes due to the presence of a large blood vessel located medially in the frontal dura. Therefore, the authors are most likely reaching mPFC deep layers where PYR neurons produce fast spikes at high rates. If this is the case, this would make it difficult to sort the spiking of PYR from that of INs based on the spike kinetics and rate. The authors used opto-tagging of PVIs in a set of experiments. It would be reassuring to confirm that the spike waveform and kinetics that they extracted from PVIs are similar to those they assigned as INTs in their experiments with no opto-tagging. Identified PVIs should be statistically different from putative PYRs (not responding to light). Although opto-tagging of PVIs can solve this issue, the amount of cells isolated remains low and the number of animals is not stated. Opto-tagged cells are subsequently used for further analyses but the statistical value of those remain unclear. Since the entire interpretation of the rest of the results depend on this result, this must be clarified.

As correctly pointed out by the reviewer, we indeed targeted deep layers of the mPFC (~0.4 mm lateral of the midline; see also the histological information about the recordings sites that is now included in Figure 1 – figure supplement 1), where higher spike rates are expected compared to superficial layers. To assess whether this might have influenced the identification of putative INTs, we separately plotted the duration and asymmetry index used to classify the neurons in PYRs and putative INTs for Disc1 and control mice. This analysis yielded well separated clusters in both cases. In addition, as suggested by the reviewer, we compared the kinetic properties (spike duration and asymmetry index) and rates of PYRs, putative INTs, and optotagged PVIs. In both genotypes, ANOVA analysis followed by Tukey post-hoc testing revealed significant differences between the PYRs and both groups of INTs, both for rate (smaller in PYRs) and kinetic properties (longer spikes of PYRs) while we found no difference between putative INTs and PVIs. These results thus suggest that the method used to identify INTs works reliably. These new data are now shown in the revised Fig. 1a and the new Figure 1 – figure supplement 2 and mentioned in line 89 ff. (85 without tracked changes) of the revised manuscript.

We agree that the number of experiments using PVI opto-tagging is low (n=3 mice per genotype, this information is now included in the main text in line 93 ff. (88 ff. without tracked changes)). However, our analysis of spike transmission probability using the population of untagged putative fast-spiking INTs revealed similar results as for the sample of optogenetically identified PVIs. We view the PVI optotagging experiment as an additional confirmation that the difference in firing rate and spike transmission did likely not arise from sampling from different INT types in Disc1 and control mice, as pointed out in line 80 (76 without tracked changes) of the revised manuscript. The limitation of the low number of PVIs in our study is critically reflected in the revised discussion in line 249 ff. (229 without tracked changes).

3) Proportion of gamma coupled neurons. The authors mention the use of pairwise phase consistency (PPC). PPC is a good method to measure phase coupling independent of differences in firing rates. However, it is not entirely clear how PPC is used to measure the extent of phase locking. In the methods, the authors mention that they ran the PPC analysis after determining significant phase locking with Rayleigh's test. Moreover, they provide PPC values for high gamma oscillations but not for other frequency ranges. Perhaps, it would be better to test significant coupling of all units by nonrandom spike-phase distributions crossing a confidence interval, estimated by Monte Carlo methods from independent surrogate data set. These can be obtained upon randomly jittering each spike times. Indeed, PPC values estimated by the authors for high gamma are higher for PYR than INT (Fig. 1- Fig. Suppl 4 b). This is at odds with previously published observations in V1 (e.g. Perrenoud et al., PLoS Biol. 2016 PMID: 26890123). Given the existing reports of reduced excitatory transmission in DISC-1 mice, phase locking of PYR to other frequency bands might be affected.

Following the reviewer’s suggestion we have revised our phase-coupling analysis. First, Perrenoud et al (2016) show that gamma oscillations occur in short bursts of high power. To better reflect the coupling of putative INTs to those transient gamma events, we restricted the phase-coupling analysis to epochs within the largest quintile of gamma amplitude (assessed by the envelope of the gamma-filtered signal obtained by Hilbert transformation). Second, instead of the Rayleigh test, we obtained for each unit randomized spike trains by shuffling the inter-spike intervals (500 iterations). Significant phase locking was then obtained by testing whether two consecutive bins of the phase histogram exceeded the 95th percentile of the random distribution. This analysis was performed separately for the low (20-40 Hz) and high gamma bands (60-90 Hz) for both putative INTs and PYRs. Third, the depth of phase coupling was assessed by PPC for all significantly phase-coupled neurons. While this metric is more robust against changes in spike rates than traditional measures, it is still not completely independent of it. Perrenoud et al, for instance, showed using spike sub-sampling that the reliability in estimating PPC depends on spike rate (with >1000 spikes being optimal). However, our data set of PYRs contained fewer than 1000 spikes during high gamma events (mean Disc1: 657 ± 32, mean control: 840 ± 43). To better account for the effect of rate dependence, we restricted the analysis to neurons with >250 spikes. To further limit the potential impact of different spike counts across neurons, we used random subsampling with a fixed spike number of 250 (100 iterations per cell), computed PPC in each iteration, and averaged over the PPC estimates per cell. Finally, in response to the reviewers point 1, the results of all neurons (PYR and INT separately) were then averaged for each mouse.

Consistent with our original analysis, we found a significantly reduced proportion of phase-coupled INTs but unaltered PPC of significantly coupled INTs to the high gamma band. Moreover, we observed no significant effects for low gamma oscillations or for the phase-coupling of PYRs to either low or high gamma bands. These results are now shown in the new Fig. 4 and the new Figure 4 – figure supplement 1, and are described in line 170 ff. (154 without tracked changes) of the revised manuscript. In addition, we provide a detailed explanation of the revised phase coupling analysis, including a formal description how PPC is computed, in the Methods section of the revised manuscript in line 524 ff. (486 without tracked changes).

Using the revised phase-coupling analysis, we observed comparable PPC values of significantly coupled PYRs (0.013) and INTs (0.014) to high gamma in control mice. While the improved analysis thus resolved the paradoxical finding of lower PPC in INTs, we did not observe weaker phase-coupling of PYRs as reported in Perrenoud et al. (2016). A possible explanation for this discrepancy might be genuine differences in gamma coupling of the PYR population between visual cortex (Perrenoud et al., 2016) and the prefrontal cortex (our study), which will require further investigation in future.

Reviewer #3 (Public Review):

In the present study, the authors aim to assess network activity alterations in the prefrontal cortex of mice with a deletion variant in the schizophrenia susceptibility gene DISC1 ("DISC1 mutants"). Using silicon probe in vivo recordings from the prefrontal cortex, they find that mutant mice show reduced firing rates of fast-spiking interneurons, reduced spike transmission efficacy from pyramidal cells to interneurons, and enhanced synchronization and activation of cell assemblies. The authors conclude that "interneuron pathology is linked with the abnormal coordination of pyramidal cells, which might relate to impaired cognition in schizophrenia."

The cellular and circuit basis of psychiatric disorders has received strong interest in the recent past. In particular, alterations of the "excitation-inhibition balance" in cortical circuits has been the focus of extensive scrutiny (reviewed in pmid 22251963). Specifically, in both human samples as well as in mouse models, disruption of interneuron development and function have been implicated in the pathogenesis of schizophrenia. In the DISC1 mouse model, studies have reported disrupted interneuron development (e.g. pmid 23631734, 27244370), reduced numbers of GABAergic neurons (e.g. pmid 18945897), reduced inhibition from GABAergic neurons ex vivo (e.g. pmid 32029441), and reduced firing rates of fast-spiking neurons in vivo in the basal forebrain (pmid 34143365).

The present manuscript makes a potentially important contribution to this question by probing the microcircuitry of the prefrontal cortex in vivo in the DISC1 mouse model of schizophrenia. It goes beyond previous work in assessing circuit dynamics in vivo in more detail, albeit with indirect methods. The experiments and analysis have generally carefully been performed, though the statistical analysis raises some questions. The advances made by the present work compared to previous studies could be delineated more clearly.

We thank the reviewer for praising the analysis of our data ‘…have generally carefully been performed..’ and the ‘important contribution’ of our work to the field.

See also Emma "A woman may not marry a man merely because she is asked, or because he is attached to her" (chapter 7) and Mansfield Park "I think it ought not to be set down as certain that a man must be acceptable to every woman he may happen to like himself" (Chapter 35)

Author Response

Reviewer #1 (Public Review):

Neural stem cells express cascades of transcription factors that are important for generating the diversity of neurons in the brain of flies and mammals. In flies, nothing is known about whether the transcription factor cascades are build from direct gene regulation, e.g. factor A binding to enhancers in gene B to activate its expression. Here, Xin and Ray show that one temporal factor, Slp1/2, is regulated transcriptionally via two molecularly defined enhancers that directly bind two other transcription factors in the cascade as well as integrating Notch signaling. This is a major step forward for the field, and provides a model for subsequent studies on other temporal transcription factor cascades.

Thanks for the positive comments!

Reviewer #2 (Public Review):

The manuscript addresses an important question concerning the mechanisms regulating temporal transitions in Drosophila neural progenitors called neuroblasts. Here, they concentrate on a specific transition between the transcription factors Ey and Slp1/2 that are sequentially expressed within a cascade involving at least 6 temporal transcription factors. Using a combination of new transgenes, bioinformatics and genome-wide profiling of transcription factor biding sites (Dam-ID), they functionally characterize two enhancers of the Slp1/2 genes that are active during this transition. This led to the identification of the Notch pathway as an important facilitator of the transition. They also show that Notch signaling requires cell cycle progression and that Slp1/2 is a direct target of Ey, validating the importance of transcriptional cross-regulatory interactions among the temporal transcription factors to trigger progression.

In my opinion, the study is very interesting, representing the first careful analysis of enhancers involved in temporal transitions in neural progenitors, and leading to new insights into the mechanisms promoting temporal progression.

Thanks for the positive comments!

Reviewer #3 (Public Review):

In this manuscript, the authors present data to suggest that transcriptional activation of the Slp1/2 temporal factors in the medulla neuroblasts of the developing Drosophila optic lobe is dependent on two enhancer elements. The authors concluded that these two enhancers were able to be activated by Ey and Scro, two other factors identified to be involved in the temporal cascade of the medulla NB. The authors show that cell cycle progression is necessary for Notch signaling, and that Notch signaling activates and sustains the temporal transcription factor cascade. The authors use GFP reporter assays to correlate the enhancer activity to Slp1/2 expression and used DamID to show in-vivo binding of Su(H) and Ey to the enhancer fragments.

I agree with the authors that it is important to define the mechanisms by which Notch, cell cycle control and these temporal transcription factors function through their cis-regulatory elements to establish this self-propagating cascade to generate diverse cell types during neurogenesis. However, the findings in this study offer limited new insights toward reaching this goal for a myriad of reasons. First, studies in invertebrate and vertebrate neurogenesis have agreed on the conceptual framework that transcriptional control plays a key role in regulating the generation of diverse cell types. The data showing the patterns of slp1/2 transcript simply reaffirm the proposed model as well as recently published single-cell transcriptomic analyses of fly optic lobe neuroblasts. Second, it remains unclear how physiologically relevant the enhancer analyses presented in this study are to the regulation of Slp1/2 expression, as the data can only suggest that they act redundantly to each other. It is also troubling to see that mutating binding sites of a single transcription factor appears to completely abolish enhancer activity while Slp1/2 protein expression is delayed in mutant clonal analyses. Third, the authors do not offer any explanation for how Notch signaling contributing to the timing of Slp1/2 expression, considering that Notch signaling should be active during the entire life of the neuroblast based on canonical Notch target gene expression. What action do Ey and Scro play in this timely enhancer activation as both appear to be necessary to activate the enhancers along with Notch. Fourth, many studies including the Okamoto et al., 2016 study cited in this study have contributed to our appreciation of the role of proper cell cycle control in promoting generation of diverse neurons in vertebrate neurogenesis. It is unclear to me if findings from the current study contribute to significant advancement on this regulatory link.

Thanks for raising these concerns. Here are our responses:

First, we agree that there have been great advances in this field including classical studies in the ventral nerve cord, recent studies on type II lineages and medulla including our own scRNA-seq study of medulla neuroblasts. These studies have revealed the sequential expression of transcription factors in neuroblasts of different ages, and proposed that these transcription factors form a transcriptional cascade based on the cross-regulations among them. However, these cross-regulations were based on mutant phenotypes, and in most cases, the cis-regulatory elements of these TTFs have not been characterized, and it hasn’t been studied whether these cross-regulations are direct or not. Little is known about exactly how the timing of the transition is regulated and coordinated with cell-cycle control. We have addressed these questions and identified two enhancer elements for slp1/2, and demonstrated that the previous TTF Ey, another TTF Scro, and Notch signaling directly regulate slp expression. Further we demonstrated that Notch signaling is dependent on cell cycle progression in neuroblasts, and supplying Notch signaling rescues the delay in Slp expression in cell cycle mutants. We believe this study has provided important insights in this field and is another step forward.

Second, now we provide evidence that deletion of both enhancers specifically abolished Slp1 and Slp2 expression in medulla neuroblasts.

Regarding the concerns about binding site mutation:

1) Ey: With loss of Ey, Slp is completely lost. The Ey binding site mutation phenotype is consistent with loss of Ey phenotype.

2) Su(H): For the u8772 250bp enhancer, mutating all four predicted Su(H) binding sites did abolish the reporter expression. During the revision, we generated another construct, in which we mutated the two predicted Su(H) binding sites which are perfect matches to the consensus, and found that this dramatically reduced the reporter expression. For the d5778 850bp enhancer, mutation of Su(H) binding sites caused strong glial expression which prevented us to precisely assess the neuroblast expression. Thanks to the excellent advice from review #3, we used repo-Gal4 and GFP-RNAi to remove the glial expression. This approach turned out very informative. We found that mutation of four or six out of six predicted Su(H) binding sites actually did not decrease the reporter expression in neuroblasts, suggesting that Notch signaling does not active the d5778 850bp enhancer through these binding sites. However, we think this is the explanation why this enhancer drives a delayed expression comparing to the 220bp enhancer and the endogenous Slp. In addition, this also explains why with loss of Notch signaling, endogenous Slp expression is only delayed but not completely lost. This is because although the 220bp enhancer driven expression is completely lost, the d5778 850 bp enhancer still directs a delayed expression of Slp and this expression is not dependent on Notch signaling.

3) Scro: Mutation of Scro binding sites caused a decreased expression level of the reporter, consistent with the scro RNAi phenotype on Slp, which is a decreased expression level.

Third, regarding how Notch signaling which is active in the entire neuroblast life, can act to activate Slp expression in a specific time We tested genetic interactions between Ey, Scro, and Notch in the regulation of Slp expression. We found that with loss of Ey, supplying constitutive active Notch or Scro is not sufficient to rescue Slp expression. Thus Ey as the previous TTF, may be required to prime the slp locus, so that Notch signaling and Scro can act to further activate Slp expression. Therefore, Notch signaling requires Ey to specifically further activate Slp at the correct time. We have added these experimental results and discussion.

Fourth, the Okamoto et al., 2016 study actually concluded that cell cycle progression is not required for the temporal progression. In their experimental setup, they supply Notch to maintain the un-differentiated status of cortical neural progenitors when they block cell cycle progression. The observed that temporal transition still happened, and they concluded that cell cycle progression is not required for temporal transitions. However, they didn’t consider the possibility that Notch signaling, which is itself dependent on cell cycle progression, actually rescued the possible phenotype caused by arrest of cell cycle progression. Our result demonstrated that in Drosophila medulla, supplying Notch signaling can rescue the delay in the transition to the Slp stage in cell-cycle arrested neuroblasts, and further showed that the mechanism is by direct transcriptional regulation. We believe that publication of our results will be informative to the vertebrate study, promoting vertebrate researchers to re-consider the role of cell cycle progression and Notch signaling in temporal progression.

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Reply to the reviewers

Manuscript number: RC-2022-01528

Corresponding author(s): Elena Taverna and Tanja Vogel

1. General Statements [optional]

We thank the reviewers for the comments and points they raised. We think what we have been asked is a doable task for us and we are confident we will manage to address all points in a satisfactory manner.

2. Description of the planned revisions

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

Reviewer’s comment: The manuscript investigated the role of DOT1L during neurogenesis especially focusing on the earlier commitment from APs. Using tissue culture method with single-cell tracing, they found that the inhibition of DOT1L results in delamination of APs, and promotes neuronal differentiation. Furthermore, using single cell RNA-seq, they seek possible mechanisms and changes in cellular state, and found a new cellular state as a transient state. Among differentially expressed genes, they focused on microcephaly-related genes, and found possible links between epigenetic changes led by DOT1L inhibition and epigenetic inhibition by PRC2. Based on these findings, they suggested that DOT1L could regulate neural fate commitment through epigenetic regulation. Overall, it is well written and possible links from epigenetic to metabolic regulation are interesting. However, there are several issues across the manuscript.

Response to Reviewer and planned revision:

We thank the reviewer’s 1 for her/his comments and constructive criticism.

We hope the revision plan will address the points raised by the reviewer in a satisfactory manner.

Major issues:

* *Reviewer’s comment: 1) It is not clear whether the degree of H3K79 methylation (or other histones) changes during development, and whether DOT1L is responsible for those changes. It is necessary to show the changes in histone modifications as well as the levels of DOT1L from APs to BPs and neurons, and to what extent the treatment of EPZ change the degree of histone methylation.

Response to Reviewer and planned revision:

• As for the level of DOT1L protein We tried several commercially available antibodies, but they do not work in the mouse, even after multiple attempts and optimization. So, unfortunately we will not be able to provide this piece of information.

• As for the level of DOT1L mRNA We will provide info regarding the DOT1L mRNA level in APs, BPs and neurons by using scRNAseq data from E12, E14, E16 WT cerebral cortex.

• As for the levels of H3K79methylation, we did not intend to claim that the histone methylation is responsible for the reported fate transition. We will edit the text to avoid any possible confusion. If it is deemed to be necessary to address the point raised by the reviewer, we do have 3 options, that we here in order of priority and ease of execution from our side.

• immunofluorescence with an Ab against H3K79me2 using CON and EPZ-treated hemispheres.

• FACS sort APs, BPs and neurons from CON and EPZ-treated hemispheres, followed by immunoblot for H3K79me2 to assess the H3K79me2 levels. As for the FACS sorting, we will use a combinatorial sorting in the lab on either a TUBB3-GFP or a GFP-reporter line using EOMES-driven mouse lines. This strategy has already been employed in the lab by Florio et al., 2015 and we will use it with minor modifications.
• scCut&Tag for H3K79me2 from CON and EPZ-treated hemispheres. This option entails a collaboration with the Gonzalo Castelo-Branco lab in Sweden and might therefore require additional time to be established and carried out. Reviewer’s comment:

Furthermore, the study mainly used pharmacological bath application. DOT1L has anti-mitotic effect, thus it is not clear whether the effect is coming from the inhibition of transmethylation activity.

Response to Reviewer and planned revision:

In a previous work we used a genetic model (DOT1L KO mouse) that showed microcephaly (Franz et al. 2019). For this study, we wanted to fill a gap in knowledge by understating if the DOT1L effect was mediated by its enzymatic activity. For this reason, we choose to use the pharmacological inhibition with EPZ, whose effect on DOT1L activity has been extensively reported and documented in literature (EPZ is a drug currently in phase clinical 3 studies).

The stringent focus of this study on the pharmacological inhibition is thus a step toward understanding what specific roles DOT1L can play, both as scaffold or as enzyme.

Here, we concentrate on the enzymatic function and the scaffolding function is beyond the scope of this specific study. We can further discuss and elaborate on the rationale behind this in the revised manuscript.

Reviewer’s comment:

In addition, the study assumed that the effect of EPZ is cell autonomous. However, if EPZ treatment can change the metabolic state in a cell, it would be possible that observed effects was non-cell autonomous. It would be important to address if this effect is coming in a cell-autonomous manner by other means using focal shRNA-KD by IUE.

Response to Reviewer and planned revision:

We did not claim that the effect of EPZ is cell autonomous, we are actually open on this point, as we consider both explanations to be potentially valid. We will edit the text to avoid any possible confusion on what we assume and what not.

As a general consideration, it is entirely possible that the effects are non-cell autonomous. We will comment and elaborate on that in the revised manuscript.

If the reviewer/journal considers this a point that must be addressed experimentally, then we will proceed as follows:

• DOT1L shRNA-KD via in utero electroporation, followed by either
• in situ hybridization for ASNS to check if ASNS transcript is increased upon DOT1L shRNA-KD compared to CON
• FACS sorting of the positive electroporated cells (CON and DOT1L shRNA-KD), followed by qPCR to assess the levels of ASNS
• If the reviewer wants us to check for a more downstream effect on fate, then we will immuno-stain the DOT1L shRNA-KD and CON with TUBB3 AB and/or TBR1 AB (as already done in the present version of the manuscript). Reviewer’s comment: 2) The possible changes in cell division and differentiation were found by very nice single-cell tracing system. However, changes in division modes occurring in targeted APs such as angles of mitotic division and the expression of mitotic markers were not addressed. These information is critical information to understand mechanisms underlying observed phenotype, delamination, differentiation and fate commitment.

Response to Reviewer and planned revision:

Previous effects of DOT1L manipulation on the mitotic spindle were observed in a previous paper, using DOT1L KO mouse (Franz et al. 2019). Considering that in our experiments we do use a pharmacological inhibition, we will address this point by quantifying the spindle angle in CON and EPZ-treated cortical hemispheres.

We will co-stain for DAPI to visualize the DNA/chromosomes, and for phalloidin (filamentous actin counterstain) that allows for a precise visualization of the apical surface and of the cell contour, as it stains the cell cortex.

Of note, the protocols we are referring to are already established in the lab, based on published work from the Huttner lab (Taverna et al, 2012; Kosodo et al, 2005).

Reviewer’s comment: 3) The scRNA-seq analysis indicated interesting results, but was not fully clear to explain the observed results in histology. In fact, in single cell RNA-seq, the author claimed that cells in TTS are increased after EPZ treatment, which are more similar to APs. However, in histological data, they found that EPZ treatment increased neuronal differentiation. These data conflicts, thus I wonder whether "neurons" from histology data are actually neurons? Using several other markers simultaneously, it would be important to check the cellular state in histology upon the inhibition/KD of DOT1L.

Response to Reviewer and planned revision:

The reviewer’s comment is valid, and we indeed found that TTS cells are an intermediate state between APs and neurons in term of transcriptional profile. This is the reason why we called this cell cluster transient transcriptional state.

We plan to address this point by staining for TBR1 and/or CTIP2 in CON and EPZ-treated hemispheres and to expand with this EOMES and SOX2 co-staining.

Minor issues:

Reviewer’s comment: Figure 1 - It is not clear delaminated cells are APs, BPs or some transient cells (Sox2+ Tubb3+??). It is important to use several cell type-specific and cell cycle markers simulnaneously to characterize cell-type specific identity of the analysed cells by staining. These applied to Fig1B,D,E,F,G,as well as Fig2,3.

Response to Reviewer and planned revision:

We will address this point by using a combinatorial staining scheme for several fate markers such as TUBB3, EOMES and SOX2, as suggested by the reviewer.

Reviewer’s comment: - Please provide higher magnification images of labelled cells (Fig 1H)

Response to Reviewer and planned revision:

In the revised manuscript, we will provide higher magnification for the staining.

Reviewer’s comment: - Please provide clarification on the criteria of Tis21-GFP+ signal thresholding.

Response to Reviewer and planned revision:

In the revised manuscript, we will provide a clarification on the criteria of Tis21-GFP+ signal thresholding.

Reviewer’s comment: - Splitting the GFP signal between ventricular and abventricular does not convincingly support the "more basal and/or differentiated" states after EPZ treatment.

Response to Reviewer and planned revision:

We will provide a clarification regarding this point.

Reviewer’s comment: - Please explain the presence of Tis21-GFP+ cells at the apical VZ.

Response to Reviewer and planned revision:

Tis21-GFP+ cells at the apical VZ has been extensively reported in the literature, since the first paper by Haubensak et al. regarding the generation of the Tis21-GFP+ line. In a nutshell, T Tis21-GFP+ cells are present throughout the VZ (therefore also in the apical portion) as neurogenic, Tis21-GFP positive cells are undergoing mitosis at the apical surface. Indeed, the presence of Tis-21 GFP signal have been extensively used by the Huttner lab and collaborators to score apical neurogenic mitosis. In addition, since AP undergo interkinetic nuclear migration, it follows that Tis21-GFP+ nuclei are going to be present throughout the entire VZ.

In the revised manuscript, we will explain this point and cite additional literature.

Reviewer’s comment: - Order the legends in same order as the bars.

Response to Reviewer and planned revision:

We will follow reviewers’ recommendation and order the legends accordingly.

Reviewer’s comment: Figure 2 -Fig 2B) The difference between CON and EPZ apical contacts is not clear and does not match with the graph in Fig 2E.

Response to Reviewer and planned revision:

We will explain Fig. 2B in more detail and provide additional images in the revised manuscript.

Reviewer’s comment: -Supp Fig 2 - are these injected slices cultured in control conditions? Please include this in the text and figure/figure legend

Response to Reviewer and planned revision:

In the revised manuscript, the text will be changed to address this point and provide clearer info.

Reviewer’s comment: Fig 2C) The EPZ-treated DxA555+ cells exhibit morphological change of cell shape. Is this phenotype? please comment on the image shown for EPZ treatment panel.

Response to Reviewer and planned revision:

We thank the reviewer for having raised this point.

The change in morphology might be a consequence of delamination and or of cell fate. In the revised manuscript, we will certainly better comment on this very relevant point and expand the discussion accordingly.

Reviewer’s comment: Fig 2F - 2G) Data presented on EOMES+ and TUBB3+ % are counterintuitive. The authors claimed that TUBB3+ cells are increased and neuronal differentiation is promoted. However, no changes in EOMES+ are observed. What is the explanation? Did the author check the double positive cells? These could be TSS cells?

Response to Reviewer and planned revision:

We thank the reviewer to have raised this point.

As envisioned by the reviewer, we suspect that the counterintuitive data might be due to TSS cell, which based on our scRNAseq data are expressing at the same time several cell type specific markers. It is possible that, since the treatment with EPZ is 24h long, cells (like the TTS cluster) have no time to completely eliminate the EOMES protein. If that were to be the case, then we would expect to still detect (as we indeed do) EOMES immunoreactivity.

To address this point, we will:

• analyze scRNA-seq data and check which is the extent of co-expression of Eomes and Tubb3 mRNAs in the TTS population.
• Check for EOMES and TUBB3 double positive cells in the microinjection experiment. Reviewer’s comment: Figure 2 and Figure 3) the number of pairs analyzed for EPZ is twice as that of Con for comparison of the parameters taken into account. Please include n of each graph in the figure legend of the specific panel if not the same for all panels in that figure (i.e. for figure 3)

Response to Reviewer and planned revision:

We will revise the text accordingly.

Reviewer’s comment:

Figure 3) The data indicated that the number of daughter cell pairs in EPZ samples is almost double than Control. Is this the phenotype? More numbers of daughter cells in EPZ treated samples were observed from the same number of injections? or the number of injected cells were different?

Response to Reviewer and planned revision:

Due to technical reasons, we indeed performed a higher number of injections in EPZ-treated slices. We think this is the main reason behind the difference in number.

If the reason were to be biological, one would expect to see the same trend in IUE experiments, but this is actually not the case. This does suggest/corroborate the idea that the reason behind the difference is mainly technical.

Reviewer’s comment: Figure 4)

• Please clarify if the single cell transcriptomic analysis has been performed only once, and if yes, how statistical testing to compare the cell proportion is carried out with only one batch. Fig 4G)

Response to Reviewer and planned revision:

As for the scRNAseq on microinjected cells:

the scRNA-seq analysis was done once using cells pooled from 3 different microinjection experiments performed in 3 different days.

As for the scRNAseq on IUE cells:

The scRNA-seq analysis was done once using cells pooled from 2-3 different IUE experiments performed in 3 different days.

For all scRNAseq experiments the statistical testing is achieved by intrasample comparisons according to established bioinformatics pipelines. We will better explain this point in the revised manuscript.

Reviewer’s comment: Figure 4 and 5) - Figures are not supportive of the statement regarding APs' neurogenic potential upon DOT1L inhibition. TSS transcriptomic profile resembles more progenitors than neurons. Please comment on TSS neurogenic capacity taking into account the provided GO and RNAseq.

Response to Reviewer and planned revision:

We thank Reviewer 1 for raising this point, It is indeed true that TTS resemble more AP than neurons (as indicated in the Fig. S5B, C). We took that to indicate the fact that these cells are transient and therefore still maintain some AP features. Interestingly, TTS downregulate cell division markers, suggesting a restriction of proliferative potential, as one would expect for cells with an increased neurogenic potential. We will discuss this point in the revised manuscript.

Reviewer’s comment: - Please provide GO analysis for APs and BPs.

Response to Reviewer and planned revision:

Following the reviewer’s suggestion, we will incorporate a more careful and in-depth analysis in the revised version of the manuscript.

Reviewer’s comment: - Reconstruct figure 5A by listing genes in the same order in both Con and EPZ and prioritize EPZ-Con differences instead of cell-cell differences.

Response to Reviewer and planned revision:

We will revise Figure 5A based on the reviewer’s comment.

Reviewer’s comment:

Moreover, the presented genes in the heatmap is not the same in two conditions (i.e. NEUROG1 is present in EPZ but absent in Con). Please justify.

Response to Reviewer and planned revision:

This observation is based on different activities of transcription factor networks in the control and EPZ condition. They are not supposed to be the same as the cell states are altered and different TF are expressed and active upon the treatment in the diverse cell types. In a revised manuscript we will justify this point.

Reviewer’s comment: Fig 5D)

• Please explain why binding of EZH2 on the promoter of Asns is strongly reduced in comparison to a mild significant reduction of H3K79me/H3K27me3 in EPZ compared to Control.

Response to Reviewer and planned revision:

Several explanations are possible

First, the variation can be due to batch effects.

Second, the acute reduction of EZH2 might not be directly accompanied by a reduced histone mark, which is reduced either by cell division or by demethylases. The two processes of getting rid of the mark might be slower than the reduction of EZH2 presence at the respective site.

Based on the reviewer’s comment, we will explain this point in the revised manuscript.

• *

Reviewer’s comment:

Also is the changed directly medicated by DOT1L?

Please test whether DOT1L can bind the promoter of Asns.

Response to Reviewer and planned revision:

To address this relevant issue we will proceed with the following protocol:

• electroporate a tagged version of DOT1L into ESCs
• select ESCs and differentiate them into NPC_48h.
• treat NPC with DMSO (Con) or EPZ
• harvest CON and EPZ-treated NPC
• perform ChIP-qPCR DOT1L at the Asns promoter Reviewer’s comment: Please provide the expression patterns of DOT1L and Asns during neuronal differentiation.

Response to Reviewer and planned revision:

As for Dot1l

Dot1l expression was shown in Franz et al 2019, by ISH from E12.5 to E18.5.

As for Asns

We will provide E14.5 in situ staining of Asns in the developing mouse brain using the Gene Paint database (see Figure below).

We will also show immunostainings for ASNS at mid-neurogenesis, provided that Ab against ASNS works in the mouse.

Other General comments:

Reviewer’s comment: Please Indicate VZ, SVZ and CP on the side of the pictures/ with dot lines in the pictures both for primary figures and supplementary.

Response to Reviewer and planned revision:

We will revise the figures accordingly.

Reviewer’s comment: - The Results and figures sometimes do not support the statement made by the authors

Response to Reviewer and planned revision:

We will carefully check on this and eliminate any overinterpretation or non-supported statements from the text.

• Schemes are not informative/explanatory enough, i.e. time windows of treatment and sample collection, culture conditions details.

Response to Reviewer and planned revision:

We will revise the schemes to include more details. In particular, we plan to add a supplementary figure with a detailed visual description of the protocol, to match the detailed description presented in the materials and methods.

Reviewer’s comment: - A more extensive characterization of TTS cells in terms of differentiation progression and integration would be enlightening

Response to Reviewer and planned revision:

In general, we are facing two main challenges while studying the TTS population: one is the lack of a specific marker gene for TTS, the other is the relatively small size of the TTS subpopulation.

For these reasons, our ability to carry on an in-depth analysis of this cell state is limited.

Considering the reviewer’s comment, in the revised manuscript we will expand the analysis ad characterization of the differentiation potential of TTS using RNA velocity trajectory.

We can also expand the discussion on this point.

Reviewer’s comment: - Picture quality can be improved, provide high magnification images.

Response to Reviewer and planned revision:

We will revise the figures to include higher magnification images.

Reviewer #1 (Significance (Required)):

Reviewer’s comment: The study could be important for the specific field in neural development. It aims to understand mutations in respective genes and brain malformation. If the link between epigenetic and metabolic changes is clearly shown, it will be interesting. However, the current manuscript is still rather descriptive, and clear mechanistic insights were not provided. The study have potentials and additional data will strength the value of study.

Response to Reviewer and planned revision:

We will address the direct impact of DOT1L and H3K79me2 on the Asns gene locus during the revision (see the rationale of the experimental strategy also in the revision plan above). We hope we will thus provide a mechanistic link between epigenetics and altered metabolome.

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

Reviewer’s comment: Appiah et al. present a concise manuscript that provides details and possible mechanisms of their previous work (Franz et al., 2019; Ferrari et al., 2020). The study uses diverse lines of investigation to arrive at most conclusions. However, as interesting as the data is, we find that at the present state, it is not sufficient to prove that, indeed, the asparagine metabolism is regulated by DOTL1/PRC2 crosstalk. The neurogenic shift presented in the first part of the paper is not comprehensive and, therefore, not very convincing. The quality of images provided in the main and supplementary data is less than ideal. Additional data analysis and interpretation of the scRNA seq data may be needed. The authors finally conclude with rescue experiments done in culture and in-vivo, which we believe is the stand-out part of this study. Overall the manuscript has some interesting observations that are often over-interpreted with less supporting data. The manuscript reads well but requires additional data and changes in the claims/interpretation to be suited for publication.

Response to Reviewer and planned revision:

In the revised manuscript, we hope we will address the comments and concerns raised by the reviewer in a satisfactory manner. Comments

Reviewer’s comment: 1) Abstract: Is this statement correct: "DOT1L inhibition led to increased neurogenesis driven by a shift from asymmetric self-renewing to symmetric neurogenic divisions of APs. AP undergoes symmetric division for self-renewal and asymmetric neurogenic divisions.

Response to Reviewer and planned revision:

Based on the current literature (cit. Huttner and Kriegstein), AP undergo:

• symmetric division for proliferative division at early stages of neurogenesis
• asymmetric self-renewing division, generating an AP and a BP at mid neurogenesis. This division is also described as neurogenic, as it produces a BP, that is a step further than AP in term of neurogenic potential.
• symmetric consumptive division at late neurogenesis To avoid any possible confusion, we will re-phrase the sentence to include the adjective “consumptive” and specify the composition of the progeny.

In the revised manuscript, the sentence will read as follow:

"DOT1L inhibition led to increased neurogenesis driven by a shift of APs from asymmetric self-renewing (generating one AP and one BP) to symmetric consumptive divisions (generating two neurons)"

Reviewer’s comment: All the data is based on treatments with EPZ (DOTL1 inhibitor), yet no information is shown to support its targeted activity in this system. A proof of principle in the chosen experimental system is missing; for instance, examining the activity or protein level of DOTL1 and decreased methylation of the target(s) is essential.

Response to Reviewer and planned revision:

EPZ is a well characterized drug, that has been used previously in our lab and by others as well.

As for our lab, the information regarding the inhibitor, its activity and efficiency in inhibiting DOT1L towards H3K79me2 was shown in Franz et al. Supplementary Fig. S6 D, E.

In the present manuscript, an additional confirmation that EPZ targets DOT1L in regard to its H3K79me2 activity is shown in Fig. 5D.

We would refer to this information more explicitly in a revised manuscript.

Reviewer’s comment: 2) Figure 1: The scoring of centrosomes and cilia is insufficient to conclude delamination and increase in basal fates. The effect could be on ciliogenesis or centrosome tethering to the apical end-feet of the AP, and other possible explanations for this observation also exist. The images are too small; larger images or graphic representations could be helpful in addition to the data.

Response to Reviewer and planned revision:

We did not intend to claim that the change in centrosome location demonstrate delamination, but only that it suggests delamination. This criterion has been extensively used as a proxy for delamination by several labs working on the cell biology of neurogenesis, such Huttner and Gotz labs. If the issue persists, we can re-phrase in a more cautious way the text referring to Figure 1 to highlight that the data only suggest delamination.

Response to Reviewer and planned revision:

To make a statement regarding delamination, I would like to see either the dynamics of delamination (organotypic slices images), staining with BP markers, or morphological changes of AP (staining that will reveal loss of adherence) or comparable data to support the observation. In my opinion Supp. Figure 1 is insufficient; the single image is not convincing; I would like to see 3D reconstruction and better-quality images.

Response to Reviewer and planned revision:

We can certainly provide better images and co-stain with relevant markers.

We think it is beyond the scope of the manuscript embarking in live imaging as we are not studying the dynamics of delamination per se.

Reviewer’s comment: Tis21 data (1H), again of low quality, is only a single piece of evidence and the conclusion "suggesting that the acquisition of a basal fate was paralleled by a switch to neurogenesis" is premature. I think other cell cycle exit reporters, Fucci markers, pHis, BrdU, NeuroD, or Tbr2 reporters (Li et al., 2020, (Haydar and Sestan labs)) to name a few, are necessary to establish the conclusions. The authors should show other markers such as PAX6, EOMES, or other upper-layer markers upon cell cycle exit in the SVZ/CP. These additional experiments will assist in cell fate analysis.

Response to Reviewer and planned revision:

We completely understand the points raised by the reviewer, and we plan to address them by co-staining with PAX6/SOX2, PH3 and/or EOMES.

We think establishing the Fucci or EOMES mouse system is beyond the scope of the manuscript. In addition, given the present setting of all labs involved, it would be logistically unattainable (see also comments in the section below).

We think the co-staining scheme and plan will be informative enough to satisfactory address the concerns raised by the reviewer.

Reviewer’s comment: 2) Figure 2: The microinjection experiments are elegant; the images, however, do not complement the experiment. The images of the microinjected cells seem not to be reconstructed from z-stacked optical slices, so often, processes are not continuous (panel B, for example); therefore, it is not clear if an apical process is indeed missing or just not seen.

Response to Reviewer and planned revision:

The mentioned images are reconstructed from continuous Z-stacks, as we always do given the type of data. We can provide better reconstructions and/or additional images.

Reviewer’s comment:

The data analysis should include other parameters; BrdU staining could have given information on cell cycle exit, PAX6, SOX2, and EOMES on the location of the cells in the VZ/sVZ. The quality of images showing EOMES and TUBB3 staining is so low that it makes the reader doubt the validity of the quantifications. "Taken together, these data suggest that the inhibition of DOT1L might favor the acquisition of a neuronal over BP cell fate" This interpretation should be subjected to more investigations. It is possible that this treatment just accelerates the AP-> BP -> Neuronal fate. The author's claim needs to be backed by additional experiments or be changed.

Response to Reviewer and planned revision:

To address this point, we will include in the revised manuscript staining and co-staining with PAX6, SOX2 (see also response above) and provide a BrdU labeling experiment.

Reviewer’s comment: 3) Figure 3: The experiment concept and its performance are impressive, yet the data is insufficient. The images in A that are supposed to be representative show two cells; their location is not clear, and the expression of GFP is not clear; in fact, both pairs seem to be GFP negative (not clear what is the threshold for background). Staining with anti-GFP and a second method to follow neurogenesis is necessary.

Response to Reviewer and planned revision:

We did use different staining methods and schemes to follow neurogenesis. As specified above, we will deepen our analysis by using additional markers, such as TBR1.

Reviewer’s comment: 4) On page 9, lines 8-10, the authors claim that their number of cells was "sufficient" for single-cell analysis; the numbers are Response to Reviewer and planned revision:

In the revised manuscript, we will include the analysis of how many cells are needed to identify cluster of 6 cell types in this paradigm, based for example on the algorithms developed in Treppner et al. 2021.

Reviewer’s comment: 5) The authors use Seurat and RaceID without their appropriate citations in the first mention during the results. The authors also stop immediately after DEG analysis along with clustering. The authors could analyze their RNA-seq data with a trajectory; to say the least, the identification/characterization of TTS and neurons as Neurons I, II, and III are insufficient. There could be multiple ways to show the "fate" of cells in the isolated FACS, which the authors have missed.

Response to Reviewer and planned revision:

We will include the respective citations in a revised manuscript. We provide already differentiation trajectories but will include other methods, including scVelo of FateID to extend the trajectory analyses. We kindly ask the reviewer to also refer to the comments above regarding the TTs cluster characterization as part of our effort to provide a better picture of the different clusters.

Reviewer’s comment: 6) The authors detected candidates like Fgfr3, Nr2f1, Ofd1, and Mme as part of their treated (different approaches) datasets (from their DEG analysis). They correctly cite Huang et al., 2020 but fail to give us a sense of the consequences of these gene dysregulations. The authors can also validate if these proteins are expressed in their treated cells.

Response to Reviewer and planned revision:

In the revised manuscript we will comment on the function of the four genes mentioned.

In addition, we will validate the expression of these genes on protein and transcriptional level through immunostainings -provided that antibodies are working in our system- or smFISH, respectively.

Reviewer’s comment: 7) The authors list a few GO terms (page 10, lines 1-10) and associate them with reduced proliferation; they must cite relevant studies. The authors can also add supplementary data showing which genes in their data correspond to these GO terms.

Response to Reviewer and planned revision:

We thank the reviewer for pointing out the missing citations.

We of course agree on the need to add them, and we will do so in the revised manuscript.

Reviewer’s comment: 8) On Page 11, lines 3-7, the authors describe their method to arrive at the 17 targets with TF activity from the previous analysis. Can the authors describe the method used to correlate the two? The reviewer understands this could be MEME analysis or analysis of earlier datasets of Ferrari et al. 2020. But it must be explicitly stated, and a few examples in supplementary need to be exemplified as this analysis is key to discovering the three metabolic genes.

Response to Reviewer and planned revision:

In the revised manuscript, we will clarify the exact analysis that resulted in the identification of the 17 target genes, using the specific tool for gene network analysis, that is based on our scRNA-seq data alone, but not on the Ferrari et al 2020 data set.

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

n/a

4. Description of analyses that authors prefer not to carry out

Reviewer’s comment: Tis21 data (1H), again of low quality, is only a single piece of evidence and the conclusion "suggesting that the acquisition of a basal fate was paralleled by a switch to neurogenesis" is premature. I think other cell cycle exit reporters, Fucci markers, pHis, BrdU, NeuroD, or Tbr2 reporters (Li et al., 2020, (Haydar and Sestan labs)) to name a few, are necessary to establish the conclusions. The authors should show other markers such as PAX6, EOMES, or other upper-layer markers upon cell cycle exit in the SVZ/CP. These additional experiments will assist in cell fate analysis.

Response to Reviewer and planned revision:

As pointed out above, we think establishing the Fucci or EOMES mice system is beyond the scope of the manuscript as it will not provide more information than the ones we will obtain from systematic and extensive co-staining experiments. In addition, all labs involved are facing a logistic issue (animal house not ready yet, construction works etc) that made the importing and setting up of the colony unattainable for the next 6-10months. If the reviewer and/or the editorial board think this is a major point compromising the entire revision, we kindly ask to contact us again so that we can discuss the issue and arrive to a shared conclusion.

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Referee #2

Evidence, reproducibility and clarity

Appiah et al. present a concise manuscript that provides details and possible mechanisms of their previous work (Franz et al., 2019; Ferrari et al., 2020). The study uses diverse lines of investigation to arrive at most conclusions. However, as interesting as the data is, we find that at the present state, it is not sufficient to prove that, indeed, the asparagine metabolism is regulated by DOTL1/PRC2 crosstalk. The neurogenic shift presented in the first part of the paper is not comprehensive and, therefore, not very convincing. The quality of images provided in the main and supplementary data is less than ideal. Additional data analysis and interpretation of the scRNA seq data may be needed. The authors finally conclude with rescue experiments done in culture and in-vivo, which we believe is the stand-out part of this study. Overall the manuscript has some interesting observations that are often over-interpreted with less supporting data. The manuscript reads well but requires additional data and changes in the claims/interpretation to be suited for publication.

Comments

1. Abstract: Is this statement correct: "DOT1L inhibition led to increased neurogenesis driven by a shift from asymmetric self-renewing to symmetric neurogenic divisions of APs". AP undergoes symmetric division for self-renewal and asymmetric neurogenic divisions.

All the data is based on treatments with EPZ (DOTL1 inhibitor), yet no information is shown to support its targeted activity in this system. A proof of principle in the chosen experimental system is missing; for instance, examining the activity or protein level of DOTL1 and decreased methylation of the target(s) is essential. <br /> 2. Figure 1: The scoring of centrosomes and cilia is insufficient to conclude delamination and increase in basal fates. The effect could be on ciliogenesis or centrosome tethering to the apical end-feet of the AP, and other possible explanations for this observation also exist. The images are too small; larger images or graphic representations could be helpful in addition to the data.

To make a statement regarding delamination, I would like to see either the dynamics of delamination (organotypic slices images), staining with BP markers, or morphological changes of AP (staining that will reveal loss of adherence) or comparable data to support the observation. In my opinion Supp. Figure 1 is insufficient; the single image is not convincing; I would like to see 3D reconstruction and better quality images.

