now our problem is navigating the sheer amount of things compressed and actually being able to effectively consult them

reminds me of our discussion about the move away from the 'solitary genius' of western tradition to a more collaborative approach towards knowledge-building in which everyone specializes in something different

interesting that Bush characterizes the war as little more than an 'exhilarating' blip in the careers of professional scientists

This is a really interesting way to think about smartphones/tablets; there's less of a burden on us to carry a ton much information in our minds because it's so easy to pull out our phones and reference almost anything in the world.

I read online somewhere that humans were originally only supposed to live in small groups of 25 people or less, and that we are not built to handle the thousands of people we see on social media in a day. I do not know if the first part is accurate, but I believe we are no meant to see as much as we do. While we can interact much more than just face to face allows, we don't need to, and it may be too much. The amount of tragedy we witness everyday online certainly isn't healthy and I don't think our brains are supposed to handle all of that at once. So I agree that access to a larger society is definitely a double-edged sword.

I remember in middle school every year for our digital literacy class we had to do a lesson about our digital footprint. And it really got instilled in us that you need to be careful what you put online because it will never go away. I remember we would be told stories of people losing their job because of something they posted about years before and even if they didn't use that platform anymore they got fired because their company didn't want to be associated with someone like that. I think this idea should be instilled into anyone who uses the internet because it is really helpful.

Author Response

Reviewer #2 (Public Review):

Klein et al. have developed a high-throughput tracker to evaluate operant conditioning in Drosophila larvae. Employing this device, they train larvae to prefer bending towards one specific side (left or right), by using as unconditioned stimulus (US) the optogenetic activation of dopaminergic and serotoninergic neurons, demonstrating that larvae are able to perform this behaviour. Furthermore, they show that serotoninergic neurons alone are sufficient to mediate the reward signal, and that specifically serotoninergic neurons in the VNC are required for this behaviour. However, they do not show whether serotoninergic VNC neurons are sufficient. The results are interesting and novel. Operant conditioning had been shown for Drosophila adult. Furthermore, the existence of VNC circuits sufficient for operant conditioning had been shown for other species, as the authors point out in the discussion. Nonetheless, the genetic dissection to identify serotonine expressing neurons as mediators of operant conditioning in the Drosophila larva, and the identification of VNC serotonine cells as necessary are new. Furthermore, given the experimental advantages of the Drosophila larva, including genetic accessibility and a full connectome, the findings open the door to future research into the circuit mechanisms of operant conditioning. I have some comments that I think would be important to address.

The high-throughput tracker is impressive. However, there is no sufficient documentation to ensure that an expert would be able to easily reproduce it. All of the hardware assembly files, the list of materials, as well as the electronic circuit maps and all of the required software needs to be appropriately documented and uploaded onto a public repository. This is a basic requirement when publishing new hardware/software, particularly in an open journal such as eLife.

We have now included all the documentation and CAD files for the high-throughput tracker. The software is publicly available in the following Github repository (https://github.com/ZlaticLab/multi-larva-tracker-scripts-public). The CAD files are available in the Supplementary materials of the paper.

• The differences observed in the results of operant conditioning are very subtle (see for example figure 3c), which means that it is extremely important that statistic analyses are correctly made. The sample number (n) for these experiments is really high (n>100) and for what I understood is not equivalent to the number of animals, because the same animal can generate n >1, eg. n = 2 or n =3 if it collides one or two times, as each time it collides a new identity is given to the larvae. This means that the datapoints collected are not independent, and I think in that case a Wilcoxon rank-sum test is not the appropriate test to take. I recommend the authors and eLife editors to consult with an expert in this type of statistics. Alternatively, the authors could, for each experiment, take into account only the data from larvae that did not collide, and for those that collide only take into account the data before the collision. This can be calculated easily as they just need to exclude from their analysis in each experiment all of the larval IDs where the ID is larger than the initial number of larvae identified by the software.

We apologise if we did not clarify sufficiently that we only took into account (for each time bin) larvae that did not collide. Within the Materials and methods, we describe how objects retained for analysis had to satisfy several criteria. The first criterion is that the object needed to be detected in every frame of the given 60 s bin. In this way, the object identity is stable throughout the bin - a reflection that the object did not collide with another object. In other words, within a single time bin, the same animal only contributes once. Text has been added to the Materials and methods to clarify that this first criterion is selecting for larvae that did not collide.

The reviewer mentions that Wilcoxon rank-sum test is not the appropriate nonparametric test for dependent samples. We agree. In accordance with this, the test used for within-bin comparisons was Wilcoxon signed-rank, which is also nonparametric but is for dependent samples. We believe, then, that there is no need to reconsider the statistical tests used.