Tis21 data (1H), again of low quality, is only a single piece of evidence and the conclusion "suggesting that the acquisition of a basal fate was paralleled by a switch to neurogenesis" is premature. I think other cell cycle exit reporters, Fucci markers, pHis, BrdU, NeuroD, or Tbr2 reporters (Li et al., 2020, (Haydar and Sestan labs)) to name a few, are necessary to establish the conclusions. The authors should show other markers such as PAX6, EOMES, or other upper-layer markers upon cell cycle exit in the SVZ/CP. These additional experiments will assist in cell fate analysis. 2. Figure 2: The microinjection experiments are elegant; the images, however, do not complement the experiment. The images of the microinjected cells seem not to be reconstructed from z-stacked optical slices, so often, processes are not continuous (panel B, for example); therefore, it is not clear if an apical process is indeed missing or just not seen. The data analysis should include other parameters; BrdU staining could have given information on cell cycle exit, PAX6, SOX2, and EOMES on the location of the cells in the VZ/sVZ. The quality of images showing EOMES and TUBB3 staining is so low that it makes the reader doubt the validity of the quantifications. <br /> "Taken together, these data suggest that the inhibition of DOT1L might favor the acquisition of a neuronal over BP cell fate" This interpretation should be subjected to more investigations. It is possible that this treatment just accelerates the AP-> BP -> Neuronal fate. The author's claim needs to be backed by additional experiments or be changed. 3. Figure 3: The experiment concept and its performance are impressive, yet the data is insufficient. The images in A that are supposed to be representative show two cells; their location is not clear, and the expression of GFP is not clear; in fact, both pairs seem to be GFP negative (not clear what is the threshold for background). Staining with anti-GFP and a second method to follow neurogenesis is necessary. 4. On page 9, lines 8-10, the authors claim that their number of cells was "sufficient" for single-cell analysis; the numbers are <500 for all samples. The authors need to justify this statement or articles that carefully analyze the number required for such a conclusion as references. 5. The authors use Seurat and RaceID without their appropriate citations in the first mention during the results. The authors also stop immediately after DEG analysis along with clustering. The authors could analyze their RNA-seq data with a trajectory; to say the least, the identification/characterization of TTS and neurons as Neurons I, II, and III are insufficient. There could be multiple ways to show the "fate" of cells in the isolated FACS, which the authors have missed. 6. The authors detected candidates like Fgfr3, Nr2f1, Ofd1, and Mme as part of their treated (different approaches) datasets (from their DEG analysis). They correctly cite Huang et al., 2020 but fail to give us a sense of the consequences of these gene dysregulations. The authors can also validate if these proteins are expressed in their treated cells. 7. The authors list a few GO terms (page 10, lines 1-10) and associate them with reduced proliferation; they must cite relevant studies. The authors can also add supplementary data showing which genes in their data correspond to these GO terms. 8. On Page 11, lines 3-7, the authors describe their method to arrive at the 17 targets with TF activity from the previous analysis. Can the authors describe the method used to correlate the two? The reviewer understands this could be MEME analysis or analysis of earlier datasets of Ferrari et al. 2020. But it must be explicitly stated, and a few examples in supplementary need to be exemplified as this analysis is key to discovering the three metabolic genes.

Significance

Appiah et al. present a concise manuscript that provides details and possible mechanisms of their previous work (Franz et al., 2019; Ferrari et al., 2020). The study uses diverse lines of investigation to arrive at most conclusions. However, as interesting as the data is, we find that at the present state, it is not sufficient to prove that, indeed, the asparagine metabolism is regulated by DOTL1/PRC2 crosstalk. The neurogenic shift presented in the first part of the paper is not comprehensive and, therefore, not very convincing. The quality of images provided in the main and supplementary data is less than ideal. Additional data analysis and interpretation of the scRNA seq data may be needed. The authors finally conclude with rescue experiments done in culture and in-vivo, which we believe is the stand-out part of this study.

Overall the manuscript has some interesting observations that are often over-interpreted with less supporting data. The manuscript reads well but requires additional data and changes in the claims/interpretation to be suited for publication.

There's an interesting dichotomy between streaming information and batched data here, but it isn't well delineated and doesn't add much to the discussion as a result. Perhaps distilling it down may help? There's a kernel of something useful here, but it isn't immediately apparent.

Relation to stock and flow or the idea of the garden and the stream?

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Referee #1

Evidence, reproducibility and clarity

Summary:

In this study, the authors consider the problem of inferring transcription dynamics from smFISH data. They distinguish between two important experimental situations. The first one considers measurements of mature mRNAs, while the second one considers measurements of nascent mRNA through fluorescent probes targeting PP7 stem loops. The former problem has been previously dealt with extensively, but less work has been done on the context of the latter. The inference approaches are based on maximum likelihood estimation, from which point estimates for promoter-switching and transcription rates are obtained. The study focuses on steady state measurements only. The authors perform several analyses using synthetic data to understand the limitations of both approaches. They find that inference from nascent mRNA is more reliable than inference from mature mRNA distributions. Moreover, they show that accounting for different cell-cycle stages (G1 vs G2) is important and that pooling measurements across the cell-cycle can lead to quantitatively and even qualitatively different inferences. Both approaches are then used to analyze transcription in an experimental system in yeast, for which they find evidence of gene dosage compensation. I consider this an interesting and relevant study, which will appeal to the systems- and computational biology community. The paper is well written and the (computational) methods are described in detail. The experimental description is quite minimal and could profit from further details / explanations. I have several technical criticisms and questions, which I believe should be addressed before publication. Since I am a theorist, I will comment predominantly on the statistical / computational aspects.

Major comments/questions:

-A key reference that is missing is Fritzsch et al. Mol Syst Biol (2018). In this work, the authors have used nascent mRNA distributions and autocorrelations (obtained from live-imaging) to infer promoter- and transcription dynamics. I believe this work should be appropriately cited and discussed.

Synthetic case study:

-Inference and point estimates. The authors use a maximum-likelihood framework to extract point estimates of the parameters. Subsequently, relative absolute differences are used to assess the accuracy of the inference. However, as far as I have understood, this is performed for only a single simulated dataset, for each considered parameter configuration. The resulting metric, however, does not really capture the inference accuracy, since it is based on a single (random) realization of the MLE. I would recommend to at least repeat the inference multiple times for different realizations of the simulated dataset (per parameter configuration) to get a better feeling of the distribution of the MLE (e.g., its bias / variance). Alternatively, identifiability analyses based on the Fisher information could be performed for (some of) the different parameter configurations although this may be computationally more demanding.

-It would be useful to include confidence intervals based on profile likelihoods also for the synthetic case study, in particular for the 6 reported datasets. I would also find it helpful to see comprehensive profile likelihood plots for the key results / parameter inferences in the supplement. This would also provide useful insights into the identifiability of the parameters.

Experimental case study:

-Validation against live-cell data. In the simulation of the autocorrelation function, what was the ratio of cells initialized in G1 / G2, respectively? I'd expect this to have direct influence on the simulated ACF. Moreover, a linear fit is used to correct for "non-stationary effects" in the ACF that supposedly stem from cell-cycle dynamics. First, I don't think this terminology is really accurate, since non-stationarity would lead to an ACF that depends on two parameters (tau_1 and tau_2). I suppose the goal of the linear correction is to remove slow / static population heterogeneity? If yes, wouldn't it be easier / more direct to also change the simulations to non-synchronized cell-cycles? In this case, they should also display the very slow / static components as displayed in the data, which would eliminate the need for the post-hoc correction. I was also wondering whether other statistics (e.g., mean, variance, distributions) match between the simulations and the live-cell experiment? This could provide further validation of the inferred parameters.

-If I understood correctly, the signal intensity of the measured transcription spot is normalized by the median cytoplasmic spot brightness. Since the normalized intensity of a single complete transcript is 1, the cumulative intensity should give a lower bound on the nascent mRNAs. The histograms in Fig. 4b show intensity values in the range of 30, which would mean that at least 30 transcripts contribute to the transcription spot. The total number of nucleoplasmic and cytoplasmic mRNA, however, is in the range of 10 (Fig. 3a). I am probably missing something but how can we reconcile these numbers? The authors mention that the brightest spot just counts for one transcript, but argue that this has negligible influence on mature RNA counts. Could this be a possible explanation for the mismatch?

Minor comments:

-In the experimental case study, the authors argue that the "correct" inference result is the one that accounts for cell-cycle stage, while the other one termed "incorrect". I find this terminology too strong, since every estimate is subject to uncertainty.

-Page 2: "... in a asynchronous population" -> "... in an asynchronous population"

-Page 7: "...parameters sets 3 and 4" -> "...parameter sets 3 and 4"

-Figures 5a and 6a: parameter names and units should go on the y-axis.

Significance

Quantifying kinetic parameters from incomplete and noisy experimental data is a core problem in systems biology. I therefore consider this manuscript to be very relevant to this field. The contribution of this manuscript is largely methodological, although its potential usefulness is demonstrated using experimental data in yeast.

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Reply to the reviewers

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

In recent years, the field has investigated crosstalk between cGMP and cAMP signaling (PMID: 29030485), lipid and cGMP signaling (PMID: 30742070), and calcium and cGMP signaling (PMID: 26933036, 26933037). In contrast to the Plasmodium field, which has benefited from proteomic experiments (ex: PMID 24594931, 26149123, 31075098, 30794532), second messenger crosstalk in T. gondii has been probed predominantly through genetic and pharmacological perturbations. The present manuscript compares the features of A23187- and BIPPO-stimulated phosphoproteomes at a snapshot in time. This is similar to a dataset generated by two of the authors in 2014 (PMID: 24945436), except that it now includes one BIPPO timepoint. The sub-min​​ute phosphoproteomic timecourse following A23187 treatment in WT and ∆cdpk3 parasites is novel and would seem like a useful resource.

CDPK3-dependent sites were detected on adenylate cyclase, PI-PLC, guanylate cyclase, PDE1, and DGK1. This motivated study of lipid and cNMP levels following A23187 treatment. The four PDEs determined to have A23187-dependent phosphosites were characterized, including the two PDEs with CDPK3-dependent phosphorylation, which were found to be cGMP-specific. However, cGMP levels do not seem to differ in a CDPK3- or A23187-dependent manner. Instead, cAMP levels are elevated in ∆cdpk3 parasites. This would seem to implicate a feedback loop between CDPK3, the adenylyl cyclase, and PKA/PKG: CDPK3 activity reduces adenylyl cyclase activity, which reduces PKA activity, which increases PKG activity. The authors don't pursue this direction, and instead characterize PDE2, which does not have CDPK3-dependent phosphosites, and seems out of place in the study

Response:

We agree with reviewer 1 that a feedback loop between CDPK3, the adenylyl cyclase and PKA/PKG is certainly one of several possibilities (and we acknowledge this in the manuscript).

We felt, however, that given the observation that A23187 and BIPPO treatment leads to phosphorylation of numerous PDEs (hinting at the presence of an Ca2+-regulated feedback loop), it was entirely relevant to study these in greater detail. Coupled with the A23187 egress assay on ΔPDE2 parasites - our findings suggest that PDE2 plays an important role in this signalling loop (an entirely novel finding). While PDE2 appears to exert its effects in a CDPK3-independent manner (indeed suggesting that CDPK3 might exert its effects on cAMP levels in a different fashion), this does not detract from the important finding that PDE2 is one of the (likely numerous) components that is regulated in a Ca2+-dependent feedback loop to regulate egress.

We have modified our writing to better reflect the fact that our decision to pursue study of the PDEs was not solely CDPK3-centric.

While we feel that our reasoning for studying the PDEs is solid, we appreciate that further clarification on the putative CDPK3-Adenylate cyclase link would make it easier for the reader to follow the rationale.

We have not studied the direct link between CDPK3 and the Adenylate Cyclase β in more detail, as ACβ alone was shown to not play a major role in regulating lytic growth (Jia et al., 2017).

**MAJOR COMMENTS**

1.Some of the key conclusions are not convincing.

The data presented in Figure 6E, F, and G and discussed in lines 647-679 are incongruent. In Figure 6E, the plaques in the PDE2+RAP image are hardly visible; how can it be that the plaques were accurately counted and determined not to differ from vehicle-treated parasites?

Are the images in 6E truly representative? Was the order of PDE1 and PDE2 switched? The cited publication by Moss et al. 2021 (preprint) is not in agreement with this study, as stated. That preprint determined that parasites depleted of PDE2 had significantly reduced plaque number and plaque size (>95% reduction); and parasites depleted of PDE1 had a substantially reduced plaque size but a less substantial reduction in plaque number.

Response:

The plaques for PDE2+RAP were counted using a microscope since they are difficult to see by eye. We thank the reviewer for detecting our incorrect reference to Moss et al. (2021). This has been corrected in the text. We confirm, however, that the images in 6E are representative of what we observed and do indeed differ from what was seen by Moss et al.. We have acknowledged this clearly in the text.

The differences cannot easily be explained other than by the different genetic systems used. Further studies of the individual PDEs will likely illuminate their role in invasion/ growth, but we feel this would be beyond the scope of this study.

Unfortunately, the length of time required for PDE depletion (72h) is incompatible with most T. gondii cellular assays (typically performed within one lytic cycle, 40-48h). Although the authors performed the assays 3 days after initial RAP treatment, is there evidence that non-excised parasites don't grow out of the population. This should be straightforward to test: treat, wait 3 days, infect onto monolayers, wait 24-48h fix, and stain with anti-YFP and an anti-Toxoplasma counterstain. The proportion of the parasite population that had excised the PDE at the time of the cellular assays will then be known, and the reader will have a sense of how complete the observed phenotypes are. As a reader, I will regard the phenotypes with some level of skepticism due to the long depletion time, especially since a panel of PDE rapid knockdown strains (depletion in __Response:

1. Cellular assays using KO parasites are commonly performed at the point at which protein depletion is detected. Both our western blots and plaque assay results demonstrate that, at the point of assay, there is no substantial outgrowth of non-excised parasites. The original manuscript also includes PCRs performed at the 72 hr time point (See Fig. 6B) to support this.
2. We appreciate the reviewer’s comment re the panel of PDE KD strains. The reviewer notes that there are substantial limitations to conditional KO systems, which similarly applies to KD systems - there are notable pros and cons to each approach. When designing our strategy (pre-publication of the Moss et al., 2022), we made a deliberate decision to use conditional KO strains in light of the fact that residual protein levels in KD systems can cause significant problems, particularly for membrane proteins (all of the investigated PDEs have a transmembrane domain). Tagging of proteins with the degradation domain can have further issues, leading to protein mis-localisation, which we have experienced with several unrelated proteins in the lab.

   The authors should qualify some of their claims as preliminary or speculative, or remove them altogether.

The claims in lines 240-260 are confusing. It seems likely that the two drug treatments have at least topological distinctions in the signaling modules, given that cGMP-triggered calcium release is thought to occur at internal stores, whereas A23187-mediated calcium influx likely occurs first at the parasite plasma membrane.The authors' proposed alternative, that treatment-specific phosphosite behavior arises from experimental limitations and "mis-alignment", is unsatisfying for the following reasons: (1) From the outset, the authors chose different time frames to compare the two treatments (15s for BIPPO vs. 50s for A23187); (2) the experiment comprises a single time point, so it does not seem appropriate to compare the kinetics of phosphoregulation. There is still value in pointing out which phosphosites appear treatment-specific under the chosen thresholds, but further claims on the basis of this single-timepoint experiment are too speculative. Lines 264-267 and 281-284 should also be tempered.

Relatedly, graphing of the data in Figure 1G (accompanying the main text mentioned above) was confusing. Why is one axis a ratio, and the other log10 intensity? What does log10 intensity tell you without reference to the DMSO intensity? Wouldn't you want the L2FC(A23187) vs. L2FC(BIPPO) comparisons? Could you use different point colors to highlight these cases on plot 1E? Additionally, could you use a pseudocount to include peptides only identified in one treatment condition on the plot in 1E? (Especially since these sites are mentioned in lines 272-278 but are not on the plot)

Response:

1. The kinetics of the responses to A23187 and BIPPO are very different. This is why treatment timings are purposely different as they were selected to align pathways to a point where calcium levels peak just prior to calcium re-uptake. We make no mention of kinetic comparisons, and merely demonstrate that at the chosen timepoints, overall signalling correlation is very high. The observation that most of the sites that behave differently between conditions sit remarkably close to the threshold for differential regulation (in the treatment condition where they are not DR - see Fig. 1G) led us to speculate that many of these sites are likely on the cusp of differential regulation. While it is entirely possible that some of these differences are, in fact, treatment specific (and we clearly acknowledge this in the text), we simply state that we cannot confidently discern clear signalling features that allow us to distinguish between the two treatments. We feel that this is an entirely relevant observation given the observed preponderance of both A23187 and BIPPO-dependent DR phosphosites on proteins in the PKG signalling pathway (as current models place this upstream of Ca2+release).
2. Log10 intensity only serves to spread the data for easier visualisation. The only comparison being made relates to the LFCs. Fig. 1Gi shows the LFC scores (x axis) for all sites regulated following A23187 treatment (for which peptides were also identified in BIPPO treatment). On this plot we have highlighted the sites that are differentially regulated following BIPPO but not A23187 treatment (with red showing the DRup and blue showing the DRdown sites). This demonstrates that many of the sites that are regulated following BIPPO but not A23187 treatment cluster close to the threshold for differential regulation in the A23187 dataset - suggesting that many of these sites are likely on the cusp of differential regulation. Fig. 1Gii shows the reverse. While we could highlight the above-mentioned sites on the plot in Fig. 1E, we do not feel that it would demonstrate our point as clearly.

We feel that including a pseudocount on Fig. 1E for peptides lacking quantification in one treatment condition would be visually misleading as the direct correlation being made in Fig. 1E is BIPPO vs A23187 treatment. The sites mentioned in lines 272-278 in the original manuscript (now lines 268-276) are available in the supplement tables.

3.Additional experiments would be essential to support the main claims of the paper.

Genetic validation is necessary for the experiments performed with the PKA inhibitor H89. H89 is nonspecific even in mammalian systems (PMID: 18523239) and in this manuscript it was used at a high concentration (50 µM) The heterodimeric architecture of PKA in apicomplexans dramatically differs from the heterotetrameric enzymes characterized in metazoans (PMID: 29263246), so we don't know what the IC50 of the inhibitor is, or whether it inhibits competitively. Two inducible knockdown strains exist for PKA C1 (PMID: 29030485, 30208022). The authors could request one of these strains and construct a ∆cdpk3 in that genetic background, as was done for the PDE2 cKO strain. Estimated time: 3-4 weeks to generate strain, 2 weeks to repeat assays.

Response:

1. While we appreciate that H89 is not 100% specific for PKA, this is not our only line of evidence that cAMP levels are altered. We demonstrate that cAMP levels are elevated in CDPK3 KO parasites – further substantiating our finding.

The H89 concentration used in our experiment is in keeping with/lower than the concentrations used in other Toxoplasma publications (Jia et al., 2017), and both the Toxoplasma and Plasmodium fields have shown convincingly that H89 treatment phenocopies cKD/cKO of PKA (see Jia et al., 2017; Flueck et al., 2019).

While we agree that the genetic validation suggested by reviewer 1 would serve to further support our findings (though it would not provide further novel insights), the suggested time frame for experimental execution was not realistic. Line shipment, strain generation, subcloning and genetic validation would take substantially longer than 3-4 weeks.

cGMP levels are found to not increase with A23187 treatment, which is at odds with a previous study (lines 524-560). The text proposes that the differences could arise from the choice of buffer: this study used an intracellular-like Endo buffer (no added calcium, high potassium), whereas Stewart et al. 2017 used an extracellular-like buffer (DMEM, which also contains mM calcium and low potassium). An alternative explanation is that 60 s of A23187 treatment does not achieve a comparable amount of calcium flux as 15 s of BIPPO treatment, and a calcium-dependent effect on cGMP levels, were it to exist, could not be observed at the final timepoint in the assay. The experiments used to determine the kinetics of calcium flux following BIPPO and A23187 treatments (Fig. 1B, C) were calibrated using Ringer's buffer, which is more similar to an extracellular buffer (mM calcium, low potassium). In this buffer, A23187 treatment would likely stimulate calcium entry from across the parasite plasma membrane, as well as across the membranes of parasite intracellular calcium stores. By contrast, A23187 treatment in Endo buffer (low calcium) would likely only stimulate calcium release from intracellular stores, not calcium entry, since the calcium concentration outside of the parasite is low. Because calcium entry no longer contributes to calcium flux arising from A23187 treatment, it is possible that the calcium fluxes of A23187-treated parasites at 60 s are "behind" BIPPO-treated parasites at 15 s. The researchers could control these experiments by *either* (i) performing the cNMP measurements on parasites resuspended in the same buffer used in Figure 1B, C (Ringer's) or (ii) measuring calcium flux of extracellular parasites in Endo buffer with BIPPO and A23187 to determine the "alignment" of calcium levels, as was done with intracellular parasites in Figure 1C. No new strains would have to be generated and the assays have already been established in the manuscript. Estimated time to perform control experiments with replicates: 2 weeks. This seems like an important control, because the interpretation of this experiment shifts the focus of the paper from feedback between calcium and cGMP signaling, which had motivated the initial phosphoproteomics comparisons, to calcium and cAMP signaling. Further, the lipidomics experiments were performed in an extracellular-like buffer, DMEM, so it's unclear why dramatically different buffers were used for the lipidomics and cNMP measurements.

Response:

While the initial calibration experiments to measure calcium flux were indeed performed in Ringer’s buffer, the parasites were intracellular. We therefore chose to measure cNMP concentrations of extracellular parasites syringe lysed in Endo buffer, which is better at mimicking intracellular conditions than any other described buffer.

As the reviewer suggested, we measured the calcium flux of extracellular parasites in Endo buffer upon stimulation with either A23187 or BIPPO.

We found that peak calcium response to BIPPO in Endo buffer was similar to that of intracellular parasites (~15 seconds post treatment) (See Supp Fig. 6A). Upon treatment with A23187, extracellular parasites in Endo buffer had a much faster response compared to their intracellular counterparts, with peak flux measured at ~25 seconds post treatment (see Supp Fig. 6B). This indeed does suggest that extracellular parasites in Endo buffer behave differently to A23187 compared to their intracellular counterparts. However, peak calcium response is still occuring within the experimental time course and is not being missed, as the reviewer worries. Moreover, since we are able to detect increased cAMP levels in A23187 treated parasites, Ca2+ flux appears sufficient to alter cNMP signalling.

We did notice however that the intensity of the calcium flux was much weaker in Endo buffer compared to intracellular parasites (see Supp Fig. 6B). We found that this was due to the lack of host-derived Ca2+, since supplementation of Endo buffer with 1 uM CaCl2 restored the intensity of the calcium response to match that of intracellular parasites (see Supp Fig. 6C). We therefore decided to repeat our cGMP measurements, this time using extracellular parasites in Endo buffer supplemented with 1 uM CaCl2. However, we found no differences in cGMP levels in the response to ionophore under these conditions (now Supp Fig. 6D) compared to the previous experiments, so the conclusions from the previous data do not change.

As for the lipidomics experiments, we chose to use DMEM so that our dataset could be compared with other published lipidomic datasets (Katris et al., 2020; Dass et al., 2021) where DMEM was also used as a buffer when measuring global lipid profiles of parasites.

We now acknowledge in the paper that Endo buffer has its shortcomings, and that this could be the reason why we do not detect changes in cGMP concentrations. We do, however, believe that Endo buffer is the best alternative to intracellular parasites and is supported by its consistent use in numerous publications studying Toxoplasma signalling (McCoy et al., 2012; Stewart et al., 2017).

Additional information is required to support the claim that PDE2 has a moderate egress defect (lines 681-687). T. gondii egress is MOI-dependent (PMID: 29030485). Although the parasite strains were used at the same MOI, there is no guarantee that the parasites successfully invaded and replicated. If parasites lacking PDE2 are defective in invasion or replication, the MOI is effectively decreased, which could explain the egress delay. Could the authors compare the MOIs (number of vacuoles per host cell nuclei) of the vehicle and RAP-treated parasites at t = 0 treatment duration to give the reader a sense of whether the MOIs are comparable?

Response:

Since PDE2 KO parasites have a substantial growth defect, we did notice that starting MOIs were consistently lower for the RAP-treated samples compared to the DMSO-treated samples. However, this was also the case for PDE1 KO parasites where we did not see an egress delay. We also found that the egress delay was still evident for ∆CDPK3 parasites, despite having higher starting MOIs than WT parasites in our experiments. Therefore there does not appear to be a link between starting MOIs and the egress delay.

To be sure of our results, we also performed egress assays where we co-infected HFFs with mCherry-expressing WT parasites (WT ∆UPRT) and GFP-expressing PDE2 cKO parasites that were treated with either DMSO or RAP or ∆CDPK3 parasites. This recapitulated our previous findings, confirming the deletion of PDE2 leads to delay in A23187-mediated egress.

4.A few references are missing to ensure reproducibility.

The manuscript states that the kinetic lipidomics experiments were performed with established methods, but the cited publication (line 497) is a preprint. These are therefore not peer reviewed and should be described in greater detail in this manuscript, including any relevant validation.

Response:

We thank the reviewer for pointing this out. We have included a greater description of the methods used in the materials and methods section such that the experiment is reproducible, as per the reviewer’s suggestion. We decided to still make mention of the BioRxiv preprint since we thought it was appropriate for the reader to be informed of ongoing developments in the field.

Please cite the release of the T. gondii proteomes used for spectrum matching (lines 972-973).

Response:

We have included this as per the reviewer’s suggestion.

Please include the TMT labeling scheme so the analysis may be reproduced from the raw files.

Response:

We have included this as per the reviewer’s suggestion in Supp Fig. 3A.

5.Statistical analyses should be reviewed as follows:

Have the authors examined the possibility that some changes in phosphopeptide abundance reflect changes in protein abundance? This may be particularly relevant for comparisons involving the ∆cdpk3 strain. Did the authors collect paired unenriched proteomes from the experiments performed? Alternatively, there may be enriched peptides that did not change in abundance for many of the proteins that appear dynamically phosphorylated.

Response:

We did not collect unenriched proteomes from the experiments performed (although we did perform unenriched mixing checks to ensure equal loading between samples), and believe that this wasn’t a necessity for the following reasons:

1. For within-line treatment analyses, treatment timings are so short (a maximum of 15-50s in the single timepoint experiment) that it would be unlikely to detect substantial changes in protein abundance. Moreover, these unlikely events would affect all phosphosites across a protein, and therefore be detectable.

In our CDPK3 dependency timecourse experiments, we normalise both the WT and ∆CDPK3 strain to 0s, and measure signalling progression over time. Therefore, any difference at timepoints that are not “0” are not originating from basal differences. We also see a consistent increase/decrease in phosphosite detection across the sub-minute timecourse, further confirming that the observed changes are truly down to dynamic changes in phosphorylation and not protein levels.

In the single timepoint CDPK3 dependency analyses (44 regulated sites identified, Data S2), we acknowledge that there could be some risk of altered starting protein abundance between lines. However, if protein abundance were responsible for the changes in phosphosite detection, we would expect all phosphosites across the protein to shift, and we do not observe this. Moreover, when we look at these CDPK3 dependent proteins and compare their phosphosite abundance in untreated WT and ∆CDPK3 lines, we find that for each protein, either all or the majority of phosphosites detected are unchanged (highlighting that there is no substantial difference in this protein’s abundance between lines). Where there are phosphosite differences between lines, these are only ever on single sites on a protein while most other sites are unchanged - implying that these are changes to basal phosphorylation states and not protein levels.

It seems like for Figs. 3B and S5 the maximum number of clusters modeled was selected. Could the authors provide a rationale for the number of clusters selected, since it appears many of the clusters have similar profiles.

The number of clusters is chosen automatically by the Mclust algorithm as the value that maximizes the Bayes Information Criterion (BIC). BIC in effect balances gains in model fit (increasing log-likelihood) against increasing the number of parameters (i.e. number of clusters).

Please include figure panel(s) relating to gene ontology. Relevant information for readers to make conclusions includes p-value, fold-enrichment or gene ratio, and some sort of metric of the frequency of the GO term in the surveyed data set. See PMID: 33053376 Fig. 7 and PMID: 29724925 Fig. 6 for examples or enrichment summaries. Additionally, in the methods, specify (i) the background set, (ii) the method used for multiple test correction, (iii) the criteria constituting "enrichment", (iv) how the T. gondii genome was integrated into the analysis, (v) the class of GO terms (molecular function, biological process, or cellular component), (vi) any additional information required to reproduce the results (for example, settings modified from default).

Response:

We have included the additional information requested in the materials and methods.

We purposely did not include GO figure panels as our analyses are being done across many clusters, making it very difficult to display this information cohesively. We have included all data in Tables S2-S5. These tables included all the relevant information on p-value, enrichment status, ratio in study/ratio in population, class of GO terms etc.

The presentation of the lipidomics experiments in Figure 4A-C is confusing. First, the ∆cdpk3/WT ratio removes information about the process in WT parasites, and it's unclear why the scale centers on 100 and not 1. Second, the data in Figure S6 suggests a more modest effect than that represented in Fig. 4; is this due to day to day variability? How do the authors justify pairing WT and mutant samples as they did to generate the ratios?

Response:

This is a common strategy used by many metabolomics experts (Bailey et al., 2015; Dass et al., 2021; Lunghi et al., 2022). We had originally chosen to represent the data as a ratio since this form of representation helps get rid of the variability that arises between experiments and allows us to see very clear patterns which would otherwise go unnoticed. This variability arises from the amount of lipids in each sample which varies between parasites in a dish, the batch of FBS and DMEM used, and the solutions and even room temperature used to extract lipids on a given day.

However, we agree with the reviewer that depicting the data in Figure 4A-C as a ratio of ∆CDPK3/WT parasites can be confusing, so we have now changed the graphs, plotting WT and ∆CDPK3 levels instead, and have moved the ratio of ∆CDPK3/WT to the Supplementary Figure 5.

The significance test seems to be performed on the difference between the WT and ∆cdpk3 strains, but not relative to the DMSO treatment? Wouldn't you want to perform a repeated measures ANOVA to determine (i) if lipid levels change over time and (ii) if this trend differs in WT vs. mutant strain?

Response:

The reviewer correctly points out that ANOVA is often used for time courses, but we must point out that it is not always strictly appropriate since it can overlook the purpose of the individual experiment design, which in this case is, 1) to investigate the role of CDPK3 compared to the WT parental strain, and 2) specifically to find the exact point at which the DAG begins to change after stimulus to match the proteomics time course.

Our data is clearly biassed towards earlier time points where we have 0, 5, 10, 30, 45 seconds where DAG levels are mostly unchanged compared to the single timepoint 60 seconds which shows a significant difference in DAG using our method of statistical comparison by paired two tailed t-test. Therefore, it would be unwise to use ANOVA when we really want to see when the A23187 stimulus takes effect, which appears to be after the 45 second mark. Therefore, analysing the data by ANOVA would likely provide a false negative result, where the result is non-significant but there is clearly more DAG in WT than CDPK3 after 60 seconds. T-tests are commonly used when comparing the same cell lines grown in the same conditions with a test/treatment, and in this case the test/treatment is CPDK3 present or absent (Lentini et al., 2020).

In the main text, it would be preferable to see the data presented as the proteomics experiments were in Figure 4B and 4C, with fold changes relative to the DMSO (t = 0) treatment, separately for WT and ∆cdpk3 parasites.

Response:

We have now changed the way that we represent the data, plotting %mol instead of the ratio.

Signaling lipids constitute small percentages of the overall pool (e.g. PMID: 26962945), so one might not necessarily expect to observe large changes in lipid abundance when signaling pathways are modulated. Is there any positive control that the authors could include to give readers a sense of the dynamic range? Maybe the DGK1 mutant (PMID: 26962945)?

Response:

DGK1 is maybe not a good example because the DGK1 KO parasites effectively “melt” from a lack of plasma membrane integrity ((Bullen et al., 2016), so this would likely be technically challenging. We don’t see the added value in including an additional mutant control since we can already see the dynamic change over time from no difference (0 seconds) to significant difference (60 seconds) between WT and CDPK3 for DAG and most other lipids. We already see a significant difference between WT and CDPK3 after 60 seconds for DAG, and we can clearly see in sub-minute timecourses the changes or not at the specific points where the A23187 is added (0-5 seconds), the parasites acclimatise, for the A23187 to take effect (10-30 seconds) and for the parasite lipid response to be visible by lipidomics (45-60 +seconds).

Figure 4E: are the differences in [cAMP] with DMSO treatment and A23187 treatment different at any of the timepoints in the WT strain? The comparison seems to be WT/∆cdpk3 at each timepoint. Does the text (lines 562-568) need to be modified accordingly?

Response:

In WT (and ∆CDPK3) parasites, [cAMP] is significantly changed at 5s of A23187 treatment (relative to DMSO). We have modified our figures to include this analysis. The existing text accurately reflects this.

Figure 6I: is the difference between PDE2 cKO/∆cdpk3 + DMSO or RAP significant?

Response

In our original manuscript, there was no statistical difference in [cAMP] between PDE2cKO/∆CDPK3+DMSO and PDE2cKO/∆CDPK3+DMSO+RAP, likely due to the variation between biological replicates. To overcome the issues in variability between replicates, we have now included more biological replicates (n=7). This has led to a significant difference in [cAMP] between PDE2cKO/∆CDPK3 DMSO- and RAP-treated parasites and between PDE2cKO DMSO- and RAP-treated parasites (now Fig. 6I).

**MINOR COMMENTS**

1.The following references should be added or amended:

Lines 83-85: in the cited publication, relative phosphopeptide abundances of an overexpressed dominant-negative, constitutively inactive PKA mutant were compared to an overexpressed wild-type mutant. In this experimental setup, one would hypothesize that targets of PKA should be down-regulated (inactive/WT ratios). However, the mentioned phosphopeptide of PDE2 was found to be up-regulated, suggesting that it is not a direct target of PKA.

Response:

We thank the reviewer for spotting this error, we have now modified our wording.

Cite TGGT1_305050, referenced as calmodulin in line 458, as TgELC2 (PMID: 26374117).

Response:

We have included this as per the reviewer’s suggestion.

Cite TGGT1_295850 as apical annuli protein 2 (AAP2, PMID: 31470470).

Response:

We have included this as per the reviewer’s suggestion.

Cite TGGT1_270865 (adenylyl cyclase beta, Acβ) as PMID: 29030485, 30449726.

Response:

We have included this as per the reviewer’s suggestion.

Cite TGGT1_254370 (guanylyl cyclase, GC) as PMID: 30449726, 30742070.

Response:

We have included this as per the reviewer’s suggestion.

Note that Lourido, Tang and David Sibley, 2012 observed that treatment with zaprinast (a PDE inhibitor) could overcome CDPK3 inhibition. The target(s) of zaprinast have not been determined and may differ from those of BIPPO (in identity and IC50). The cited study also used modified CDPK3 and CDPK1 alleles, rather than ∆cdpk3 and intact cdpk1 as used in this manuscript. That is to say, the signaling backgrounds of the parasite strains deviate in ways that are not controlled.

Response:

While it is true that zaprinast targets have not been unequivocally identified, zaprinast-induced egress is widely thought to be the result of PKG activation, a conclusion that is further supported by the finding that Compound 1 completely blocks zaprinast-induced egress (Lourido, Tang and David Sibley, 2012). Similarly, BIPPO-induced egress is inhibited by chemical inhibition of PKG by Compound 1 and Compound 2 (Jia et al., 2017). Moreover, like zaprinast, BIPPO has been clearly shown to partially overcome the ∆CDPK3 egress delay (Stewart et al., 2017).

2.The following comments refer to the figures and legends:

Part of the legend text for 1G is included under 1H.

Response:

This has been corrected

Figure 1H: The legend mentions that some dots are blue, but they appear green. Please ensure that color choices conform to journal accessibility guidelines. See the following article about visualization for colorblind readers: https://www.ascb.org/science-news/how-to-make-scientific-figures-accessible-to-readers-with-color-blindness____/ . Avoid using red and green false-colored images; replace red with a magenta lookup table. Multi-colored images are only helpful for the merged image; otherwise, we discern grayscale better. Applies to Figures 1B, 5C, 6D. (Aside: anti-CAP seems an odd choice of counterstain; the variation in the staining, esp. at the apical cap, is distracting.)

Response:

We thank reviewer #1 for bringing this to our attention, and have modified our colour usage for all IFAs and Figures 1H and 3E.

We chose CAP staining as the antibody is available in the laboratory and stains both the apical end (which has been shown to contain several proteins important for signalling as well as PDE9) and the parasite periphery, the location of CDPK3.

Figure 1B: When showing a single fluorophore, please use grayscale and include an intensity scale bar, since relative values are being compared.

Response:

We have modified this as per the reviewer’s suggestion

Figure 1C: it is difficult to compare the kinetics of the calcium response when the curves are plotted separately. Since the scales are the same, could the two treatments be plotted on the same axes, with different colors? Additionally, according to the legend, a red line seems to be missing in this panel.

Response:

Fig1C is not intended to compare kinetics, merely to show peak calcium release in each separate treatment condition. We have removed mention of a red line in the figure legend.

Figure 2A: Either Figure S4 can be moved to accompany Figure 2A, or Figure 2A could be moved to the supplemental.

Figure S4 has now been incorporated into Figure 2.

Reviewer #1 (Significance (Required)):

This manuscript would interest researchers studying signaling pathways in protozoan parasites, especially apicomplexans, as CDPK3 and PKG orthologs exist across the phylum. To my knowledge, it is the first study that has proposed a mechanism by which a calcium effector regulates cAMP levels in T. gondii. Unfortunately, the experiments fall short of testing this mechanism.

Response:

We thank reviewer #1 for their comments, but disagree with their assessment that the key points of the manuscript “fall short of experimental testing”.

1. We demonstrate that, following both BIPPO and A23187 treatment, there is differential phosphorylation of numerous components traditionally believed to sit upstream of PKG activation (as well as several components within the PKG signalling pathway itself).
2. We show that some of these sites are CDPK3 dependent, and that deletion of CDPK3 leads to changes in lipid signalling and an elevation in levels of cAMP (dysregulation of which is known to alter PKG signalling).
3. We show that pre-treatment with a PKA inhibitor is able to largely rescue this phenotype.
4. We demonstrate that a cAMP-specific PDE is phosphorylated following A23187 treatment (i.e. Ca2+ flux)
5. We show that this cAMP specific PDE plays a role in A23187-mediated egress.
6. While the latter PDE may not be directly regulated by CDPK3, these findings suggest that there are likely several Ca2+-dependent kinases that contribute to this feedback loop.

   Reviewer #2 (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, Dominicus et al investigate the elusive role of calcium-dependent kinase 3 during the egress of Toxoplasma gondii. Multiple functions have already been proposed for this kinase by this group including the regulation of basal calcium levels (24945436) or of a tyrosine transporter (30402958). However, one of the most puzzling phenotypes of CDPK3 deficient tachyzoites is a marked delay in egress when parasites are stimulated with a calcium ionophore that is rescued with phosphodiesterase (PDE) inhibitors. Crosstalk between, cAMP, cGMP, lipid and calcium signalling has been previously described to be important in regulating egress (26933036, 23149386, 29030485) but the role of CDPK3 in Toxoplasma is still poorly understood.

Here the authors first take an elegant phosphoproteomic approach to identify pathways differentially regulated upon treatment with either a PDE inhibitor (BIPPO) and a calcium ionophore (A23187) in WT and CDPK3-KO parasites. Not much difference is observed between BIPPO or A23187 stimulation which is interpreted by the authors as a regulation through a feed-back loop.

The authors then investigate the effect of CDPK3 deletion on lipid, cGMP and cAMP levels. The identify major changes in DAG, phospholipid, FFAs, and TAG levels as well as differences in cAMP levels but not for cGMP. Chemical inhibition of PKA leads to a similar egress timing in CDPK3-KO and WT parasites upon A23187 stimulation.

As four PDEs appeared differentially regulated in the CDPK3-KO line upon A23187, the authors investigate the requirement of the 4 PDEs in cAMP levels. They show diverse localisation of the PDEs with specificities of PDE1, 7 and 9 for cGMP and of PDE2 for cAMP. They further show that PDE1, 7 and 9 are sensitive to BIPPO. Finally, using a conditional deletion system, they show that PDE1 and 2 are important for the lytic cycle of Toxoplasma and that PDE2 shows a slightly delayed egress following A23187 stimulation.

**Major comments:**

-Are the key conclusions convincing?

The title is supported by the findings presented in this study. However I am not sure to understand why the authors imply a positive feed back loop. This should be clarified in the discussion of the results.

Response:

We believe in a positive feedback loop as, upon A23187 treatment (resulting in a calcium flux), ΔCDPK3 parasites are able to egress, albeit in a delayed manner. This egress delay is substantially, but not completely, alleviated upon treatment with BIPPO (a PDE inhibitor known to activate the PKG signalling pathway). In conjunction with our phosphoproteomic data (where we see phosphorylation of numerous pathway components upstream of PKG upon BIPPO and A23187 treatment - both in a CDPK3 dependent and independent manner), these observations suggest that calcium-regulated proteins (CDPK3 among them) feed into the PKG pathway. As deletion of CDPK3 delays egress, it is reasonable to postulate that this feedback is one that amplifies egress signalling (i.e. is positive).

The phosphoproteome analysis seems very strong and will be of interest for many groups working on egress. However, the key conclusion, i.e. that a substrate overlaps between PKG and CDPK3 is unlikely to explain the CDPK3 phenotype, seems premature to me in the absence of robustly identified substrates for both kinases.

Response:

We certainly do not fully exclude the possibility of a substrate overlap but do lean more heavily towards a feedback loop given (a) the inability to clearly detect treatment-specific signalling profiles and (b) the phospho targets observed in the A23187 and BIPPO phosphoproteomes. We have further clarified our reasoning, and overall tempered our language in the manuscript as per the reviewer’s suggestion.

I am not sure there is a clear key conclusion from the lipidomic analysis and how it is used by the authors to build their model up. Major changes are observed but how could this be linked with CDPK3, particularly if cGMP levels are not affected?

Response:

Our phosphoproteomic analyses identify several CDPK3-dependent phospho sites on phospholipid signalling components (DGK1 & PI-PLC), suggesting that there is indeed altered signalling downstream of PKG. To test whether these lead to a measurable phenotype, we performed the lipidomics analysis. We did not pursue this arm of the signalling pathway any further as we postulated that the changes in the lipid signalling pathway were less likely to play a role in the feedback loop. Nevertheless, we felt that it was worthwhile to include these findings in our manuscript as they support the conclusions drawn from the phosphoproteomics - namely that lipid signalling is perturbed in CDPK3 mutants. We, or others, may follow up on this in future.

We agree with the reviewer that it is surprising that cGMP levels remain unchanged in our experiments when we treat with A23187. Given the measurable difference in cAMP levels between WT and ΔCDPK3 parasites, we postulate that CDPK3 directly or indirectly downregulates levels of cAMP. This would, in turn, alter activity of the cAMP-dependent protein kinase PKAc. Jia et al. (2017) have shown a clear dependency on PKG for parasites to egress upon PKAc depletion, but were also unable to reliably demonstrate cGMP accumulation in intracellular parasites. Similarly, their hypothesis that dysregulated cGMP-specific PDE activity results in altered cGMP levels has not been proven (the PDE hypothesised to be involved has since been shown to be cAMP-specific).

While it is possible that our collective inability to observe elevated cGMP levels is explained by the sensitivity limits of the assay, it is similarly possible that cAMP-mediated signalling is exerting its effects on the PKG signalling pathway in a cGMP-independent manner.

The evidence that CDPK3 is involved in cAMP homeostasis seems strong. However, the analysis of PKA inhibition is a bit less clear. The way the data is presented makes it difficult to see whether the treatment is accelerating egress of CDPK3-KO parasites or affecting both WT and CDPK3-KO lines, including both the speed and extent of egress. This is important for the interpretation of the experiment.

Response:

Fig. 4F shows that there is a significant amount of premature egress in both WT and ∆CDPK3 parasites following 2 hrs of H89 pre-treatment (consistent with previous reports that downregulation of cAMP signalling stimulates premature egress). When we subsequently investigated A23187-induced egress rates of the remaining intracellular H89 pre-treated parasites (Fig. 4Gi-ii) we found that the ∆CDPK3 egress delay was largely rescued. We have moved Fig. 4F to the supplement (now Supp Fig. 5E) in order to avoid confusion between the distinct analyses shown in 4F (pre-treatment analyses) and 4G (egress experiment). These experiments provided a hint that cAMP signalling is affected, which we then validate by measuring elevated cAMP levels in CDPK3 mutant parasites.

The biochemical characterisation of the four PDE is interesting and seems well performed. However, PDE1 was previously shown to hydrolyse both cAMP and cGMP (____https://doi.org/10.1101/2021.09.21.461320____) which raises some questions about the experimental set up. Could the authors possibly discuss why they do not observe similar selectivity? Could other PDEs in the immunoprecipitate mask PDE activity? In line with this question, it is not clear what % of "hydrolytic activity (%)" means and how it was calculated.

The experiments describing the selectivity of BIPPO for PDE1, 7 and 9 as well as the biological requirement of the four tested PDEs are convincing.

Response:

We believe that the disagreement between our findings and those published by Moss and colleagues are due to the differences in experimental conditions. We performed our assays at room temperature for 1 hour with higher starting cAMP concentrations (1 uM) compared to them. They performed their assays at 37ºC for 2 hours with 10-fold lower starting cAMP concentrations (0.1 uM). We have now repeated this set of experiments using the Moss et al. conditions, and find that PDEs 1, 7 and 9 can be dual specific, while PDE2 is cAMP-specific, thereby recapitulating their findings (Now included in the revised manuscript under Supp Fig. 7B). However, we also now performed a timecourse PDE assay using our original conditions and show that the cAMP hydrolytic activity for PDE1 can only be detected following 4 hours of incubation, compared to cGMP activity that can be detected as early as 30 minutes, suggesting that it possesses predominantly cGMP activity (See Supp Fig. 7C). We therefore believe that our experimental setup is more stringent, because if one starts with a lower level of substrate and incubates for longer and at a higher temperature, even minor dual activity could make a substantial difference in cAMP levels. Our data suggests that the cAMP hydrolytic activity of PDEs 1, 7 and 9 is substantially lower than the cGMP hydrolytic activity that they display.

We have also included a clear description of how % hydrolytic activity was calculated in the methods section.

-Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

The claim that CDPK3 affects cAMP levels seems strong however the exact links between CDPK3 activity, lipid, cGMP and cAMP signalling remain unclear and it may be important to clearly state this.

Response:

We have modified our wording in the text to more clearly describe our current hypothesis and reasoning.

-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.

I think that the manuscript contains a significant amount of experiments that are of interest to scientists working on Toxoplasma egress. Requesting experiments to identify the functional link between above-mentioned pathways would be out of the scope for this work although it would considerably increase the impact of this manuscript. For example, would it be possible to test whether the CDPK3-KO line is more or less sensitive to PKG specific inhibition upon A23187 induced?

-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.

The above-mentioned experiment is not trivial as no specific inhibitors of PKG are available. Ensuring for specificity of the investigated phenotype would require the generation of a resistant line which would require significant work.

__Response: __We agree that this would be an interesting experiment to further substantiate our findings. As indicated by the reviewer, however, the lack of specific inhibitors of PKG means a resistant line would likely be required to ensure specificity.

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

It is not clear how the % of hydrolytic activity of the PDE has been calculated.

Response: We have included a clearer description of how % hydrolytic activity was calculated in the methods section.

-Are the experiments adequately replicated and statistical analysis adequate?

This seems to be performed to high standards.

**Minor comments:**

-Specific experimental issues that are easily addressable.

I do not have any comments related to minor experimental issues.

-Are prior studies referenced appropriately?

Most of the studies relevant for this work are cited. It is however not clear to me why some important players of the "PKG pathway" are not indicated in Fig 1H and Fig 3E, including for example UGO or SPARK.

Response:

We have modified Fig 1H and 3E to include all key players involved in the PKG pathway.

-Are the text and figures clear and accurate?

While all the data shown here is impressive and well analysed, I find it difficult to read the manuscript and establish links between sections of the papers. The phosphoproteome analysis is interesting and is used to orientate the reader towards a feedback mechanism rather than a substrate overlap. But why do the authors later focus on PDEs and not on AC or CNBD, as in the end, if I understand well, there is no evidence showing a link between CDPK3-dependent phosphorylation and PDE activity upon A23187 stimulation?

Response:

We thank reviewer#2 and appreciate their constructive feedback re the flow of the manuscript.

Our key findings from the phosphoproteomics study were that 1) BIPPO and A23187 treatment trigger near identical signalling pathways, 2) that both A23187 and BIPPO treatment leads to phosphorylation of numerous components both upstream and downstream of PKG signalling (hinting at the presence of an Ca2+-regulated feedback loop) and 3) several of the abovementioned components are phosphorylated in a CDPK3 dependent manner.

While several avenues of study could have been pursued from this point onwards, we chose to focus on the feedback loop in a broader sense as its existence has important implications for our general understanding of the signalling pathways that govern egress.

We reasoned that, given the differential phosphorylation of 4 PDEs following A23187 and BIPPO treatment (none of which had been studied in detail previously), it was relevant to study these in greater detail.

Coupled with the A23187 egress assay on PDE2 knockout parasites - our findings suggest that PDE2 plays a role in the abovementioned Ca2+ signalling loop. While PDE2 may not exert its effects in a CDPK3-dependent manner (and CDPK3 may, therefore, alter cAMP levels in a different fashion), this does not detract from the important finding that PDE2 is one of the (likely numerous) components that is regulated in a Ca2+-dependent feedback loop to facilitate rapid egress.

We have modified our wording to better reflect our rationale for studying the PDEs irrespective of their CDPK3 phosphorylation status.

While we feel that our reasoning for studying the PDEs is solid, we do appreciate that further clarification on the putative CDPK3-Adenylate cyclase link would elevate the manuscript substantially. However, given the data that the ACb is not playing a sole role in the control of egress, this is likely a non-trivial task and requires substantial work.

It is also unclear how the authors link CDPK3-dependent elevated cAMP levels with the elevated basal calcium levels they previously described. This is particularly difficult to reconcile particularly in a PKG independent manner.

Response:

We previously postulated that elevated Ca2+ levels allowed ΔCDPK3 mutants to overcome a complete egress defect, potentially by activating other CDPKs (e.g. CDPK1). It is similarly plausible that elevated Ca2+ levels in ΔCDPK3 parasites may lead to elevated cAMP levels in order to prevent premature egress.

As noted in our previous responses, we acknowledge that our inability to detect cGMP is surprising. However, given the clarity of our cAMP findings, and the phosphoproteomic evidence to suggest that various components in the PKG signalling pathway are affected, we postulate that we are either unable to reliably detect cGMP due to sensitivity issues, or that cAMP is exerting its regulation on the PKG pathway in a cGMP-independent manner. As noted previously, while the link between cAMP and PKG signalling has been demonstrated by Jia et al., it is not entirely clear how this is mediated.

The presentation of the lipidomic analysis is also not really clear to me. Why do the authors show the global changes in phospholipids and not a more detailed analysis?

Response:

We performed a detailed phospholipid profile of WT and ∆CDPK3 parasites under normal culture conditions. However, due to the sheer quantity of parasites required for this detailed analysis, we were unable to measure individual phospholipid species in our A23187 timecourse. We therefore opted to measure global changes following A23187 stimulation.

As the authors focus on the PI-PLC pathway, could they detail the dynamics of phosphoinositides? I understand that lipid levels are affected in the mutant but I am not sure to understand how the authors interpret these massive changes in relationship with the function of CDPK3 and the observed phenotypes.

Response:

Our phosphoproteomic analyses identified several CDPK3-dependent phospho sites on phospholipid signalling components (DGK1 & PI-PLC), suggesting that (in keeping with all of our other data), there is altered signalling downstream of PKG. To test whether these changes lead to a measurable phenotype, we performed the lipidomics analysis. Following stimulation with A23187, we found a delayed production of DAG in ∆CDPK3 parasites compared to WT parasites. Since DAG is required for the production of PA, which in turn is required for microneme secretion, our finding can explain why microneme secretion is delayed in ∆CDPK3 parasites, as previously reported (Lourido, Tang and David Sibley, 2012; McCoy et al., 2012).

We did not follow this arm of the signalling pathway any further as we postulated that the changes in the lipid signalling pathway were less likely to play a role in the feedback loop. Nevertheless, we felt that it was worthwhile to include these findings in our manuscript as they support the conclusions drawn from the phosphoproteomics - namely that lipid signalling is perturbed in CDPK3 mutants. We, or others, may follow up on this in future.

Finally, the characterisation of the PDEs is an impressive piece of work but the functional link with CDPK3 is relatively unclear. It would also be important to clearly discuss the differences with previous results presented in this this preprint: https://doi.org/10.1101/2021.09.21.461320____.

My understanding is while the authors aim at investigating the role of CDPK3 in A23187 induced egress, the main finding related to CDPK3 is a defect in cAMP homeostasis that is not linked to A23187. Similarly, the requirements of PDE2 in cAMP homeostasis and egress is indirectly linked to CDPK3. Altogether I think that important results are presented here but divided into three main and distinct sections: the phosphoproteomic survey, the lipidomic and cAMP level investigation, and the characterisation of the four PDEs. However, the link between each section is relatively weak and the way the results are presented is somehow misleading or confusing.

Response:

As mentioned in a previous response, we chose to study PDEs in greater detail because of our observation that both A23187 and BIPPO treatments lead to their phosphorylation (hinting at the presence of a Ca2+regulated feedback loop). We were particularly intrigued to study the cAMP specific PDE, as CDPK3 KO parasites suggested that cAMP may play a role in the Ca2+ feedback mechanism. As PDE2 may not be directly regulated by CDPK3, Ca2+ appears to exert its feedback effects in numerous ways. We have modified our wording to better reflect our rationale for studying the PDEs irrespective of their CDPK3 phosphorylation status.

-Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

This is a very long manuscript written for specialists of this signalling pathway and I would suggest the authors to emphasise more the important results and also clearly state where links are still missing. This is obviously a complex pathway and one cannot elucidate it easily in a single manuscript.

Response:

We have included an additional summary in our conclusions to better illustrate our findings and clarify any missing links.

Reviewer #2 (Significance (Required)):

-Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

This is a technically remarkable paper using a broad range of analyses performed to a high standard.

-Place the work in the context of the existing literature (provide references, where appropriate).

The cross-talk between cAMP, cGMP and calcium signalling is well described in Toxoplasma and related parasites. Here the authors show that, in Toxoplasma, CDPK3 is part of this complex signalling network. One of the most important finding within this context is the role of CDPK3 in cAMP homeostasis. With this in mind, I would change the last sentence of the abstract to "In summary we uncover a feedback loop that enhances signalling during egress and links CDPK3 with several signalling pathways together."

Response:

In light of feedback received from several reviewers, we have made our wording less CDPK3 centric - as our findings relate in part to CDPK3 and, in a broader sense, to a Ca2+ driven feedback loop.

The genetic and biochemical analyses of the four PDEs are remarkable and highlight consistencies and inconsistencies with recently published work that would be important to discuss and will be of interest for the field.

__Response: __We thank reviewer#2 and agree that the PDE findings are of significant importance to the field.

While I understand the studied signalling pathway is complex, I think it would be important to better describe the current model of the authors. In the discussion, the authors indicate that "the published data is not currently supported by a model that fits most experimental results." I would suggest to clarify this statement and discuss whether their work helps to reunite, correct or improve previous models.

__Response: __We have expanded on the abovementioned statement to clarify that the presence of a feedback loop is a major pillar of knowledge required for the complete interpretation of existing signalling data.

Could the authors also speculate about a potential role of PDE/CDPK3 in host cell invasion as cAMP signalling has be shown to be important for this process (30208022 and 29030485)?

__Response: __Existing literature (Jia et al., 2017) suggests that perturbations to cAMP signalling play a very minor role in invasion since parasites where either ACα or ACβ are deleted show no impairment in invasion levels. We currently do not have substantial data on invasion, and are not sure that pursuing this is valuable given the minor phenotypes observed in other studies.

-State what audience might be interested in and influenced by the reported findings.

This paper is of great interest to groups working on the regulation of egress in Toxoplasma gondii and other related apicomplexan pathogens.

-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 working on the cell biology of apicomplexan parasites.

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

**Summary:**

Dominicus et al aimed to identify the intersecting components of calcium, cyclic nucleotides (cAMP, cGMP) and lipid signaling through phosphoproteomic, knockout and biochemical assays in an intracellular parasite, Toxoplasma gondii, particularly when its acutely-infectious tachyzoite stage exits the host cells. A series of experimental strategies were applied to identify potential substrates of calcium-dependent protein kinase 3 (CDPK3), which has previously been reported to control the tachyzoite egress. According to earlier studies (PMID: 23226109, 24945436, 5418062, 26544049, 30402958), CDPK3 regulated the parasite exit through multiple phosphorylation events. Here, authors identified differentially-regulated (DR) phosphorylation sites by comparing the parasite samples after treatment with a calcium ionophore (A23178) and a PDE inhibitor (BIPPO), both of which are known to induce artificial egress (induced egress as opposed to natural egress). When the DCDPK3 mutant was treated with A23187, its delayed egress phenotype did not change, whereas BIPPO restored the egress to the level of the parental (termed as WT) strain, probably by activating PKG.

The gene ontology enrichment of the up-regulated clusters revealed many probable CDPK3-dependent DR sites involved in cyclic nucleotide signaling (PDE1, PDE2, PDE7, PDE9, guanylate and adenylate cyclases, cyclic nucleotide-binding protein or CNBP) as well as lipid signaling (PI-PLC, DGK1). Authors suggest lipid signaling as one of the factors altered in the CDPK3 mutant, albeit lipidomics (PC, PI, PS, PT, PA, PE, SM) showed no significant change in phospholipids. To reveal how the four PDEs indicated above contribute to the cAMP and cGMP-mediated egress, they examined their biological significance by knockout/knockdown and enzyme activity assays. Authors claim that PDE1,7,9 proteins are cGMP-specific while PDE2 is cAMP-specific, and BIPPO treatment can inhibit PDE1-cGMP and PDE7-cGMP, but not PDE9-cGMP. Given the complexity, the manuscript is well structured, and most experiments were carefully designed. Undoubtedly, there is a significant amount of work that underlies this manuscript; however, from a conceptual viewpoint, the manuscript does not offer significant advancement over the current knowledge without functional validation of phosphoproteomics data (see below). A large body of work preceding this manuscript has indicated the crosstalk of cAMP, cGMP, calcium and lipid signaling cascades. This work provides a further refinement of the existing model In a methodical sense, the work uses established assays, some of which require revisiting to reach robust conclusions and avoid misinterpretation. The article is quite interesting from a throughput screening point of view, but it clearly lacks the appropriate endorsement of the hits.The authors accept that identifying the phosphorylation of a protein does not imply a functional role, which is a major drawback as there is no experimental support for any phosphorylation site of the protein identified through phosphoproteomics. In terms of the mechanism, it is not clear whether and how lipid turnover and cAMP-PKA signaling control the egress phenotype (lack of a validated model at the end of this study).

Response:

We thank reviewer #3 for their comments, but respectfully disagree with their assessment that the work presented does not advance current knowledge.

1. We demonstrate that, following both BIPPO and A23187 treatment, there is differential phosphorylation of numerous components traditionally believed to sit upstream of PKG activation (as well as numerous components within the PKG signalling pathway itself). While it may have been inferred from previous studies that A23187 and BIPPO signalling intersect, this has never been unequivocally demonstrated - nor has a feedback loop ever been shown.

We provide a novel A23187-driven phosphoproteome timecourse that further bolsters the model of a Ca2+-driven feedback loop.

We show that deletion of CDPK3 leads to a delay in DAG production upon stimulation with A23187.

We show that some of the abovementioned sites are CDPK3 dependent, and that deletion of CDPK3 leads to elevated levels of cAMP (dysregulation of which is known to alter PKG signalling).

We show that pre-treatment with a PKA inhibitor is able to largely rescue this phenotype.

We demonstrate that a cAMP-specific PDE is phosphorylated following A23187 treatment (i.e. Ca2+ flux)

We show that this cAMP specific PDE plays a role in egress.

While the latter PDE may not be directly regulated by CDPK3, these findings suggest that there are likely several Ca2+-dependent kinases that contribute to this feedback loop.

We also firmly disagree with the reviewer’s assertion that without phosphosite characterisation, we have no support for our model. Following treatment with A23187 (and BIPPO), we clearly show broad, systemic changes (both CDPK3 dependent and independent) across signalling pathways previously deemed to sit upstream of calcium flux. Given the vast number of proteins involved in these signalling pathways, and the multitude of differentially regulated phosphosites identified on each of them, it is highly likely that the signalling effects we observe are combinatorial. Accordingly, we believe that mutating individual sites on individual proteins would be a very costly endeavour which is unlikely to substantially advance our understanding of signalling during egress. Moreover, introducing multiple point mutations in a given protein to ablate phosphorylation may lead to protein misfolding and would therefore not be informative. One of the key aims of this study was to assess how egress signalling pathways are interconnected, and we believe we have been able to show strong support for a Ca2+-driven feedback mechanism in which both CDPK3 and PDE2 play a role through the regulation of cAMP.

While we agree with the reviewer’s statement that a large body of work preceding this manuscript has indicated the crosstalk of cAMP, cGMP, calcium and lipid signalling cascades, a feedback loop has not previously been shown. We believe that this finding is absolutely central to facilitate the complete interpretation of existing signalling data. Furthermore, no previous studies have gone to this level of detail in either proteomics or lipidomics to analyse the calcium signal pathway in any apicomplexan parasite. We argue that the novelty in our manuscript is that it is a carefully orchestrated study that advances our understanding of the signalling network over time with subcellular precision. The kinetics of signalling is not well understood and we believe that our study is likely the first to include both proteomic and lipidomic analyses over a timecourse during the acute lytic cycle stage of the disease. In doing so, we found evidence for a feedback loop that controls the signalling network spatiotemporally, and we characterise elements of this feedback in the same study.

**Major Comments:**

Based on the findings reported here there is little doubt that BIPPO and A23187-induced signaling intersect with each other, as very much expected from previous studies. The authors selected the 50s and 15s post-treatment timing of A23187 and BIPPO, respectively for collecting phosphoproteomics samples. At these time points, which were shown to peak cytosolic Ca2+, parasites were still intracellular (Line #171). How did authors make sure to stimulate the entire signaling cascade adequately, particularly when parasites do not egress within the selected time window? There is significant variability between phosphosite intensities of replicates (Line #186), which may also be attributed to insufficient triggers for the egress across independent experiments. This work must be supported by in vitro egress assays with the chosen incubation periods of BIPPO and ionophore treatment (show the induced % egress of tachyzoites in the 50s and 15s).

Response:

1. We appreciate that the reviewer acknowledges that our data clearly shows that BIPPO and A23187-induced signalling intersect. While this may have been expected from previous studies, this has not previously been shown - and is therefore valuable to the field. Specifically, the fact that A23187-treatment leads to phosphorylation of targets normally deemed to sit upstream of calcium release is entirely novel and adds a substantial layer of information to our understanding of how these signalling pathways work together.

Treatments were purposely selected to align pathways to a point where calcium levels peak just prior to calcium reuptake. At these chosen timepoints, we clearly show that overall signalling correlation is very high. We know from our egress assays using identical treatment concentrations (Fig. 2C), that the stimulations used are sufficient to result in complete egress. We are simply comparing signalling pathways at points prior to egress.

As mentioned in point 2, we show convincingly that the treatments used are sufficient to trigger complete egress. As detailed clearly in the text, we believe that these variations in intensities between replicates are due to slight differences in timing between experiments (this is inevitable given the very rapid progression of signalling, and the difficulty of replicating exact sub-minute treatment timings). We demonstrate that the reporter intensities associated with DR sites correlate well across replicates (Supp Fig. 3C), suggesting that despite some replicate variability, the overall trends across replicates is very much consistent. This allows us to confidently average scores to provide values that are representative of a site’s phosphorylation state at the timepoint of interest.

The reviewer’s suggestion that we should demonstrate % egress at the 50s and 15s treatment timepoints is obsolete - we state clearly in the text that parasites have not egressed at these timepoints. Our egress assays (Fig. 2C) further support this.

The authors discuss that CDPK3 controls the cAMP level and PKA through activation of one or more yet-to-be-identified PDEs(s). cAMP could probably also be regulated by an adenylate cyclase, ACbeta that was found to have CDPK3-dependent phosphorylation sites. If CDPK3 is indeed a regulator of cAMP through the activation of PDEs or ACbeta, it would be expected that the deletion of CDPK3 would perturb the cAMP level, resulting in dysregulation of PKAc1 subunit, which in turn would dysregulate cGMP-specific PDEs (PMID: 29030485) and thereby PKG. All these connections need to explain in a more clear manner with experimental support (what is positive and what is negatively regulated by C____DPK3).

Response:

1. We do not firmly state that CDPK3 regulates cAMP by phosphorylation of a PDE - this is one of the possibilities addressed. We acknowledge the possibility that this could also be via the adenylate cyclase (see line 792).

PMID: 29030485 demonstrates clearly a link between cAMP signalling and PKG signalling, but does not demonstrate how this is mediated. The authors postulate that a cGMP-specific PDE is dysregulated given their observation that PDE2 is differentially phosphorylated in a constitutively inactive PKA mutant, however this was not validated experimentally. We and others (Moss et al., 2022), however, demonstrate that PDE2 is cAMP-specific. This suggests that the model built by PMID: 29030485 requires revisiting. We acknowledge clearly in the text that Jia et al. have shown a link between cAMP and PKG signalling, and hypothesise that CDPK3’s modulation of cAMP levels may affect this (this is in keeping with our phosphoproteomic data).

Moreover, the egress defect is not due to a low influx of calcium in the cytosol because when the ionophore A23187 was added to the CDPK3 mutant, its phenotype was not recovered. Rather, the defect may be due to the low or null activity of PKG that would activate PI4K to generate IP3 and DAG. The latter would be used as a substrate by DGK to generate PA that is involved in the secretion of micronemes and Toxoplasma egress. In this context, authors should evaluate the role of CDPK3 in the secretion of micronemes that is directly related to the egress of the parasite.

1. We agree with the reviewer on their point about calcium influx, and have already acknowledged in the text that the feedback loop does not control release of Ca2+ from internal stores as disruption of CDPK3 does not lead to a delay in Ca2+

We agree, and clearly address in the text, that the egress defect could be due to altered PKG/phospholipid pathway signalling.

(Lourido, Tang and David Sibley, 2012; McCoy et al., 2012) have both previously shown that microneme secretion is regulated by CDPK3. We therefore do not deem it necessary to repeat this experiment, but have made clearer mention of their findings in our writing.

When the Dcdpk3 mutant with BIPPO treatment was evaluated, it was observed that the parasite recovered the egress phenotype. It is concluded that CDPK3 could probably regulate the activity of cGMP-specific PDEs. CDPK3 could (in)activate them, or it could act on other proteins indirectly regulating the activity of these PDEs. Upon inactivation of PDEs, an increase in the cGMP level would activate PKG, which will, in turn, promote egress. From the data, it is not clear whether any phosphorylation by CDPK3 would activate or inactivate PDEs, and if so, then how (directly or indirectly). To reach unambiguous interpretation, authors should perform additional assays.

Response:

As mentioned previously, given the abundance of differentially regulated phosphosites, we do not believe that mutating individual sites on individual proteins is a worthwhile or realistic pursuit.

We clearly show systematic A23187-mediated phosphorylation of key signalling components in the PKA/PKG/PI-PLC/phospholipid signalling cascade, and demonstrate that several of these are CDPK3-dependent. We demonstrate that CDPK3 alters cAMP levels (and that the ∆CDPK3 egress delay in A23187 treated parasites is largely rescued following pre-treatment with a PKA inhibitor). We similarly demonstrate that A23187 treatment leads to phosphorylation of numerous PDEs, including the cAMP specific PDE2, and show that PDE2 knockout parasites show an egress delay following A23187 treatment. While PDE2 may not be directly regulated by CDPK3 (suggesting other Ca2+ kinases are also involved), these findings collectively demonstrate the existence of a calcium-regulated feedback loop, in which CDPK3 and PDE2 play a role (by regulating cAMP).

We acknowledge that we have not untangled every element of this feedback loop, and do not believe that it would be realistic to do so in a single study given the number of sites phosphorylated and pathways involved. We do believe, however, that we have shown clearly that the feedback loop exists - this in itself is entirely novel, and of significant importance to the field.

On a similar note, a possible experiment that can be done to improve the work would be to treat the CDPK3 mutant with BIPPO in conjunction with a calcium chelator (BAPTA-AM) to reveal, which proteins are phosphorylated prior to activation of the calcium-mediated cascades?

Response:

We agree that this would be an interesting experiment to carry out but would involve significant work. This could be pursued in another paper or project but is beyond the scope of this work.

The manuscript claims that PDE1, PDE7, PDE9 are cGMP specific, and BIPPO inhibits only cGMP-specific PDEs. All assays are performed with 1-10 micromolar cAMP and cGMP for 1h. There is no data showing the time, protein and substrate dependence. Given the suboptimal enzyme assays, authors should re-do them as suggested here. (1) Repeat the pulldown assay with a higher number of parasites (50-100 million) and measure the protein concentration. (2) Set up the PDE assay with saturating amount of cAMP and cGMP, which is critical if the PDE1,7,9 have a higher Km Value for cAMP (means lower affinity) compared to cGMP. An adequate amount of substrate and protein allows the reaction to reach the Vmax. Once you have re-determined the substrate specificity (revise Fig 5D), you should retest BIPPO (Fig 5E) in the presence of cAMP and cGMP. It is very likely that you would find the same result as PDE9 and PfPDEβ (BIPPO can inhibit both cAMP and cGMP-specific PDE), as described previously

We have repeated our assay using the exact same conditions outlined by Moss et al. This involved using a similar number of parasites, a longer incubation time of 2 hours at a higher temperature (37ºC) and with a lower starting concentration of cAMP (0.1 uM). We demonstrate that we are able to recapitulate both the Moss et al. and Vo et al. (see Supp Fig. 7B). However, we noticed that these reactions were not carried out with saturating cAMP/cGMP concentrations, since all reactions had reached 100% completion at the end of the assay whereby all substrate was hydrolysed. We therefore believe that based on our original assay, as well as the new PDE1 timecourse that we have performed (Supp Fig. 7C), that PDEs 1, 7 and 9 display predominantly cGMP hydrolysing activity, with moderate cAMP hydrolysing activity.

We also repeated the BIPPO inhibition assay using the Moss et al. conditions, and still observe that the cGMP activity of PDE1 is the most potently inhibited of all 4 PDEs. We also see moderate inhibition of the cAMP activities of PDE1 and PDE9, suggesting that cAMP hydrolytic activity can also be inhibited. Interestingly, the cGMP hydrolytic activities of PDEs 7 & 9, which were previously inhibited using our original assay conditions, no longer appear to be inhibited. This is likely due to the longer incubation time, which masks the reduced activities of these two PDEs following treatment with BIPPO.

The authors did not identify any PKG substrate, which is quite surprising as cAMP signaling itself could impact cGMP. Authors should show if they were able to observe enhanced cGMP levels in BIPPO-treated sample (which is expected to stimulate cGMP-specific PDEs). The author mention their inability to measure cGMP level but have they analyzed cGMP in the positive control (BIPPO-treated parasite line)? Why have they focused only on CDPK3 mutant, whereas in their phosphoproteomic data they could see other CDPKs too? It could be that other CDPK-mediated signaling differs and need PKA/PKG for activation.

In the title, the authors have mentioned that there is a positive feedback loop between calcium release, cyclic nucleotide and lipid signaling, which is quite an extrapolation as there is no clear experimental data supporting such a positive feedback loop so the author should change the title of the paper.

Response:

1. As addressed in our previous response to the reviewer, PMID: 29030485 demonstrates clearly a link between cAMP signalling and PKG signalling, but does not confirm how this is mediated. The authors surmise that a cGMP-specific PDE is dysregulated (although the PDE hypothesised to be involved has since been shown to be cAMP-specific), but are similarly unable to detect changes in cGMP levels. This suggests that their model may be incomplete.

The BIPPO treatment experiment suggested by the reviewer was already included in the original manuscript (see Fig. 4D in original manuscript, now Fig. 4E). With BIPPO treatment we are able to detect changes in cGMP levels.

We did not deem it to be within the scope of this study to study every single other CDPK. We chose to study CDPK3, as its egress phenotype was of particular interest given its partial rescue following BIPPO treatment. We reasoned that its study may lead us to identify the signalling pathway that links BIPPO and A23187 induced signalling.

As addressed in greater detail in our response to reviewer #2, the fact that the feedback loop appears to stimulate egress implies that it is positive.

**Minor Comments:**

Materials & Methods

Explanation of parameters is not clear (Line #360-367). Phosphoproteomics with A23187 (8 micromolar) treatment in CDPK3-KO and WT, for 15, 30 and 60s at 37{degree sign}C incubation with DMSO control. Simultaneously passing the DR and CDPK3 dependency thresholds: CDPK3-dependent phosphorylation

__Response: __We have modified the wording to make this clearer as per the reviewer’s suggestion.

Line #368: At which WT-A23187 timepoint did the authors identify 2408 DR-up phosphosites (15s, 30s or 60s)? Or consistently in all? It should be clarified?

__Response: __As already stated in the manuscript (see line 366 in original manuscript, now line 1047), phosphorylation sites were considered differentially regulated if at any given timepoint their log2FC surpassed the DR threshold.

A23187 treatment of the CDPK3-KO mutant significantly increased the cAMP levels at 5 sec post-treatment, but BIPPO did not show any change. The authors concluded that BIPPO presumably does not inhibit cAMP-specific PDEs. However, the dual-specific PDEs are known to be inhibited by BIPPO, as shown recently (____https://www.biorxiv.org/content/10.1101/2021.09.21.461320v1____). Authors do confirm that BIPPO-treatment can inhibit hydrolytic activity of PfPDEbeta for cAMP as well as cGMP (Line #612). Besides, it was shown in Fig 5E that BIPPO can partially though not significantly block cAMP-specific PDE2. The statements and data conflict each other under different subtitles and need to be reconciled. Elevation of basal cAMP level in the CDPK3 mutant indicates the perturbation of cAMP signaling, however BIPPO data requires additional supportive experiments to conclude its relation with cAMP or dual-specific PDE.

Response:

1. The manuscript to which the reviewer refers does not use BIPPO in any of their experiments. They show that continuous treatment with zaprinast blocks parasite growth in a plaque assay, but do not test whether zaprinast specifically blocks the activity of any of the PDEs.

Having repeated the PDE assay using the Moss et al. conditions (as outlined above), we are now able to recapitulate their findings, showing that PDEs 1, 7 and 9 can display dual hydrolytic activity while PDE2 is cAMP specific. As explained further above, we believe that our original set of experiments are more stringent than the Moss *et al. * To confirm this, we also performed an additional experiment, incubating PDE1 for varying amounts of time using our original conditions (1 uM cAMP or 10 uM cGMP, at room temperature). This revealed that PDE1 is much more efficient at hydrolysing cGMP, and only begins to display cAMP hydrolysing capacity after 4 hours of incubation.

We also measured the inhibitory capacity of BIPPO on the PDEs using the Moss *et al. * During the longer incubation time, it seems that BIPPO is unable to inhibit PDEs 7 and 9, while with the more stringent conditions it was able to inhibit both PDEs. We reasoned that since BIPPO is unable to inhibit these PDEs fully, the residual activity over the longer incubation period would compensate for the inhibition, eventually leading to 100% hydrolysis of the cNMPs. We also see that while the cGMP hydrolysing capacity of PDE1 is completely inhibited, its cAMP hydrolysing capacity is only partially inhibited. These findings and the fact that PDE2 is not inhibited by BIPPO are in line with our experiments where we measured [cAMP] and showed that treatment with BIPPO did not lead to alterations in [cAMP].

The method used to determine the substrate specificity of PDE 1,2,7 and 9 resulted in the hydrolytic activity of PDE2 towards cAMP, while the remaining 3 were determined as cGMP-specific. However, PDE1 and PDE9 have been reported as being dual-specific (Moss et al, 2021; Vo et al, 2020), which questions the reliability of the preferred method to characterize substrate specificity by the authors. It is also suggested to use another ELISA-based kit to double check the results.

Response:

As outlined above, we have repeated the assay using the conditions described by Moss et al. (lower starting concentrations of cAMP, 2 hour incubation period at 37ºC) and find that we are able to recapitulate the results of both Moss et al. and Vo et al.. However, using the Moss et al. conditions, the PDEs have hydrolysed 100% of the cyclic nucleotide, suggesting that these conditions are less stringent than the ones we used originally using higher starting concentrations of cAMP and incubating for 1 hour only at room temperature. With enzymatic assays it is always important to perform them at saturating conditions (as already suggested by the reviewer) and therefore we believe that our original conditions are more stringent than the results using the Moss et al. conditions.

Line #607-608: Authors found PDE9 less sensitive to BIPPO-treatment and concluded PDE2 as refractory to BIPPO inhibition; however, the reduction level of activity seems similar as seen in PDE9-BIPPO treated sample? This strong statement should be replaced with a mild explanation.

__Response: __We have tempered our wording as per the reviewer’s suggestion

Figures and legends:

The introductory model in Fig S1 is difficult to understand and ambiguous despite having it discussed in the text. For example, CDPK1 is placed, but only mentioned at the beginning, and the role of other CDPKs is not clear. In addition, the arrows in IP3 and PKG are confusing. The location of guanylate and adenylate cyclase is wrong, and so on... The figure should include only the egress-related signaling components to curate it. The illustration of host cell in orange color must be at the right side of the figure in connection with the apical pole of the parasite (not on the top). Figure legend should also be rearranged accordingly and citations of the underlying components should be included (see below).

__Response: __We have modified Supp Fig. 1 as per the suggestions of reviewer#2 and #3. We have now modified the localisations of the proteins and have also removed the lines showing the cross talk between pathways. We have also highlighted to the reader that this is only a model and may not represent the true localisations of the proteins, despite our best efforts.

In Figure 5D, would you please provide the western blot analysis of samples before and after pulling down to demonstrate the success of your immunoprecipitation assay. Mention the protein concentration in your PDE enzyme assay. Please refer to the M&M comments above to re-do the enzyme assays.

Response:

We have now included western blots for the pull downs of PDEs 1, 2, 7 and 9 (Supp Fig. 7A). We chose not to measure protein concentrations of samples since all experiments were performed using the same starting parasite numbers, and we do not see large differences in activities between biological replicates of the PDEs.

Figure legend 1C: Line #194: There is no red-dotted line shown in graph! Correct it!

__Response: __We have modified this.

Figure 4Gi-ii: Shouldn't it be labelled i: H89-treatment and ii: A23178, respectively instead of DMSO and H89? (based on the text Line #579).

__Response: __Our labelling of Fig. 4Gi-ii is correct as panel i parasites were pre-treated with DMSO, while panel ii parasites were pre-treated with H89. Subsequent egress assays on both parasites were then performed using A23187.

We have modified the figures to include mention of A23187 on the X axis, and modified the figure legend to clarify pre-treatment was performed with DMSO and H89 respectively.

Bibliography:

Line #57 and 58: Citations must be selected properly! Carruthers and Sibley 1999 revealed the impact of Ca2+ on the microneme secretion within the context of host cell attachment and invasion, not egress as indicated in the manuscript! Similar case is also valid for the reference Wiersma et al 2004; since the roles of cyclic nucleotides were suggested for motility and invasion. Also notable in the fact that several citations describing the localization, regulation and physiological importance of cAMP and cGMP signaling mediators (PMID: 30449726 , 31235476 , 30992368 , 32191852 , 25555060 , 29030485 ) are either completely omitted or not appropriately cited in the introduction and discussion sections.

Response:

We have modified the citations as per the reviewer’s suggestions. We now cite Endo et al., 1987 for the first use of A23187 as an egress trigger, and Lourido, Tang and David Sibley, 2012 for the role of cGMP signalling in egress. We also cite all the GC papers when we make first mention of the GC. We have also removed the Howard et al., 2015 citation (PMID: 25555060) when referring to the fact that BIPPO/zaprinast can rescue the egress delay of ∆CDPK3 parasites.

Grammar/Language

Line #31: After "cAMP levels" use comma

Response:

We have modified this.

36: Sentence is not clear. Does conditional deletion of all four PDEs support their important roles? If so, the role in egress of the parasite?

Response:

We have clarified our wording as per the reviewer’s suggestion. We state that PDEs 1 and 2 display an important role in growth since deletion of either these PDEs leads to reduced plaque growth. We have not investigated exactly what stage of the lytic cycle this is.

40: "is a group involving" instead of "are"

Response:

We found no mention of “a group involving” in our original manuscript at line 40 or anywhere else in the manuscript, so we are unsure what the reviewer is referring to.

108: isn't it "discharge of Ca++ from organelle stores to cytosol"?

__Response: __We thank the reviewer for spotting this error. We have now modified this sentence.

120: "was" instead of "were"

__Response: __Since the situation we are referencing is hypothetical, then ‘were’ is the correct tense.

Reviewer #3 (Significance (Required)):

There is a significant amount of work that underlies this manuscript; however, from a conceptual viewpoint, the manuscript does not offer significant advancement over the current knowledge without functional validation of phosphoproteomics data. In terms of the mechanism, it is not clear whether and how lipid turnover and cAMP-PKA signaling control the egress phenotype (lack of a validated model at the end of this study).In a methodical sense, the work uses established assays, some of which require revisiting to reach robust conclusions and avoid misinterpretation.

Compare to existing published knowledge

A large body of work preceding this manuscript has indicated the crosstalk of cAMP, cGMP, calcium and lipid signaling cascades. This work provides a further refinement of the existing model. The article is quite interesting from a throughput screening point of view, but it clearly lacks the appropriate endorsement of the hits.

Response:

Please refer to our first response to reviewer #3 for our full rebuttal to these points. We respectfully disagree with the assessment that the work presented does not advance current knowledge.

Audience

Field specific (Apicomplexan Parasitology)

Expertise

Molecular Parasitology

References

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Bullen, H. E. et al. (2016) ‘Phosphatidic Acid-Mediated Signaling Regulates Microneme Secretion in Toxoplasma Article Phosphatidic Acid-Mediated Signaling Regulates Microneme Secretion in Toxoplasma’, Cell Host & Microbe, pp. 349–360. doi: 10.1016/j.chom.2016.02.006.

Dass, S. et al. (2021) ‘Toxoplasma LIPIN is essential in channeling host lipid fluxes through membrane biogenesis and lipid storage’, Nature Communications. Springer US, 12(1). doi: 10.1038/s41467-021-22956-w.

Endo, T. et al. (1987) ‘Effects of Extracellular Potassium on Acid Release and Motility Initiation in Toxoplasma gondii’, The Journal of Protozoology, 34(3), pp. 291–295. doi: 10.1111/j.1550-7408.1987.tb03177.x.

Flueck, C. et al. (2019) Phosphodiesterase beta is the master regulator of camp signalling during malaria parasite invasion, PLoS Biology. doi: 10.1371/journal.pbio.3000154.

Howard, B. L. et al. (2015) ‘Identification of potent phosphodiesterase inhibitors that demonstrate cyclic nucleotide-dependent functions in apicomplexan parasites’, ACS Chemical Biology, 10(4), pp. 1145–1154. doi: 10.1021/cb501004q.

Jia, Y. et al. (2017) ‘ Crosstalk between PKA and PKG controls pH ‐dependent host cell egress of Toxoplasma gondii ’, The EMBO Journal, 36(21), pp. 3250–3267. doi: 10.15252/embj.201796794.

Katris, N. J. et al. (2020) ‘Rapid kinetics of lipid second messengers controlled by a cGMP signalling network coordinates apical complex functions in Toxoplasma tachyzoites’, bioRxiv. doi: 10.1101/2020.06.19.160341.

Lentini, J. M. et al. (2020) ‘DALRD3 encodes a protein mutated in epileptic encephalopathy that targets arginine tRNAs for 3-methylcytosine modification’, Nature Communications. Springer US, 11(1). doi: 10.1038/s41467-020-16321-6.

Lourido, S., Tang, K. and David Sibley, L. (2012) ‘Distinct signalling pathways control Toxoplasma egress and host-cell invasion’, EMBO Journal. Nature Publishing Group, 31(24), pp. 4524–4534. doi: 10.1038/emboj.2012.299.

Lunghi, M. et al. (2022) ‘Pantothenate biosynthesis is critical for chronic infection by the neurotropic parasite Toxoplasma gondii’, Nature Communications. Springer US, 13(1). doi: 10.1038/s41467-022-27996-4.

McCoy, J. M. et al. (2012) ‘TgCDPK3 Regulates Calcium-Dependent Egress of Toxoplasma gondii from Host Cells’, PLoS Pathogens, 8(12). doi: 10.1371/journal.ppat.1003066.

Moss, W. J. et al. (2022) ‘Functional Analysis of the Expanded Phosphodiesterase Gene Family in Toxoplasma gondii Tachyzoites’, mSphere. American Society for Microbiology, 7(1). doi: 10.1128/msphere.00793-21.

Stewart, R. J. et al. (2017) ‘Analysis of Ca2+ mediated signaling regulating Toxoplasma infectivity reveals complex relationships between key molecules’, Cellular Microbiology, 19(4). doi: 10.1111/cmi.12685.

Vo, K. C. et al. (2020) ‘The protozoan parasite Toxoplasma gondii encodes a gamut of phosphodiesterases during its lytic cycle in human cells’, Computational and Structural Biotechnology Journal. The Author(s), 18, pp. 3861–3876. doi: 10.1016/j.csbj.2020.11.024.

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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).

In this manuscript, Dominicus et al investigate the elusive role of calcium-dependent kinase 3 during the egress of Toxoplasma gondii. Multiple functions have already been proposed for this kinase by this group including the regulation of basal calcium levels (24945436) or of a tyrosine transporter (30402958). However, one of the most puzzling phenotypes of CDPK3 deficient tachyzoites is a marked delay in egress when parasites are stimulated with a calcium ionophore that is rescued with phosphodiesterase (PDE) inhibitors. Crosstalk between, cAMP, cGMP, lipid and calcium signalling has been previously described to be important in regulating egress (26933036, 23149386, 29030485) but the role of CDPK3 in Toxoplasma is still poorly understood.

Here the authors first take an elegant phosphoproteomic approach to identify pathways differentially regulated upon treatment with either a PDE inhibitor (BIPPO) and a calcium ionophore (A23187) in WT and CDPK3-KO parasites. Not much difference is observed between BIPPO or A23187 stimulation which is interpreted by the authors as a regulation through a feed-back loop. The authors then investigate the effect of CDPK3 deletion on lipid, cGMP and cAMP levels. The identify major changes in DAG, phospholipid, FFAs, and TAG levels as well as differences in cAMP levels but not for cGMP. Chemical inhibition of PKA leads to a similar egress timing in CDPK3-KO and WT parasites upon A23187 stimulation.

As four PDEs appeared differentially regulated in the CDPK3-KO line upon A23187, the authors investigate the requirement of the 4 PDEs in cAMP levels. They show diverse localisation of the PDEs with specificities of PDE1, 7 and 9 for cGMP and of PDE2 for cAMP. They further show that PDE1, 7 and 9 are sensitive to BIPPO. Finally, using a conditional deletion system, they show that PDE1 and 2 are important for the lytic cycle of Toxoplasma and that PDE2 shows a slightly delayed egress following A23187 stimulation.

Major comments:

-Are the key conclusions convincing?

The title is supported by the findings presented in this study. However I am not sure to understand why the authors imply a positive feed back loop. This should be clarified in the discussion of the results. The phosphoproteome analysis seems very strong and will be of interest for many groups working on egress. However, the key conclusion, i.e. that a substrate overlaps between PKG and CDPK3 is unlikely to explain the CDPK3 phenotype, seems premature to me in the absence of robustly identified substrates for both kinases.

I am not sure there is a clear key conclusion from the lipidomic analysis and how it is used by the authors to build their model up. Major changes are observed but how could this be linked with CDPK3, particularly if cGMP levels are not affected?

The evidence that CDPK3 is involved in cAMP homeostasis seems strong. However, the analysis of PKA inhibition is a bit less clear. The way the data is presented makes it difficult to see whether the treatment is accelerating egress of CDPK3-KO parasites or affecting both WT and CDPK3-KO lines, including both the speed and extent of egress. This is important for the interpretation of the experiment.

The biochemical characterisation of the four PDE is interesting and seems well performed. However, PDE1 was previously shown to hydrolyse both cAMP and cGMP (https://doi.org/10.1101/2021.09.21.461320) which raises some questions about the experimental set up. Could the authors possibly discuss why they do not observe similar selectivity? Could other PDEs in the immunoprecipitate mask PDE activity? In line with this question, it is not clear what % of "hydrolytic activity (%)" means and how it was calculated. The experiments describing the selectivity of BIPPO for PDE1, 7 and 9 as well as the biological requirement of the four tested PDEs are convincing.

-Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

The claim that CDPK3 affects cAMP levels seems strong however the exact links between CDPK3 activity, lipid, cGMP and cAMP signalling remain unclear and it may be important to clearly state this.

-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.

I think that the manuscript contains a significant amount of experiments that are of interest to scientists working on Toxoplasma egress. Requesting experiments to identify the functional link between above-mentioned pathways would be out of the scope for this work although it would considerably increase the impact of this manuscript. For example, would it be possible to test whether the CDPK3-KO line is more or less sensitive to PKG specific inhibition upon A23187 induced?

-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.

The above-mentioned experiment is not trivial as no specific inhibitors of PKG are available. Ensuring for specificity of the investigated phenotype would require the generation of a resistant line which would require significant work.

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

It is not clear how the % of hydrolytic activity of the PDE has been calculated.

-Are the experiments adequately replicated and statistical analysis adequate?

This seems to be performed to high standards.

Minor comments:

-Specific experimental issues that are easily addressable.

I do not have any comments related to minor experimental issues.

-Are prior studies referenced appropriately?

Most of the studies relevant for this work are cited. It is however not clear to me why some important players of the "PKG pathway" are not indicated in Fig 1H and Fig 3E, including for example UGO or SPARK.

-Are the text and figures clear and accurate?

While all the data shown here is impressive and well analysed, I find it difficult to read the manuscript and establish links between sections of the papers. The phosphoproteome analysis is interesting and is used to orientate the reader towards a feedback mechanism rather than a substrate overlap. But why do the authors later focus on PDEs and not on AC or CNBD, as in the end, if I understand well, there is no evidence showing a link between CDPK3-dependent phosphorylation and PDE activity upon A23187 stimulation? It is also unclear how the authors link CDPK3-dependent elevated cAMP levels with the elevated basal calcium levels they previously described. This is particularly difficult to reconcile particularly in a PKG independent manner.

The presentation of the lipidomic analysis is also not really clear to me. Why do the authors show the global changes in phospholipids and not a more detailed analysis? As the authors focus on the PI-PLC pathway, could they detail the dynamics of phosphoinositides? I understand that lipid levels are affected in the mutant but I am not sure to understand how the authors interpret these massive changes in relationship with the function of CDPK3 and the observed phenotypes.

Finally, the characterisation of the PDEs is an impressive piece of work but the functional link with CDPK3 is relatively unclear. It would also be important to clearly discuss the differences with previous results presented in this this preprint: https://doi.org/10.1101/2021.09.21.461320. My understanding is while the authors aim at investigating the role of CDPK3 in A23187 induced egress, the main finding related to CDPK3 is a defect in cAMP homeostasis that is not linked to A23187. Similarly, the requirements of PDE2 in cAMP homeostasis and egress is indirectly linked to CDPK3. Altogether I think that important results are presented here but divided into three main and distinct sections: the phosphoproteomic survey, the lipidomic and cAMP level investigation, and the characterisation of the four PDEs. However, the link between each section is relatively weak and the way the results are presented is somehow misleading or confusing.

-Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

This is a very long manuscript written for specialists of this signalling pathway and I would suggest the authors to emphasise more the important results and also clearly state where links are still missing. This is obviously a complex pathway and one cannot elucidate it easily in a single manuscript.

Significance

-Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

This is a technically remarkable paper using a broad range of analyses performed to a high standard.

-Place the work in the context of the existing literature (provide references, where appropriate).

The cross-talk between cAMP, cGMP and calcium signalling is well described in Toxoplasma and related parasites. Here the authors show that, in Toxoplasma, CDPK3 is part of this complex signalling network. One of the most important finding within this context is the role of CDPK3 in cAMP homeostasis. With this in mind, I would change the last sentence of the abstract to "In summary we uncover a feedback loop that enhances signalling during egress and links CDPK3 with several signalling pathways together."

The genetic and biochemical analyses of the four PDEs are remarkable and highlight consistencies and inconsistencies with recently published work that would be important to discuss and will be of interest for the field.

While I understand the studied signalling pathway is complex, I think it would be important to better describe the current model of the authors. In the discussion, the authors indicate that "the published data is not currently supported by a model that fits most experimental results." I would suggest to clarify this statement and discuss whether their work helps to reunite, correct or improve previous models.

Could the authors also speculate about a potential role of PDE/CDPK3 in host cell invasion as cAMP signalling has be shown to be important for this process (30208022 and 29030485)?

-State what audience might be interested in and influenced by the reported findings.

This paper is of great interest to groups working on the regulation of egress in Toxoplasma gondii and other related apicomplexan pathogens.

-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 working on the cell biology of apicomplexan parasites.

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Reply to the reviewers

Manuscript number: RC-2022-01384

Corresponding author(s): Mary O’Riordan and Basel Abuaita

1. General Statements [optional]

We appreciated the positive feedback and helpful suggestions from the reviewers that pointed to a need for more clarity regarding the central focus of the study. Our goal was to take an unbiased approach to evaluating the role of neutrophils during S. Typhimurium (STM) infection of human intestinal epithelial cells (IEC), using human intestinal organoids as a model. An abundance of data point to important inflammatory roles for neutrophils during STM infection of human intestine but the critical mechanisms involved have not been fully elucidated. New data now included in the revised manuscript provide strong support for human PMN-derived IL1-beta as a driver of epithelial cell shedding in STM-infected HIOs, consistent with known differences in local inflammation between human and mouse infection, and this is the focus of the current study. Our data did not support a significant role for human neutrophils in controlling luminal bacterial numbers, but instead the primary human PMNs robustly stimulated epithelial cell responses that led to decreased intraepithelial bacteria. Several recent studies have suggested that caspase-1 is not a critical inflammasome component during STM infection of IEC, which instead use non-canonical inflammasomes, including caspases-4 and -11. Our data point to a human neutrophil-intrinsic function for caspase-1 and IL1-beta that contributes to the inflammatory tone of the intestinal milieu early in STM infection.

2. Point-by-point description of the revisions

Reviewer #1

Major comments:

Some important links are missing to fully support the mechanistic model proposed:* *

1- PMN activity

The authors may strengthen their evidence of PMN activities presented in lines 135 to 143 and in Fig.S2 and S3. In particular, the authors claim that PMNs form NETs in PMN-HIOs but the evidence displayed are limited. In fact, Fig S2 shows the same condition and same staining as Fig 1B but the MPO-positive structures are different. Clarification in the text or the figure would be welcome. Besides, as the authors insist on the relevance of NETs in the discussion, it seems that a clear demonstration and characterization of these structures in the PMN-HIO model would highly benefit the manuscript.

While we commented on NETs in our original manuscript, our conclusions do not rely on the presence or absence of NETs. We have therefore removed the NET data and the reference to NETs. While NETs are potentially interesting in the context of intestinal infection, we understand the reviewer's concern about NETs and anticipate that a more quantitative characterization of NETs may be challenging given the structure and variability of the PMN-HIOs.

Regarding the analyses of the culture supernatants (Fig.S3), only 3 out of the 5 displayed datasets are commented on in the text. The data obtained for BD2 and N-Gal should be either commented or removed from the figure. The author further suggests that Elafin expression in presence of PMN may restrict PMNs' ability to kill Salmonella. Repeating the experiment displayed in Fig S1 in the presence of Elafin as well as in the presence of the supernatant extracted from HIOs and PMN-HIOs would clarify the potential inhibition of PMN killing capacity in the PMN-HIO model.

We now include a sentence on the antimicrobials BD2 and N-GAL to the text (line 135-136). Elafin is one of many molecules that could potentially affect the ability of PMNs to kill Salmonella. We repeated the experiments in S3 Fig with recombinant Elafin. There was a very weak effect on killing in the presence of Elafin, however Elafin can also kill Salmonella directly, complicating interpretation of these experiments. We have now added a sentence in the Discussion to speculate that Elafin is one example of how the epithelium may inhibit the ability of PMNs to kill (line 366-372). These data are not central to our main conclusions and are only intended to provide context to the reader about possible explanations for why PMNs can kill Salmonella directly, but do not significantly alter total bacterial numbers in the HIO model.

The author proposed that infected and uninfected cells are extracted from the epithelium due to PMN activation, suggesting that Salmonella infection of epithelial cells is only indirectly involved in cell shedding. This is an interesting hypothesis that could be tested by measuring cell shedding in a non-infected but PMN-activated (for instance with PMA) PMN-HIO model. This would clarify further the role of PMN in controlling epithelial response to the infection.

We tested this possibility by microinjecting LPS into the lumen of PMN-HIOs (S6 Fig). There was significantly less TUNEL+ signal in LPS-injected PMN-HIOs compared to STM-infected PMN-HIOs, suggesting that active Salmonella infection is required for shedding of both infected and uninfected cells in the presence of PMNs__. __

2- Specificity of RNA-seq profiling:

The authors analyzed the transcriptomic profiling of PMN-HIOs and HIOs infected or not. While these experiments bring to light an interesting difference in inflammasome/cell death transcriptomic programs at the scale of the co-culture model, it is not possible to conclude from which cell type these transcriptomic shifts emerge. To clarify this, the authors stain the co-culture for ASC and observe that ASC-positive cells are PMNs. They conclude that PMNs are most likely the primary site of caspase-1 dependent production of IL1. While their model is theoretically consistent, more direct proofs are necessary to conclude on the cell-type specific transcriptomic program during infection of PMN-HIO and could be obtained by FACS sorting of the cells prior to RNA-seq, for instance using MPO to detect PMNs and E-cadherin to detect epithelial cells.

We now provide evidence that pretreating PMNs with an irreversible Caspase-1 inhibitor before co-culturing with STM-infected HIOs prevented accumulation of luminal TUNEL+ cells (Fig 6B,C). Additionally, IL-1β treatment in the absence of PMNs recapitulated the cell death phenotype of the infected PMN-HIOs (Fig 6D,E) suggesting Caspase-1 activity in PMNs and IL-1β production are necessary for epithelial cell death in the PMN-HIOs.

3- Roles of cytokine

After showing an increased expression/release of IL1 and IL1RA in infected PMN-HIOs, the authors move on to testing the role of caspases on cell shedding. Yet, they do not test the impact of IL1 and IL1RA on cell shedding. As, according to their proposed model, IL1 is acting upstream of caspase-1 to promote cell shedding, testing cell shedding in infected PMN-HIOs in the presence of an IL1 inhibitor would clarify that link. The author also proposed that the decrease of IL33 in PMN-HIOs compared to HIOs could be due to PMN processing, which would give an additional role to PMNs in controlling the epithelial response to infection. In the context of this manuscript, it would be highly relevant to test this hypothesis by measuring the rate of cleaved IL-33.

We now provide data to address these questions about IL-1 signaling. HIOs were microinjected with recombinant IL-1β during STM infection and PMN-HIOs were also treated with IL1RA during STM infection. Cell shedding was measured under these conditions in Fig. 6D-F. Cell shedding was dependent on IL-1 signaling and the model has been updated to reflect this.

We also concentrated supernatants from STM-infected HIOs and PMN-HIOs, probed for cleaved IL33 via western blot and did see some cleavage. However, without being able to block this process it is not possible to conclude what role cleaved IL33 has during infection in the PMN-HIO and IL-1β seems to be sufficient to drive the cell shedding phenotype. Since the status of IL33 is not central to our conclusions, we have removed these data from the manuscript.

4- Roles of caspase

The interpretations of the role of Caspases to restrict bacteria burden are unclear and should be revised (see also minor comment). It appears that both Caspase-1 and Caspase-3 are necessary for efficient cell shedding (Fig4B), Caspase-1 (but not Caspase-3) decreases intraluminal bacteria burden (Fig4C) and Caspase-3 (but not Caspase-1) decreases epithelium-associated bacteria (Fig4D). To reconcile these observations with the hypothesis that cell shedding is responsible for the decrease of intraluminal and epithelium-associated bacterial burden, one may propose that caspase-3 (but not caspase-1) induces cell shedding of mainly non-infected cells (possibly bacteria-associated) and caspase-1 (but not caspase-3) induced cell shedding of infected cells. This could be tested by measuring the % of infected extruded cells upon caspase inhibitor treatments. In addition, these data don't allow to propose that Caspase-3 activation happens downstream of Caspase-1 as suggested by the authors in their abstract figure.

It is difficult to accurately quantify the percent infected cells that are extruded since both infected and uninfected cells are extruded into a luminal space full of bacteria, which may associate with uninfected cells post-extrusion. However, we did observe cells positive for cleaved Caspase-3 when HIOs were treated with IL-1β leading us to infer that Caspase-1 mediated cytokine signaling through IL1R can trigger downstream Caspase-3 activation (Fig. 6G). We have expanded the Discussion to talk about differing roles of Caspases on bacterial burden and association with the epithelium (lines 374-397).

Minor comments:

The majority of the points listed below can be addressed with further analyses of pre-acquired data sets:

Fig1E/1F/4D: each green dot is not likely to be individual bacteria but rather a cluster of bacterium (based on their size). So the y-axis in Fig 1E and Fig4D should not be #STM.

Y-axis labels have been changed to #STM objects

Fig2A: Variations in organoid size and epithelial thickness can be observed between figures. In particular, in Fig 2A, the HIO seems much younger than the other ones displayed in the manuscript.

There is considerable natural variability between HIOs and between batches, a phenomenon observed by many HIO researchers (Hofer et al. Nature Reviews Materials 2021). HIOs were all treated the same way prior to infection, and based on our extensive observations, epithelial thickness does not correlate significantly with a particular experimental condition, as we now show in S10 Fig.

Line 176 to 178, the authors mentioned the TUNEL+ cells in the mesenchyme but rule out the possibility that this phenotype could be infection or PMN-dependent because it is observed in the different conditions. As the picture displayed in Fig2A suggests high differences in the number of TUNEL+ cells in the mesenchyme under the 4 tested conditions, the authors should still quantify this phenomenon (possibly in the supplementary).

This is likely an artifact of culturing and not due to the infection or PMNs. There is variability between HIO batches in the amount of TUNEL signal in mesenchymal cells (for example HIOs in Fig 4A and 5A have very low or no TUNEL positivity in the mesenchyme).

"DAPI" should be written in blue.

This has been corrected.

Fig2C: Could the authors comment on the % of E-cadherin cells that are also TUNEL+? Is it 100%?

On average about 75% of TUNEL+ cells are E-cadherin+. We think that this may be an underestimate because E-cadherin staining intensity decreases in many cells after shedding. This is commented on in the text (lines 178-179).

Fig 2D: The point made on lines 182 to 186 that HIOs contain TUNEL + cells retained in the epithelial lining in the absence of PMNs is not very strongly supported by Fig 2D. Quantification of the number of intraepithelial TUNEL+ cells in the 4 compared conditions would make a more solid case.

We quantified TUNEL intensity in epithelial cells retained in the monolayer (S7 Fig). We do note that there is some variability in this phenotype that correlates with different batches of HIOs__.__

Fig2E: This experiment should be completed with a quantification of the percentage of TUNEL+ cells that are also cleaved caspase3-positive. The data, as currently displayed, do not prove that the cells negative for cleaved caspase 3 are apoptotic cells and thus do not support the sentence "suggesting that multiple forms of cell death were occurring in the PMN-HIO" (line 194).

Cells negative for cleaved Caspase-3 that are TUNEL+ may be undergoing some other form of cell death that is not Caspase-3 dependent, such as necrosis. This possibility is consistent with the decreased TUNEL signal observed upon inhibition of Caspase-4 (Fig 5A,B)__. __However, we have reworded our conclusion to identify more clearly what the data indicate, and where we are drawing inferences.

Fig3A: "IL1RN" should be changed for "IL1RA (IL1RN)" for consistency with Fig 3B.

The heatmap shows gene expression data so IL1RN is more consistent with the gene nomenclature. However, we have added an asterisk to the label on the heatmap, along with a sentence in the figure legend to elucidate.

Fig 4C: The authors should provide the percentage of infected cells rather than the number of bacteria per cell (this information can be included in supplementary).

Percent infected cells has been moved to Fig 4C and the number of bacteria per cell has been moved to Fig 4D__.__

FigS2: The different thicknesses of the epithelial layer observed between PBS and STM panels suggest a difference in scale. This may be double-checked by the authors.

The images are scaled similarly – as noted earlier (S10 Fig), there is considerable natural variability between HIOs that is not correlated with any experimental condition in this study.

Line 197-199, the authors claimed that uninfected cells may be observed in the cell lumen. This seems hard to observe/conclude at this resolution. The authors may show a non-infected cell at higher magnification. __

We have added higher magnification images, uninfected cells are indicated with white arrows in S8 Fig.

Discussion: Some important points should be added to the discussion. In particular, what is the fate of intracellular salmonellae after cell shedding? Can the bacterium survive cell apoptosis and burst out of the cell to re-infect the epithelium or be transferred to phagocytic cells during the clearance of intraluminal apoptotic cells? Previous studies showed that cytosolic hyper-replication could fuel cell shedding. The importance of bacterial load in PMN-induced cell shedding could be discussed.

We have expanded the discussion to elaborate on what may happen to shed cells. One useful feature of the HIOs is that the enclosed lumen allows us to capture the cells to fully measure the extent of cell shedding, however in the intestine where there is flow these cells would be washed away and could help to reduce bacterial load in the intestine. This point is now made in lines 386-388 in the discussion.

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

Major concerns

1) The authors show that only ~5% of the neutrophils have migrated to the lumen, which is a barely noticeable increase compared to PBS treated organoids. Does this reflect that the mucosal layer of the organoids might not produce neutrophil chemoattractants and that neutrophil recruitment during Salmonella is a bystander effect from a different cell type?

This number indicates that PMNs are ~5% of total cells in the PMN-HIO (including epithelial and mesenchymal cells) during Salmonella infection (not that only 5% of PMNs added migrated). Moreover, PMNs were added to a well containing multiple HIOs. We also show that HIOs do produce neutrophil chemoattractants during infection (S1 Fig).

2) How quickly are neutrophils recruited to the HIOs? The authors show one time point of 8 hours. Related to the relatively low number of neutrophils seen in their HIOs, is this perhaps a result of the time point they chose? Will they see more neutrophils recruited if they go longer?

It is likely that 5% of total cells in the PMN-HIO represents a significant recruitment of PMNs, and our data clearly indicate a marked effect on the infected epithelium. PMNs can cause substantial tissue damage, and their recruitment and activation is known to be tightly regulated. Due to the short-lived nature of human PMNs it would be difficult to extend this experiment to later timepoints. We have experimentally characterized PMN migration at 24h and by that time, most of the PMNs that we observe are non-viable, thus we focused our studies earlier.

3) The authors show that PMNs did not kill STM in their organoids, but they do in pure culture. Is this simply because of the low levels of neutrophils present in their HIOs, which would result in lower concentrations of antimicrobials being produced in the HIO lumen? If the authors are able to get higher levels of neutrophils in their HIOs, would they see increased bacterial killing?

Neutrophils have both inflammatory signaling and microbicidal functions. For example, Cho, et al (PLoS Pathogens 2012) find that neutrophil-derived IL-1 beta is sufficient to support abscess formation in the innate immune response to Staphylococcus aureus soft tissue infection. Similarly, a recent study showed that activation of neutrophils by keratinocyte defensins in a S. aureus skin infection led to neutrophil IL1 beta and CXCL2 release that amplified antibacterial defenses (Dong, et al Immunity 2022). Moreover, in the native environment of the gut with extensive microbiome colonization, direct neutrophil microbicidal activity might be less effective against infection than signaling. Recruitment of higher levels of neutrophils in vivo or in the HIO might exacerbate damage of the epithelial barrier. In the discussion, we speculate there may be proteins, like Elafin, that are upregulated during infection and inhibit some neutrophil functions as a trade-off to control host tissue damage. We reason that our data strongly support an inflammatory signaling role for neutrophils to promote innate immune responses of the intestinal epithelium.

4) Related to the above point, if the authors treat their HIOs with known neutrophil chemoattractants, can they increase the number of neutrophils that migrate into their organoids?

High levels of chemoattractants are already being produced in the HIO in response to infection (S1 Fig). The most effective number of neutrophils in the context of intestinal infection may not be the highest number, given that neutrophils can cause tissue damage. Since we see a marked phenotype with the neutrophils that are recruited, we propose that this PMN-HIO model reveals important inflammatory signaling roles for PMNs to promote intestinal epithelial immune function.

5) The authors speculate that Salmonella may "employ specific mechanisms to overcome PMN effector functions in the HIO luminal environment". Are any such mechanisms known? If so, the authors could test this hypothesis by repeating these experiments with Salmonella mutants in which these mechanisms are ablated. In this case, they should see increased killing of Salmonella by PMNs in the HIO lumen.

The focus of this study was to test how PMNs contribute to the host response against wildtype Salmonella. In the PMN-HIO model, we find that neutrophils direct a robust epithelial cell extrusion response, impacted intracellular bacterial numbers, and that Salmonella luminal colonization is not affected by PMNs. Thus, our data are pointing to an important inflammatory role for neutrophils in the infected intestine. Indeed, reliance on direct bactericidal mechanisms in the intestinal lumen which in vivo would be colonized with the microbiota might be a losing strategy for neutrophils, which would be hugely outnumbered.

6) Furthermore there is no information of the activation status of the neutrophils. How does the surface expression of CD16 CD62L, CD66 and CD11b look between the migrated and non-migrated and between infected and uninfected controls? Did the neutrophils de-granulate? Are they CD63+ or is the high levels of NGAL and S100 proteins an effect of lysis? The authors should also be careful in claiming that there is NETosis as the image in the supplement look more like an artifact than actual NETs.

Our new findings suggest that IL-1 production by PMNs is the biggest factor in driving the cell death phenotype. We have also added a figure with CD63 staining. We were able to visualize some localization of CD63 to the cell surface of PMNs, consistent with degranulation (S4 Fig).

7) Why does ASC translocate to the nucleus? Is the IL-1b cleavage mediated through Caspase-1 or Caspase-11? The neutrophils stained positive in the lumen appear to be intact, does this mean that pyroptosis does not occur, or does the IL-1b come from cells that did not migrate through the mucosal membrane? Staining for IL-1 and the different caspases might help resolve this question.

ASC does not appear to be translocating to the nucleus. In Fig 3D the green signal (ASC) is primarily excluded from the DAPI-stained area. In this human model, Caspase-11 is not present, and inhibition of Caspase-1 is sufficient to block the cell shedding phenotype (Fig. 5A,B and Fig. 6B,C). We are unable to distinguish whether IL-1 is being produced by intact PMNs or PMNs that are undergoing pyroptosis. Unfortunately, there are not suitable antibodies for fixed immunofluorescence staining for cleaved Caspase-1, and as a secreted protein, IL-1 beta likely will not remain localized with the producer cell.

8) The authors comment that there is substantial TUNEL staining in the mesenchyme independent of STM or PMNs, however, there is no explanation for why this happens. Does this have any downstream effects on the neutrophils that doesn't migrate towards the lumen?

TUNEL positivity in the mesenchyme is likely an artifact of culturing and we have noted this in the text (line 169-172). The extent of TUNEL+ mesenchymal cells appears to be dependent on the batch of HIOs as not all HIOs exhibit this phenotype (for example Figs 4A and 6B). In contrast, the extent of TUNEL+ luminal cells is significantly dependent on the presence of PMNs and Salmonella.

Minor comments

1) The authors should remove that MPO is neutrophil-specific, monocytes are known to have higher MPO expression than neutrophils.

In this controlled co-culture system there are no monocytes, therefore we have modified our text to indicate that MPO is used as a neutrophil marker in the PMN-HIOs (line 161).

2) If the authors performed flow cytometry as they say, they should provide the flow plots and the gating strategy they used in the supplement.

Representative flow plots for the data presented in Fig 1A are now included in S2 Fig. The data shown in Figs. 1A and S2 Fig are not gated.

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

Major Comments

1.Overall the study is convincing and it is well-conducted. This reviewer found it surprising that the PMNs did not alter the total levels of STM in the HIOs as neutrophils are expected to control the infection. Can the authors elaborate on if the intraepithelial numbers are reduced, what happens to STM in the lumen? It would be convincing if the authors can extend the infection timeline to see if the neutrophils are capable of killing luminal STM. *

One of the limitations of the HIO model is the lack of flow in the lumen. It is likely that shed cells would be removed from the body following extrusion in vivo. In the HIOs, since the cells are trapped in the lumen, Salmonella could then reinvade and so this phenotype might be even stronger in a model that incorporates flow. We have added this point to the discussion (lines 387-390). Due to the short-lived nature of PMNs, it is difficult to extend the infection beyond 8h. While in vitro experiments with just neutrophils and STM as we and others have performed might set the expectation that neutrophils would alter luminal bacterial levels, there is little to no direct evidence that neutrophil bactericidal activity is critical in the context of the intestinal environment (vs. releasing ROS or inflammatory signals that may have complex indirect effects). Indeed, an advantage of the HIO model is that we are able to test the function of neutrophils in a multi-component system, but one that is still sufficiently simplified that we can do some mechanistic analysis.

2-It would be powerful to conduct the caspase inhibition on neutrophils prior to HIO co-culturing to convincingly show that the effects of caspase inhibition effect neutrophils which in turn effect the epithelium disrupting the epithelial load of STM.

We appreciated this suggestion. We pretreated the PMNs with a Caspase-1 inhibitor for 1h prior to co-culture with infected HIOs. We found that this was sufficient to block TUNEL cell accumulation in the lumen of infected PMN-HIOs. These results are now presented in Fig 6B,C.

3- While other caspases are well-established to be involved in Salmonella-related cell death and epithelial shedding, why did the authors picked caspase 3 but not caspase 4/5 to show activation in Fig 2?

We have now also tested the role of Caspase-4 on cell shedding using z-LEVD-fmk inhibitor. Consistent with prior published studies, we found that Caspase-4 inhibition reduced the accumulation of TUNEL-positive cells in the PMN-HIO lumen. These results are presented in Fig 5. There are no detectable levels of Caspase-5 in the HIOs (S9 Fig).

Minor comments

Fig 1C It is not clear how the total bacterial burden was determined. Please include details such as the timepoint and sufficient details of the technique both in the results section and the legend.

These details have been added in the figure legend (line 605-607). Briefly, HIOs were washed with PBS and homogenized in PBS at 8hpi. CFU/HIO were enumerated by serial dilution and plating on LB agar.

• Fig S2. Authors claim that the PMNs form NETs in the lumen, however, the marker used in the immunostaining is MPO. Although a NETting is seen in the images, MPO staining is not sufficient to claim these are NETs. Additional staining is required to show if the neutrophils in the lumen are intact or formed NETs*.

As noted in response to Reviewer #1, although we commented on NETs in our original manuscript, our conclusions do not rely on the presence or absence of NETs and our new data implicates PMN IL-1 as necessary and sufficient for the cell shedding phenotype. We have therefore removed the NET data and the reference to NETs. While NETs are potentially interesting in the context of intestinal infection, we understand the reviewer's concern about NETs and anticipate that a more quantitative characterization of NETs may be challenging given the structure and variability of the PMN-HIOs.

This work has been published in GigaByte Journal under a CC-BY 4.0 license (https://doi.org/10.46471/gigabyte.66), and has published the reviews under the same license. These are as follows.

Reviewer 1. Linzhou Li

Are the data and metadata consistent with relevant minimum information or reporting standards? No. Geographic location (country and/or sea, region, latitude and longitude) is missing, as well as environmental context.

Is there sufficient data validation and statistical analyses of data quality? No. The genome size and gene number of Dendrobium hybrid cultivar ‘Emma White’ differ greatly from the published Dendrobium genomes (e.g. Zhang et. al Scientific Reports 2016, Zhang et. al Horticulture Research 2021, Han et. al Genome Biology and Evolution 2020...). Specifically, the authors assembled a smaller genome and predicted a larger number of genes compared with the previous study. Therefore, I strongly suspect that the assembled genome is incomplete and fragmented, resulting in more fragmental genes.

Is the validation suitable for this type of data? No. There's not enough raw data (~24Gb) to assemble a 600Mb (or ~1.2Gb from the previous study) genome. I highly recommend the authors get more raw data and do a genome survey.

Additional Comments: The Complete BUSCOs only account for 16.6% which is quite low. The authors explain that the large loss of BUSCOs is due to the fact that the mutant genome has a lot of specific sequences, but these genes are very conserved in plants and should not be easily mutated.

Reviewer 2. Stephanie Chen

Is the language of sufficient quality? No. Most of the manuscript is written in a sufficient quality, but there are certain parts that require revision to improve readability. Please see detailed comments on the Word document.

Are all data available and do they match the descriptions in the paper? No. The SRA link is coming up as a permission error, but I assume it will be released once the paper is available. There is no information on where to access the annotation file.

Is the data acquisition clear, complete and methodologically sound? No. The contiguity (635,396 contigs, N50 of 1,620 bp) and completeness (16.60 %) of the genome is quite low and this may limit its downstream uses. It would be good to incorporate some long-reads or increased sequencing coverage to improve your genome. There are a number of chromosome-level Dendrobium genomes that are available (e.g. D. chrysotoxum and D. huoshanense) and scaffolding off these may be attempted to improve the assembly. Scaffolding from existing assemblies may be a good option if generating more sequencing reads is not feasible.

Is there sufficient detail in the methods and data-processing steps to allow reproduction? No. Some details on the DNA extraction and library preparation steps are missing. In the methods section, there are also missing details for multiple programs in terms of the version and parameters (e.g. BUSCO version and database used, QUAST version, AUGUSTUS version, details on adapter removal and trimming). It is mentioned 'similarity score and description of each gene was filtered out using in-house pipeline'. The script and details of the pipeline are not provided; please add a reference or details in the manuscript e.g. link to GitHub repository.

Is there sufficient data validation and statistical analyses of data quality? No. The reporting and interpretation of BUSCO results ('BUSCO version 5.2.2 analysis reveals 913 (56.57%) single-copy orthologs doesn’t match with any data bases indicates the unique and possible uncharacterized sequences in mutant genome of Dendrobium hybrid cultivar') needs to be revisited. There needs to be additional validation of the gene annotation (e.g. BUSCO, comparison with existing Dendrobium annotations) and also some validation of the genome size (e.g. GenomeScope and comparison with reported flow cytometry measures).

Is the validation suitable for this type of data? Yes. The type of validation in the manuscript (BUSCO) is suitable to assess genome completeness, but reporting and discussion of the results needs to be revised. Some additional validation is also needed (see box above).

Additional Comments: In this manuscript, the authors provide a draft genome of a gamma-ray induced mutant of a Dendrobium hybrid cultivar using Illumina sequencing that will assist with future breeding efforts and studies. However, I am not convinced of the genome's usefulness in its current form. There are some methods that need to be described in more detail to be reproducible. Revisions will also help improve the readability of the manuscript. As page and line numbers are not provided on the manuscript, please find additional comments directly added to manuscript file attached.

https://gigabyte-review.rivervalleytechnologies.com/download-api-file?ZmlsZV9wYXRoPXVwbG9hZHMvZ3gvRFIvMzA2L1Jldmlld19TQ185Njc2XzA1MDIyMl9HaWdhYnl0ZV9HYW1tYSBXR1MgZGF0YW5vdGUgKDEpLmRvY3g~

Re-review: Thank you to the authors for addressing the previous comments on the manuscript. I generally find the revisions satisfactory, although have some follow up comments. The addition of details on the genetic origin of the Dendrobium ‘Emma White’ hybrid cultivar and requested details on bioinformatic tool versions/parameters have strengthened the manuscript. The authors have not followed up on the suggestion to improve the genome via scaffolding, but provide an explanation that existing chromosome-level assemblies/sequencing data of Dendrobium species are not suitable as they are not related to the hybrid cultivar the authors studied, implying that they are highly diverged and scaffolding would not meaningfully improve the genome. Given this information, I think the Dendrobium ‘Emma White’ hybrid cultivar genome can still be useful for orchid breeding efforts despite low contiguity and completeness. However, I do not agree with the author’s point of, “Third, we used low coverage genome analysis with short reads of gamma mutant Dendrobium hybrid cultivar, as it was the first case study and obtained SRA, genome assembly and TSA accessions from NCBI. The genome assemblies of Dendrobium species from earlier studies used both long reads and short reads in their study. Construction of scaffolding from such database species using our contigs may be skewed and shall give unreliable data based on above points mentioned. Hence, I opinioned that suggestion given by Reviwer 2 on scaffolding suggestion may not be correct point.” Even if different types of sequencing technologies were used in comparison to Emma White genome, the availability of a contiguous closely related reference genome would still be useful for reference-guided scaffolding of the draft genome and well as comparative analyses. Lines 107-109: Reorder sentence to make the order of the steps clear i.e. adapter removal and quality filtering before assembly with MaSuRCA. Also, on the MaSuRCA GitHub (https://github.com/alekseyzimin/masurca), it says “Avoid using third party tools to pre-process the Illumina data before providing it to MaSuRCA, unless you are absolutely sure you know exactly what the preprocessing tool does. Do not do any trimming, cleaning or error correction. This will likely deteriorate the assembly.” Did the authors find that the pre-processing meaningfully improved the quality of the assembly, compared to if the raw reads were input straight into the assembler? Please justify the preprocessing of reads. Suggest to reword lines 137-139 “BUSCO version 5.2.2 analysis reveals 913 (56.57%) single-copy orthologs doesn’t match with any data bases indicates the impact from evolutionary development of hybrid cultivars and influence of gamma radiation. It is because, the genome of ‘Emma White’ hybrid cultivar of Dendrobium derived from five unique different species is complex genome and continuously hybridized repeatedly 11 times over a period of 68 years with selection process for economic trait improvement” to make the explanation clearer and also to include the number and/or percentage of complete BUSCOs. This was flagged in the previous comments, but not fully resolved and would benefit from revisiting the interpretation of BUSCO results. There are a large number of missing BUSCOs in your assembly, likely related to low contiguity (as well as radiation which is mentioned). Can you discuss if/how this may be a limitation for using this genome in further studies? You suggest that the BUSCOs are not found in the assembly due to many rounds of trait selection and radiation. It is possible that some of the BUSCOs are indeed missing from the particular plant sequenced, but how can you be certain that this is due to the breeding history and radiation applied as implied in the text, and not low genome contiguity? Some papers which characterised gamma irradiation-induced mutations in plants (DOIs: 10.1093/jrr/rraa059, 10.1186/s12864-019-6182-3, 10.1534/g3.119.400555) indicate that it is unlikely as many as 913 BUSCO genes have been affected. Even with stronger doses of radiation than used on the orchid, the number of mutations/genes affected is much lower. The genus name needs to be consistently italicised throughout the manuscript.

Re-re-review: Thank you to the authors for addressing the previous comments on the manuscript. The authors have followed up on the suggestion scaffold the genome by using the published Dendrobium huoshanense genome to scaffold their draft genome using RagTag. This is an appropriate tool to use and has improved the contiguity of the draft assembly which is good to see. In the methods, the version of RagTag is missing, as are the parameters used to run the program. Please also provide specification on the specific RagTag utilities used (correct, scaffold, patch and/or merge). The authors have added genome statistics for two other orchids and the scaffolded assembly in Table 1, however, have not added BUSCO results for their scaffolded assembly in Table 2. Also, can the authors provide a comment on if the low BUSCO values may be related to the fragmented assembly as brought up in the previous round of review? It will be interesting to see if BUSCO has improved with the scaffolding. BUSCO results for the other two species, D. catenatum and D. huoshanense, would also be a good point of comparison and this is relatively simple and quick to add. The authors could consider concatenating Table 1 and 2 in this case. The draft assembly has improved, and the authors should report numbers on the final version of the assembly presented in the paper (i.e. the scaffolded assembly) in terms of the analysis they have run. In the results and discussion section, it appears some of the statistics (e.g. 96,529 genes, 216,232 SSRs) still refer to the first draft assembly. The authors have clarified that raw reads were used as input into MaSuRCA (line 111) and have now included the necessary detail for the input and parameterisation of the program. Line 157-159: “Taxonomical analysis of mutant Dendrobium at raw sequence data also revealed limited synteny with its closest Dendrobium catenatum species at below 9% and genetically heterogeneous with outcrossing nature”. Details of how this analysis was done is missing from the methods. It may be more appropriate to perform synteny analysis at the genome level and compare the published D. catenatum genome with the scaffolded Dendrobium hybrid genome.

Editors comment: Additional Editorial Board assessment and feedback was received during the review process.

Reviewer #1 (Public Review):

The present study aims to define the main immune cell subsets found in the hemolymph of the white shrimp, P. vannamei. This is significant because this species is heavily farmed around the world to meet the demand of the human consumption market. Yet, farmed shrimp suffer from infectious diseases and therefore we need to understand how their immune system works to design strategies that decrease infection losses.

Classification of crustacean (and other invertebrates) hemocytes is difficult due to the lack of antibodies to use traditional flow cytometry approaches. Furthermore, hemocyte purification is not easy, cells die and clump, again precluding flow cytometry studies. Thus, the majority of what we know about shrimp hemocytes is based on morphological classification. This study contributes significantly to advancing our knowledge of shrimp Immunobiology by defining hemocyte subsets based on their transcriptional profiles.

Another strength of the paper is that some function in vivo assays (phagocytosis) are presented in an attempt to validate the single-cell data. The authors frame their question or try to frame their question with a more evolutionary angle, such as whether the macrophage-like cell is the evolutionary precursor of human macrophages. I think that this question is not really achievable because the evolution of innate immune systems may have diverged in many branches of the metazoan tree of life. The authors, however, identify gene markers that are conserved in macrophages from shrimp and humans and that is a fair conclusion. There are some methodological caveats to the study and the manuscript needs to be heavily edited to improve language as well as to increase the depth of the interpretation.

In summary, there are interesting findings in this manuscript but the manuscript needs to be significantly improved so that its quality and impact are elevated.

Author Response

Reviewer #1 (Public Review):

The manuscript by Dr Riley and colleagues reports a novel link between molecular clock operative in skeletal muscle and titin mRNA, encoding for essential regulator of sarcomere length and muscular strength. Surprisingly, this clock-mediated regulation of titin occurs at the level of splicing, as demonstrated by SDS-VAGE analyses of skeletal muscle from muscle-specific Bmal1KO mice compared to Bmal1wt counterpart. Concomitant with switch of predominant isoform of titin, skeletal muscle of muscle specific Bmal1KO mice exhibited irregular sarcomere length. Moreover, the authors show that this shift of titin splice is causal for such sarcomere length irregularity and for altered sarcomere length in muscle from the mice with compromised clock function. Importantly, the authors provide compelling evidence that Rbm20, encoding for RNA-binding protein that mediates splicing of titin, is cooperatively regulated by Bmal1-Clock heterodimer and MyoD, via enhancer element in intron 1 of Rbm20, thus identifying Rbm20 as a novel direct clock-regulated gene in the skeletal muscle. Strikingly, rescue of Rbm20 in muscle specific Bmal1KO animals' results in rescue of titin splicing pattern and protein size, suggesting that Rbm20 mediates the regulatory effect of Bmal1 on titin splicing and represents a mechanistic link between the clock and regulator of sarcomere length and regularity.

We thank reviewer 1 for the very kind comments. We agree that the circadian regulation of titin in any capacity is surprising. We are excited about the implications of our work for cardiac muscle and its therapeutic potential in human skeletal muscle.

Reviewer #2 (Public Review):

In this work the authors investigated whether deleting the BMAL1 gene, an integral component of the cellular clock that drives the circadian rhythms of cells, affects the giant protein titin. They report that deleting BMAL1 in skeletal muscle alters the splicing of titin and that this might underlie an increase in sarcomere length dispersion. They show that the effect is through the titin splicing factor RBM20. This work has high novelty and has the potential to add to our understanding of muscle physiology. It is unclear whether splicing of skeletal muscle titin indeed undergoes a circadian rhythm. This could be easily checked using protein gels or RNA seq in muscle samples collected at different times of the day.

We appreciate the question and recognize that our original manuscript did not clearly outline that the circadian clock regulates both rhythmic and non-rhythmic gene expression. In this study, the target of the muscle clock is expression of Rbm20 mRNA which is not a rhythmically expressed gene in muscle. This has now been addressed in the manuscript.

Based on the estimated titin turnover and incorporation rates of titin (Cadar et al., 2014), we do not believe that skeletal muscle titin splicing undergoes a circadian rhythm. However, we believe our data highlights the growing recognition of the molecular clock in regulating non-rhythmic processes. We have added data from a chronic phase advance model of circadian disruption with wildtype mice and identify that disrupted circadian rhythms are sufficient to change Rbm20 expression in skeletal muscle (Figure 5).

This work would be more convincing if the sarcomere length dispersion was investigated in greater detail. Showing this in one muscle type only (TA), in muscles fixed at one length only, and not showing sarcomere length dispersion in the rescue experiment of Figure 6, is rather limited.

We agree that our analysis of sarcomere length dispersion across joint angles would be interesting but we think it is beyond the scope of this study. As noted above, the premise of this study emerged from our early work in which we found that skeletal muscle from 2 different genetic mouse models of circadian disruption, Bmal1 KO mice as well as the Clock mutant mice, exhibit decreased maximum specific force with significant disruptions to sarcomere structure (Andrews et al., PNAS, 107 (44) 19090-19095 2010). The primary focus of this study was to address the mechanistic link between the muscle circadian clock, its transcriptional targets with a focus on sarcomere structure and our first clue was with the expression of titin isoforms. We included analysis of sarcomere length as an outcome measure because it is a fundamental feature of skeletal muscle, it has links to mechanical function and it is a structure that can be modified by titin spliceforms.

A small increase in sarcomere length variation as suggested in Figure 2 is unlikely to have a great functional consequence. If it were, how can muscles that express naturally long titin isoforms (soleus, EDL, diaphragm, etc), function well?

We did not intend to suggest that we see an increase in sarcomere length in Figure 2 and have clarified the figure and text accordingly. The change we see is related to the variability of sarcomere length; we do not see any change in the average sarcomere length. The topic of titin spliceform specialization and the contribution to sarcomere structure and function across different muscle groups (soleus vs. EDL vs. Diaphragm) is a really interesting question but beyond the scope of this study.

Reviewer #3 (Public Review):

This manuscript is using an inducible and skeletal muscle specific Bmal1 knockout mouse model (iMSBmal1-/-) that was published previously by the same group. In this study, they utilized the same mouse model and further investigated the effect of a core molecular clock gene Bmal1 on isoform switching of a giant sarcomeric protein titin and sarcomere length change resulted from titin isoform switching. Lance A. Riley et al found that iMSBmal1-/- mouse TA muscle expressed more longer titin due to additional exon inclusion of Ttn mRNA compared to iMSBmal+/+ mice. They observed that sarcomere length did not significantly change but more variable in iMSBmal1-/- muscle compared to iMSBmal+/+ muscle. In addition, they identified significant exon inclusion in the proximal Ig region, so they measured the proximal Ig length domain and confirmed that proximal Ig domain was significantly longer in iMSBmal1-/- muscle. Subsequently, they experimentally generated a shorter titin in C2C12 myotubes and observed that the shorter titin led to the shorter sarcomere length. Since RBM20 is a major regulator of Ttn splicing, they determined RBM20 expression level, and found that RBM20 expression was significantly lower in iMSBmal1-/- muscle. The reduced RBM20 expression was regulated by the molecular clock controlled transcriptional factor MyoD1. By performing a rescue experiment in vivo, the authors found that rescue of RBM20 in iMSBmal1-/- TA muscle restored titin isoform expression, however, they did not measure whether sarcomere length was restored. These data provide new information that the molecular cascades in the circadian clock mechanism regulate RBM20 expression and downstream titin isoform switching and sarcomere length change. Although the conclusion of this manuscript is mostly supported by the data, some aspects of experimental design and data analysis need be clarified and extended.

Strengths:

This paper links the circadian rhythms to skeletal muscle structure and function through a new molecular cascade: the core clock component Bmal1-transcription factor MyoD1-RBM20 expression-titin isoform switching-sarcomere length change.

Utilization of muscle specific bmal1 knockout mice could rule out the confounding factors from the molecular clock in other cell types

The authors performed the RNA sequencing and label free LC-MS analyses to determine the exon inclusion and exclusion through a side-by-side comparison which is a new approach to identify individual alternative spliced exons via both mRNA level and protein level.

We agree that the side-by-side analysis from RNAseq and LC-MS data are novel and provides a foundation for others wanting to study both titin mRNA and protein. In this version, we have expanded this work to include samples from our Rbm20 rescue model (Figure 6). Similarly, to our approach in the muscle specific Bmal1 knockout model, these results confirm our RNA-seq results and indicate that LC-MS is a suitable method to measure titin protein isoform. We note that while more work is needed to confirm the broad utility of the LC-MC approach, it may be a suitable alternative to RNA-seq for measuring region-specific, and possibly exon-specific, changes in titin isoform expression.

Weaknesses:

Both RBM20 expression and titin isoform expression varies in different skeletal muscles. The authors only detected their expression in TA muscle. It is not clear why the authors only chose TA muscle.

The reviewer, like Reviewer 2, raises a good point about muscle specificity as this is a significant challenge for research in the field of skeletal muscle. As we noted above, our primary focus was on the TA because our goal was to study the molecular links between the muscle circadian clock and titin expression with inclusion of analysis of a structural outcome, sarcomere length variability. This muscle is well suited for the combination of approaches employed. We recognize the limits of using a single muscle, but we note that the we used ChIPseq data that provided the initial clues that CLOCK and BMAL1 bind to a site within intron 1 of the Rbm20 gene came from gastrocnemius and not TA muscle samples . Our targeted ChIP-PCR confirms that CLOCK and BMAL1 bind to the same intron 1 location from TA muscle samples. In addition, we have included data from quadriceps and TA muscles in our chronic jet lag model in which we use an environmental manipulation to disrupt the muscle clocks. We believe that the edits to the text and inclusion of this data strengthen and extends our findings to other muscles through circadian disruption and not only a genetic knockout model.

The sarcomere length data are self-contradictory. The authors stated that sarcomere length was not significantly changed in muscle specific KO mice in Line 149, however, in Line 163, the measurements showed significantly longer in muscle specific KO muscle. The significance is also indicated in Figures 2C and 3B.

We apologize for the miscommunication. The significance indicated in Figure 2C refers to the significant difference in variability of sarcomere length and not a significant difference in sarcomere length. The difference in Figure 3B is to indicate a slightly longer but significantly different from control sarcomere length, but also a significant difference in sarcomere length variability. To make this difference clear, we have changed the symbol for significantly different variability from * to # in both Figures 2C and 3B. We hope this clarifies our findings.

Manipulating titin size using U7 snRNPs linking to the changes in sarcomere length and overexpressing RBM20 to switch titin size are the concepts that have been proved. These data do not directly support the impact of muscle specific Bmal1 KO on ttn splicing and RBM20 expression

We agree that the use of U7 snRNPs does not directly support the impact of muscle specific Bmal1 KO on titin splicing and RBM20 expression; however, that was not the goal of this set of experiments. Several papers have recently indicated titin’s role as a sarcomeric ruler (Tonino 2017, Brynnel 2018), but none of them have investigated the proximal Ig domain that we identified as regulated by the circadian clock disruption. Because of this, we thought it necessary to show this region specifically contributes to sarcomere length using our cell culture model. Further, we think this point strengthens our study as it suggests that in the absence of a clock effect, altering the proximal Ig domain of titin directly alters sarcomere length adding to the growing evidence base that titin acts as a sarcomeric ruler. We have edited the text of the results and the discussion to clarify this point.

There is no evidence to show if interrupted circadian rhythms in mice change RBM20 expression and ttn splicing, which is critical to validate the concept that circadian rhythms are linked to Ttn splicing through RBM20.

We recognize this concern and have performed a new study in which we used a model of chronic jet lag in normal adult C57BL6 mice as a model to disrupt the muscle clock (Wolff, Duncan and Esser, JAP 2013). This new data has been added in Figure 5 and shows that by altering the lights on: lights off schedule every 4 days for 8 weeks, mimicking repeated jet lag, we disrupt Rbm20 expression in TA and gastrocnemius muscle (note, this is new data for both the muscle and clock fields). Concomitant with changes in clock gene expression we reported in 2013, we found that mRNA expression of Rbm20 is altered as well. These findings confirm that normal muscle clock disruption is sufficient to alter expression of Rbm20.

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

This is already a full revision, not a revision plan. All points were carefully addressed. TMF

July 28, 2022

RE: Review Commons Refereed Preprint #RC-2022-01555

Dear Dr. Fuchs,

Thank you for sending your manuscript entitled "Dissecting the invasion of Galleria mellonella by Yersinia enterocolitica reveals metabolic adaptations and a role of a phage lysis cassette in insect killing" to Review Commons. We have now completed the peer review of the manuscript. Please find the full set of reports below.