-The finding that serotoninergic neurons in the VNC, which with the line they used amount to only 2 neurons per VNC hemisegment, are required for operant conditioning is very interesting. It would be great if they could also test whether they are sufficient. It seems that they would just need to make two split Gal4 lines one for tsh and one for tph, so the experiment does not seem too difficult and would significantly add to their findings.

Generating new intersections is beyond the scope of this already large study which has been significantly impacted by the pandemic. We have therefore added the following sections below explaining that we have identified candidate serotonergic neurons that are required for operant learning and that identifying specific single neuron types that may be sufficient would be an exciting avenue for future follow-up work.

In the Results section entitled, “Serotonergic VNC neurons may play role in operant conditioning of bend direction” we have added:

“The Tph-Gal4 expression pattern contains two neurons per VNC hemisegment (with the exception of a single neuron in each A8 abdominal hemisegment, Huser2012). Future experiments exclusively targeting a single serotonergic neuron per VNC hemisegment could be valuable in determining whether they are sufficient for operant learning.”

In the Discussion section entitled: “Automated operant conditioning of Drosophila larvae”

“Furthermore, developing sparser lines that target single serotonergic and dopaminergic neuron types will enable the identification of the smallest subsets of neurons that are sufficient for providing the operant learning signal. Behavioural experiments with these genetic lines may have the added benefit of mitigating conflicting or non-specific reinforcement signalling.”

Author Response

Reviewer #1 (Public Review):

The manuscript is clear and well-written and provides a novel and interesting explanation of different illusions in visual numerosity perception. However, the model used in the manuscript is very similar to Dehaene and Changeux (1993) and the manuscript does not clearly identify novel computational principles underlying the number sense, as the title would suggest. Thus, while we were all enthusiastic about the topic and the overall findings, the paper currently reads as a bit of a replication of the influential Dehaene & Changeux (1993)-model, and the authors need to do more to compare/contrast to bring out the main results that they think are novel.

Major concerns:

1) The model presented in the current manuscript is very similar to the Dehaene and Changeux 1993 model. The main difference is in the implementation of lateral inhibition in the DoG layer where the 1993 model used a recurrent implementation, and the current model uses divisive normalization (see minor concern #1). The lateral inhibition was also identified as a critical component of numerosity estimation in the 1993 model, so the novelty in elucidating the computational principles underlying the number sense in the current manuscript is not evident.

If the authors hypothesize that the particular implementation of lateral inhibition used here is more relevant and critical for the number sense than the forms used in previous work (e.g., the recurrent implementation of the 1993 model or the local response normalization of the more recent models), then a direct comparison of the effects of the different forms is necessary to show this. If not, then the focus of the manuscript should be shifted (e.g., changing the title) to the novel aspects of the manuscript such as the use of the model to explain various visual illusions and adaptation and context effects.

Thank you for bringing up these issues. We acknowledge that there was a lack of clear explanations for the key differences between the proposed model and that of Dehaene & Changeux (hereafter D&C). Please see our revisions below where we: 1) explain the D&C model and its limitations in more in detail; 2) our critical changes to the D&C model; and 3) how those critical changes allow a novel way to explain numerosity perception.

The paragraph in the Introduction where we first introduce D&C is modified to read:

“The computational model of Dehaene and Changeux (1993) explains numerosity detection based on several neurocomputational principles. That model (hereafter D&C) assumes a one-dimensional linear retina (each dot is a line segment), and responses are normalized across dot size via a convolution layer that represents combinations of two attributes: 1) dot size, as captured by difference-of-Gaussian contrast filters of different widths; and 2) location, by centering filters at different positions. In the convolution layer, the filter that matches the size of each dot dominates the neuronal activity at the location of the dot owing to a winner-take-all lateral inhibition process. To indicate numerosity, a summation layer pools the total activity over all the units in the convolution layer. While the D&C model provided a proof of concept for numerosity detection, it has several limitations as outlined in the discussion. Of these, the most notable is that strong winner-take-all in the convolution layer discretizes visual information (e.g., discrete locations and discrete sizes yielding a literal count of dots), which is implausible for early vision. As a result, the output of the model is completely insensitive to anything other than number in all situations, which is inconsistent with empirical data (Park et al., 2021).”

The revised Discussion describes our critical modifications to D&C and their consequences.