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

In this manuscript Saenger et al. concentrate on the pathophysiological details of insect larvae infection by Yersinia enterocolitica. The authors studied the colonisation, proliferation, tissue invasion, and killing activity of the bacteria in Galleria mellonella larvae. Their study provides valuable evidence for the biological relevance of Tc toxins and a neighboring holin-endolysin cassette during establishment of Y. enterocolitica infection in Galleria mellonella larvae through the oral route. The findings of the authors provide important novel insights, that can be used for the development of Tc toxins as biopesticides.

In general, this is a nice study. The data and the methods are presented well so that they can be reproduced and the key conclusions convincing.

Unfortunately, the manuscript is sloppily written in some places, including grammatical and formatting errors. Citations regarding the structure and mechanism of action of Tc toxins are arbitrarily chosen, often taking the wrong ones and important aspects are left out. I highly recommend that the authors read the review of Roderer and Raunser 2019 that nicely describes and summarizes the molecular mechanism of Tc toxins.

Answer: We have now improved the writing of the manuscript and corrected several errors and typos. In particular, the review by Roderer and Raunser, as well as other literature in the field, is now considered and cited in the text.

The abstract ends with a speculation: "Suggesting that this dual lysis cassette is an example for a phage-related function that has been adapted for the release of a bacterial toxin" - this is likely true, but not proven in this work. What if it is used for the release of something else like extracellular DNA needed for biofilm formation (see https://doi.org/10.1038/ncomms11220)?

Answer: This sentence was carefully written as a hypothesis strengthened by the data obtained in our study. Experimental evidence for this assumption is the strong correlation of toxin and HE cassette phenotypes of mutants (see abstract), the highly conserved localisation of the cassette within Tc loci of distinct bacterial genera (see discussion for literature), and the synchronic regulation of both the toxin and the lysis genes (manuscript in preparation). Moreover, strain W22703 is unable to form biofilms in contact with invertebrates (Spanier et al., AEM 2010). There, also in accordance with other reviewers, we would like to keep this statement in the text. However, to address this interesting point, we now mention the finding of Turnbull et al. in the discussion (see last paragraph).

In addition to that, several outstanding issues must be addressed:

1. Line 45 3-D structural analysis of the tripartite Tc suggests a 4:1:1 stoichiometry of the A, B and C subunits, with the A subunit forming a cage-like pentamer that associates with a tightly bound 1:1 sub-complex of B and C. This is wrong. The stoichiometry is 5:1:1 and the structure is not a cage. The statement was taken from citation 3. However, citation 3 should not be used, since the stoichiometry as well as the structure that was determined there is wrong. Use Landsberg et al. 2012 PNAS, Gatsogiannis et al. 2013 Nature instead.

Answer: We apologize for misunderstanding the literature. Reference Lee et al. was removed here, and the two papers plus Meusch et al. (Nature, 2014) are now cited. The stoichiometry was corrected, “cage” was removed.

"Few bacteria are known to successfully colonize and infect invertebrates" - needs a reference.

Answer: This was modified to “Several bacteria…”, and we cite the recent paper by Weber and Fuchs (in press) that in Table 7g lists more than 40 bacterial species pathogenic towards insects.

"Their oral insecticidal activity is comparable to that of the Bacillus thuringiensis- (Bt)- toxin" - reference missing.

Answer: The reference is now cited (Bowen et al., Science 1998). Please see the last paragraph of the paper.

"Type a, type b and type c" subunits is not usual for the literature. Please use TcA, TcB, TcC. A-, B-, and C-components should be abbreviated as TcA, TcB and TcC respectively in order to be in line with recent literature on the topic.

Answer: This was corrected accordingly.

Is TccC an ADP-ribosyltransferase or does it have a different biochemical activity?

Answer: This is unknown with respect to the Tc of Y. enterocolitica. In the introduction, we now refer on P. luminescens and do not further attribute such a function to the TcC of Y. enterocolitica. In the abstract, we replaced “ADP-ribosylating” with “toxic”.

"The toxic and highly variable carboxyl-terminus of TccC that has recently been demonstrated to ADP-ribosylate actin and Rho-GTPases" - this is only certain for TccC3 and TccC5 from P. luminescens. There are many such C-termini, called HVRs which have not had their activities determined yet, see here: https://doi.org/10.1371/journal.ppat.1009102

Answer: We agree and cite this article. See also the response to comment 5 above.

"is probably followed by receptor-mediated endocytosis" - more recent references exist for the receptor binding of Tc toxins.

Answer: We added two references pointing to glycans as receptors of the Tc (line 52).

"A pH decrease then triggers the injection of a translocation channel formed by the pentameric TcaA subunits into the endosomal vacuole, followed by the subsequent release of the BC subcomplex into the cytosol of the target cell" - this again is incorrect. Please read the above mentioned review and correct this passage accordingly.

Answer: We agree. This phrase was rewritten to “The attachment of the Tc to the host cell membrane is either followed by receptor-mediated endocytosis or release of the ADP-ribosyltransferase into the target cell {Landsberg, 2011 #738;Sheets, 2011 #742}{Meusch, 2014 #788}. In a pH-dependent manner, the TcA translocation channel injected into the membrane of the host cell. Conformational changes then allow the toxic component to be released into the translocation channel of TcA and from there into the cytosol {Meusch, 2014 #788}{Roderer, 2019 #871}.” (Lines 51-56)

What is meant by "environmental Yersinia species"?

Answer: This was corrected to “…and in Y. mollaretii.”

In the relevant W22703 pathogenicity island sequence (https://www.ncbi.nlm.nih.gov/nuccore/AJ920332) previously submitted by the same group, something odd is going on with the TcA component: it appears to be split into three polypeptides (tcaA, tcaB1, tcaB2). In the manuscript you state TcA is made up from only tcaA and tcaB. Could you please address this?

Answer: Shotgun sequencing was performed 15 years ago, and mapping revealed a frameshift within tcaB that resulted in the split annotation of tcaB. Even if this frameshift is not the result of a sequencing error, it obviously does not result in Tc inactivation. As this frameshift was not identified in most other Tc-PAI of yersiniae, we assume our statement to be correct.

"And their products were recently shown to act as a holin and an endolysin, respectively" - missing reference.

Answer: The reference is now cited (Springer et al., JB 2018).

"Its Tc proteins are produced at environmental temperatures, but silenced at 37{degree sign}C." versus "Remarkably, HolY and ElyY lyse Y. enterocolitica at body temperature, but not at 15{degree sign}C". Please address the issue that HolY/ElyY lyse the bacteria at temperatures where Tc proteins are not produced.

Answer: In the absence of in vitro conditions activating the HE gene cassette, we used the pBAD system to artificially overexpress the two genes and showed cell lysis at 37°C, but not at 15°C (Springer et al., JB, 2018). This finding points to a lack of cell lysis as prerequisite for TC release and strengthens the hypothesis of a new secretion system as now corroborated in the last paragraph of the discussion. To avoid confusion of readers, the sentence was removed from the manuscript.

"Nematodes, which are easily maintained in the laboratory without raising ethical issues, have successfully been used to identify virulence-related genes in a broad set of bacterial pathogens" - what is the relevance of this for the current manuscript?

Answer: Invertebrates are introduced here as infection models. Nematodes are mentioned here for two reasons: yersiniae are nematocidal due to the Tc, and their immune system is less elaborated than that of G. mellonella, thus explaining its preferred use as insect model. We shortened the sentence by deleting the phrase in commas.

Fig. 1C - no description is given for the labels 1-8.

Answer: This is given below figures 1E-H. The labels are valid for all figure panels to ease reading.

"The hemolymph of these cadavers was found full of Y. enterocolitica cells" - injected CFUs are provided here, but not final CFUs in the cadavers (although referred to in a later section). Please address this.

Answer: These were preliminary experiments to identify the optimal infection dose. Hemolymph content was plated, but cell numbers in the hemolymph were not enumerated. This sentence therefore now reads: “…and the hemolymph of these cadavers contained Y. enterocolitica cells.” (lines 113-114).

What is the inducing agent used for pACYC-tcaA and pACYC-HE? Why would "slight leakiness of the pBAD-promoter" make pBAD-tccC non-inducible? Were colonies taken from the cadavers to verify that the bacteria still contained these plasmids?

Answer: Within pACYC, the genes tcaA and hlyY/elyY (HE) are under control of their own promoters as indicated in Table S2. In general, pACYC vectors are often and successfully used for complementation due to middle copy number.

This now reads “Due to the slight leakiness of the pBAD-promoter, arabinose was not added to further induce tccC transcription.” (lines 133-134).

The presence of the plasmids in vivo was confirmed by periodic plating on selective and non-selective plates, not revealing differences in cell numbers.

Can the authors please address the TD50 of 1.83 days for W22703 ΔHE/pACYC-HE versus 3.67 days for WT bacteria? This would mean that the former kill larvae twice as fast as usual. I would not call this "did not significantly differ in their insecticidal activity".

Answer: This statement is indeed not very intuitive given the variations of the TD50-values. However, the significance here (and elsewhere in the text) is based on a statistical calculation. For the Kaplan-Meier-plot, we used an application (K.T.Bogen, Advances in Molecular Toxicology, 2016; Exponent Health Sciences, Oakland, CA, United States; Johann Kummermehr, Klaus-Rüdiger Trott, Stem Cells, 1997; Academic Press, London, San Diego) based on all data of a graph. However, to consider this point and to not confuse the readers, the phrase was modified to “…did not significantly differ in their insecticidal activity from that of the parental strain W22703 after one week, demonstrating…” (lines 135-138).

Fig. 2 is missing survival data for larvae infected with tcaA, HE, and tccC KO bacteria.

Answer: These data are shown and are equal to the LB-control, e. g. the survival rate of larvae infected with strains W22703 lacking HE, tcaA, or tccC were 100%.

"And a slight colouring of some of the larvae from one h p.i. on (data not shown)" - best show the data or remove this statement.

Answer: Although we observed this phenomenon regularly, monitoring and documentation cannot be provided and would not substantially strengthen the manuscript. We therefore deleted this phrase.

The infection of larvae by W22703 ΔtccC/pBAD-tccC is missing, the other bacterial variants are present. Please address this.

Answer: Infections with W22703 DtccC are not shown to not overload the figure, please see the panel below. W22703 DtccC/pBAD-tccC infections have not been documented by photos. Figure legend 3 now reads “Infections with W22703 DtccC and DtccC/pBAD-tccC are not shown.”

"initially proliferated from an application dose of 4.0 × 105 CFU and 4.0 × 105 CFU, respectively, to 2.2 × 106 CFU and 2.8 × 106 CFU, but could not be detected from day three on. This finding strongly suggests that TcaA is involved in adherence to epithelial cells and thus in midgut colonization". Please address the "initially proliferated" (which day post-infection?), their elimination from the larvae (how, why?), why the tccC KO bacteria were more virulent than tcaA KO bacteria, and where the suggestion about TcaA involvement specifically in adherence comes from.

Answer: “initially proliferated” was rewritten to “proliferated within the first day p.i.”. (line 163)

Elimination: This now reads “…was completely absent six days p.i., probably due to passage through the gut followed by excretion”. (lines 161-162)

In our view, the tccC knockout mutant is not more virulent than W22703 DtcaA (se Fig. 2), but replicates during the first day post infection, whereas the cell numbers of the tcaA KO mutant strongly decrease already within the first 24 h p.i.. This prompted us to speculate that Tc is involved in two infection steps, e.g. adherence and hemocyte inactivation. For clarity, this sentence was modified to: “This discrepancy suggests that TcaA is involved in adherence to epithelial cells and thus in midgut colonization, without requiring TccC.” (lines 165-166)

In Fig. 4, the CFUs for W22703 ΔtccC/pBAD-tccC are essentially the same as for the other rescued KOs and WT, while in the text a point about weaker growth is made. Is this justified? Also, even though the CFU data is present here, data on infection of larvae by W22703 ΔtccC/pBAD-tccC is missing unlike the other bacterial variants. Please explain.

Answer: We agree that this part of the results is misleading. We want to stress that the complementation very well restores the phenotype of the wildtype. The weaker growth of DtccC may be due to the distinct vector system used here. This part was there shortened and rephrased to: “When larvae were infected with 4.0 × 105 CFU of the DtcaA and DHE mutants, and with 1.4 × 106 CFU of strain W22703 DtccC/pBAD-tccC, all of which carrying the deleted genes on recombinant plasmids, the bacterial burden at days one to six p.i. increased approximately to that of the parental strain W22703 applied with 9.0 × 105 CFU, indicating a successful complementation of the gene deletions.”

” (lines 166-170).

Missing data on W22703 ΔtccC/pBAD-tccC infection in Fig. 3, please the answer to point 20 above.

Fig. 6b - The presence of an anti-RFP signal is not obvious in any of the bottom row images. The top row images are missing the same kind of annotation provided for Fig. 6a, without which non-histologists will find understanding the figure difficult.

Answer: The anti-RFP signal is visible only on the left photo of the bottom panel, and not in the other three photos as explained in the text. We understand that the signals are not very strong, but they are visible on the screen.

"In the absence of the lysis cassette, however, TcaA::Rfp was not detected despite the presence of W22703 ΔHE tcaA::rfp cells." + "To test whether or not the promoter of the lysis cassette is active in vivo, we infected G. mellonella larvae with strain W22703 PHE::rfp. Although Y. enterocolitica cells densely proliferated within the hemolymph (FIG. 6B), no staining signal that would point to the presence of TcaA was obtained, possibly due to no or weak PHE activity." Does this mean that without HE, tcaA does not express?

Answer: No, we performed Western Blots showing that TcaA is detected in cells lacking HE. Therefore, a negative feedback regulation (e. g. increasing intracellular amounts of TcaA repress its own transcription) can be excluded. This is also in line with the low transcriptional activity of the lysis cassette in vivo (new Fig. S1B).

"These data suggest that the HE cassette is responsible for the extracellular activity of the insecticidal Tc." Please explain how the preceding paragraph leads to this conclusion.

Answer: This was poorly written and now reads “…for the transport…” (line 224).

"As expected, bacterial cells, e.g. Y. enterocolitica, are visible in the hemolymph obtained from W22703-infected animals, but not in all other preparations." - which figure are the authors referring to?

Answer: We have indeed identified, but not immunostained, bacterial cells in those preparations, but they are not visible in Fig. 7. This sentence was removed. However, the presence of W22703, but not its tc-PAIYe-mutants, in the hemolymph is demonstrated in Fig. 6A.

"To delineate the transcriptional profile of Y. enterocolitica during infection of G. mellonella, we applied immunomagnetic separation to isolate Y. enterocolitica from the larvae 12 h and 24 h after infection" - do the authors store the bacteria for up to 24 h at 4 {degree sign}C, as indicated in the methods section?

Answer: Yes, the probes were stabilized with RNAlater and then stored up to 24 h to synchronize all samples of one experiment.

"The endolysin located within Tc-PAIYe was significantly up-regulated after 24 h, but not after 12 h, pointing to its possible role in the release of the Tc" - I could not find the endolysin in Table S1. Could the authors mark it clearly? Also, why is the holin also not upregulated?

Answer: The endolysin gene is lacking in Table S1 due to its FC=1.02. We now added a table to Fig. S1 that shows the FC values of all genes from Tc-PAIYe. The FC-value of holin gene is 0.87, thus pointing to a very slight transcription of this lysis gene as discussed, thus preventing cell death.

"This is in line with the fact that a T3SS is lacking in strain W22703" - Is a complete genomic sequence available for this strain, so readers could validate this statement?

Answer: The genome sequence is available, and the reference is now cited (line 358). The common virulence plasmid of yersiniae, pYV that encodes the T3SS, is missing in this strain. We do not mention here the presence of a second, but probably incomplete, chromosomally encoded T3SS in strain W22703 do not overload the manuscript.

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

This is a very, very nice study as it actually describes the role of different Tc toxin components in a model infection system using an important bacterium- really for the first time in a properly controlled manner. The mutants lacking either the syringe (AB) or the bullet (C) make 'sense' for a loss of function perspective. The description of the phage cassette in loss of function is also interesting and could do with some more speculation? For example, some groups of Photorhabdus bacteria release their oral toxicity (Tc's) into their bacterial supernatants- whereas in others it remains cell associated. The likely role of this phage cassette in this process should be discussed (is cell suicide required for release?).

Answer: We now discuss the possibly role of the lysis cassette in more detail, including the possibility that a subpopulation commits cell suicide (see lines 375-396).

Reviewer #2 (Significance (Required)):

This is highly significant finding as despite all of the very elegant structural studies done on these important toxins there is still very little work in vivo. These studies clearly show the role of the different components of these ABC toxins in vivo. It should be published with priority.

Congratulations to the authors.

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

Summary: The authors analyze the phases of infection of Galleria mellonella by Yersinia enterocolitica following forced oral feeding. They study different phases of infection, including survival within the gut and invasion of the hemolymph. By analyzing differences in the genes up- and down regulated, they show that for example transporters for food sources from the hemocoel are regulated for making those sources available for the bacteria.

Major comments: This is an interesting paper demonstrating genes of Y. enterocolitica dependent for colonization, growth and crossing of the epithelial gut barrier in G. mellonella.

Major points which have to be addressed:

Introduction: line 54: the BC subcomplex is not released into the cytosol! It is only the hypervariable region (enzymatic part) which enters the cytosol. This has to be corrected.

Answer: This has been corrected accordingly.

Fig.2/3: Why have different CFU been used for the distinct bacterial strains? This does not allow a direct comparison of their toxicity. For me the dead larvae shown in Fig. 3 are not represented in Fig 2 (data are not concordant), because of the loss before day one depicted in Fig. 2: The curves should be normalized to the same starting point (should be 100 %)?

Answer: We would like to stress here that infection doses are hard to reproduce if frozen and diluted stocks are used. We decided for overnight culture to better mimic natural conditions and controlled each culture for its viable cell numbers by plating. Moreover, we choose the infection doses in a conservative manner, e.g. the number of mutants was higher than that of the parental strain.

The data of Fig. 3 are concordant with Fig. 2 for two reasons: First, this experiments was performed in replicates with a total of 36 larvae per strain (see Fig. 2 legend), so that representative photos are shown. Second, larvae were considered dead if they failed to respond to touch, and many larvae without strong sign of melanisation were already killed.

We analysed the algorithmus of the Kaplan-Meier-plot. All graphs start at 100%, this is now mentioned in the legend. There are no data between day 0 and day 1, and a stepwise graph is essential for this plot.

Fig. 3: Why is the strain W22703 delta tccC/pBAD - tccC missing in the data set?

Infections with W22703 DtccC are not shown to not overload the figure, please see the panel below. Answer: W22703 DtccC/pBAD-tccC infections have not been documented by photos. Figure legend 4 now reads “Infections with W22703 DtccC and DtccC/pBAD-tccC are not shown.”

Minor: line 221: "the" is doubled

Answer: This has been corrected accordingly.

Reviewer #3 (Significance (Required)):

The manuscript shows the use of G. mellonella as a straight foreward method to study gene functions of pathogenic bacteria, a significant knowledge for scientists of the field.

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

Summary: Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

Answer: There are already three sections that summarize the results and the methods applied, namely the abstract, the last paragraph of the introduction, and the conclusion following the discussion. In our view, a further summary would overload the manuscript. Nevertheless, depending on the journal the manuscript will be published in, an additional authors´ summary would be provided.

Outlines proposed role of lysis cassette in oral infection of Galleria as a model insect for host pathogen interaction, data which is fortified through use of histology and RNAseq.

Introduction could extend to additional background eg Aleniz et al and other entomopathogen transcriptome data, more so other studies using Yersinia and Galleria as a model (refer references provided in the below comments)

Answer: We again carefully screened PubMed for studies in the field and added few papers. However, in vivo transcriptome analyses are still rare, as indicated by a lack of a respective investigations with the highly relevant entomopathogen Photorhabdus luminescens. The literature suggested by the reviewer is now cited in the introduction and the discussion (see below for details).

The strength of the paper lies in understanding the progression of the disease in the insect host as mentioned L316-317 and clearance of the bacteria via in TcaA mutant

Major comments: - Are the key conclusions convincing? Yes for mode of action Fig 5 could have additional panels -this is a strength of the paper

Answer: We agree that this time course is a strength of the paper, and we carefully selected representative photos. There are several to be shown, but to our view, they are rather illustrative than providing a substantial additional value.

Fig 6 legend could better describe the observed insect components

Answer: The insect components are now indicated in Fig. 6B and in Fig. 5.

Figure 7 may be lost in PDF conversion -the figure appears un resolved? are there more high resolution photos

Answer: Fig. 7 was present in the merged PDF provided by the publisher. We used the photos with the best resolution.

• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? the data provided is in places rudimentary (i.e. validation of the role of the lysis cassette in virulence) and could be bolstered with the construction and use of a lysis translational reporter etc I was left unsure how the HE::rfp and TcA::rfp constructs were made. I had assumed red florescent protein however it appears an antibody is used. This needs to be clarified as I then found it hard to interpret the results.

Answer: The transcriptional PHE::rfp fusion is mentioned in the results section, but immunostaining failed probably due to a very low promoter activity (line 223). This is well in line with the transcriptome data. Please see a detailed answer how the HE::rfp and tcaA::rfp were constructed below. We applied the RFP-antibody for two reasons: first, fluorescence microscopy did not reveal clear red fluorescence in the tissue sections, and second, a TcaA antibody failed to match quality criteria for this purpose.

It appear l114-125 that their may be enough data to derive a LD50 values and or LT value at a fixed dose - if so reporting this data of interest. It may also allude as to why a 10e5 dose was selected for subsequent expts

Answer: This is an interesting point. The LD50 (dose of cells that kills 50% of all larvae) is usually not calculated in publications in this field of research, because its calculation requires a very huge separate data set that cannot be used to answer the questions addressed here. Such a dat set is not available. We published the dose-dependent toxicity of Y.enterocolitica W22703 upon subcutaneous injection, and from these data, we determined a LD50 for this strain of approximately 2 x 104 cells. The paper is cited in our manuscript. The 10E05 dose was selected due to our preliminary work and the reproducibility of the experimental phenotypes.

• 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. Use of lysis the reporter - discuss commonalties of the in host transcriptome with other Yersinia Galleria systems eg Paulson etc al (refer below). Are there any thoughts on the host range of this Yersinia and can this be placed in a pathogen host evolutionary context?

Answer: Paulson et al. are now cited twice in the text. The host range of Yersinia enterocolitica has not been investigated to our knowledge. However, its nematocidal activity has been described by Spanier et al., and Manduca sexta larvae, the tobacco hornworm, is also killed by W22703 (see references). Moreover, there are two copies of tccC in the genome of strain W22703 encoding the cytotoxic Tc subunit with its hypervariable C-terminus that is assumed to contribute to host specificity. This is discussed in very detail by Song et al. (see references).

Evolution: Yes, this has been addressed by Waterfield et al. 2004 (see references) where insects are hypothesized as a source of emerging pathogens. We placed our findings in the context of this article in lines 91-94 and 305-310.

• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. Yes

• Are the data and the methods presented in such a way that they can be reproduced? yes but I think some vector construction methodology is missing e.g. ::rfp (refer above)

Answer: The plasmids used to construct the two strains W22703 tcaA::rfp and W22703 PHE::rfp are listed in Table S2. References for details are given (Starke et. al., 2013, Starke and Fuchs, 2014). Briefly, we used a suicide vector (pUTs) carrying the gene encoding the red fluorescent protein (RFP). This vector replicates in E. coli helper strains such as SM10, but not in Y. enterocolitica. Strain SM10 is now listed in Table 2. Following conjugation, the construct is chromosomally inserted upon recombination via the fragments cloned into the plasmid. In case of tcaA, we cloned the 3´-end of the gene to generate a translational fusion, and in case of HE its promoter, resulting in a transcriptional fusion with the reporter RFP.

Fig 2 I am a little lost mortality seems quick on day 0 is this a result of aberrant injection damage mortality or are the authors observing a different effect across mutants through the initial 24 hours? If data available could this time plot be extended out 0-24 hours. The dash used for W222703 tcaA /TccC look similar can a different symbol be used.

Answer: The reviewer is right that the mortality is high on the first day. However, larvae monitoring for up to nine days is a standard in the literature. No data are available for a better resolution of the first 24 h that, however, were investigated in more detail in the time course of Fig. 5. Moreover, we observed changes in motility and colouring of some of the larvae from one h p.i. on (data not shown). Aberrant injection damage was avoided, and damaged larvae or larvae that not completely took up the infection solution were not further considered in the experiment. This is mentioned in lines 107-109.

A different symbol is now used for W222703 DtccC /pBAD-tccC.

• Are the experiments adequately replicated and statistical analysis adequate? Yes

Minor comments: - Specific experimental issues that are easily addressable. - Are prior studies referenced appropriately? Other entomopathogenic transcriptome studies could be compared to and or cross referenced (I have provided references in the response

Answer: Repetition of our answer above: We again carefully screened PubMed for studies in the field and added few papers. However, in vivo transcriptome analyses are still rare, as indicated by a lack of a respective investigations with the highly relevant entomopathogen Photorhabdus luminescens. The literature suggested by the reviewer is now cited in the introduction and the discussion (see below for details).

I am unsure on the use of immuno pulldown and efficiency of recovering the Yersinia using this method as opposed to direct sequencing total RNA has this method been used in other systems,

Answer: Isolating RNA from in vivo probes of infected insects encounters two challenges: first, a possible contamination with commensal bacteria, and a too high amount of host RNA that reduces the number of sequence reads. This might be the reason for the relatively low sequence depth found in related papers in the field of in vivo transcriptomics. We overcame these problems by immunomagnetic separation that is easily applicable and enriches the samples with respect to Yersinia cells, this is now mentioned in the results. We also cite a study (Prax et al., in which we established the protocol of IMS.

• Are the text and figures clear and accurate? Yes though in places better naming of insect components could be listed

Answer: This was done, see above.

• Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

As listed above potential use of reporters and or comparison and transcriptome analysis to other systems and an evolutionary pathogen host context (refer comments above) would strengthen the manuscript

Answer: Please see answer to comments above. We explained the use of the reporter fusions, and put the transcriptome analysis into the context of related studies.

Minor comments as per below When first mentioned good to state the larval instar used

Answer: We used larvae of instar 5-6 according to Jorjao et al. (2018), this is now mentioned and cited in the M&M section, line 434.

l 78 lon protease? what type? this is an important SOS protease affecting many regulatory systems please clarify

Answer: This is a Lon A endopeptidase, and its function for the temperature-dependent activity of the lysis cassette has ben described (Springer et al. 2021, see references). Its relevance for the thermodependent regulation of Yersinia virulence has been documented by Herbst et al. (PMID: 19468295) and Jackson et al. (https://doi.org/10.1111/j.1365-2958.2004.04353.x).

l103-113 an description of the elemental tract which is depicted, perhaps this could be placed in the Fig. 1 figure legend

Answer: We agree and substantially shortened the first paragraph of the results. Relevant aspects are now mentioned in Figure legend 2, redundancies with the figure legend were removed.

l 133 use of the word larvae in place of the word animals might be more appropriate

Answer: This was corrected accordingly.

l 133 clarify delta HE mutant description when first mentioned

Answer: The abbreviation HE is now introduced in the introduction in line 74.

Lines 220-234 hard to follow mainly as I am unsure how then strains are constructed, perhaps clarify what rfp is how was it made :: demotes and insertion but yet then they seek to detect TcaA? I could not find the methodology on its or HE::rfp construction

Answer: The plasmids used to construct the two strains W22703 tcaA::rfp and W22703 PHE::rfp are listed in Table S2. References for details is given (Starke et. Al., 2013, Starke et al. 2014). Briefly, we used a suicide vector (pUTs) carrying the gene encoding the red fluorescent protein (RFP). Following conjugation, the construct is chromosomally inserted upon recombination via the fragments cloned into the plasmid. In case of tcaA, we cloned the 3´-end of the gene to generate a translational fusion, and in case of HE its promoter, resulting in a transcriptional fusion with the reporter RFP.

Please see above why we used RFP-antibodies to detect TcaA.

l247 immuno-magnetic separation to isolate Yersinia - is there an efficiency behind this method, might be good to mention (I am unfamiliar with this technique)

Answer: We here repeat our answer to the point above: Isolating RNA from in vivo probes of infected insects encounters two challenges: first, a possible contamination with commensal bacteria, and a too high amount of host RNA that reduces the number of sequence reads. This might be the reason for the relatively low sequence depth found in related papers in the field of in vivo transcriptomics. We overcame these problems by immunomagnetic separation that is easily applicable and enriches the samples with respect to Yersinia cells, this is now mentioned in the results. We also cite a study (Prax et al., in which we established the protocol of IMS.

l313 alludes to role of Tca in hemoceol which contradicts an earlier statements in l 130 please clarify

Answer: The reviewer is right. The sentence in former line 130 (now lines 123-124) was corrected to “…suggesting that the Tc plays a main role in the initial phases of infection”. This statement does not exclude its activity towards hemocytes. Moreover, subcutaneous infection is very artificial and was therefore replaced by oral application in our study to mimic natural routes of infection. This is now elaborated in more detail in the discussion (Lines 305-310).

For clarity table 1 could colour highlight (different colours) tc and lysis genes

Answer: We now added a table to Fig. S1 that shows the FC values of all genes from Tc-PAIYe.

CROSS-CONSULTATION COMMENTS I am in agreement with all points of reviewer 1 who has a clear understanding on Tc toxin composition TcA pentamer etc. Being familiar to the field I regret I did not pick up on these errors

Answer: This has been corrected according to R1.

Point 13 agree and should possibly bring in other researchers who have used Galleria as a model. It also needs to be kept in mind that the target host for many Tcs has yet to be determined hence the importance of oral activity of this isolate

Answer: This has been corrected according to R1.

I am similarly in agreement with comments of reviewer 3

Reviewer 4 I over looked the LT50 data -- apologies but agree with reviewer 1 where WT should be the more potent strain --I still think if possible LD50 for WT would be of value more so to define its oral activity

Answer: We repeat our answer from above. This is an interesting point. The LD50 (dose of cells that kills 50% of all larvae) is usually not calculated in publications in this field of research, because its calculation requires a very huge separate data set that cannot be used to answer the questions addressed here. Such a dat set is not available. We published the dose-dependent toxicity of Y.enterocolitica W22703 upon subcutaneous injection, and from these data, we determined a LD50 for this strain of approximately 2 x 104 cells. The paper is cited in our manuscript. The 10E05 dose was selected due to our preliminary work and the reproducibility of the experimental phenotypes.

Reviewer #4 (Significance (Required)):

SECTION B - Significance ========================

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

Extends from work of Fuchs - research group Extends from work of Palmer et al on lysis cassettes as potential T10SS Extends from work off Vesga Pseudomonas and Paulson Yersinia(refs provided below) on insect transcriptomics

Of interest and possibly understated is the oral activity of enterocolitica in the insect host as mentioned L316-317 and how this might relate to the lifestyle/evolution of this microbe further elaboration here would be of interest

Answer: We agree that this is an important aspect. Therefore, we added the following sentences here: “In contrast to subcutaneous injection in the use of insect larvae as model for bacterial virulence properties towards mammals, oral application mimics natural routes of infection that in particular take place during the bioconversion of animal cadavers by bacteria, fungi, and larvae {Carter, 2007 #879}. Together with the broad cytocidal host spectrum of bacterial toxins {Mendoza-Almanza, 2020 #880}, investigation of yet neglected natural infections of invertebrates will contribute to a better understanding of microbial pathogenicity {Waterfield, 2004 #480}.” (lines 305-310)

• Place the work in the context of the existing literature (provide references, where appropriate).

Relevant Transcriptome papers which could be referred to in the discussion i.e. are similar genes in play or is their a point of difference? https://doi.org/10.1093/g3journal/jkaa024;https://doi.org/10.1038/s41396-020-0729-9; https://doi.org/10.1099/mic.0.000311

Answer: Paulson et al. mainly address virulence factors, whereas metabolism is not uncovered. We now cite similarities with respect to hemolysis and iron scavenging. The focus of Vesga et al. is on the interaction of a plant pathogen with wheat and two insect hosts, including their transcriptome. Although metabolic details are missing, there is an interesting overlap with the paper by Vesga et al. (hemocoel as permissive environment for proliferation) and a difference (upregulation of chitinases was not observed) that are now cited in the discussion. The Alenzi paper mainly investigated the general virulence of Y. enterocolitica strain. We cite its finding on the importance of motility, thus confirming our transcriptome analysis.

• State what audience might be interested in and influenced by the reported findings. The oral activity of enterocolitica towards Galleria of interest and an evolutionary context insect vs mammalian activity in the discussion could be provided. Potential role of TcaA in gut association For the targeted journal I feel additional technical data is required and a broader context to other global systems (bacterial species) provided

Answer: All points were addressed carefully and in detail. We refer to our answers to points detailed above.

• 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. Reviewers expertise entomopathogens, their toxins and pathogen ecology

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Reply to the reviewers

We thank the both the reviewers for their constructive comments. Please see our point-by-point response to all the comments.

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

• Summary *
• The authors of this manuscript confirm data found by others by determining replication kinetics of the ancestral B.6 SARS-CoV-2 virus, Delta and Omicron BA.1 and BA.2 in Calu-3 cells. The authors quantify barrier integrity between variants and interferon induction to conclude that Delta is more cytopathic and induced less interferon than Omicron, possibly leading to its increased pathogenesis. In addition the authors identify CuCl2 and FeSO4 as potential antivirals. *

*Major comments *

1. *__Reviewer comment: __The author's argue that Omicron's slower replication on Calu-3 cells correlates with mild disease, however many publications show that Omicron replicates more efficiently/ rapidly in primary human airway cultures: *
2. Hui et al., (Nature, 2022) doi: https://doi.org/10.1038/s41586-022-04479-6*
3. Peacock et al., (bioRxiv) doi: https://doi.org/10.1101/2021.12.31.474653*
4. Lamers et al., (bioRxiv) doi: https://doi.org/10.1101/2022.01.19.476898 * Response: Previous reports including the citations indicated by the reviewer have shown that the Omicron variant replicates at a lower levels in lung tissue as compared to cells of bronchial origin or upper respiratory tract. In fact, Omicron variant was shown not to productively infect at all in alveolar type II cells. Omicron replication was severely compromised in Calu-3 cells grown in 96-well plates (https://doi.org/10.1080/22221751.2021.2023329) which is consistent with our observations.

*__Reviewer comment: __Can the authors explain why air-liquid grown Calu-3 cells appear to display similar viral titers for Omicron and Delta at 24 and 36 h.p.i (Figure 5B), however lower viral replication in Figure 3B? If the cells in Figure 3B are submerged, then the authors should identify why ALI grown Calu-3 cells are more susceptible to Omicron. *

Response: Cells were grown in plastic multi-well plates for growth curve experiments shown in Figure 3. The cells in this condition are not polarized and the virus titers are the total amount of virus released into the culture supernatant. The infection conditions in Figure 5 is under air-liquid culture conditions, from polarized cells. Therefore, the virus titers are only from the basolateral chamber. The outcomes of figure 3 and figure 5 are not comparable due to these technical differences. We will add this explanation in the results section.

*__Reviewer comment: __The authors suggest that Delta disrupts epithelial barrier integrity to a larger extent compared to B.6 and Omicron, however this may be due to fewer infected cells (despite equal viral titers, the nucleocapsid staining in Figure 2 and 5C suggests fewer infected cells). Have the authors imaged B.6 or Omicron at a later timepoint (or normalized virus input for equal infected cells) to determine barrier integrity when the amount of infected cells is equal? Alternatively, the authors should discuss this as a possible limitation of their study, especially since they argue this is a major reason why Delta has a growth advantage (lines 345 to 349). *

Response: We performed confocal imaging of transwells from air-liquid interface model using a 20X objective and have obtained data to show that the percent of infected cells is similar between Omicron and Delta variant. We will include this data in the revised manuscript. In an in vitro system, once the infection is set in, the infected cells eventually die and the TEER reaches background levels. We are proposing a delay in disruption of barrier integrity most probably due to lower cytopathogenicity of the Omicron variant. As per the reviewer’s suggestion, we will discuss the possible limitation of the models and provide additional interpretations.

Minor comments *A) __Reviewer comment: __Line 118: Implications of this sentence are too strong. The authors have not shown the causality of Ct values and transmission, therefore they should reword the sentence: "indicating a high viral burden in patients during this period resulting in increased transmission of the virus among the contacts" to "likely attributing to increased transmission..." *

Response: We will correct this.

*__B) Reviewer comment: __Line 289: The authors suggest that infection with the Omicron variant generated higher levels of antibodies to the Delta variant, however these individuals are already vaccinated and elicit cross-neutralizing antibodies against Delta even before their Omicron infection. Therefore the Delta response is boosted and the Omicron response is essentially a primary response since vaccination elicits almost no cross-protection in itself. Therefore the authors should compare primary Delta infected individuals to primary Omicron infected individuals to determine cross-protection levels. *

Response: We agree with the reviewer’s argument. Please note that the two vaccines used in India are against the ancestral virus (inactivated) or the spike protein expressed by the adenovirus vector backbone. As over 90% of the population in India have been fully vaccinated with these two vaccines and a majority of them may also have been infected with delta variant and now with omicron, it is practically impossible to compare primary delta cases vs primary omicron cases at this stage. As part of another study in mid 2021, after the second wave of COVID-19 infections due to the Delta variant in India, we randomly selected 55 samples which had a detectable FRNT50 value for the delta variant, to test for their ability to neutralize the Omicron variant. Only twenty of the 55 samples had detectable levels of neutralizing antibodies against the Omicron variant. By assigning a FRNT50 value of 10 for the samples which had no detectable levels of antibodies in the starting dilution (1:20) of the assay, we obtained a GMT of 22.5 (95% CI: 16, 31) for these 55 samples. This value was 20-fold lower than the GMT of Delta variant which was 404 (95% CI:248, 658). This clearly indicates that even during the peak of delta wave, there were barely any cross-reactive antibodies to the Omicron variant. This study was recently published [NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-022-31170-1]. It would be interesting to eventually compare the antibody responses in reinfections with other sub-lineages of Omicron variant which is beyond the scope of our manuscript. We will add this description in the results and discussion section of the revised manuscript.

*C) __Reviewer comment: __There appears to be no reference to Figure 6G, however this reference is most likely missing from line 306. *

Response: Thank you for bringing this to our notice. We will insert the reference to Figure 6G.

*D) __Reviewer comment: __Line 359-362: The authors suggest that waning antibody titers increase susceptibility to new variants of concern, however their cohort already possessed very low antibody titers against Omicron a month after vaccination (Figure 7F) suggesting they could be equally susceptible to Omicron 1 and 6 months after vaccination. *

Response: Please note that nine out of 15 samples had FRNT50 value above the level of detection after vaccination in June 2021. The number of samples positive for Omicron antibodies reduced to six out of 15 by Dec 2021 suggesting that relatively more people were without protective antibodies for Omicron variant by Dec 2021. Around 70% of the population was seropositive by Aug 2021 (https://doi.org/10.1016/j.ijid.2021.12.353) and most adults in India received both doses of their vaccine after June 2021 which would have boosted the humoral and cellular response to SARS-CoV-2. This is corroborated in a recently published report, where we showed that 36 out of 55 previously infected subjects had neutralizing antibodies for the Omicron variant after receiving a single dose of inactivated vaccine. Therefore, in the context of hybrid immunity in India, we speculate that waning antibody titers could have played a significant role in the emergence and spread of Omicron variant in addition to the ability of the Omicron variant to escape neutralization, replicate more efficiently in the upper respiratory tract etc., The fact that booster doses of vaccines developed against the ancestral virus/viral protein was capable of increasing the level of neutralizing antibodies to omicron variant suggests that the level of antibodies above a certain threshold may play a significant role in protecting against the omicron variant.

Reviewer #1 (Significance (Required)):

• __Reviewer comment: __Many of the conclusions based on replication and barrier integrity may not represent the situation in primary human tissues and does not explain the rapid spread of Omicron. In addition, interferon induction has already been described for these variants and this finding is not novel. The manuscripts most interesting and novel finding is the role of CuCl2 and FeSO4 as antivirals. It would be interesting to test these salts in primary human airway cultures. *

Response: The study was conducted in the months of Jan-March 2022 and the first version of the results were uploaded on a preprint server in March 2022. The process of journals handling the manuscript and obtaining reviews is not under our control. We cannot argue to defend the comments on novelty when the Omicron variant is barely six months old and new variants continue to emerge. The deluge of publications should not result in reviewers branding most of the efforts as not novel or insignificant. We have been trying since three months to obtain primary cells but the distributors are unable to supply the same. We will continue to try to obtain cells from one or the other source. Transwells are back-ordered with expected delivery dates in three months. Meanwhile, we now have HBEC3-KT cells which are normal human bronchial epithelial cells immortalized with CDK4 and hTERT. We will perform the inhibition experiments in these cell lines to convince the reviewers.

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

*In the manuscript entitled "BA.1 and BA.2 sub-lineages of Omicron variant have comparable replication kinetics and susceptibility to neutralization by antibodies" the authors assess the kinetics of growth of SARS-CoV-2 variants in Calu-3 cells and their effects on epithelial junction, and the interferon response. The authors also analyze the capacity of metal salts to block SARS CoV-2 replication in Calu-3 cells. Finally, the authors characterize the ability of vaccinated and/or COVID-19 patients to develop neutralizing antibodies to different variants using FRNT and specific binding assays (ELISA). *

• The paper largely confirms several previous reports on the replication capacity and interferon responses of the different variants. Although the title and abstract focus on the Omicron sub-lineages, the paper is mostly focused on comparing original CoV2, with Kappa, Delta and Omicron. *

• Figures 1-5 compare the replication kinetics, interferon responses, and epithelial barrier disruption of Kappa, Delta and the original Omicron (B.1.1.529) to the original B6 variant. On a separate note, Figure 7 shows the ability of metal salts (especially iron, copper, and zinc) to block viral RNA-dependent RNA polymerase activity (RdRp) in vitro. The authors also show the effect on virus replication in Calu-3 cells (Delta and Omicron B.1.1.529 only). The data mainly focus on the variants, the Delta and the Omicron (BA.1.1.529 and not the BA.1 and BA.2 sub-lineages) except in Fig 6A, B, G. *

• __Reviewer comment: __Most importantly, a major limitation of the paper is that when human samples are analyzed, the authors assume that the patients have been infected with a specific variant according to the "peak" of infection, but sequencing is never performed. When neutralization and binding of antibodies are analyzed, the information on the patients is unclear - for example, were the patients exposed to Delta or Omicron or any of their sub-lineages? What was the vaccination status of SARS CoV-2 positive patients? And why non-tested individuals showing symptoms were included in the study (lines 302-304)? *

Response: We thank the reviewer for the comments. Over 90% of the population in India is vaccinated. All the participants of the study have been vaccinated in 2021. The participants were enrolled into the study almost 4 weeks after recovery from illness. We have enrolled participants who have reported to have had fever or COVID-19-like symptoms in the preceding weeks with or without confirmed RT-PCR test results. Testing is an individual and voluntary choice now. Therefore, it would be difficult to find RT-PCR confirmed cases. Our assumption about exposure is based on a nationwide sequencing effort of thousands of samples every week and this approach is reliable and credible. As indicated in the text and in the supplementary figure, Omicron lineages BA.1 followed by BA.2 were the circulating virus lineages since Jan 2021 in India.

*__Reviewer comment: __The authors show that BA.1 and BA.2 have similar replication kinetics in Calu-3 cells and induce similar neutralizing antibodies in the patients tested. However, there is a large disconnection with the rest of the paper that is mostly focused on Kappa, Delta, and Omicron B.1.1.529. Also, no comparisons between these variants and BA.1 or BA.2 have been shown. Similarly, a large assumption in the paper is that the patients who tested positive for COVID-19 have had "natural Omicron infection" (lines 36-37; lines 307-311) when it could be any other variants or Omicron sub-lineages as well. *

Response: Please note that the B.1.1.529 which was used at the beginning of the study is the BA.1 sub-lineage which has been compared with Kappa and Delta variants. BA.2 emerged at later stages and therefore we have compared the kinetics and neutralization titer between BA.1 and BA.2. It is unreasonable to expect to repeat all the comparisons with BA.2 considering the cost and challenges of working in a BSL-3 environment. The initial version of this data was uploaded on preprint server in March 2022 when only two sub-lineages of Omicron namely BA.1 and BA.2 existed. Our data from the national SARS-CoV-2 sequencing consortium clearly shows that there were no other sub-lineages circulating at that time.

Reviewer #2 (Significance (Required)):

*__Reviewer comment: __In light of the fact that most of the paper does not look at the subvariants BA.1 and BA.2 of Omicron- either the authors compare BA.1 and BA.2 more comprehensively with Omicron B.1.1.529 or rewrite the conclusions and claims of the current paper. Similar to the experiments comparing B6 with Kappa, Delta and Omicron, Omicron B.1.1.529 should be compared similarly to BA.1 and BA.2 in a separate figure. In any case, the novelty compared to other papers -also cited by the authors- remains limited. *

Response: We will revise the conclusions and claims of the paper as per the suggestions. Please see our response to reviewer 1 with regards to the novelty of our observations. The B.1.1.529 variant was later classified as the BA.1 variant. Our study was uploaded on the preprint server in March 2022 and the entire review process has taken four months. It is unfair to now demand comparison of BA.2 with Kappa or Delta variant which does not add any additional value to our observations.

*__Reviewer comment: __In addition to the concerns mentioned above, there are more pressing variants circulating right now, such as BA.4 and BA.5. These variants are not referred in the paper. It might be beyond the scope of the paper, but including more analyses with BA.1, BA.2 (as the ones done with B.1.1.529) and adding some key data with BA.3, BA.4, BA.5 might substantially increase the relevance and importance of the paper. *

Response: Please see our comments above. Our efforts are continuing in this direction to further look at antibody responses and replication kinetics of newer variants which have emerged recently. However, the scarcity of positive clinical samples and lower probability of getting samples that would be suitable for virus isolation are the challenges we are dealing with. We think testing newer variants which have emerged during the review process is certainly valuable but is extremely difficult under the current circumstances. We will have to apply to seek import permits to obtain these sub-lineages or enrol patients with symptoms and keep testing them to isolate, culture the virus and obtain whole genome sequence. We will have to establish neutralization assays with newer sub-variants to test in parallel with other Omicron lineages. All this is beyond the scope of our manuscript and will take few months of paper work and experimentation.

Reviewer #3 (Public Review):

The present study aims to elucidate posterior cingulate cortex (PCC) function with both single-unit and population-level depth electrodes. The results clearly show that the dorsal PCC (dPCC) is involved in executive functions (search and add), but that it also contains neurons that are selective for episodic memory (past and future) and rest conditions. With this impressive study design, the authors are able to reconcile discrepancies between human and primate studies. Furthermore, the derived conclusion that PCC function is more diverse than merely its participation in the DMN is of great importance for the field. Thus, I believe that this work will have a great impact on how we think about the PCC, by (1) emphasizing its participation in executive processes and (2) providing evidence of distinct single-unit response profiles that do not manifest on a population level.

The main strength of this work is the combination of population-level measurements that clearly show the participation of dPCC in executive processes with microelectrode single-unit measurements and an unsupervised hierarchical clustering approach that allows for the identification of 4 distinct SU response profiles within the dPCC. In addition, the population-level electrodes mostly engaged in executive function cluster around an fMRI meta-analysis peak related to executive processing derived from neurosynth, providing a bridge to human fMRI research.

Nevertheless, there is one concern regarding the data collected within the ventral PCC (vPCC) in this study and the way the authors integrated it into their conclusions.

Specifically, the conclusion that "Together, they [the findings] inform a view of PCC as a heterogeneous region composed of dorsal and ventral subregions specializing in executive and episodic processing respectively" may not be completely supported by the data. The dPCC macroelectrode data does clearly show a functional specialization in executive processing, but does the data from vPCC presented in this manuscript also support the claim? While taking a closer look at the vPCC data, several inconsistencies stood out: First, the total number of vPCC electrodes was much smaller (6 vs 29 microelectrodes and one microwire probe that was not analyzed). Second, it is not clear which of the presented electrodes in figure 3 were considered to be ventral. From comparing figure 3 with the dorsal/ventral split displayed in figure 1B, it seems as if only one electrode was unambiguously placed in vPCC. Third, BBG statistics of these 6 electrodes are not presented, thus the claim that they show vPCC functional specialization is not statistically supported.

Author Response

Reviewer #1 (Public Review):

Jones et al. investigated the relationship between scale free neural dynamics and scale free behavioral dynamics in mice. An extensive prior literature has documented scale free events in both cortical activity and animal behavior, but the possibility of a direct correspondence between the two has not been established. To test this link, the authors took advantage of previously published recordings of calcium events in thousands of neurons in mouse visual cortex and simultaneous behavioral data. They find that scale free-ness in spontaneous behavior co occurs with scale free neuronal dynamics. The authors show that scale free neural activity emerges from subsets of the larger population - the larger population contains anticorrelated subsets that cancel out one another's contribution to population-level events. The authors propose an updated model of the critical brain hypothesis that accounts for the obscuring impact of large populations on nested subsets that generate scale free activity. The possibility that scale free activity, and specifically criticality, may serve as a unifying theory of brain organization has suffered from a lack of high-resolution connection between observations of neuronal statistics and brain function. By bridging theory, neural data, and behavioral dynamics, these data add a valuable contribution to fields interested in cortical dynamics and spontaneous behavior, and specifically to the intersection of statistical physics and neuroscience.

Strengths:

This paper is notably well written and thorough.

The authors have taken a cutting-edge, high-density dataset and propose a data-driven revision to the status-quo theory of criticality. More specifically, due to the observed anticorrelated dynamics of large populations of neurons (which doesn't fit with traditional theories of criticality), the authors present a clever new model that reveals critical dynamics nested within the summary population behavior.

The conclusions are supported by the data.

Avalanching in subsets of neurons makes a lot of sense - this observation supports the idea that multiple, independent, ongoing processes coexist in intertwined subsets of larger networks. Even if this is wrong, it's supported well by the current data and offers a plausible framework on which scale free dynamics might emerge when considered at the levels of millions or billions of neurons.

The authors present a new algorithm for power law fitting that circumvents issues in the KS test that is the basis of most work in the field.

Weaknesses:

This paper is technically sound and does not have major flaws, in my opinion. However, I would like to see a detailed and thoughtful reflection on the role that 3 Hz Ca imaging might play in the conclusions that the authors derive. While the dataset in question offers many neurons, this approach is, from other perspectives, impoverished - calcium intrinsically misses spikes, a 3 Hz sampling rate is two orders of magnitude slower than an action potential, and the recordings are relatively short for amassing substantial observations of low probability (large) avalanches. The authors carefully point out that other studies fail to account for some of the novel observations that are central to their conclusions. My speculative concern is that some of this disconnect may reflect optophysiological constraints. One argument against this is that a truly scale free system should be observable at any temporal or spatial scale and still give rise to the same sets of power laws. This quickly falls apart when applied to biological systems which are neither infinite in time nor space. As a result, the severe mismatch between the spatial resolution (single cell) and the temporal resolution (3 Hz) of the dataset, combined with filtering intrinsic to calcium imaging, raises the possibility that the conclusions are influenced by the methods. Ultimately, I'm pointing to an observer effect, and I do not think this disqualifies or undermines the novelty or potential value of this work. I would simply encourage the authors to consider this carefully in the discussion.

R1a: We quite agree with the reviewer that reconciling different scales of measurement is an important and interesting question. One clue comes from Stringer et al’s original paper (2019 Science). They analyzed time-resolved spike data (from Neuropixel recordings) alongside the Ca imaging data we analyzed here. They showed that if the ephys spike data was analyzed with coarse time resolution (300 ms time bins, analogous to the Ca imaging data), then the anticorrelated activity became apparent (50/50 positive/negative loadings of PC1). When analyzed at faster time scales, anticorrelations were not apparent (mostly positive loadings of PC1). This interesting point was shown in their Supplementary Fig 12.

This finding suggests that our findings about anticorrelated neural groups may be relevant only at coarse time scales. Moreover, this point suggests that avalanche statistics may differ when analyzed at very different time scales, because the cancelation of anticorrelated groups may not be an important factor at faster timescales.

In our revised manuscript, we explored this point further by analyzing spike data from Stringer et al 2019. We focused on the spikes recorded from one local population (one Neuropixel probe). We first took the spike times of ~300 neurons and convolved them with a fast rise/slow fall, like typical Ca transient. Then we downsampled to 3 Hz sample rate. Next, we deconvolved using the same methods as those used by Stringer et al (OASIS nonnegative deconvolution). And finally, we z-scored the resulting activity, as we did with the Ca imaging data. With this Ca-like signal in hand, we analyzed avalanches in four ways and compared the results. The four ways were: 1) the original time-resolved spikes (5 ms resolution), 2) the original spikes binned at 330 ms time res, 3) the full population of slow Ca-like signal, and 4) a correlated subset of neurons from the slow Ca-like signal. Based on the results of this new analysis (now in Figs S3 and S4), we found several interesting points that help reconcile potential differences between fast ephys and slow Ca signals:

1. In agreement with Sup Fig 12 from Stringer et al, anticorrelations are minimal in the fast, time-resolved spike data, but can be dominant in the slow, Ca-like signal.

2. Avalanche size distributions of spikes at fast timescales can exhibit a nice power law, consistent with previous results with exponents near -2 (e.g. Ma et al Neuron 2019, Fontenele et al PRL 2019). But, the same data at slow time scales exhibited poor power-laws when the entire population was considered together.

3. The slow time scale data could exhibit a better power law if subsets of neurons were considered, just like our main findings based on Ca imaging. This point was the same using coarse time-binned spike data and the slow Ca-like signals, which gives us some confidence that deconvolution does not miss too many spikes.

In our opinion, a more thorough understanding of how scale-free dynamics differs across timescales will require a whole other paper, but we think these new results in our Figs S3 and S4 provide some reassurance that our results can be reconciled with previous work on scale free neural activity at faster timescales.

Reviewer #2 (Public Review):

The overall goal of the paper is to link spontaneous neural activity and certain aspects of spontaneous behavior using a publicly available dataset in which 10,000 neurons in mouse visual cortex were imaged at 3 Hz with single-cell resolution. Through careful analysis of the degree to which bouts of behavior and bouts of neural activity are described (or not) by power-law distributions, the authors largely achieve these goals. More specifically, the key findings are that (a) the size of bouts of whisking, running, eye movements, and pupil dilation are often well-fit by a power-law distribution over several decades, (b) subsets of neurons that are highly correlated with one of these behavioral metrics will also exhibit power-law distributed event sizes, (c) neuron clusters that are uncorrelated with behavior tend to not be scale-free, (d) crackling relationships are generally not found (i.e. size with duration exponent (if there is scaling) was not predicted by size power-law and duration power-law), (e) bouts of behavior could be linked to bouts of neural activity. In the second portion of the paper, the authors develop a computational model with sets of correlated and anti-correlated neurons, which can be accomplished under a relatively small subset of connection architectures: out of the hundreds of thousands of networks simulated, only 31 generated scale-free subsets/non-scale-free population/anti correlated e-cells/anti-correlated i-cells in agreement with the experimental recordings.

The data analysis is careful and rigorous, especially in the attention to fitting power laws, determining how many decades of scaling are observed, and acknowledging when a power-law fit is not justified. In my view, there are two weaknesses of the paper, related to how the results connect to past work and to the set-up and conclusions drawn from the computational modeling, and I discuss those in detail below. While my comments are extensive, this is due to high interest. I do think that the authors make an important connection between scale-free distributions of neural activity and behavior, and that their use of computational modeling generates some interesting mechanistic hypotheses to explore in future work.

My first general reservation is in the relationship to past work and the overall novelty. The authors state in the introduction, "according to the prevailing view, scale-free ongoing neural activity is interpreted as 'background' activity, not directly linked to behavior." It would be helpful to have some specific references here, as several recent papers (including the Stringer et al. 2019 paper from which these data were taken, but also papers from McCormick lab and (Anne) Churchland lab) showed a correlation between spontaneous activity and spontaneous facial behaviors. To my knowledge, the sorts of fidgety behavior analyzed in this paper have not been shown to be scale-free, and so (a) is a new result, but once we know this, it seems that (e) follows because we fully expect some neurons to correlate with some behavior.

R2a: We agree with the reviewer that our original introductory, motivating arguments needed improvement. We have now rewritten the last 2 paragraphs of the introduction. We hope we have now laid out our argument more clearly, with more appropriate supporting citations. In brief, the logic is this:

1. Previous theory, modeling, and experiments on the topic of scale-free neural activity suggest that this phenomenon is an autonomous, internally generated thing, independent of anything the body is doing.

2. Relatively new experiments (including those by Churchland’s lab and McCormmick’s lab: Stringer 2019; Salkoff 2020; Clancy 2019; Musall 2019) suggest a different picture with a link between spontaneous behaviors and ongoing cortical activity, but these studies did not address any questions about scale-free-ness.

3. Moreover, these new experiments show that behavioral variables only manage to explain about 10-30% of ongoing activity.

4. Is this behaviorally-explainable 10-30% scale-free or perhaps the scale-free aspects of cortical dynamics fall withing the other 70-90%. Our goal is to find out.

Digging a bit more on this issue, I would argue that results (b) and (c) also follow. By selecting subsets of neurons with very high cross-correlation, an effective latent variable has emerged. For example, the activity rasters of these subsets are similar to a population in which each neuron fires with the same time-varying rate (i.e., a heterogeneous Poisson process). Such models have been previously shown to be able to generate power-law distributed event sizes (see, eg., Touboul and Destexhe, 2017; also work by Priesemann). With this in mind, if you select from the entire population a set of neurons whose activity is effectively determined by a latent variable, do you not expect power laws in size distributions?

Our understanding is that not all Poisson processes with a time-varying rate will result in a power law. It is quite essential that the fluctuations in rate must themselves be power-law distributed. As a clear example of how this breaks down, consider a Poisson rate that varies according to a sine wave with fixed period and amplitude. In this case, the avalanche size distribution is definitely not scale-free, it would have a clear typical scale. Another point of view on this comes from some of the simplest models used to study criticality – e.g. all-to-all connected probabilistic binary neurons (like in Shew et al 2009 J Neurosi). These models do generate spiking with a time-varying Poisson rate when they are at criticality or away from criticality. But, only when the synaptic strength is tuned to criticality is the time-varying rate going to generate power-law distributed avalanches. I think the Priesmann & Shriki paper made this point as well.

My second reservation has to do with the generality of the conclusions drawn from the mechanistic model. One of the connectivity motifs identified appears to be i+ to e- and i- to e+, where potentially i+/i- are SOM and VIP (or really any specific inhibitory type) cells. The specific connections to subsets of excitatory cells appear to be important (based on the solid lines in Figure 8). This seems surprising: is there any experimental support for excitatory cells to preferentially receive inhibition from either SOM or VIP, but not both?

R2b: There is indeed direct experimental support for the competitive relationship between SOM, VIP, and functionally distinct groups of excitatory neurons. This was shown in the paper by Josh Trachtenberg’s group: Garcia-Junco-Clemente et al 2017. An inhibitory pull-push circuit in frontal cortex. Nat Neurosci 20:389–392. However, we emphasize that we also showed (lower left motif in Fig 8G) that a simpler model with only one inhibitory group is sufficient to explain the anticorrelations and scale-free dynamics we observe. We opted to highlight the model with two inhibitory groups since it can also account for the Garcia-Junco-Clemente et al results.

In the section where we describe the model, we state, “We considered two inhibitory groups, instead of just one, to account for previous reports of anticorrelations between VIP and SOM inhibitory neurons in addition to anticorrelations between groups of excitatory neurons (Garcia-Junco-Clemente et al., 2017).”

More broadly, I wonder if the neat diagrams drawn here are misleading. The sample raster, showing what appears to be the full simulation, certainly captures the correlated/anti-correlated pattern of the 100 cells most correlated with a seed cell and 100 cells most anti-correlated with it, but it does not contain the 11,000 cells in between with zero to moderate levels of correlation.

R2c: We agree that our original model has several limitations and that one of the most obvious features lacking in our model is asynchronous neurons (The limitations are now discussed more openly in the last paragraph of the model subsection). In the data from the Garcia-Junco-Clemente et al paper above there are many asynchronous neurons as well. To ameliorate this limitation, we have now created a modified model that now accounts for asynchronous neurons together with the competing anticorrelated neurons (now shown and described in Fig S9). We put this modified model in supplementary material and kept the simpler, original model in the main findings of our work, because the original model provides a simpler account of the features of the data we focused on in our work – i.e. anticorrelated scale-free fluctuations. The addition of the asynchronous population does not substantially change the behavior of the two anticorrelated groups in the original model.

We probably expect that the full covariance matrix has similar structure from any seed (see Meshulam et al. 2019, PRL, for an analysis of scaling of coarse-grained activity covariance), and this suggests multiple cross-over inhibition constraints, which seem like they could be hard to satisfy.

R2d: We agree that it remains an outstanding challenge to create a model that reproduces the full complexity of the covariance matrix. We feel that this challenge is beyond the scope of this paper, which is already arguably squeezing quite a lot into one manuscript (one reviewer already suggested removing figures!).

We added a paragraph at the end of the subsection about the model to emphasize this limitation of the model as well as other limitations. This new paragraph says:

While our model offers a simple explanation of anticorrelated scale-free dynamics, its simplicity comes with limitations. Perhaps the most obvious limitation of our model is that it does not include neurons with weak correlations to both e+ and e- (those neurons in the middle of the correlation spectrum shown in Fig 7B). In Fig S9, we show that our model can be modified in a simple way to include asynchronous neurons. Another limitation is that we assumed that all non-zero synaptic connections were equal in weight. We loosen this assumption allowing for variable weights in Fig S9, without changing the basic features of anticorrelated scale-free fluctuations. Future work might improve our model further by accounting for neurons with intermediate correlations.

The motifs identified in Fig. 8 likely exist, but I am left with many questions of what we learned about connectivity rules that would account for the full distribution of correlations. Would starting with an Erdos-Renyi network with slight over-representation of these motifs be sufficient? How important is the homogeneous connection weights from each pool assumption - would allowing connection weights with some dispersion change the results?

R2e: First, we emphasize that our specific goal with our model was to identify a possible mechanism for the anticorrelated scale-free fluctuations that played the key role in our analyses. We agree that this is not a complete account of all correlations, but this was not the goal of our work. Nonetheless, our new modified model in Fig S9 now accounts for additional neurons with weak correlations. However, we think that future theoretical/modeling work will be required to better account for the intermediate correlations that are also present in the experimental data.

We confirmed that an Erdo-Renyi network of E and I neurons can produce scale-free dynamics, but cannot produce substantial anticorrelated dynamics (Fig 8G, top right motif). Additionally, the parameter space study we performed with our model in Fig 8 showed that if the interactions between the two excitatory groups exceed a certain tipping point density, then the model behavior switches to behavior expected from an Erdos-Renyi network (Fig 8F). Finally, we have now confirmed that some non-uniformity of synaptic weights does not change the main results (Fig S9). In the model presented in Fig S9, the value of each non-zero connection weight was drawn from a uniform distribution [0,0.01] or [-0.01,0] for excitatory and inhibitory connections, respectively. All of these facts are described in the model subsection of the paper results.

As a whole, this paper has the potential to make an impact on how large-scale neural and behavioral recordings are analyzed and interpreted, which is of high interest to a large contingent of the field.

Reviewer #3 (Public Review):

The primary goal of this work is to link scale free dynamics, as measured by the distributions of event sizes and durations, of behavioral events and neuronal populations. The work uses recordings from Stringer et al. and focus on identifying scale-free models by fitting the log-log distribution of event sizes. Specifically, the authors take averages of correlated neural sub-populations and compute the scale-free characterization. Importantly, neither the full population average nor random uncorrelated subsets exhibited scaling free dynamics, only correlated subsets. The authors then work to relate the characterization of the neuronal activity to specific behavioral variables by testing the scale-free characteristics as a function of correlation with behavior. To explain their experimental observation, the authors turn to classic e-i network constructions as models of activity that could produce the observed data. The authors hypothesize that a winner-take-all e-i network can reproduce the activity profiles and therefore might be a viable candidate for further study. While well written, I find that there are a significant number of potential issues that should be clarified. Primarily I have main concerns: 1) The data processing seems to have the potential to distort features that may be important for this analysis (including missed detections and dynamic range), 2) The analysis jumps right to e-i network interactions, while there seems to be a much simpler, and more general explanation that seems like it could describe their observations (which has to do with the way they are averaging neurons), and 3) that the relationship between the neural and behavioral data could be further clarified by accounting for the lop-sidedness of the data statistics. I have included more details below about my concerns below.

Main points:

1) Limits of calcium imaging: There is a large uncertainty that is not accounted for in dealing with smaller events. In particular there are a number of studies now, both using paired electro-physiology and imaging [R1] and biophysical simulations [R2] that show that for small neural events are often not visible in the calcium signal. Moreover, this problem may be exacerbated by the fact that the imaging is at 3Hz, much lower than the more typical 10-30Hz imaging speeds. The effects of this missing data should be accounted for as could be a potential source of large errors in estimating the neural activity distributions.

R3a: We appreciate the concern here and agree that event size statistics could in principle be biased in some systematic way due to missed spikes due to deconvolution of Ca signals. To directly test this possibility, we performed a new analysis of spike data recorded with high time resolution electrophysiology. We began with forward-modeling process to create a low-time-resolution, Ca-like signal, using the same deconvolution algorithm (OASIS) that was used to generate the data we analyzed in our work here. In agreement with the reviewer’s concern, we found that spikes were sometimes missed, but the loss was not extreme and did not impact the neural event size statistics in a significant way compared to the ground truth we obtained directly from the original spike data (with no loss of spikes). This new work is now described in a new paragraph at the end of the subsection of results related to Fig 3 and in a new Fig S3. The new paragraph says…

Two concerns with the data analyzed here are that it was sampled at a slow time scale (3 Hz frame rate) and that the deconvolution methods used to obtain the data here from the raw GCAMP6s Ca imaging signals are likely to miss some activity (Huang et al., 2021). Since our analysis of neural events hinges on summing up activity across neurons, could it be that the missed activity creates systematic biases in our observed event size statistics? To address this question, we analyzed some time-resolved spike data (Neuropixel recording from Stringer et al 2019). Starting from the spike data, we created a slow signal, similar to that we analyzed here by convolving with a Ca-transient, down sampling, deconvolving, and z-scoring (Fig S3). We compared neural event size distributions to “ground truth” based on the original spike data (with no loss of spikes) and found that the neural event size distributions were very similar, with the same exponent and same power-law range (Fig S3). Thus, we conclude that our reported neural event size distributions are reliable.

However, although loss of spikes did not impact the event size distributions much, the time-scale of measurement did matter. As discussed above and shown in Fig S4, changing from 5 ms time resolution to 330 ms time resolution does change the exponent and the range of the power law. However, in the test data set we worked with, the existence of a power law was robust across time scales.

2) Correlations and power-laws in subsets. I have a number of concerns with how neurons are selected and partitioned to achieve scale-free dynamics. 2a) First, it's unclear why the averaging is required in the first place. This operation projects the entire population down in an incredibly lossy way and removes much of the complexity of the population activity.

R3b: Our population averaging approach is motivated by theoretical predictions and previous work. According to established theoretical accounts of scale-free population events (i.e. non-equilibrium critical phenomena in neural systems) such population-summed event sizes should have power law statistics if the system is near a critical point. This approach has been used in many previous studies of scale-free neural activity (e.g. all of those cited in the introduction in relation to scale-free neuronal avalanches). One of the main results of our study is that the existing theories and models of critical dynamics in neural systems fail to account for small subsets of neurons with scale-free activity amid a larger population that does not conform to these statistics. We could not make this conclusion if we did not test the predictions of those existing theories and models.

2b) Second, the authors state that it is highly curious that subsets of the population exhibit power laws while the entire population does not. While the discussion and hypothesizing about different e-i interactions is interesting I believe that there's a discussion to be had on a much more basic level of whether there are topology independent explanations, such as basic distributions of correlations between neurons that can explain the subnetwork averaging. Specifically, if the correlation to any given neuron falls off, e.g., with an exponential falloff (i.e., a Gaussian Process type covariance between neurons), it seems that similar effects should hold. This type of effect can be easily tested by generating null distributions using code bases such as [R3]. I believe that this is an important point, since local (broadly defined) correlations of neurons implying the observed subnetwork behavior means that many mechanisms that have local correlations but don't cluster in any meaningful way could also be responsible for the local averaging effect.

R3c: We appreciate the reviewer’s effort, trying out some code to generate a statistical model. We agree that we could create such a statistical model that describes the observed distribution of pairwise correlations among neurons. For instance, it would be trivial to directly measure the covariance matrix, mean activities, and autocorrelations of the experimental data, which would, of course, provide a very good statistical description of the data. It would also be simple to generate more approximate statistical descriptions of the data, using multivariate gaussians, similar to the code suggested by the reviewer. However, we emphasize, this would not meet the goal of our modeling effort, which is mechanistic, not statistical. The aim of our model was to identify a possible biophysical mechanism from which emerge certain observed statistical features of the data. We feel that a statistical model is not a suitable strategy to meet this aim. Nonetheless, we agree with the reviewer that clusters with sharp boundaries (like the distinction between e+ an e- in our model) are not necessary to reproduce the cancelation of anticorrelated neurons. In other words, we agree that sharp boundaries of the e+ and e- groups of our model are not crucial ingredients to match our observations.

2c) In general, the discussion of "two networks" seems like it relies on the correlation plot of Figure~7B. The decay away from the peak correlation is sharp, but there does not seem to be significant clustering in the anti-correlation population, instead a very slow decay away from zero. The authors do not show evidence of clustering in the neurons, nor any biophysical reason why e and i neurons are present in the imaging data.

R3d: First a small reminder: As stated in the paper, the data here is only showing activity of excitatory neurons. Inhibitory neurons are certainly present in V1, but they are not recorded in this data set. Thus we interpret our e+ and e- groups as two subsets of anticorrelated excitatory neurons, like those we observed in the experimental data. We agree that our simplified model treats the anticorrelated subsets as if they are clustered, but this clustering is certainly not required for any of the data analyses of experimental data. We expect that our model could be improved to allow for a less sharp boundary between e+ and e- groups, but we leave that for future work, because it is not essential to most of the results in the paper. This limitation of the model is now stated clearly in the last paragraph of the model subsection.

The alternative explanation (as mentioned in (b)) is that the there is a more continuous set of correlations among the neurons with the same result. In fact I tested this myself using [R3] to generate some data with the desired statistics, and the distribution of events seems to also describe this same observation. Obviously, the full test would need to use the same event identification code, and so I believe that it is quite important that the authors consider the much more generic explanation for the sub-network averaging effect.

R3e: As discussed above, we respectfully disagree that a statistical model is an acceptable replacement for a mechanistic model, since we are seeking to understand possible biophysical mechanisms. A statistical model is agnostic about mechanisms. We have nothing against statistical models, but in this case, they would not serve our goals.

To emphasize our point about the inadequacy of a statistical model for our goals, consider the following argument. Imagine we directly computed the mean activities, covariance matrix, and autocorrelations of all 10000 neurons from the real data. Then, we would have in hand an excellent statistical model of the data. We could then create a surrogate data set by drawing random numbers from a multivariate gaussian with same statistical description (e.g. using code like that offered by reviewer 3). This would, by construction, result in the same numbers of correlated and anticorrelated surrogate neurons. But what would this tell us about the biophysical mechanisms that might underlie these observations? Nothing, in our opinion.

2d) Another important aspect here is how single neurons behave. I didn't catch if single neurons were stated to exhibit a power law. If they do, then that would help in that there are different limiting behaviors to the averaging that pass through the observed stated numbers. If not, then there is an additional oddity that one must average neurons at all to obtain a power law.

R3f: We understand that our approach may seem odd from the point of view of central-limit-theorem-type argument. However, as mentioned above (reply R3b) and in our paper, there is a well-established history of theory and corresponding experimental tests for power-law distributed population events in neural systems near criticality. The prediction from theory is that the population summed activity will have power-law distributed events or fluctuations. That is the prediction that motivates our approach. In these theories, it is certainly not necessary that individual neurons have power-law fluctuations on their own. In most previous theories, it is necessary to consider the collective activity of many neurons before the power-law statistics become apparent, because each individual neurons contributes only a small part to the emergent, collective fluctuations. This phenomenon does not require that each individual neuron have power-law fluctuations.

At the risk of being pedantic, we feel obliged to point out that one cannot understand the peculiar scale-free statistics that occur at criticality by considering the behavior of individual elements of the system; hence the notion that critical phenomena are “emergent”. This important fact is not trivial and is, for example, why there was a Nobel prize awarded in physics for developing theoretical understanding of critical phenomena.

3) There is something that seems off about the range of \beta values inferred with the ranges of \tau and $\alpha$. With \tau in [0.9,1.1], then the denominator 1-\tau is in [-0.1, 0.1], which the authors state means that \beta (found to be in [2,2.4]) is not near \beta_{crackling} = (\alpha-1)/(1-\tau). It seems as this is the opposite, as the possible values of the \beta_{crackling} is huge due to the denominator, and so \beta is in the range of possible \beta_{crackling} almost vacuously. Was this statement just poorly worded?

R3g: The point here is that theory of crackling noise predicts that the fit value of beta should be equal to (1-alpha)/(1-tau). In other words, a confirmation of the theory would have all the points on the unity line in the rightmost panels of Fig9D and 9E, not scattered by more than an order of magnitude around the unity line. (We now state this explicitly in the text where Fig 9 is discussed.) Broad scatter around the unity line means the theory prediction did not hold. This is well established in previous studies of scale-free brain dynamics and crackling noise theory (see for example Ma et al Neuron 2019, Shew et al Nature Physics 2015, Friedman et al PRL 2012). A clearer single example of the failure of the theory to predict beta is shown in Fig 5A,B, and C.

4) Connection between brain and behavior:

4a) It is not clear if there is more to what the authors are trying to say with the specifics of the scale free fits for behavior. From what I can see those results are used to motivate the neural studies, but aside from that the details of those ranges don't seem to come up again.

R3h: The reviewer is correct, the primary point in Fig 2 is that scale-free behavioral statistics often exist. Beyond this point about existence, reporting of the specific exponents and ranges is just standard practice for this kind of analysis; a natural question to ask after claiming that we find scale behavior is “what are the exponents and ranges”. We would be remiss not to report those numbers.

4b) Given that the primary connection between neuronal and behavioral activity seems to be Figure~4. The distribution of points in these plots seem to be very lopsided, in that some plots have large ranges of few-to-no data points. It would be very helpful to get a sense of the distribution of points which are a bit hard to see given the overlapping points and super-imposed lines.

R3i: We agree that this whitespace in the figure panels is a somewhat awkward, but we chose to keep the horizontal axis the same for all panels of Fig 4B, because this shows that not all behaviors, and not all animals had the same range of behavioral correlations. We felt that hiding this was a bit misleading, so we kept the white space.

4c) Neural activity correlated with some behavior variables can sometimes be the most active subset of neurons. This could potentially skew the maximum sizes of events and give behaviorally correlated subsets an unfair advantage in terms of the scale-free range.

Author Response

Reviewer #1 (Public Review):

In this study, Scalabrino et al. show persistent cone-mediated RGC signaling despite changes in cone morphology and density with rod degeneration in CNGB1 mouse model of retinitis pigmentosa. The authors use a linear-nonlinear receptive field model to measure functional changes (spatial and temporal filters and gain) across the RGC populations with space-time separable receptive fields. At mesopic and photopic conditions, receptive field changes were minor until rod death exceeded 50%; while response gain decreased with photoreceptor degeneration. Using information theory, the authors evaluated the fidelity of RGC signaling demonstrated that mutual information decreased with rod loss, but cone-mediated RGC signaling was relatively stable and was more robust for natural movies than artificial stimulus. This work reveals the preservation of cone function and a robustness in encoding natural movies across degeneration. This manuscript is the first demonstration of using information theory to evaluate the effects of neural degeneration on sensory coding. The study uses a systematic evaluation of rod and cone function in this model of rod degeneration to make the following findings: (1) cone function persists for 5-7 months, (2) spatial and temporal changes to the ganglion cell receptive fields were not monotonic with time, (3) mutual information between spikes and photopic stimuli remained relatively constant up to 3-5 months, and (4) information rates were higher for natural movies than for checkerboard noise stimuli.

The strengths of this paper include the following:

A systemic evaluation of potentially confusing data. The authors do an excellent job of organizing the results in terms of light levels and time points. The results themselves are confusing and difficult to draw across metrics, but the data are presented as clearly as possible. The work is especially well executed and presented.

The insight that cone responses remain relatively stable despite rod loss. The study clearly demonstrates that despite cone loss and morphological changes, cone-mediated responses remain robust and functional.

The application of information theory to degeneration is the first of its kind and the study clearly shows the utility of the metric.

The results are thoughtfully interpreted.

We thank the reviewer for these comments.

The weaknesses of this study include the following:

The inability to follow the same ganglion cell types over time is a major weakness that could confound the interpretation in terms of whether the changes are happening from artifacts of the recording method or from dynamic changes in the pooled population of ganglion cells. Is there even a single cell class, for example the ON-OFF direction-selective ganglion cells, that this group has so well quantified on the MEA, that the study could track over time, in addition to examining the pooled population changes over time? Tracking a single cell type for each of the metrics would make the population data more convincing or could clearly show that not all ganglion cells follow the population trend.

As suggested by the reviewer, we have added a cell type that is tracked through all the analyses: ON brisk sustained RGCs. Example receptive field mosaics, temporal receptive fields, and spike train autocorrelation functions for WT and 4M Cngb1neo/neo animals are shown in Figure 2-figure supplement 1E-F. These RGCs follow the trends displayed by the larger populations of RGCs in each analysis. We chose this cell type because they are readily identified by their spike train autocorrelation functions compared to other RGC types and they have approximately space-time separable receptive fields (RFs). There are many text changes associated with adding an analysis of the ON Brisk sustained RGCs (see lines 202-207; 227-229; 264-267, etc).

We chose not to focus on direction selective RGCs because we are analyzing the spatial and temporal RFs of RGCs in Figures 3-5 and direction-selective RGCs do not have space-time separable RFs (see example in Figure 2C-D). Thus, those cells could not be used to track those receptive field properties across degeneration. Also, we did not collect responses to drifting gratings or bar responses across a range of speeds or contrasts, so we are unable to reliably distinguish the different types of direction-selective RGCs (e.g., ON vs ON-OFF) from these data.

While the non-monotonic changes are interesting, they are also difficult to make sense of. Can the authors speculate in the Discussion what could be underlying mechanisms that give rise to non-monotonic changes. In the absence of potential mechanisms, the concern of recording artifacts arises.

Thank you for raising this point. We have added some speculation for the cause of these non-monotonic changes in the Discussion (lines 455-462). “While we do not know why non-monotonic changes are occurring for some RF properties, they largely occurred in the 3-5M range. During this time, there is a transient decrease in the rate of rod death (4-5M) and cone death begins (Figure 1). Consequently, there may be complex changes to retinal circuitry as the retina reacts to a temporary stabilization in rod numbers and an acceleration in cone death. Intracellular studies of the light-driven synaptic currents impinging onto bipolar cells and RGCs during this time will be important for understanding the origin of these non-monotonic changes in RF properties.”

The mutual information calculation seems to be correlated with the spike rate despite the argument made in Fig 10E-F. Can the authors show this directly by calculating the bits per spike in Figures 8 and 9? Of all the metrics, the gain function and the mutual information seem to be more consistent with each other. Can the authors demonstrate or refute a connection between the spike rate and information rates?

We added a supplementary figure to each of the information figures (see for Figures 8-10 figure supplement 1) showing the trends hold after dividing the information rate by the spike rate. Certainly, changing spike rates are contributing, but there are also clear changes in the bits/spike plots (Figure 8-figure supplement 1D; Figure 9-figure supplement 1D, Figure 10-figure supplement 1D).

Can the authors provide an explanation for why the mutual information calculation remains stable despite lower SNR and lower gain, especially after the contributions of oscillations have been ruled out?

The mutual information depends more strongly on the precision of spiking (both in terms of time and spike number within a small time bin) than the mean spike rate (averaged over the stimulus). Diminishing the total number of spikes (because of reduced gain) will have a relatively small effect on the information rate if the spike trains continue to exhibit low variability (high precision). Indeed, spike generation by RGCs is distinctly sub-Poisson (Berry, Warland, and Meister 1997), indicating it can exhibit relatively high information rates even when spike rates are relatively low. We clarified this in Results at lines 493-496.

Lack of age-matched WT controls to accompany the different time points. It is known that photoreceptor degeneration can occur naturally in WT mice. Though the authors have used controls pooled from across the ages used in the CNGB1 mutants, it would be informative to know if there are age-dependent changes in any of the metrics for WT mice.

WT recordings were pooled from retinas from littermate control mice between 2 and 7 months of age (n=3 2M, n=1 each 4M, 6M, 7M). We have added data points from individual retinal recordings to the figure supplements for Figure 2-6 and 8-10 to illustrate the consistency between these recordings, which allowed us to confidently pool the results.

Can the authors elaborate on why cone function persists despite the rod loss and morphological changes? This is unique for other models of rod loss and is worth extra discussion.

This is something we are also very interested in, but outside the scope of this study. The Sampath Lab (co-author and collaborator) has data from single cell recordings in late stage rd10 retinas that show abnormal cone signaling (and structure similar to the 7M Cngb1neo/neo cones), yet relatively normal cone bipolar cell and horizontal cell responses. Thus, somehow there is either compensation or a high level of redundancy in the transmission of signals from cones to 2nd-order neurons that makes the responses of the 2nd-order neurons robust to deteriorating cone function. These results suggest our observations in Cngb1neo/neo mice are not unique to this model of RP. Future experiments are needed to understand how this compensation is occurring.

Reviewer #2 (Public Review):

In this study, the authors assess the decline of retinal function in a mouse model of slow photoreceptor degeneration - the Cngb1neo/neo. Rod loss occurs between 1-7 months and complete cone loss occurs by 8-9 months. The authors characterize cone loss in the first 7 months and find that 70% of cones are still there at 7 months, though their outer segments are highly degraded. They then use MEA recordings to characterize retinal function using a variety of measures. First, they use spike-triggered averaging to determine the spatial and temporal receptive fields, restricting this analysis to RGCs that have separable spatial and temporal receptive fields. They find that both rod and cone receptive fields are surprisingly intact over the first 5 months, identifying primarily a reduction in contrast response functions (and a reduction in the number of rods that are light responsive-though this is not quantified). Second, they show that oscillatory activity does not appear until after photoreceptors are completely deteriorated-in sharp contrast to other PR degeneration models (e.g. rd10) in which oscillatory activity appears while there are still light-evoked responses. Third, they use information theory to assess the reliability of signaling. When examining the 10% of RGCs with the highest information rates they see a significant decrease at mesoscopic light levels, while information rates were mostly stable at photopic light levels. Finally, they showed that at photopic light levels, the mutant retinas conveyed more information about natural movies than a repeating checkerboard, and this was maintained across light levels.

My primary question is whether this represents a significant advance. There have been many studies regarding the changing retinal circuits in various rodent models of photoreceptor degeneration. The authors make a few arguments regarding the uniqueness of this study.

One is that this is a novel analysis that is not limited to particular cell types but rather characterized the retinal as a "whole". But in this point is also its weakness. First, one cannot speak to the retinal as a "whole" since they state that there is a reduction in the number of light-responsive cells across degeneration - yet they do not quantify it. This seems incredibly important to know because even presuming the remaining cells have perfect receptive field structure if only 10% of cells are left, assessing the receptive fields of only the remaining cells is clearly not a characterization of the retention of visual function.

We never claim that we have assessed the “retina as a whole”. We do state that we are measuring certain features of RGC signaling that reflect the “net changes” induced by photoreceptor degeneration (e.g., changes in photoreceptor function, retinal rewiring, homeostatic mechanisms, etc.) on those features. In fact, we are explicit that we are only measuring certain RF properties in certain RGC types, such as the linear spatial and temporal RFs in cells with space-time separable RFs: Figure 2 makes this point explicitly. We do not measure changes in direction-selectivity, object motion sensitivity, orientation selectivity, edge detection, looming detection, luminance encoding, chromatic opponency, contrast adaptation, motion reversal signaling, etc., because doing so would produce a manuscript with at least one figure for every RGC type (e.g., 45 figures). This would clearly be an unreasonable amount for a single study.

We agree with the Reviewer that explicitly quantifying the number of light responsive RGCs is important, and we now include this information as a function of degeneration time point in Figure 2-figure supplement 1. Under photopic conditions, this fraction is quite stable until 5M and then begins to deteriorate. We also observe a decrease in the number of RGCs with space-time separable RFs at 5M (Figure 2F), suggesting (but not proving) that these RGCs are representative of changes across all RGCs. We also described these results in the Results (lines 167-174).

Second, it is hard to assess whether this mouse model is better than existing models for human disease. Their phenotype is different than the rat model of this same disease. It also shows a lack of oscillatory activity that is apparent in rd models.

We are not making the claim that this model is better than other models. Each model has value. However, because the degeneration in this model is relatively slow, it may be more representative of changes that occur in slower forms of human retinal degeneration (emphasis on “may be”). This is a discussion point, not something that we are aiming to prove. We also believe the utility of a model depends on the questions being asked. In this case, we aimed to track changes over time during photoreceptor loss to better understand the extent to which retinal output is impaired.

Also, retinitis pigmentosa is a heterogenous disease with a spectrum of phenotypes that may or may not be genotype specific. A patient with a PDE6B mutation presents with differing phenotypes than a patient with CNGB1 mutation, despite both having an RP diagnosis. It is fallacy to assume a mouse is the exact same as a human, just as it is incorrect to assume clinical presentations are identical for all patients for one broad disease that is known to have a diverse set of underlying causes. Studying a range of models is thus essential to understanding the disease. Given that mutations causing RP have different impacts on retinal signaling, we believe it is important to contextualize findings to their mutation. We make this point in Discussion: Comparison to previous studies of RGC signaling in retinitis pigmentosa (beginning on line 436).

Finally, the model we study does not lack oscillatory activity, it simply arises later than in rd1 or rd10 mice and does so only after all the photoreceptors have died (Figure 7). To our knowledge, it is not clear when or even if RGCs exhibit oscillations in human patients with RP. We discuss why oscillation might arise at different time points in different genetic models of RP in lines 555-570.

Reviewer #3 (Public Review):

In the manuscript by Scalabrino et al. a rigorous characterization of the functionality of retinal ganglion cells in a mouse model of rod photoreceptor degeneration is presented. The authors analyzed the degeneration of cone photoreceptors, which is known to be linked to rod degeneration. Based on the time course of cone degeneration they investigated the functional properties of retinal ganglion cells aged between 1 month and seven months.

The most interesting finding is robust preservation of functional properties, as reflected in little changes of the receptive fields (spatial and temporal characteristics) or signaling fidelity/information rate. In contrast to other mouse models, the present one shows no oscillatory activity until a complete loss of cone photoreceptors occurred at an age of nine months.

Although the receptive fields of retinal ganglion cells remain nearly intact, the number of ganglion cells with identifiable receptive fields decreases significantly with age (Fig.2F). Could the authors comment, if this might imply a "patchy" vision?

Visual field loss is a predominant clinical observation in patients with retinitis pigmentosa, including those with Cngb1 mutations. We connect to this observation in the Discussion at lines 521-529: “At the latest stages of photoreceptor degeneration in the Cngb1neo/neo mice (5-7M), we did observe a decrease in the fraction of RGCs with spike rates that were strongly modulated by checkerboard noise (Supplemental Figure 2). It is possible these RGCs were losing their light response completely, or that changes in their light response properties made them relatively unresponsive to checkerboard noise. If the former, it is possible that light responsive RGCs are becoming sparser at the later stages of degeneration which may result in inhomogeneous, or “patchy”, visual sensitivity described by RP patients (see reviews by Hull et al., 2017; Nassisi et al., 2021).”

Reviewer #4 (Public Review):

Scalabrino et al. report the remarkable persistence of cone-driven retinal ganglion cell responses in a mouse model of retinitis pigmentosa (i.e., Cngb1 KO mice). The authors first map the time course of primary rod and secondary cone degeneration in Cngb1 KO mice. Approximately 30% of rods are gone at one month (1M), and all rods are lost by 7M in Cngb1 KO retinas. The cone morphology changes progressively as rods degenerate, cone outer segments shrink and are largely absent by 5M. Cones die between 8-9M. Scalabrino et al. next perform multielectrode array recordings from wild-type and Cngb1 KO retinas from 1M to 5M in mesopic and photopic stimulus conditions. They find that spatiotemporal receptive fields remain relatively stable in the face of photoreceptor degeneration, whereas contrast gain gradually decreases. Oscillatory spontaneous ganglion cell activity emerges late (~9M) in Cngb1 KO mice compared to other retinal degeneration models. Finally, the authors analyze mutual information between stimuli (white noise and naturalistic movies) and ganglion cell spikes trains and find that the encoding of the most informative ganglion cells is preserved relatively late into photoreceptor degeneration and that information rates decline less in photopic vs. mesopic conditions and for naturalistic movies vs. white noise stimuli.

Overall, this is an exciting study that shows remarkable preservation of cone-driven ganglion cell light responses in advanced stages of a retinitis pigmentosa model when most rods have died, and cone morphologies are dramatically altered. The results are presented clearly in the text and figures and are scholarly discussed. Nonetheless, the authors should address a few specific comments to clarify and better support some of the conclusions they draw.

Specific comments:

1) In describing the results on information encoding, the authors write and show data (panels A of Figures 8-10) that suggest that most ganglion cells, even in recordings from wild-type retinas, respond unreliably to white noise stimuli and naturalistic movies. Why does such a large fraction of cells have such low repeat reliability? Does this reflect unreliable spike detection and sorting, poor cell or tissue health, or true variability in the responses of healthy retinal ganglion cells. The latter does not seem to align with results from patch-clamp recordings targeted to specific ganglion cell types. The limited repeat reliability also raises questions about how well the linear-nonlinear model, which the authors use to compare responses between wild-type and Cngb1 KO mice of different ages, predicts the responses of these cells. Comparing model parameters (receptive field size, temporal filtering, and contrast sensitivity) between genotypes and ages only makes sense if the model is a good description in the acquired datasets.

We agree with the reviewer that this is an important point to be clear about. In Figures 8-10 some RGCs exhibit high repeatability, some exhibit low repeatability as quantified by their information rates. The reviewer is concerned about those cells with low repeatability and the ability of capturing their responses with an LN model. This is a valid concern, but to be clear, we are not fitting an LN model to cells with low information rates. In Figures 3-6, where an LN model is being used to estimate the spatial and temporal components of the RFs, we are fitting a subset of all the RGCs: those with space-time separable RFs (see Figure 2). Those particular cells exhibit high information rates and highly reproducible responses, and an LN model captures ~60% of the explainable variance in the spike rate (see Figure 2-figure supplement 1A-B; also see lines 157-151). This is typical for LN models that approximately predict the responses of RGCs to checkerboard noise. Thus, we think the LN model reasonably captures the responses of cells for which we use the LN model. The information rate estimates include these cells as well as other cells that are not well described by an LN model. Note, the LN model is not used to calculate the mutual information rates. We have added text in the Results (lines 324-327) to clarify this.

In addition, the information rates we estimated in mouse are consistent with past studies from guinea pig (Koch et al, 2004 and Koch et al, 2006). We think cells with very low repeatability are not well driven by checkerboard noise or the particular 10s natural movies we showed. We have updated the example neurons to better reflect the reliability of the cells near the median of the MI distributions in Figures 8-10.

2) The authors should, maybe in figure supplements and parts of the main figures, break results down by recordings. Inter-experimental variability has been well documented (e.g., Shah et al. Neuron 2022, Zhao et al Sci Rep 2020), and it would be reassuring to see that the conclusions drawn by the authors are supported by statistics in which n = number of recordings (e.g., there is a somewhat difficult to explain broadening of temporal filters in 4M Cngb1 KO retinas that recover by 5M).

We agree that inter-experiment variability can be large and is important to control for. We now show all the analyses broken down by experiment in Supplemental Figures (2, 3, 4, 5, 6, 8, 9, and 10) for each analysis. None of the trends we describe or highlight in the manuscript were driven by inter-experiment variability.

3) At different points in their manuscript, the authors conclude that their results "suggest that homeostatic mechanisms in the retina serve to compensate for deteriorating photoreceptors" (or similar). I think that this may well be the case. However, in its present form, the study provides no evidence that retinal circuits in Cngb1 KO mice change to preserve function compared to the alternative that the observed stability is evidence for functional redundancy or resilience in retinal circuits (as they are) without the need for adjustments. Distinguishing between these alternatives would be conceptually important. For example, Care et al. Cell Rep 2019 and Care et al. Cell Rep 2020 used partial stimulation to activate fewer photoreceptors and compare light responses in downstream neurons to those in retinas with fewer photoreceptors. Other studies have directly observed changes in circuit wiring in models of retinal degeneration. If the authors cannot provide experimental evidence for homeostatic changes, it would be good to reflect this in the interpretation and discussion.

The reviewer raises a terrific point and potential alternative interpretation. We agree. We have not been able to identify an equivalent analysis to that in Care et al. 2019 that we can run that will cleanly distinguish between these two possibilities, without doing many more experiments across timepoints of degeneration. We have thus rewritten portions of the Introduction and the Discussion to recognize the potential of this alternative interpretation.

Introduction (lines 39-44): Alternatively, homeostatic plasticity or redundancy in retinal circuitry may compensate for photoreceptor loss (Care et al., 2020; Lee et al., 2021; Shen et al., 2020). Such mechanisms could facilitate reliable signaling at the level of retinal output, despite deterioration in photoreceptor function. Identifying the extent to which changes in photoreceptor morphology impact retinal output will inform treatment timepoints for gene therapies aimed at halting rod loss to preserve cone-mediated vision.

Discussion (lines 514-520): There are two potential classes of mechanisms for this compensation. First, homeostatic plasticity has been documented in models of photoreceptor loss in which the retina remodels to preserve signal transmission (Care et al., 2019; Keck et al., 2013, 2011, 2008; Leinonen et al., 2020; Shen et al., 2020). Alternatively, functional redundancy within the circuit could explain how robust retinal signaling is retained longer than the changes in cone morphology would suggest (Care et al., 2020). This study did not distinguish between the two compensation models.

4) The authors do not attempt to classify retinal ganglion cells into functional types as functional changes from degeneration may confound such classifications. However, it would be beneficial to separate some categorical response types (direction-selective ON-OFF and ON ganglion cells, maybe orientation-selective [horizontal, vertical, ON, OFF] ganglion cells) and compare how their responsiveness, reliability, and information encoding change with degeneration. This would provide additional insights and address concerns that changes caused by degeneration may be obscured by the differences between ganglion cell types in the present analysis.

We agree. We now track ON brisk sustained RGCs across degeneration time points for the RF analyses and mutual information analyses. These RGCs are likely the ON sustained alpha cells because they generate very large spikes on the MEA as would be expected for cells with large somata. Example receptive field mosaics, temporal receptive fields, and spike train autocorrelation functions for WT and 4M Cngb1neo/neo animals are shown in Figure 2-figure supplement 1E-F. These RGCs follow the trends displayed by the larger populations of RGCs in each analysis. We chose this cell type because they are readily identified by their spike train autocorrelation functions compared to other RGC types and they have approximately space-time separable receptive fields (RFs). There are many text changes associated with adding an analysis of the ON Brisk sustained RGCs (see lines 202-207; 227-229; 264-267, etc).

We chose not to focus on direction selective RGCs because we are analyzing the spatial and temporal RFs of RGCs in Figures 3-5 and direction-selective RGCs do not have space-time separable RFs (see example in Figure 2C-D). Thus, those cells could not be used to track those receptive field properties across degeneration. Also, we did not collect responses to drifting gratings or bar responses across a range of speeds or contrasts, so we are unable to reliably distinguish the different types of direction-selective RGCs (e.g., ON vs ON-OFF) from these data.

Historical Hypermedia: An Alternative History of the Semantic Web and Web 2.0 and Implications for e-Research. .mp3. Berkeley School of Information Regents’ Lecture. UC Berkeley School of Information, 2010. https://archive.org/details/podcast_uc-berkeley-school-informat_historical-hypermedia-an-alte_1000088371512. archive.org.

https://www.ischool.berkeley.edu/events/2010/historical-hypermedia-alternative-history-semantic-web-and-web-20-and-implications-e.

https://www.ischool.berkeley.edu/sites/default/files/audio/2010-10-20-vandenheuvel_0.mp3

Interface as Thing - book on Paul Otlet (not released, though he said he was working on it)

• W. Boyd Rayward 1994 expert on Otlet
• Otlet on annotation, visualization, of text
• TBL married internet and hypertext (ideas have sex)
• V. Bush As We May Think - crosslinks between microfilms, not in a computer context
• Ted Nelson 1965, hypermedia

t=540

• Michael Buckland book about machine developed by Emanuel Goldberg antecedent to memex
• Emanuel Goldberg and His Knowledge Machine: Information, Invention, and Political Forces (New Directions in Information Management) by Michael Buckland (Libraries Unlimited, (March 31, 2006)
• Otlet and Goldsmith were precursors as well

four figures in his research: - Patrick Gattis - biologist, architect, diagrams of knowledge, metaphorical use of architecture; classification - Paul Otlet, Brussels born - Wilhelm Ostwalt - nobel prize in chemistry - Otto Neurath, philosophher, designer of isotype

Paul Otlet

• wrote bibliography on law
• book: Something on Bibliography #wanttoread
• universal decimal classification system
• mundaneum
• Le Corbusier - architect worked with Otlet for building for Mundaneum; See: https://socks-studio.com/2019/05/05/the-shape-of-knowledge-the-mundaneum-by-paul-otlet-and-henri-la-fontaine/

Otlet was interested in both the physical as well as the intangible aspects of the Mundaneum including as an idea, an institution, method, body of work, building, and as a network.<br /> (#t=1020)

Early iPhone diagram?!?

(roughly) armchair to do the things in the web of life (Nelson quote) (get full quote and source for use) (circa 19:30)

compares Otlet to TBL

Michael Buckland 1991 <s>internet of things</s> coinage - did I hear this correctly? https://en.wikipedia.org/wiki/Internet_of_things lists different coinages

Turns out it was "information as thing"<br /> See: https://hypothes.is/a/kXIjaBaOEe2MEi8Fav6QsA

sugane brierre and otlet<br /> "everything can be in a document"<br /> importance of evidence

The idea of evidence implies a passiveness. For evidence to be useful then, one has to actively do something with it, use it for comparison or analysis with other facts, knowledge, or evidence for it to become useful.

transformation of sound into writing<br /> movement of pieces at will to create a new combination of facts - combinatorial creativity idea here. (circa 27:30 and again at 29:00)<br /> not just efficiency but improvement and purification of humanity

put things on system cards and put them into new orders<br /> breaking things down into smaller pieces, whether books or index cards....

Otlet doesn't use the word interfaces, but makes these with language and annotations that existed at the time. (32:00)

Otlet created diagrams and images to expand his ideas

Otlet used octagonal index cards to create extra edges to connect them together by topic. This created more complex trees of knowledge beyond the four sides of standard index cards. (diagram referenced, but not contained in the lecture)

Otlet is interested in the "materialization of knowledge": how to transfer idea into an object. (How does this related to mnemonic devices for daily use? How does it relate to broader material culture?)

Otlet inspired by work of Herbert Spencer

space an time are forms of thought, I hold myself that they are forms of things. (get full quote and source) from spencer influence of Plato's forms here?

Otlet visualization of information (38:20)

S. R. Ranganathan may have had these ideas about visualization too

atomization of knowledge; atomist approach 19th century examples:S. R. Ranganathan, Wilson, Otlet, Richardson, (atomic notes are NOT new either...) (39:40)

Otlet creates interfaces to the world - time with cyclic representation - space - moving cube along time and space axes as well as levels of detail - comparison to Ted Nelson and zoomable screens even though Ted Nelson didn't have screens, but simulated them in paper - globes

Katie Berner - semantic web; claims that reporting a scholarly result won't be a paper, but a nugget of information that links to other portions of the network of knowledge.<br /> (so not just one's own system, but the global commons system)

Mention of Open Annotation (Consortium) Collaboration:<br /> - Jane Hunter, University of Australia Brisbane & Queensland<br /> - Tim Cole, University of Urbana Champaign<br /> - Herbert Van de Sompel, Los Alamos National Laboratory annotations of various media<br /> see:<br /> - https://www.researchgate.net/publication/311366469_The_Open_Annotation_Collaboration_A_Data_Model_to_Support_Sharing_and_Interoperability_of_Scholarly_Annotations - http://www.openannotation.org/spec/core/20130205/index.html - http://www.openannotation.org/PhaseIII_Team.html

trust must be put into the system for it to work

coloration of the provenance of links goes back to Otlet (~52:00)

Creativity is the friction of the attention space at the moments when the structural blocks are grinding against one another the hardest. —Randall Collins (1998) The sociology of philosophers. Cambridge, MA: Harvard University Press (p.76)

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

Learn more at Review Commons

Reply to the reviewers

Reply to the reviewers

1. General Statements

It is the common view of all three reviewers that we have not utilized adequate in vitro/biochemical evidence to support the idea that SATB1 protein undergoes liquid-liquid phase separation. We do agree with the reviewers that our manuscript lacks biochemical evidence to support such notion. Though we find it quite interesting and we would like to suggest for the first time in the field of chromatin organization and function, based upon the action of SATB1, that this protein does exist in at least two polypeptide isoforms (764 and 795 amino acids long) which display different phase separation propensity and therefore confer different actions in regulating the (patho)physiological properties of a murine T cell.

Every single research group that works on SATB1, considered so far only a single protein isoform, that is, the shorter isoform of 764 amino acids and no tools, such as isoform-specific antibodies have been developed to discriminate the two isoforms and thus being able to assign unique functions to each isoform. We do understand that such a report, suggesting the presence of two protein isoforms, with potentially quite diverse functions, would question (not necessarily by the authors of this manuscript, since no such comment is included in our manuscript) the conclusions drawn in the literature assigning all biochemical properties to a single, short isoform of SATB1. Moreover, all the genetically modified mice that have been analyzed so far (including our group), deleted both Satb1 isoforms. Our future research approaches should, from now on, consider unraveling the isoform-specific functions of SATB1 and their involvement in physiology and disease. This could also deem useful to explain the quite diverse, both positive and negative effects of SATB1 in transcription regulation. Another major objection of the reviewers was that we should provide cumulative supporting evidence for the existence of the long SATB1 isoform, or at least evaluate the specificity of our custom-made antibody.

Taking under consideration the aforementioned constructive criticism of the three reviewers we would like to perform (most of the suggested experiments have already been performed) additional experiments to support our claims in the manuscript. These experiments are described below as a point-by-point reply to each point raised by the reviewers.

In line with the aforementioned rationale, we propose the title of our manuscript to change into “Two SATB1 isoforms display different phase separation propensity”, if our manuscript is considered for publication.

1. Description of the planned revisions

**Reviewer #1**:

4) Lack of in vitro reconstitution experiments with purified long and short SATB1

**PLANNED EXPERIMENT #1**

We do realize this shortcoming of our work. We have to note that purifying recombinant SATB1 protein is quite a challenging task, yet we 1. cloned both Satb1 cDNAs for the long and short isoforms, 2. we successfully expressed both proteins in great quantity and quality and we are willing to perform these experiments if our work is considered for publication.

This proposed experiment has also been requested by Reviewers #2 and #3.

**Reviewer #2**:

1. Moreover, an important and direct experiment would be to clone the long isoform in a suitable vector and overexpress in the cell line (as done for the canonical isoform in Supp Fig 1a). This would unequivocally show the efficacy of the antibody and thus the following usage of the same for various assays.

**PLANNED EXPERIMENT #2**

This is a great suggestion. We have cloned the long and short Satb1 cDNAs in pEGFP-C1 vector. We will transfect these plasmids in NIH 3T3 fibroblasts and we will perform Western blot analysis, utilizing the antibody raised against the extra 31 amino acids long peptide present only in the long SATB1 isoform, for the following samples: 1. NIH-3T3 whole cell protein extracts, 2. protein extracts from NIH 3T3 fibroblasts transiently transfected with the pEGFP-C1 plasmid, 3. protein extracts from NIH 3T3 fibroblasts transiently transfected with the pEGFP-long_Satb1_ plasmid and 4. protein extracts from NIH 3T3 fibroblasts transiently transfected with the pEGFP-short_Satb1_ plasmid.

This experiment will consist another proof regarding the specificity of the antibody raised against the extra 31 amino acids long peptide present only in the long SATB1 isoform.

**Minor comments:**

1. On pg 6, related to Figure 1, the authors mention 'It should also be noted that when investigating the SATB1 protein levels, we have to bear in mind that the antibodies targeting the N-terminus of SATB1 protein cannot discriminate between the short and long isoforms'. The authors reason that their sizes are too close. It is indeed possible, and widely studied in biochemistry to assess various factors on protein migration (such as PTMs). The authors should validate this aspect (as it is important as per their premise) and perform separation based on charge as well and also use a commercial antibody to validate the same.

(Experiments already performed)

We have adapted the text so that it does not imply that the two isoforms cannot be separated by size. This part in lines 102-107 then reads: “It should also be noted that when investigating the SATB1 protein levels, we have to bear in mind that the antibodies targeting the N-terminus of SATB1 protein cannot discriminate between the short and long isoforms, thus we can only compare the amount of the long SATB1 isoform to the total SATB1 protein levels in vivo conditions. To overcome this limitation and to specifically validate the presence of the long SATB1 protein isoform in primary murine T cells, we designed a serial immunodepletion-based experiment (Fig. 1e, Supplementary Fig. 1a).”

Moreover, in the revised version of the manuscript we now provide a number of additional proofs supporting the presence of the long isoform and also the specificity of the long isoform-specific antibody. As evident in the text cited above, in the revised Fig. 1e,f and revised Supplementary Fig. 1a,b; we present two immunodepletion experiments which should alone address the Reviewer’s concerns. Moreover, we added Supplementary Fig. 1c; demonstrating that the long isoform-specific antibody does not detect any protein in cells with conditionally depleted SATB1 (Satb1_fl/fl_Cd4-Cre+), supporting its specificity. The custom-made and publicly available antibodies targeting all SATB1 isoforms were also verified in Supplementary Fig. 1d. Moreover, the long isoform and all isoform antibodies display similar localization in the nucleus (Supplementary Fig. 1e; their co-localization based on super-resolution microscopy is also quantified in Supplementary Fig. 5a).

In our accompanying revised manuscript Zelenka et al., 2022 (https://doi.org/10.1101/2021.07.09.451769), we will provide yet another piece of evidence, consisting of bacterially expressed short and long SATB1 protein isoforms detected by western blot using either the long isoform-specific or the non-selective all SATB1 isoform antibodies.

**PLANNED EXPERIMENT #3**

Although we think that in the revised version of the manuscript, we have provided enough proof about the existence of the long isoform in primary murine thymocytes we would like to try the following approach as suggested by this Reviewer.

The pI of the two SATB1 isoform is quite similar. The pI of the short SATB1 isoform is 6.09 and for the long SATB1 isoform is 6.18. We will perform 2D PAGE coupled to Western blotting utilizing the antibodies detecting the long and all SATB1 isoforms. Given the fact that both isoforms are post-translationally modified to a various degree, it will be extremely difficult to discriminate between the long and short unmodified versus the long and short post-translationally modified proteins especially in the absence of a specific antibody only for the short isoform.

**Reviewer #3**

1. Hexanediol is another assay frequently used in phase-separation studies. However, hexanediol has many deleterious effects on the cell, even at a fraction of the concentration normally used in phase-separation studies. Authors should show controls of cell viability, control proteins that do not phase-separate, etc. See https://www.jbc.org/article/S0021-9258(21)00027-2/fulltext.

Secondly, hexanediol treatment should cause phase-separated protein aggregates to disperse. It is difficult to determine from the images whether or not the aggregates actually disperse or there is just less protein. In any case, small aggregates remain even after treatment, and this appears different from most other hexanediol experiments reported in the literature where the signals become more dispersed and uniform. This is likely because the samples are fixed.

One of the main features of using hexanediol in phase-separation is to show that upon washout, LLPS aggregates can reform. Because the cells are fixed, the critical aspect of this assay is not performed. A washout and LLPS recovery would control for cell viability issues described above and would provide the opportunity to show that total SATB1 protein levels did not change, but its distribution did, which is the essence of this assay in the context of LLPS. This review from the Tjian group is very informative and may be a good resource:

http://genesdev.cshlp.org/content/33/23-24/1619

In line with our reply to point #1 of this Reviewer (page 26 of this document), we should again emphasize that we utilized the hexanediol treatment in primary murine developing T cells as this is the only way to investigate the properties of SATB1 speckles under physiological conditions. This also explains why some small insoluble structure remains after the hexanediol treatment. Note that under physiological conditions, there is a contribution of several protein variants (such as differential PTMs) out of which some will tend to form more stable structures while others could undergo LLPS. It is not clear how the washout experiment could be applied in the primary cell conditions that include cell fixation as the heterogeneity and big variation among cells would make such data analysis highly unreliable.

**PLANNED EXPERIMENT #1**

As we answered to point #4 of Reviewer 1 (page 2), we propose the following experiment. Although the purification of recombinant SATB1 protein is quite a challenging task, yet we 1. cloned both Satb1 cDNAs for the long and short isoforms, 2. we successfully expressed both proteins in great quantity and quality and we are willing to perform in vitro reconstitution experiments if our work is considered for publication.

1. The major difference between the long and short isoform of SATB1 is the 31aa segment within the IDR. However the authors find that neither the long or short isoform SATB1 forms LLPS aggregates, and the IDR alone forms aggregates in the cytoplasm (Fig5) but they do not respond to Cry2 light activation. When forced to localize to the nucleus, it does not form aggregates as well (Fig6). The short isoform also did not form any aggregates. These results seem to argue against any isoform specific phase-separation. This experiment seems critical for the story, yet it does not support their overall conclusions. The authors might consider using a different cell line or perhaps do an in vitro assay using purified protein.

I am not certain what to make of the cytoplasmic aggregation, which appears to not form upon localization to the nucleus. Because of this, it is difficult to place weight on the significance of the S635A mutation and the role that a phosphorylation of SATB1 contributes to phase-separation, let alone function There are many additional points of concern, but the ones listed above are perhaps the most significant in terms of the overall conclusions of the paper.

In Fig. 5c we show that the full length long SATB1 isoform often aggregates unlike the short isoform. These data are accompanied with the results for the IDR region, where the situation is even more obvious (Fig. 5f,g). However, in the latter, we have to bear in mind the absence of the multivalent N-terminal part of the protein which seems to be essential for the overall phase behavior of the protein as indicated in Fig. 4b,c.

**PLANNED EXPERIMENT #1**

To further support LLPS of SATB1, we are considering performing the following in vitro experiment, as we answered to point #4 of Reviewer 1 (page 2). Although the purification of recombinant SATB1 protein is quite a challenging task, yet we 1. cloned both Satb1 cDNAs for the long and short isoforms, 2. we successfully expressed both proteins in great quantity and quality and we are willing to perform in vitro reconstitution experiments if our work is considered for publication.

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

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

This paper looks at an important nuclear matrix protein SATB1, which is a well known global chromatin organizer and help chromatin loop attach to the nuclear matrix. The paper starts with identification of novel short and long form of SATB1. Both the isoform consist of a prion like low complexity domains, but the long isoform additionally contain an extra EPF domain next the Prion like low complexity domain. The paper reports that in murine cells the long isoform is 3-4 fold more abundant than the short isoform. By using STED microscopy they show SATB1 foci lie next to transcription sites in the nucleus. They conclude by looking at the spherical shape of the SATB1 foci and the susceptibility of SATB1 staining after 1,6 hexanediol treatment that SATB1 forms the small foci in the nucleus due to LLPS. The authors also use RAMAN spectroscopy to conclude a change in nuclear chemical space in absence of SATB1 but without much explanation about which chemical bond or nuclear sub structure change correspond to the change in principal component analysis from Raman spectroscopy. The authors use the light inducible aggregation cry2 tag with the PrD domain of SATB1 and compare it with the Cry2-FUS-LC domain to conclude that the SATB1 LC domain can undergo LLPS. The authors hint at involvement of RNA and also DNA in the LLPS of the SATB1 but without going into any detail. Reviewer: The paper reports that in murine cells the long isoform is 3-4 fold more abundant than the short isoform.

Actually, in page 5 (lines 94-96) of the manuscript we write: “We confirmed that in murine thymocytes the steady state mRNA levels of the short Satb1 transcripts were about 3-5 fold more abundant compared to the steady state mRNA levels of the long Satb1 transcripts (Fig. 1d).” Although the steady state mRNA levels of the long isoform are less abundant compared to the shorter isoforms, the long isoform protein levels are almost comparable to the short isoform as deduced based on immunofluorescence experiments. Moreover, Using our two immunodepletion experiments we quantified the difference, estimating the long isoform being 1.5× to 2.62× less abundant than the short isoform (Fig. 1f and Supplementary Fig. 1b; compare lanes 2 & 3 at the lower panel). • Regarding the RAMAN spectroscopy experiments please see Minor Comment #1 of this Reviewer (page 10).

The key conclusions of the paper are- A) SATB1 undergoes LLPS. But this conclusion is drawn after correlative experiments as detailed below-

This conclusion is indeed made based on correlative experiments only for the primary murine T cells, which do not allow for any targeted experiments. However, the use of in vitro cell lines allowed us to validate these findings using the optogenetic approaches, utilizing additional experimentation.

1) observation of spherical punctae by STED-which could also seem spherical due to their small size. The resolution limit achieved by the STED microscopy used in this paper is not determined or mentioned clearly.

In the revised version of the manuscript, we have specified the resolution of our systems, for STED in Lines 745-746: ”This system enables super-resolution imaging with 35 nm lateral and 130 nm axial resolution.” and for SIM in Lines 759-761: “Images were acquired over the majority of the cell volume in z-dimension with 15 raw images per plane (five phases, three angles), providing ~120-135 nm lateral and ~340-350 nm axial resolution for 488/568 nm lasers, respectively.” The size of the observed speckles is thus above the resolution limit with sizes ranging between 40-80 nm.

The resolution of our systems is routinely verified by the following methods: The resolution of our OMX (SIM-3D) system was tested using ARGO-SIM slide containing a pattern of 36 µm long lines with gradually increasing spacing ranging from (left to right) 0 to 390 nm, with a step of 30 nm (Fig. 1 below). Our SIM system was able to clearly resolve two lines separated by 120 nm.

2) No live cell FRAP experiment with fluorescent SATB1 long or short isoform to show that these foci are liquid like

We did perform FRAP experiments for the SATB1 N-terminus optogenetic construct as demonstrated in Fig. 4f. We did not perform FRAP in the primary murine T cells as this is not technically feasible without creating a new mouse line with fluorescently labeled protein. In the revised version of the manuscript, we additionally performed FRAP experiments for the full length short and long isoform of SATB1 labeled with EGFP and transfected into the NIH-3T3 cell line (Supplementary Figure 6f).

5) LLPS is strongly coupled to the cellular concentration of the proteins. Authors should quantify the cellular concentration of the long and short isoform in the cells.

We did consider protein concentration in our analyses of optogenetic constructs in Fig. 4b,d,e and Supplementary Fig. 6a,b,c. Quantifying the physiological cellular concentration of short and long SATB1 protein isoforms in primary T cells is impossible due to the inherent inability to discriminate between the isoforms by two antibodies, in the absence of Satb1 isoform-specific knockout mice.

However, an approximation of the cellular concentration can be obtained from our immunodepletion experiments. On top of the original immunodepletion experiment that we now present in Supplementary Fig. 1a,b; in the revised version of the manuscript we have repeated the experiment in Fig. 1e,f. Comparison of the two bands for the long and short SATB1 isoforms in the lower panel of the western blot figures suggest that the long SATB1 isoform protein levels are 1.5× to 2.62× less abundant than the short isoform, according to the original and new immunodepletion experiment, respectively. This is now also included in the main text in Lines 110-116: “This experiment can also be used for approximation of the cellular protein levels of SATB1 isoforms in primary murine thymocytes. Comparison of the two bands for long (lane 2) and short SATB1 (lane 3) isoform in the lower panel of Fig. 1f and Supplementary Fig. 1b, suggests that the long SATB1 isoform protein levels may be about 1.5× to 2.62× less abundant than the short isoform, according to the two replicates of our immunodepletion experiment, respectively.”

Major conclusion B)- SATB1 regulates transcription and splicing.

This was also shown previously and in this paper they show the close proximity of the transcription site and SATB1 foci by microscopy. Hexanediol treatment which lead to loss of colocalization between FU foci and SATB1 is also taken as an evidence in regulation of transcription is not right as the transcription foci itself can be dissolved using 1,6 Hexanediol. Although the rate of transcription is not measured quantitatively.

As mentioned in comment #3 (page 29) of this Reviewer, unfortunately there is no better tool to investigate these questions in primary cells than using microscopy approaches in conjunction with hexanediol treatment. However, we should also note that there is an accompanying manuscript from our group that is currently being under revision in another journal (preprint available: Zelenka et al., 2021; https://doi.org/10.1101/2021.07.09.451769). In the preprint manuscript, we showed that: 1. the long SATB1 isoform binding sites have increased chromatin accessibility than what expected by chance (Fig. 3b), 2. there is a drop in chromatin accessibility at SATB1 binding sites in Satb1 cKO mouse (Fig. 3c) and 3. this drop in chromatin accessibility is especially evident at the transcription start sites of genes (Supplementary Fig. 1i)

We believe that, together these data suggest a direct involvement of SATB1 in transcription regulation. Also note the vast transcriptional deregulation that occurs in Satb1 cKO T cells, affecting the expression of nearly 2000 genes (Fig. 2f, this revised manuscript). That is why we believe that the co-localization analysis, using super-resolution microscopy, presented in Fig. 2c and quantified in Fig. 3g, represents a nice additional support to our claims. Moreover, in the revised version of the manuscript we now present a positive correlation between SATB1 binding and deregulation of splicing (Supplementary Fig. 4d) which also supports its direct involvement in the regulation of transcriptional and co-transcriptional processes.

In the revised version of the manuscript we have made this clear in Lines 182-194: “Satb1 cKO animals display severely impaired T cell development associated with largely deregulated transcriptional programs as previously documented19,37,38. In our accompanying manuscript19, we have demonstrated that long SATB1 isoform-specific binding sites (GSE17344619) were associated with increased chromatin accessibility compared to randomly shuffled binding sites (i.e. what expected by chance), with a visible drop in chromatin accessibility in Satb1 cKO. Moreover, the drop in chromatin accessibility was especially evident at the transcription start site of genes, suggesting that the long SATB1 isoform is directly involved in transcriptional regulation. Consistent with these findings and with SATB1’s nuclear localization at sites of active transcription, we identified a vast transcriptional deregulation in Satb1 cKO with 1,641 (922 down-regulated, 719 up-regulated) differentially expressed genes (Fig. 2f). Specific examples of transcriptionally deregulated genes underlying SATB1-dependent regulation are provided in our accompanying manuscript19. Additionally, there were 2,014 genes with altered splicing efficiency (Supplementary Fig. 4d-e; Supplementary File 3-4). We should also note that the extent of splicing deregulation was directly correlated with long SATB1 isoform binding (Supplementary Fig. 4d).”

Major conclusion C)-Post transcriptional modification is important for SATB1 function.

This point is just barely touched upon in the last figure of the paper

We would not call the identification of the novel phosphorylation site as a main conclusion of our manuscript. Though, it is already known that posttranslational modifications of SATB1 are important for its function as they can function as a molecular switch rendering SATB1 into either an activator or a repressor (Kumar et al., 2006; https://doi.org/10.1016/j.molcel.2006.03.010).

In the revised manuscript, we support the effect of serine phosphorylation on the DNA binding capacity of SATB1 by another experiment. We have performed DNA affinity purification experiments utilizing primary thymocyte nuclear extracts treated with phosphatase (Supplementary Fig. 7b). We found that SATB1’s capacity to bind DNA (RHS6 hypersensitive site of the TH2 LCR) is lost upon treatment with phosphatase (Supplementary Fig. 7c). These results are in line with the data presented in Supplementary Fig. 7d, indicating the lost ability of SATB1 to bind DNA upon mutating the discovered phosphorylation site S635. Given the importance of posttranslational modifications of proteins on LLPS, we found it relevant to include it in our manuscript. Even more so, when we identified SATB1 aggregation, upon mutation of this phospho site (Fig. 6d).

Overall I find that the major conclusion-point A and B, is based on very indirect experiments and needs much more convincing data and the role of SATB1 LLPS in cells should be demonstrated more rigorously. And conclusion C is barely described and needs a lot more cell biological and genetic evidence.

One of the major assets of our work is that most of our data are based on the analysis of primary murine T cells and thus investigating the biological roles of the endogenous SATB1 protein, under physiological conditions. We apologize that we did not make it clear to this Reviewer, that our system has certain inherent limitations due to the utilization of primary cells.

I do not recommend publishing the paper in current state. The story needs much more experiment to convincingly prove the major conclusions. Further, the MS needs more careful thinking and presentation to make it streamlined.

We hope that in the revised version we have significantly improved the quality of our manuscript by implementing the suggested changes.

Minor comments: One of the major flaw of the paper is the use too many techniques without proper explanation. E.g. use of STED and RAMAN microscopy need controls and explanation on what is being quantified. The use of Raman microscopy to quantify the nuclear environment of nucleus is not related to the chromatin organization or LLPS of SATB1 at all. And no information is provided at all which aspect of nuclear organization is being measured in Raman and what it means for the LLPS of SATB1.

We do provide quite a thorough explanation of Raman spectroscopy and the underlying quantification in Lines 224-231: “we employed Raman spectroscopy, a non-invasive label-free approach, which is able to detect changes in chemical bonding. Raman spectroscopy was already used in many biological studies, such as to predict global transcriptomic profiles from living cells42, and also in research of protein LLPS and aggregation43–47. Thus we reasoned that it may also be used to study phase separation in primary T cells. We measured Raman spectra in primary thymocytes derived from both WT and Satb1 cKO animals and compared them with spectra from cells upon 1,6-hexanediol treatment. Principal component analysis of the resulting Raman spectra clustered the treated and non-treated Satb1 cKO cells together, while the WT cells clustered separately (Fig. 3h).” We also do provide controls as the method was performed on both treated and untreated WT and Satb1 cKO cells.

Regarding the RAMAN spectroscopy experiments we now provide more information on the changes of chemical bonds altered between wild type and Satb1 cKO thymocytes. Following principal component analysis, we have extracted the two main principal components that were used for the clustering of our data. The differences are presented in Supplementary Fig. 5d.

We do realize that RAMAN spectroscopy, although a quite novel approach utilized to study LLPS, has not been used to study LLPS in live cells. If deemed proper we are willing to avoid presenting these results in this manuscript.

Similarly for Hexanediol treatment, duration of treatment is missing. Hexanediol can also dissolve the liquid like transcription foci. And hence a decrease in correlation between SATB1 foci and FU foci cannot be taken as a measure of SATB1 foci connection to transcription alone

The duration of hexanediol treatment was 5 minutes as presented in Line 724 and in the revised version of the manuscript also in Lines 1206-1207. We should also note that additionally, we performed experiments with different hexanediol concentrations and timing varying from 1 minute to 10 minutes with results consistent with the data presented.

It is not very clear how many times the STED or Raman microscopy is done on how many samples and biological replicates. Similarly for RNA sequencing number of samples and description of controls are missing. Also if the sequencing data is made publicly available is not clear.

Data availability is clearly stated in Lines 506-509: “RNA-seq experiments and SATB1 binding sites are deposited in Gene Expression Omnibus database under accession number GSE173470 and GSE173446, respectively. The other datasets generated and/or analyzed during the current study are available upon request.”

The Reviewer’s token is “wjwtmeeeppovzqx”.

RNA sequencing was performed in a biological triplicate for each genotype as stated in the GEO repository and now also in Line 566 of the revised manuscript.

In Lines 180-181, we also state that it was performed on Satb1 cKO animals and WT mice as a control: “we performed stranded-total-RNA-seq experiments in wild type (WT) and Satb1fl/flCd4-Cre+ (Satb1 cKO) murine thymocytes”.

In Lines 739-740, we now also state that all imaging approaches were performed on at least two biological replicates (different mice) and please also note the fact that all findings were based on data from both STED and 3D-SIM methods, allowing to minimize detection of artifacts. In the Raman spectroscopy figure, each point represents measurements from an individual cell and for each condition we used 2-5 biological replicates (Lines 831-832 & Line 1169).

Similarly, in Lines 129-132 we provided a quite detailed description of differences between STED and 3D-SIM, even though these techniques are not that rare as Raman spectroscopy in biology research.

Additional control is needed to report the resolution limit of Superresolution techniques-STED and 3D-SIM systems used by them.

We have already provided this information in our reply to comment #1 of this Reviewer (pages 6-7): In the revised version of the manuscript, we have specified the resolution of our systems, for STED in Lines 745-746: ”This system enables super-resolution imaging with 35 nm lateral and 130 nm axial resolution.” and for SIM in Lines 759-761: “Images were acquired over the majority of the cell volume in z-dimension with 15 raw images per plane (five phases, three angles), providing ~120-135 nm lateral and ~340-350 nm axial resolution for 488/568 nm lasers, respectively.” The resolution of our systems is routinely verified by the following methods: The resolution of our OMX (SIM-3D) system was tested using ARGO-SIM slide containing a pattern of 36 µm long lines with gradually increasing spacing ranging from (left to right) 0 to 390 nm, with a step of 30 nm (Fig. 1 below). Our SIM system was able to clearly resolve two lines separated by 120 nm.

Would be very helpful if the zonation was plotted for the FluoroUridine (FU) also to show that Zone1 (heterochromatin) is completely depleted of FU, and is present in other regions.

In the revised version of the manuscript, we performed the suggested analysis and in Supplementary Fig. 3a we now show that indeed FU is significantly less localized to Zone 1 (heterochromatin) and has the most abundant localization in Zones 3 and 4, similar to the localization of SATB1 protein, as demonstrated in Fig. 2b.

Scale bar needed figure 3d

In the revised version of the manuscript, we included scale bars which are both 0.5 µm (line 1213).

Perfectly rounded SATB1 foci- this does not mean LLPS. For LLPs measurement, protein condensate dynamics measurement by FRAP or fusion experiments is required. What is the size of condensates? and cellular concentration of SATB1? Will SATB1 undergo LLPS in vitro at similar concentrations? does SATB1 interact with DNA or RNA to undergo LLPS ?

We toned down this sentence which now reads: “Here we demonstrated its connection to transcription and found that it forms spherical speckles (Fig. 1g), markedly resembling phase separated transcriptional condensates. (Lines 200-202)”.

Moreover, as explained in earlier replies to comments of this Reviewer, we cannot perform FRAP on primary murine T cells without generating a new mouse line. We did, however, use FRAP and other in vitro approaches including visualization of droplet fusion in ex vivo experiments utilizing cell lines. Moreover, we are willing to demonstrate the LLPS properties of SATB1 on in vitro purified SATB1 protein as indicated in the suggested experiment of Point#4 (page 2).

After careful reading of the MS I conclude that the main conclusions of the paper are very preliminary and need much more detailed experiments. So does not qualify to get published at all at this stage.

**Reviewer #1 (Significance)**:

The present manuscript tries to connect the phase separation of SATB1 to understanding the mechanism of SATB1 function in cells. One of the major hallmarks of phase separation is dynamic, liquid-like behaviour and in absence of these measurements, it is very difficult to say that the current manuscript has made any contribution to showing that SATB1 can phase separate.

The presence of 2 isoforms of SATB1 is a novel finding and the paper could have focused more on this. E.g. elucidate expression of the isoform during thymocyte development and maturation.

As a reviewer my expertise are cell biology experiments, microscopy, in vitro reconstitution assays, RNA binding proteins, RNA and RBP condensate formation. And I feel that the reconstitution experiments are an important tool for understanding phase behaviour of proteins and also to gauge if this behaviour can occur or not in cellular concentration and conditions.

I do not have sufficient expertise in Raman microscopy and hence the information provided in the MS on this part was not enough to understand the experiment and conclusions drawn from it.

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

The authors have reported the existence of a 'long' SATB1 isoform which also undergoes LLPS. The authors tried to draw multiple comparisons and pointed out distinction between phase properties of SATB1 isoforms. The authors also touch upon two functional roles of SATB1. Although a wide array of assays are used, the data presented and hence the manuscript makes multiple transitions into disparate hypotheses without diving deep into a single hypothesis. As a result, the connections drawn are unclear, and do not converge at best. The authors have used number of techniques, however, the results do not support their conclusions and they appear hastily drawn. It is not clear why the authors jump from one context to the other, discussing LLPS first, then transcription, splicing, post-translational modification and finally cancer. The link between all of these isn't clear and not fully supported by data. It appears that the authors wish to focus on Satb1's physiological role in development, hence the data on breast cancer is confusing. Thus, this work suffers from multiple pitfalls. Specific comments are given below:

Major comments 1. Importantly, in Fig 1d, there is no statistics shown. There is no mention of number of replicates as well in the legends. Proper statistical evaluation is critical for interpreting this result.

Please note that Fig. 1d only serves as a control to the sequencing experiment in Fig. 1b. In Line 566, we now state that for the RNA-seq: “A biological triplicate was used for each genotype.” To validate these data, we further designed a RT-qPCR experiment which was performed on three technical replicates from a male and female mouse. We now state this in Line 636. For the low number of samples, statistical tests are not accurate but we still added t test into the figure Fig. 1d and specified it also in the figure legend in Line 1169-1170.

1. Figure 1f presents one of the weakest evidences in the manuscript. There are a number of corrections needed. Firstly, being their major and only validation figure for their custom antibody, the immunoblot is not clean, bands are fuzzy. Importantly, as the authors claim that the antibody is highly specific to 'long' SATB1, after the IP there should be only a single band (like input) of Satb1 long. But that does not seem to be the case, rather an array of bands are visible below (lane 2 top panel). This could easily mean that the shorter isoforms or non-specific protein bands are also pulled down with the 'long' form specific antibody. Therefore, raising a critical concern regarding the specificity of the antibody.

• The long antibody was raised in mice inoculated with the extra peptide present in the long isoform only. Therefore, the capacity of this antibody precipitating the shorter isoforms, which do not express the sequence of the extra peptide (EP, Figure 1a) in not possible. • We have repeated the immunodepletion experiment and we now provide the results in Fig. 1f and Supplementary Fig. 1b. The western blot in Fig. 1f is now cleaner and supports quite convincingly the presence of a long SATB1 isoform. Given the lack of isoform-specific knockouts which we could utilize to immunoprecipitate or detect the different isoforms in a single cell (or cell population), the utilized approach of immunodepletion and subsequent western blotting is the approach we thought of implementing. • As shown in Fig. 1f and Supplementary Figure 1b, the long isoform SATB1 antibody has the capacity to recognize the long isoform in murine thymocyte protein extracts but not the short SATB1 isoform (please compare lane 3 in the two western blots utilizing either the antibody for the long isoform -top panel - or the antibody that detects both isoforms (lower panel). • We have performed Immunofluorescence experiments utilizing the antibody detecting the long SATB1 isoform in thymocytes isolated from either C57BL/6 or Satb1 cKO mice. The antibody is specific to the SATB1 protein since there is no signal in immunofluorescence experiments utilizing the knockout cells (Supplementary Figure 1c). • We have performed Immunofluorescence experiments utilizing thymocytes and the antibody detecting the long SATB1 or a commercially available antibody detecting all SATB1 isoforms. The pattern of SATB1 subnuclear localization is similar for both antibodies (Supplementary Figure 1e). • In our accompanying revised manuscript Zelenka et al., 2022 (https://doi.org/10.1101/2021.07.09.451769), we provide yet another piece of evidence, consisting of bacterially expressed short and long SATB1 protein isoforms detected by western blot using either the long isoform-specific or the non-selective all SATB1 isoforms antibodies. • Regarding the additional bands detected in the immunoprecipitation experiment presented in the original Supplementary Figure 1b (lane 2), it is not surprising that additional bands appear in a sample of protein extracts that is used for several hours for the immunoprecipitation experiments, while the “input” sample simply denotes protein extract that is frozen at -80oC right after the preparation of protein extracts until use. It is well-established that SATB1 is the target of proteases which might as well be active during the immunoprecipitation steps (2 consecutive immunoprecipitation steps take place). Therefore, the immunoprecipitated material cannot necessarily be a copy of the input material displaying a single protein band even if protease inhibitors are included in the buffers.

Taken together the experiments described here we showed that the antibody raised against the extra 31 aa long peptide, present only in the long SATB1 isoform, is specific for this isoform.

1. Related to Fig. 2 a, the authors state on Pg 5, '....the euchromatin and interchromatin regions (zones 3 & 4, Fig. 2a, b).' Although the DAPI correlation seems clear, there is no mention on how they reached the above said correlation. They should at least show a parallel speckle staining for HP1 or signature modification such as H3K4me9 STEDs for making supporting such a claim. DAPI alone is not sufficient. The authors should rectify the text thoroughly for many such interpretations without validation/reference or provide relevant data.

This is a great suggestion we have again taken under consideration and we added the following experiments and the appropriate changes in the revised version of our manuscript. • We modified the text and added a reference to Miron et al., 2020 (https://doi.org/10.1126/sciadv.aba8811) supporting our claims regarding SATB1 localization in relation to DAPI staining. • We have also added new microscopy images for HP1, H3K4me3 and fibrillarin staining and quantified the localization of FU-stained sites of active transcription in nuclear zones, to further support our claims. • This whole modified part in Lines 139-167 then reads: “ “The quantification of SATB1 speckles in four nuclear zones, derived based on the relative intensity of DAPI staining, highlighted the localization of SATB1 mainly to the regions with medium to low DAPI staining (zones 3 & 4, Fig. 2a, b). A similar distribution of the SATB1 signal could also be seen from the fluorocytogram of the pixel-based colocalization analysis between the SATB1 and DAPI signals (Supplementary Fig. 2a). SATB1’s preference to localize outside heterochromatin regions was supported by its negative correlation with HP1β staining (Supplementary Fig. 2b). Localization of SATB1 speckles detected by antibodies targeting all SATB1 isoforms and/or only the long SATB1 isoform, revealed a significant difference in the heterochromatin areas (zone 1, Fig. 2b), where the long isoform was less frequently present (see also Fig. 2a and Fig. 3c). Although, this could indicate a potential difference in localization between the two isoforms, due to the inherent difficulty to distinguish the two based on antibody staining, we refrain to draw any conclusions. The prevailing localization of SATB1 corresponded with the localization of RNA-associated and nuclear scaffold factors, architectural proteins such as CTCF and cohesin, and generally features associated with euchromatin and active transcription32. This was also supported by colocalization of SATB1 with H3K4me3 histone mark (Supplementary Fig. 2c), which is known to be associated with transcriptionally active/poised chromatin. Given the localization of SATB1 to the nuclear zones with estimated transcriptional activity32 (Fig. 2b, zone 3), we investigated the potential association between SATB1 and transcription. We unraveled the localization of SATB1 isoforms and the sites of active transcription labeled with 5-fluorouridine. Sites of active transcription displayed a significant enrichment in the nuclear zones 3 & 4 (Supplementary Fig. 3a), similar to SATB1. As detected by fibrillarin staining, SATB1 also colocalized with nucleoli which are associated with active transcription and RNA presence (Supplementary Fig. 3b). Moreover, we found that the SATB1 signal was found in close proximity to nascent transcripts as detected by the STED microscopy (Fig. 2c). Similarly, the 3D-SIM approach indicated that even SATB1 speckles that appeared not to be in proximity with FU-labeled sites in one z-stack, were found in proximity in another z-stack (Supplementary Fig. 3c). Additionally, a pixel-based colocalization of SATB1 and sites of active transcription is quantified later in the text in Fig. 3g, supporting their colocalization.”

1. The authors mention, '...of the different SATB1 isoforms, uncovered by the use of the two different antibodies, relied in the heterochromatin areas (zone 1), where the long isoform was less frequently...' There is no supporting figure number mentioned. The authors need to show a zone-by-zone comparison images for 'all iso' vs 'long' iso of SATB1. Just to reiterate, there is a need for a heterochromatin mark to unambiguously call out the distinction.

We should remind that there is an inherent difficulty to accurately compare localization of short and long SATB1 isoforms in primary cells, especially due to the lack of Satb1 isoform-specific knockout mice. There is no way to detect only the short isoform in these primary cells as there are only antibodies targeting the long or all SATB1 isoforms. Therefore, we cannot set up additional experiments probing these questions.

In line with this, in the revised version of the manuscript, we toned down our statements regarding the differential localization of the two isoforms in primary cells. We only refer to it as an indication and we support it by adding references to the relevant figures. This part now reads: “Localization of SATB1 speckles detected by antibodies targeting all SATB1 isoforms and/or only the long SATB1 isoform, revealed a significant difference in the heterochromatin areas (zone 1, Fig. 2b), where the long isoform was less frequently present (see also Fig. 2a and Fig. 3c). Although, this could indicate a potential difference in localization between the two isoforms, due to the inherent difficulty to distinguish the two based on antibody staining, we refrain to draw any conclusions. (Lines 145-150)”

1. On the same lines, '....Given the localization of SATB1 to the nuclear zones with estimated transcriptional activity (Fig. 2b, zone 3)....' How was the region labelled as transcriptionally active? For the statistical analysis of speckle count for the two antibodies' staining, the claim posited is a bit bigger. This could simply be true for that cell. The authors thus need to statistically analyse the speckle counts for multiple cells. This needs to be done for all imaging statistics done in multiple figures throughout the manuscript.

As mentioned in our reply to the two previous comments of this Reviewer, transcriptional activity in relation to the nuclear zonation is well established in the literature. To make this clear, we have now added the reference to Miron et al., 2020 (https://doi.org/10.1126/sciadv.aba8811) supporting our claims and additionally we have also included HP1, H3K4me3 and fibrillarin staining and quantification of FU signal in the nuclear zones. Moreover, it is not clear to which particular cell the comment refers to. The presented dots in Fig. 2b represent individual cells and the relative proportions of speckles in each nuclear zone are plotted on the y axis. In the revised version of the manuscript, we added into the figure the number of cells scored and we adapted the figure legend so that it is absolutely clear that we have analyzed multiple cells:

“Nuclei of primary murine thymocytes were categorized into four zones based on the intensity of DAPI staining and SATB1 speckles in each zone were counted. Images used represented a middle z-stack from the 3D-SIM experiments. The graph depicts the differences between the long and all SATB1 isoforms’ zonal localization in nuclei of primary murine thymocytes. (Lines 1189-1193)”

1. For figure 2c. the authors have used 5 Fluorouridine for nascent RNA speckles. 5FU is known to have a spread signal type (with strong association to nucleolus as well). This is not the case for the image presented 2c. The authors should resolve this by showing different sets of images.

Developing and naive T cells are very unique in terms of their metabolic features and thus they should not be directly compared with other cell types. Therefore, we would not expect to see such a spread FU pattern as previously shown for other cell types. Having said that, we could not find any reference publication that utilized super-resolution microscopy to detect localization of FU-stained sites of active transcription in developing primary T cells. However, we performed additional immunofluorescence experiments to demonstrate the colocalization or its lack between SATB1 and HP1 (Supplementary Fig. 2b), H3K4me3 (Supplementary Fig. 2c) and fibrillarin (Supplementary Fig. 3b). Moreover, we provide additional regions of SATB1 and FU staining in Supplementary Fig. 3c. The modified text reads:

“We unraveled the localization of SATB1 isoforms and the sites of active transcription labeled with 5-fluorouridine. Sites of active transcription displayed a significant enrichment in the nuclear zones 3 & 4 (Supplementary Fig. 3a), similar to SATB1. As detected by fibrillarin staining, SATB1 also colocalized with nucleoli which are associated with active transcription and RNA presence (Supplementary Fig. 3b). Moreover, we found that the SATB1 signal was found in close proximity to nascent transcripts as detected by the STED microscopy (Fig. 2c). Similarly, the 3D-SIM approach indicated that even SATB1 speckles that appeared not to be in proximity with FU-labeled sites in one z-stack, were found in proximity in another z-stack (Supplementary Fig. 3c). Additionally, a pixel-based colocalization of SATB1 and sites of active transcription is quantified later in the text in Fig. 3g, supporting their colocalization. (Lines 157-167)”

1. Fig 2 d., the authors have suddenly jumped solely to 'all iso' Satb1 here for IP MS. Is there a reason for that? The authors either need to do this with 'long iso' antibody or remove the analysis from the manuscript as it does not add to their primary aim of the manuscript. Also, the authors have only selectively talked about two clusters? What about chromatin related proteins? It is quite intuitive to have highest enrichment of these given previous literature and even IP MS data by other groups. Thus, it is necessary to revise this thoroughly or remove it.

We appreciate the acknowledgment by the Reviewer that our IP-MS data identified anticipated factors. In the revised version of the manuscript we modified the underlying text to accommodate references to these former findings revealing interactions between SATB1 and chromatin modifying complexes: “Apart from subunits of chromatin modifying complexes that were also detected in previous reports25,33–36, unbiased k-means clustering of the significantly enriched SATB1 interactors revealed two major clusters consisting mostly of proteins involved in transcription (blue cluster 1; Fig. 2d and Supplementary Fig. 4c) and splicing (yellow cluster 2; Fig. 2d and Supplementary Fig. 4c). (Lines 170-174)”

Please note that many subunits of chromatin modifying and chromatin-related complexes are in fact characterized as transcription-related factors, therefore our statements are not in disagreement with the former findings. Note also that we provide Supplementary File 1 & 2 with comprehensive description of our IP-MS data for the readers’ convenience. Please also note that we are the first group to report on the existence of the long isoform. Therefore, we find it absolutely reasonable to perform IP-MS experiment for all SATB1 isoforms which can then be used for a comparison with other publicly available datasets. We believe that there is no contradiction in this experimental setup in relation to the rest of the manuscript. We discuss the two major clusters simply because they are the two major clusters identified as indicated in Fig. 2d. Additionally, in Supplementary Fig. 4c, we provide a comprehensive description of all significantly enriched interactors including their cluster annotation and thus anyone can investigate the data if needed.

1. In relation to Fig. 2f, the authors have not mentioned any of the previously published work on Satb1 CD4 specific KO, not even the RNA seq studies the other groups have reported under the same condition. Only an unpublished reference of their own (preprint) is cited. It is imperative to show how much their data corroborates with other published studies. Additionally, what is the binding site status of dysregulated genes?

In the revised version of the manuscript, we have included the references to other studies using the same Satb1 conditional knockout. Moreover, we have clarified the relationship between SATB1 binding and gene transcription. The modified part in Lines 182-194 now reads: “Satb1 cKO animals display severely impaired T cell development associated with largely deregulated transcriptional programs as previously documented19,37,38. In our accompanying manuscript19, we have demonstrated that long SATB1 isoform specific binding sites (GSE17344619) were associated with increased chromatin accessibility compared to randomly shuffled binding sites (i.e. what expected by chance), with a visible drop in chromatin accessibility in Satb1 cKO. Moreover, the drop in chromatin accessibility was especially evident at the transcription start site of genes, suggesting that the long SATB1 isoform is directly involved in transcriptional regulation. Consistent with these findings and with SATB1’s nuclear localization at sites of active transcription, we identified a vast transcriptional deregulation in Satb1 cKO with 1,641 (922 down-regulated, 719 up-regulated) differentially expressed genes (Fig. 2f). Specific examples of transcriptionally deregulated genes underlying SATB1-dependent regulation are provided in our accompanying manuscript19. Additionally, there were 2,014 genes with altered splicing efficiency (Supplementary Fig. 4d-e; Supplementary File 3-4). We should also note that the extent of splicing deregulation was directly correlated with long SATB1 isoform binding (Supplementary Fig. 4d).”

1. In context of Figure 3a and b, the authors write .'...The long SATB1 isoform speckles evinced such sensitivity as demonstrated by a titration series with increasing concentrations of 1,6-hexanediol treatment followed...' Whereas it is apparent from the image at least that overall numbers of individual speckles are instead increased at both 2 and 5%. There is although a clear spreading of restricted speckles compared to the controls. The authors should revise their figures to substantiate the associated text. Furthermore, there needs to be 'all iso' SATB1 3D SIM imaging and not just quantitation for comparison. This is also true for panel c in order to demonstrate the effect.

In the revised Fig. 3a we provide new images which better reflect the underlying data analysis. Moreover, in Fig. 3c and Fig. 3d we provide an additional comparison between SATB1 all isoforms and long isoform staining and their changes upon hexanediol treatment, detected by both the 3D-SIM and STED approaches. It is true that upon treatment, there tend to be more speckles, however these are much smaller as they are gradually being dissolved. Depending on the treatment duration, the cells are swollen which is reflected in increased spreading of speckles. Nevertheless, the nuclear size was considered in all the quantification analyses. We believe that the new images provide better evidence of SATB1’s sensitivity to hexanediol treatment.

1. Fig. 3 d also does not clearly demonstrate what the authors have claimed '...hexanediol treatment highly decreased colocalization between...' The figure shows at best decreased signal intensity for both SATB1 and FU. We suggest that the authors should give a statistical analysis as well for the colocalization points between the two using multiple source images. Lastly, the two images shown (control and treated), there seems to be a clearly visible magnification difference. The authors should clarify this.

• In the revised version of the manuscript in Figure 3d, we have provided scale bars, which are both 0.5 µm (line 1213). The difference observed by this Reviewer is actually the main reason why we provided this image. Figure 3d demonstrates that upon hexanediol treatment, the speckles are mostly missing or significantly reduced in size, for both FU and SATB1 staining. • Moreover, the suggested statistical analysis is also provided – in Figure 3e. In Figure 3e, we performed pixel-based colocalization analysis which is a method that allows both quantification and statistical comparison of colocalization between two factors and between different conditions. Please note especially the decreased colocalization between long SATB1 isoform and FU-stained sites of active transcription in the left graph, which is in agreement with our claims in the manuscript. • Moreover, our data are compared to a negative control, i.e. 90 degrees rotated samples, which is a common method in colocalization experiments as described for example in Dunn et al., 2011 (https://doi.org/10.1152/ajpcell.00462.2010). • Additionally, we provide Costes’ P values which are based on randomly scrambling the blocks of pixels (instead of individual pixels, because each pixel’s intensity is correlated with its neighboring pixels) in one image, and then measuring the correlation of this image with the other (unscrambled) image. Please see Costes et al., 2004 (https://doi.org/10.1529%2Fbiophysj.103.038422) for more details.

1. Figure 3f. The authors show the PC plot for Raman spectroscopy for phase behaviour due to Satb1. The experiment and its related text seems misinterpreted; the authors write...' ese bonds were probably enriched for weak interactions responsible for LLPS that are susceptible to hexanediol treatment. This shifted the cluster of WT treated cells towards the Satb1 cKO cells. However, the remaining covalent bonds differentiated the WT samples from Satb1 cKO cells......' whereas the clusters are clearly far away in 3D for both WT and KO while being closer to their respective treatments. Which is also intuitive given the sensitivity of Raman spectroscopy. Thus, it is more likely to be treatment effect and KO effect as separate. Treatment of WT leads to KO like spectra is far-fetched. Thus, the authors need to show separate PCs and modify their text thoroughly.

We do not present any 3D graph hence it is not clear what the Reviewer refers to. Please also note that as stated in Lines 817-818, we used a customized Raman Spectrometer. Therefore, this approach allowed us to measure Raman spectra at cellular and even sub-cellular levels. For example, solely by utilizing Raman spectroscopy, we can now distinguish euchromatin and heterochromatin, methylated and unmethylated DNA and RNA, etc. This, together with other reports, such as Kobayashi-Kirschvink et al., 2018 (https://doi.org/10.1016/j.cels.2018.05.015) and Kobayashi-Kirschvink et al., 2022 (https://doi.org/10.1101/2021.11.30.470655), indicate a potential use of Raman in biological research. In our manuscript, we used this method as a supplementary approach, however we do find it noteworthy. We should also emphasize that in the revised Raman spectroscopy Fig. 3h, each point represents measurements from an individual cell and for each condition we used 2-5 biological replicates (Lines 831-832 & Lines 1225-1226). We specifically refer to the principal component 1 (PC1) that differentiates the samples. Therefore, there are certain spectra (representing certain chemical bonding) that allowed us to differentiate between WT and Satb1 cKO. The same type of bonding was then affected when WT samples were treated with hexanediol and we also had controls to rule out the impact of hexanediol on the resulting spectra.

1. In Fig 4. b, The authors have shown the propensity of SATB1 N terminus to phase separate using different optodroplet constructs. Although the imaging is clear, why are the regions selected not uniform when comparing various constructs?

We have selected images that would best represent each category. Please note that this was live cell imaging of photo-responsive constructs, thus there are many limitations regarding the area selection. Very often, even the brief time of bright light exposure to localize cells may trigger protein clustering. Upon disassembly, every new light exposure of the same cell then triggers much faster assembly which skews the overall results. It is therefore desired to work fast, while neglecting selection of equally sized cells. Moreover, it is not clear how would the proposed change improve the quality of our manuscript.

1. Figure 5a, the disassembly should be shown for 'long' SATB1 as well. On pg 13, the authors write '....cytoplasmic protein aggregation has been previously described for proteins containing poly-Q domains and PrLDs..' no reference given.

• In the revised version of the manuscript, we present the assembly and disassembly for both short and long full length SATB1 optogenetic constructs. To increase clarity, we present the behavior of the short and long isoforms as two separate images in Figure 5a and Figure 5b, respectively. • Moreover, we provided references to the statement regarding aggregation of PrLD and poly-Q-containing proteins in Lines 305-309, which now reads: ”Since protein aggregation has been previously described for proteins containing poly-Q domains and PrLDs8,11,38,39, we next generated truncated SATB1 constructs encoding two of its IDR regions, the PrLD and poly-Q domain and in the case of the long SATB1 isoform also the extra peptide neighboring the poly-Q domain (Fig. 1a and 4a).”

1. Fig. 5d, Is there an amino-acid specific reasoning to support the authors claim of the phase behaviour due to extra peptide? They need to show a proper control with equal extra (unrelated) peptide to show the specificity. Are the shorter isoform aggregates responsive to light?

• We have referred to the amino acid composition bias in Fig. 5c. In the revised version of the manuscript, we made this clear by showing the composition bias in the new revised Fig. 5e. The related part of the main text then reads: “Computational analysis, using the algorithm catGRANULE37, of the protein sequence for both murine SATB1 isoforms indicated a higher propensity of the long SATB1 isoform to undergo LLPS with a propensity score of 0.390, compared to 0.379 for the short isoform (Fig. 5d). This difference was dependent on the extra peptide of the long isoform. Out of the 31 amino acids comprising the murine extra peptide, there are six prolines, five serines and three glycines – all of which contribute to the low complexity of the peptide region3 (Fig. 5e).” (Lines 298-304) • Moreover, we should note that the low complexity extra peptide of the long SATB1 isoform directly extends the PrLD and IDR regions as indicated in Fig. 4a and which we now directly state in Lines 304-305: “Moreover, the extra peptide of the long SATB1 isoform directly extends the PrLD and IDR regions as indicated in the Fig. 4a.” • We show in Fig. 4, that the N terminus of SATB1 undergoes LLPS. Since this part of SATB1 is shared by both isoforms, it is reasonable to assume that both isoforms would undergo LLPS. This is also in line with the observed photo-responsiveness of both short and long full length SATB1 isoforms in CRY2 optogenetic constructs in revised Fig. 5a,b, and similar FRAP results for both short and long full length SATB1 isoform constructs transiently transfected in NIH-3T3 cells in the revised Supplementary Fig. 6f. However, the main reason why we think that the difference in LLPS propensity between the isoforms is important is because the long isoform is more prone to aggregate compared to the short isoform, as documented in Fig 5c,f,g and Supplementary Fig. 5f.

1. Fig 6c., It is important that authors show the data for NLS+short iso data as well to prove their hypothesis.

As shown in original Figure 5d, the long SATB1 isoform undergoes cytoplasmic aggregation, unlike the short SATB1 isoform (as shown in the same Figure). Therefore, an image of the NLS + short isoform would not be related to our hypothesis. Actually, we wanted to reverse the long SATB1 isoform’s relocation, from the aggregated form in the cytoplasm into the nucleus. Nevertheless, to show the complete picture, in the revised version of the manuscript in Figure 6c, we now provide data for both short and long SATB1 isoforms.

1. Fig 6d., The authors claim that mutating a specific P site changes the phase behaviour of the 'short iso'. Does it also increase for the long isoform? The authors need to confirm this in order to verify the effect of a single P site outside of oligomerization domain. ...' phosphorylation status; when phosphorylated it remains diffused, whereas unphosphorylated SATB1 is localized to PML bodies....' This being an important premise, thus should be moved to the results text.

In the revised version of the manuscript, we moved the part regarding PML in the results section, as suggested by the Reviewer. Moreover, we included additional experiments probing the impact of association between PML and two SATB1 full length isoforms on their dynamics. The modified section in Lines 357-368 now reads: “In relation to this, a functional association between SATB1 and PML bodies was already described in Jurkat cells64. We should note that PML bodies represent an example of phase separated nuclear bodies65 associated with SATB1. Targeting of SATB1 into PML bodies depends on its phosphorylation status; when phosphorylated it remains diffused, whereas unphosphorylated SATB1 is localized to PML bodies66. This is in line with the phase separation model as well as with our results from S635A mutated SATB1, which has a phosphorylation blockade promoting its phase transitions and inducing aggregation. To further test whether SATB1 dynamics are affected by its association with PML, we co-transfected short and long full length SATB1 isoforms with PML isoform IV. The dynamics of long SATB1 isoform was affected more dramatically by the association with PML than the short isoform (Supplementary Fig. 7e), which again supports a differential behavior of the two SATB1 isoforms.”

Moreover, given the localization of the discussed phosphorylation site in the DNA binding region of SATB1 we did test its impact on DNA binding as documented in the revised Supplementary Fig. 7d. Additionally, as we have noted in our answer in Major Comment C of this reviewer, to further support the effect of serine phosphorylation on the DNA binding capacity of SATB1 we have performed DNA affinity purification experiments utilizing primary thymocyte nuclear extracts treated with phosphatase (Supplementary Fig. 7b) We found that SATB1’s capacity to bind DNA (RHS6 hypersensitive site of the TH2 LCR) is lost upon treatment with phosphatase (Supplementary Fig. 7c).

1. Pg 16,. The authors have tried to explain multiple things (concepts of self-regulation, accessibility) which is quite tangential. There is no inference to Fig 6f., which is showing the opposite to what the authors had postulated. This portion should either be removed or explained with a rationale. The writing also needs to be revised thoroughly in this section. Similarly, the discussion should also be modified.

The rationale for the original Fig. 6f (revised Fig. 6g) was described in great detail in Lines 330-343 of the original manuscript. It is not clear why the Reviewer assumes that it shows the opposite to our hypothesis. As we explained, the increased accessibility allows faster read-through by RNA polymerase, and thus the exon with higher accessibility is more likely to be skipped. The exact relationship is shown in the revised Fig. 6g where the increased accessibility is associated with the expression of the short isoform, whereas the long isoform expression needs lower chromatin accessibility which allows the splicing machinery to act on the specific exon to be included. We reason that these findings are important and relevant because: 1) we suggest a potential regulatory mechanism for the SATB1 isoforms production. This is highly relevant to this manuscript given the fact that this is the first report on the existence of the long SATB1 isoform, and 2) the differential production of the long/short SATB1 isoforms has a potential relevance to breast cancer prognosis. In the revised version of the manuscript we added Fig. 6f, which now indicates the differential chromatin accessibility in human breast cancer patients and accordingly the expression of the long SATB1 isoform are associated with worse patient prognosis as indicated in Fig. 6h and Supplementary Fig. 8a,b. In the revised version of the manuscript, we substantially modified the text in Lines 374-408, to make the relevance of all these conclusions clear. The modified text now reads: “Therefore, we reasoned that a more plausible hypothesis would be based on the regulation of alternative splicing. In our accompanying manuscript19, we have reported that the long SATB1 isoform DNA binding sites display increased chromatin accessibility than what expected by chance (Fig. 3b in 19), and chromatin accessibility at long SATB1 isoform binding sites is reduced in Satb1 cKO (Fig. 3c in 19), collectively indicating that long SATB1 isoform binding promotes increased chromatin accessibility. We identified a binding site specific to the long SATB1 isoform19 right at the extra exon of the long isoform (Fig. 6e). Moreover, the study of alternative splicing based on our RNA-seq analysis revealed a deregulation in the usage of the extra exon of the long Satb1 isoform (the only Satb1 exon affected) in Satb1 cKO cells (deltaPsi = 0.12, probability = 0.974; Supplementary File 4). These data suggest that SATB1 itself is able to control the levels of the short and long Satb1 isoforms. A possible mechanism controlling the alternative splicing of Satb1 gene is based on its kinetic coupling with transcription. Several studies indicated how histone acetylation and generally increased chromatin accessibility may lead to exon skipping, due to enhanced RNA polymerase II elongation48,49. Thus the increased chromatin accessibility promoted by long SATB1 isoform binding at the extra exon of the long isoform, would increase RNA polymerase II read-through leading to decreased time available to splice-in the extra exon and thus favoring the production of the short SATB1 isoform in a negative feedback loop manner. This potential regulatory mechanism of SATB1 isoform production is supported by the increased usage of the extra exon in the absence of SATB1 in Satb1 cKO (Supplementary File 4). To further address this, we utilized the TCGA breast cancer dataset (BRCA) as a cell type expressing SATB150. ATAC-seq experiments for a series of human patients with aggressive breast cancer51 revealed differences in chromatin accessibility at the extra exon of the SATB1 gene (Fig. 6f). In line with the “kinetic coupling” model of alternative splicing, the increased chromatin accessibility at the extra exon (allowing faster read-through by RNA polymerase) was positively correlated with the expression of the short SATB1 isoform and slightly negatively correlated with the expression of the long SATB1 isoform (Fig. 6f). Moreover, we investigated whether the differential expression of SATB1 isoforms was associated with poor disease prognosis. Worse pathological stages of breast cancer and expression of SATB1 isoforms displayed a positive correlation for the long isoform but not for the short isoform (Fig. 6g and Supplementary Fig. 6c). This was further supported by worse survival of patients with increased levels of long SATB1 isoform and low levels of estrogen receptor (Supplementary Fig. 6d). Overall, these observations not only supported the existence of the long SATB1 isoform in humans, but they also shed light at the potential link between the regulation of SATB1 isoforms production and their involvement in pathological conditions.”

1. The authors should not draw conclusions based on any data which is not shown '....ed differences in chromatin accessibility at the extra exon of the SATB1 gene (data not shown), suggesting its potential involvement in alternative splicing regulation according to the "kinetic coupling" model...'. This has led to overspeculation and needs correction.

In the revised version of the manuscript, we included the ATAC-seq data from human breast cancer patients in the revised Fig. 6f. The legend of this figure now reads: “Human TCGA breast cancer (BRCA) patient-specific ATAC-seq peaks51 span the extra exon (EE: extra exon; labeled in green) of the long SATB1 isoform. Note the differential chromatin accessibility in seven selected patients, emphasizing the heterogeneity of SATB1 chromatin accessibility in cancer. Chromatin accessibility at the promoter of the housekeeping gene DNMT1 is shown as a control. (Lines 1281-1285)” Accordingly, we have also modified the main text: “ATAC-seq experiments for a series of human patients with aggressive breast cancer68 revealed differences in chromatin accessibility at the extra exon of the SATB1 gene (Fig. 6f). In line with the “kinetic coupling” model of alternative splicing, the increased chromatin accessibility at the extra exon (allowing faster read-through by RNA polymerase) was positively correlated with the expression of the short SATB1 isoform and slightly negatively correlated with expression of the long SATB1 isoform (Fig. 6g).” (Lines 395-339)”

Minor comments: 1. On pg 4, the authors state 'Here, we utilized primary murine T cells, in which we have identified two full-length SATB1 protein isoforms.' Whereas only one 'long' isoform is identified and the other is the canonical version. The authors should correct the statement.

In the revised version of the manuscript, we modified this statement as follows: ”In this work, we utilized primary developing murine T cells, in which we have identified a novel full-length long SATB1 isoform and compared it to the canonical “short” SATB1 isoform.” (Lines 64-66)”

1. Fig. 1 a , Is there a specific reason to generate a custom-made antibody for 'all' SATB1, using similar regions that are already commercially available. This becomes redundant otherwise, because there is no apparent difference in detection compared to the commercial one (Suppl. Fig 1a). Antibody generation strategy (1a) should be moved to supplementary. Additionally, authors have obtained the custom antibodies from a commercial source, therefore, the text should reflect the same alongside relevant details.

The custom-made SATB1 antibody targeting the amino-terminal region of the protein has been developed in order to be utilized for detecting the native form of the protein. Unlike commercially available antibodies raised against either short peptides or denatured forms of the protein we have utilized the native form of the amino-terminal part of the protein for raising this antibody. To be honest, this antibody has been raised in order to be utilized in ChIP-seq experiments since no commercially available antibody is of high quality for this approach. Moreover, the original Figure 1a was utilized in order to provide an overview of the SATB1 protein structure which is highly relevant to understand its biophysical properties and not for presenting the strategy for raising a custom-made antibody for SATB1.

1. Fig 3e: what is the control used here? In their Pearson correlation analysis, there seem to be significant reduction in control sets as well upon treatment. This needs to be clarified.

We used scans rotated by 90° which served as a negative control, as stated in Line 769: “SATB1 scans rotated by 90° served as a negative control for the colocalization with FU.” Note that this is a commonly used control in colocalization experiments as described for example in Dunn et al., 2011 (https://doi.org/10.1152/ajpcell.00462.2010).

Additionally, we provide Costes’ P values which are based on randomly scrambling the blocks of pixels (instead of individual pixels, because each pixel’s intensity is correlated with its neighboring pixels) in one image, and then measuring the correlation of this image with the other (unscrambled) image. Please see Costes et al., 2004 (https://doi.org/10.1529%2Fbiophysj.103.038422) for more details. Moreover, it was actually anticipated to see a decrease in colocalization upon hexanediol treatment even in the negative control, as hexanediol significantly reduces both SATB1 and FU speckles as established in Fig. 3a-d.

1. Pg 10, the authors claim that '..., thus we reasoned that it may also be used to study phase separation...' But there have been numerous reports starting from 2018, which have utilized this technique in corelation to phase behaviour (albeit individual proteins). The authors should include proper citations as they are extending an idea from the same field to their specific need.

In the revised version of the manuscript, we included relevant citations to support the use of Raman spectroscopy in LLPS research: “Raman spectroscopy was already used in many biological studies, such as to predict global transcriptomic profiles from living cells42, and also in research of protein LLPS and aggregation43–47. Thus we reasoned that it may also be used to study phase separation in primary T cells.” (Lines 225-228)”

1. For Fig 5b, there should be a comparative image for 'short' isoform.

In the revised Figure 5c we have included a comparative image for the short SATB1 isoform.

1. In the context of Figure 5c, the authors claim ...' Note also the higher LLPS propensity of the human long SATB1 isoform compared to the murine SATB1...' Why suddenly human and mouse comparisons are drawn? This figure should be moved to supplementary.

The comparison between the human and mouse SATB1 isoforms has been implemented because it is relevant for our claims regarding the increased SATB1 aggregation in human cells in relation to the revised Fig. 6f,g,h and Supplementary Fig. 6c,d. This is also discussed in Lines 479-482, which read: “This is particularly important given the higher LLPS propensity of the human long SATB1 isoform compared to the murine SATB1 (Fig. 5d). Therefore, human cells could be more susceptible to the formation of aggregated SATB1 structures which could be associated with physiological defects.”

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

Zelenka et al., focus on a T cell genome-organizing protein, SATB1, to show that SATB1 undergoes liquid liquid phase-separation (LLPS), and distinct isoforms confer different LLPS-related biophysical properties. They generate a long-isoform specific antibody and conduct several experiments to test for LLPS and compare LLPS properties between the long-isoform relative to the whole SATB1 protein population. Given that SATB1 plays important roles in T cell development and in cancer, interrogating SATB1 biophysical properties is an important question. However, there are multiple problems with the experimental setup and data that weaken their support of the conclusions. I will detail some of the major issues below:

Regarding phase-separation There are several assays to determine whether a protein undergoes LLPS. 1. One of the first the authors address is the spherocity or roundness. Indeed, formation of spherical droplets is one evidence of the liquid nature of a protein. However, the authors use fixed preparations (which can introduce artifacts), not free-floating protein, and determine roundness by showing a 2D image. Roundness should take into account the diffraction-limits of fluorescent imaging, as many structures can be imaged to appear round by the detector. There are quantifiable measurements that can be taken on 3D images to show roundness. This would best be shown using non-fixed protein.

• We thank this Reviewer for several insightful comments. Although, we agree with most of them, we should highlight the main goal of our manuscript, i.e. to investigate the SATB1 protein with an emphasis on its physiological roles in primary developing murine T cells. We highlight this already in the introduction in Line 64 “In this work, we utilized primary developing murine T cells,...” and mainly also in the respective part of the result section: “To probe differences in phase separation in mouse primary cells, without any intervention to SATB1 structure and expression, we first utilized 1,6-hexanediol treatment, which was previously shown to dissolve the liquid-like droplets34.(Lines 203-205)”

• We believe that this is a very important aspect of our study that should not be overlooked. The majority of proteins perhaps behave differently under physiological and in vitro conditions. However, due to the extensive post-translational modifications affecting the properties of SATB1, its completely different localization patterns between primary developing T cells and other cell types but especially cell lines and many other aspects, it was of utmost importance to focus our research on primary T cells. Unfortunately, this was accompanied with multiple difficulties, such as that we have to use fixed cells as this is the only way to visualize SATB1 in these cells. Alternatively, one could create a new mouse line expressing a fluorescently tagged SATB1 protein, but this is beyond the scope of our work.

• However, we should also note that many LLPS-related studies do not pay any focus on primary physiological functions of proteins and they simply focus on the investigation of protein’s artificial behavior in in vitro conditions. Having said that, we too extended our experiments in primary cells to the ex vivo studies in cell lines to further support our claims. In these experiments, we utilized live cell imaging in Fig. 4-6, quantified the spherocity in Supplementary Fig. 6, showed the ability of speckles to coalesce in Fig. 4c and also used FRAP in Fig. 4f and also in the revised version of the manuscript in Supplementary Figure 6f. Moreover, we should note that most of these experiments were designed and performed during 2017 and 2018 conforming with the standards. We are well aware of the progress in the field and impact of fixation on LLPS, as described in Irgen-Gioro et al., 2022 (https://doi.org/10.1101/2022.05.06.490956), but after over seven months of review process in another journal we also believe that these aspects should be considered not to delay further progress of the SATB1 field.

Regarding the isoform specificity of SATB1 biophysical properties 1. The authors generate a long isoform-specific antibody. However, the western blot is not convincing that this is indeed specific to the long isoform as there is a rather large smear. Can this be improved with antibody preabsorption? Since this is a key reagent for the manuscript, improvement in antibody quality is essential.

The custom-made antibody for the long isoform has been raised against the unique 31 amino acids long peptide present in the long SATB1 isoform. The polyclonal serum has undergone affinity chromatography utilizing the immobilized peptide (antigen) to purify the antibody. In the revised version of the manuscript we have included another immunodepletion experiment with cleaner bands (Fig. 1f). Moreover, please read our answer to Major comment #2 of Reviewer 1 that follows: • The long antibody was raised in mice inoculated with the extra peptide present in the long isoform only. Therefore, the capacity of this antibody precipitating the shorter isoforms, which do not express the sequence of the extra peptide (EP, Figure 1a) in not possible.

• We have repeated the immunodepletion experiment and we now provide the results in Fig. 1f and Supplementary Fig. 1b. The western blot in Fig. 1f is now cleaner and supports quite convincingly the presence of a long SATB1 isoform. Given the lack of isoform-specific knockouts which we could utilize to immunoprecipitate or detect the different isoforms in a single cell (or cell population), the utilized approach of immunodepletion and subsequent western blotting is the approach we thought of implementing.

• As shown in Fig. 1f and Supplementary Figure 1b, the long isoform SATB1 antibody has the capacity to recognize the long isoform in murine thymocyte protein extracts but not the short SATB1 isoform (please compare lane 3 in the two western blots utilizing either the antibody for the long isoform -top panel - or the antibody that detects both isoforms (lower panel).

• We have performed Immunofluorescence experiments utilizing the antibody detecting the long SATB1 isoform in thymocytes isolated from either C57BL/6 or Satb1 cKO mice. The antibody is specific to the SATB1 protein since there is no signal in immunofluorescence experiments utilizing the knockout cells (Supplementary Figure 1c).

• We have performed Immunofluorescence experiments utilizing thymocytes and the antibody detecting the long SATB1 or a commercially available antibody detecting all SATB1 isoforms. The pattern of SATB1 subnuclear localization is similar for both antibodies (Supplementary Figure 1e).

• In our accompanying revised manuscript Zelenka et al., 2022 (https://doi.org/10.1101/2021.07.09.451769), we provide yet another piece of evidence, consisting of bacterially expressed short and long SATB1 protein isoforms detected by western blot using either the long isoform-specific or the non-selective all SATB1 isoforms antibodies.

• Regarding the additional bands detected in the immunoprecipitation experiment presented in the original Supplementary Figure 1b (lane 2), it is not surprising that additional bands appear in a sample of protein extracts that is used for several hours for the immunoprecipitation experiments, while the “input” sample simply denotes protein extract that is frozen at -80oC right after the preparation of protein extracts until use. It is well-established that SATB1 is the target of proteases which might as well be active during the immunoprecipitation steps (2 consecutive immunoprecipitation steps take place). Therefore, the immunoprecipitated material cannot necessarily be a copy of the input material displaying a single protein band even if protease inhibitors are included in the buffers.

Taken together the experiments described here we showed that the antibody raised against the extra 31 aa long peptide, present only in the long SATB1 isoform, is specific for this isoform.

1. Fig 4 Optodroplet experiment appears to show that the N-terminus of SATB1 can undergo LLPS. The results of this assay show that SATB1 has a domain that can undergo phase-separation in isolation, but it does not show that the protein itself is a phase-separating protein. The FRAP assay methods are not provided by the authors, but this is important, as continued light activation means proteins are continuously forming aggregates, and the bleaching for FRAP should be balanced with the levels of Cry2 activation. A very good description of the methods is described in the original Optodroplet paper: https://www.sciencedirect.com/science/article/pii/S009286741631666X?via%3Dihub#sec4

We should note that we did follow the FRAP protocol provided by the recommended study Shin et al., 2017 (https://doi.org/10.1016/j.cell.2016.11.054). Indeed, these experiments are very tricky to perform and interpret, as every cell expresses slightly different amounts of protein which is directly associated with the different speed of optoDroplet formation, and thus its propensity to aggregate upon overactivation. On the other hand, there need to be continuous activation during the FRAP experiment as the lack of activation laser would result in fast disassembly of the optoDroplets, counteracting the FRAP results. Moreover, the optoDroplets actively move around the cell in all dimensions which makes the accurate measurement of signal intensity really challenging, even with an adjusted pinhole. Therefore, we do not think that FRAP is the best approach to examine the behavior of optoDroplets.

Either way, we have now described the detailed FRAP protocol in Lines 889-898, which read: “For the FRAP experiments, cells were first globally activated by 488 nm Argon laser illumination (alongside with DPSS 561 nm laser illumination for mCherry detection) every 2 s for 180 s to reach a desirable supersaturation depth. Immediately after termination of the activation phase, light-induced clusters were bleached with a spot of ∼1.5 μm in diameter. The scanning speed was set to 1,000 Hz, bidirectionally (0.54 s / scan) and every time a selected point was photobleached for 300 ms. Fluorescence recovery was monitored in a series of 180 images while maintaining identical activation conditions used to induce clustering. Bleach point mean values were background subtracted and corrected for fluorescence loss using the intensity values from the entire cell. The data were then normalized to mean pre-bleach intensity and fitted with exponential recovery curve in Fiji or in frapplot package in R.”

1. Description of analyses that authors prefer not to carry out

**Reviewer #1**:

Can they use the all and long isoform antibodies together, then subtract the signal from long isoform to conclude about the localization of the shorth isoform ?

We thank the Reviewer for the suggestion, though given the differential efficiency of antibodies and other limitations of imaging experiments, we do not find the suggested experiment to have a potential to improve the quality of our manuscript. However, we should note that we have performed a pixel-based colocalization experiment between the signal detected by all isoform and long isoform SATB1 antibodies. Fluorocytogram of the pixel-based colocalization, based on 3D-SIM data is provided on the left, with quantified colocalization on the right of the revised Supplementary Fig. 5a.

3) Lack of better staining with antibody against the long and short SATB1 isoforms after treatment with 1,6 Hexanediol. 1,6 Hexanediol treatment can change many other chromatin associated proteins to which SATB1 can be bound to indirectly. This experiment can

We do understand the controversy and difficulties of experiments using 1,6-hexanediol treatment. However, we have to note that there is no better approach available for the investigation of LLPS in our primary murine T cells. We did use alternative approaches in ex vivo experiments, utilizing cell lines to validate our hypothesis without the involvement of 1,6-hexanediol.

**Reviewer #2**:

1. The authors mention, '...of the different SATB1 isoforms, uncovered by the use of the two different antibodies, relied in the heterochromatin areas (zone 1), where the long isoform was less frequently...' There is no supporting figure number mentioned. The authors need to show a zone-by-zone comparison images for 'all iso' vs 'long' iso of SATB1. Just to reiterate, there is a need for a heterochromatin mark to unambiguously call out the distinction.

We should remind that there is an inherent difficulty to accurately compare localization of short and long SATB1 isoforms in primary cells, especially due to the lack of Satb1 isoform-specific knockout mice. There is no way to detect only the short isoform in these primary cells as there are only antibodies targeting the long or all SATB1 isoforms. Therefore, we cannot set up additional experiments probing these questions.

In line with this, in the revised version of the manuscript, we toned down our statements regarding the differential localization of the two isoforms in primary cells. We only refer to it as an indication and we support it by adding references to the relevant figures. This part now reads: “Localization of SATB1 speckles detected by antibodies targeting all SATB1 isoforms and/or only the long SATB1 isoform, revealed a significant difference in the heterochromatin areas (zone 1, Fig. 2b), where the long isoform was less frequently present (see also Fig. 2a and Fig. 3c). Although, this could indicate a potential difference in localization between the two isoforms, due to the inherent difficulty to distinguish the two based on antibody staining, we refrain to draw any conclusions. (Lines 145-150)”

1. Fig. 6a, The authors wished to see the effect of RNA on Satb1 nuclear localization. This is not related to the main theme of the paper, thus should be moved to supplementary (true for b as well). Importantly, the experiments should be performed with total cells to show the divergence of localization (like the paper the authors referred to) instead of matrix for clarity.

• We did not wish to see the effect of RNA on SATB1 localization. In fact, there is a long history of SATB1 research that is inherently linked with the concept of nuclear matrix, a putative nuclear structure which is highly associated with nuclear RNAs. SATB1 was described many times as a nuclear matrix protein (https://doi.org/10.1016/0092-8674(92)90432-c; https://doi.org/10.1128/mcb.14.3.1852-1860.1994; https://doi.org/10.1074/jbc.272.17.11463; https://doi.org/10.1128/mcb.17.9.5275; https://doi.org/10.1021/bi971444j; https://doi.org/10.1083/jcb.141.2.335; https://doi.org/10.1101/gad.14.5.521; https://doi.org/10.1038/ng1146).

• Moreover, our data discussed in comments 4-7 of this Reviewer, such as i. the localization of SATB1 to the nuclear zones associated with RNA and nuclear scaffold factors (Fig. 2b, Supplementary Fig. 1c), ii. colocalization of SATB1 with actively transcribed RNAs (Fig. 2c, Fig. 3g, Supplementary Fig. 2a, Supplementary Fig. 2c), iii. including its association with nucleoli (Supplementary Fig. 3b), and also iv. its computationally predicted interaction with Xist lncRNA (Agostini et al., 2013; https://doi.org/10.1093/nar/gks968) as a notable factor of nuclear matrix, all suggest that the interaction between RNA and SATB1 is plausible and potentially relevant for its function and/or at least its subnuclear localization. It is relevant even more so, when considering numerous reports on the ability of RNA-binding, poly-Q and PrLD-containing proteins to undergo LLPS https://doi.org/10.1016/j.molcel.2015.08.018; https://doi.org/10.1042/bcj20160499; https://doi.org/10.1016/j.cell.2018.03.002; https://doi.org/10.1016/j.cell.2018.06.006; https://doi.org/10.1093/nar/gkaa681), including RNAs specifically regulating LLPS behavior, especially for poly-Q and PrLD-containing proteins, such as SATB1 (https://doi.org/10.1126/science.aar7366; https://doi.org/10.1126/science.aar7432; https://doi.org/10.1016/j.ceb.2019.03.007; https://doi.org/10.1038/s41598-020-57994-9; https://doi.org/10.1016/j.molcel.2015.09.017; https://doi.org/10.1038/s41598-019-48883-x; https://doi.org/10.1038/s41467-019-11241-6).

• It should also be noted that SAF and various hnRNPs, as the most prominent proteins of nuclear matrix were many times reported to phase separate (https://doi.org/10.1016/j.molcel.2019.10.001; https://doi.org/10.1074/jbc.ra118.005120; https://doi.org/10.1016/j.celrep.2019.12.080; https://doi.org/10.1038/s41467-019-09902-7; https://doi.org/10.1016/j.molcel.2017.12.022; https://doi.org/10.1074/jbc.tm118.001189). All these aspects show that the relation between nuclear matrix, SATB1 and RNA are quite relevant to our manuscript.

• Moreover, in light of the aforementioned information, we believe that it is much clearer to follow the protocol we did – i.e. to remove soluble proteins by CSK treatment and then, upon RNase treatment, extract the released proteins using ammonium sulfate. In an experiment utilizing whole cells, one would need to microinject RNase A into the nucleus, which 1. is very challenging for primary T cells having a radius of 3-5 micrometers, 2. is of low throughput, 3. would not allow for released protein removal which would thus make the results hard to interpret. Please note that in the reference paper, the authors used cell lines overexpressing heterologous GFP-tagged proteins, which is not related to our setup.

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

Reply to the Reviewers

I thank the Referees for their...

Referee #1

1. The authors should provide more information when...

Responses + The typical domed appearance of a hydrocephalus-harboring skull is apparent as early as P4, as shown in a new side-by-side comparison of pups at that age (Fig. 1A). + Though this is not stated in the MS 2. Figure 6: Why has only...

Response: We expanded the comparison

Minor comments:

1. The text contains several...

Response: We added...

Referee #2

• PEIERLS

• (I THINK) "OUR"==="OBJETIVO", "COLECTIVO"

• (I DON'T UNDERSTAND) "promulgating the Copenhagen CONFUSION"

• (google) lo he encontrado en OTRO libro de Mermin, en referencia a una "CRITICA" de BELL al uso de la palabra "KNOWLEGDE" en las "explicaciones" de Heisenberg, y PEIERLS (!!!)

• BELL: Whose Knowlegde? Knowlegde about WHAT?

• (google) tambien aqui [MASTERPIECE-SEE 3 LAST PARAGRAPHS)

• [Peierls responde a Bell]: "That leaves the question: whose knowledge should be represented in the density matrix? In general there will be many who may have some information about the state of a physical system. Each of them has to use his or her density matrix. These may differ, as the nature and amount of knowledge may differ. People may have observed the system by different methods, with more or less accuracy; they may have seen part of the results of another physicist. However, there are limitations to the extent to which their knowledge may differ. This is imposed by the uncertainty principle. For example if one observer has knowledge of s, of our Stern-Gerlach atom, another may not know sx, since the measurement of sx would have destroyed the other person's knowledge of J2, and vice versa. This limitation can be compactly and conveniently expressed by the condition that the density matrices used by the two observers must commute with each other."

• (I THINK) Ha respondido a la pregunta??? I DONT THINK SO!

• Me recuerda la "respuesta" de Bohr a EPR
• "AMBOS" vuleven a "repetir" como "aplican" ellos la QM, pero sin "responder" a la pregunta!

Da a entender que los nuevos inventos han ampliado el poder físicos del hombre, pero no el de su mente. Por lo cual, Bush, dice que están a la mano instrumentos que, si se desarrollan adecuadamente darán al hombre acceso sobre el conocimiento.

Bush, fue el primero en describir el funcionamiento del Memex en su artículo As we may think.

Author Response

Reviewer 1

Bailon-Zambrano and colleagues were trying to answer the general question: what contributes to phenotypic variation when a gene of strong effect is mutated?

The work has several major strengths for answering this interesting question. First, they decided to study mef2ca in zebrafish for which they had previously shown that mutants displayed highly variable facial phenotypes. To learn how phenotypic variation depends on phenotypic severity, they realized they had studied more alleles, and so induced two more alleles to have three different types of molecular lesions (start codon mutation, premature stop codon, and full coding gene deletion). Investigating these alleles showed that increasingly severe alleles had more variation among individuals in the population but not necessarily more variation between the left and right sides of the face within individuals.

Over several years, these investigators had spent considerable effort to select lines of fish that segregate the start-codon mutation and have either severe or weak effects on facial phenotypes. wondered: what factors were selected out of the original genetic background that would increase or decrease phenotypic severity? They hypothesized that one or more of the five mef2 paralogs in zebrafish might help to ameliorate the phenotype in the low line or reciprocally intensify the phenotype in the high line. They studied expression of the mef2 paralogs in neural crest cells by single-cell transcriptomics. They found that paralogs were downregulated in the high-penetrance line with respect to an unselected line, a result expected if expression of the paralogs contributed to buffering phenotypic severity. This experiment has two weaknesses, first that the method only examined neural crest cells but we know that signals from the ectodermal and endodermal epithelia contribute to craniofacial morphologies by diffusible signals. If genes regulating craniofacial morphologies that act in epithelia had genetic variation that contributes to severity, those genes would not be investigated in these crest-only experiments. A minor problem (which is associated with the expense of the experiment) is that the scRNA-seq experiments compared only the high and unselected lines, not the low line. To address both problems, the investigators performed qPCR on RNAs extracted from whole heads of genetically mef2ca-wild types from the high and low line. In these qPCR experiments, however, they did not investigate the unselected line. Leaving out the low line in one approach and leaving out the unselected line in the other approach somewhat weakens the strength with which one can draw conclusions (e.g., the qPCR conclusion assumes that the unselected line would be intermediate between the two selected lines) but is unlikely to change the basic conclusions the authors drew. In addition, using whole heads in the qPCR experiments, while it has the advantage that it includes epithelia, does not distinguish between genes expressed only in the crest and genes expressed in other cell types, and these experiments did not test for any genes known to affect craniofacial development that are epithelium-specific.