“At first blush, the current model might be considered an extension of Dehaene and Changeux (1993). However, there are four ways in which the current model differs qualitatively from the D&C model. First, the D&C model is one-dimensional, simulating a linear retina, whereas we model a two-dimensional retina feeding into center-surround filters, allowing application to the two-dimensional images used in numerosity experiments (Fig. 1A). Second, extreme winner-take-all normalization in the convolution layer of the D&C model implausibly limits visual precision by discretizing the visual response. For example, the convolution layer in the D&C model only knows which of 9 possible sizes and 50 possible locations occurred. In contrast, by using divisive normalization in the current model, each dot produces activity at many locations and many filter sizes despite normalization, and a population could be used to determine exact location and size. Third, extreme winner-take-all normalization also eliminates all information other than dot size and location. By using divisive normalization, the current model represents other attributes such edges and groupings of dots (Fig. 1B) and these other attributes provide a different explanation of number sensitivity as compared to D&C. For example, the D&C model as applied to the spacing effect between two small dots (Fig. 4A) would represent the dots as existing discretely at two close locations versus two far locations, with the total summed response being two in either case. In contrast, the current model gives the same total response for a different reason. Although the small filters are less active for closely spaced dots, the closely spaced dots look like a group as captured by a larger filter, with this addition for the larger filter offsetting the loss for the smaller filter. Similarly, as applied to the dot size effect (Fig. 4B), the D&C model would only represent the larger dots using larger filters. In contrast, the current model represents larger dots with larger filters and with smaller filters that capture the edges of the larger dots, and yet the summed response remains the same in each case owing to divisive normalization (again, there are offsetting factors across different filter sizes). The final difference is that the D&C model does not include temporal normalization, which we show to be critical for explaining adaptation and context effects.”

In sum, the current model explains a wider range of effects by using representations and processes that more closely reflect early vision. The change to two-dimensions allows application to real images. The inclusion of temporal normalization allows application to temporal effects. The change from winner-take-all to divisive normalization might appear to be a parameter setting, but it’s one that produces qualitatively different results and explanations (e.g., representations of edges and groupings that are part of the explanation of selective sensitivity to number). These behaviors are consistent with empirical data and are qualitatively different from that of the D&C model. Now that we’ve highlighted the ways in which this model differs qualitatively from the D&C model, we hope that our original title still works.

Reviewer #2 (Public Review):

This is a very interesting and novel model of numerosity perception, based on known computational principles of the visual system: center-surround mechanisms at various scales, combined with divisive normalization (over space and time). The model explains, at least qualitatively, several of the important aspects of numerosity perception.

Firstly, the model makes major and minor predictions. Major: the effect of adaptation, at least 30%, as well as impendence of several densities and dot size; minor: tiny effects like irregularity, around 6%. I think it would make sense to separate these. To my knowledge, it is the first to account for adaptation, which was the major effect that brought numerosity into the realm of psychophysics: and it explains it effortlessly, using an intrinsic component of the model (divisive normalization), not with an ad-hoc add-on. This should be highlighted more. And perhaps, the fit can be more quantitative. Murphy and Burr (who they cite) showed that the adaptation is rapid. How does this fit the model? Very well, I would have thought.

Thanks for the positive evaluation of our work. In the revised manuscript, we followed the reviewer’s suggestion to highlight the novelty of the model in its explanation of numerosity adaptation. As the reviewer says, one significant aspect of our work is that the model can explain a relatively large effect of numerosity adaptation with minimal effort. To be clear, even though we call it “numerosity” adaptation, the model does not know number in any explicit way. One way to highlight this aspect, we thought, is to compare the current adaptation results to a simulation where the adaptor and target are defined along the dimensions of size or spacing. In such cases (which are now reported in Fig. S6 and S7), no reliable under- or over-estimation was observed. These results suggest that numerosity adaptation is a natural byproduct of divisive normalization working across space and time.

The question about the rapidity of adaptation is indeed an interesting one. However, the current model is not designed to simulate the effect of exposure duration on neural activity. More specifically, the current model operates across trials and stimuli (e.g., one response per stimulus), using a single parameter that captures the temporal gradient of divisive normalization from prior trials (e.g., the influence of two trials ago as compared to one trial ago). As currently formulated, the model does not address adaptation at the level of milliseconds, as would be necessary to model adaptor duration. To model adaptation at the millisecond level requires a dynamic model that not only specifies the rate of adaptation but also the rate of recovery from adaptation, such as in the visual orientation adaptation model of Jacob, Potter, and Huber (2021), which includes the dynamics of synaptic depression and synaptic recovery. In future work we hope to make such modifications to the model to expand the range of explained effects. Nevertheless, a dynamic version of the model should encompass this simpler trial-by-trial version of the model as a special case. Our goal in this study was a clear demonstration of the neural mechanisms underlying numerosity in early vision and so we have attempted to keep the model as simple as possible while still capturing neural behavior.

We have elected not to fit data and instead we explored the behavior model in a qualitative way, asking whether the commonly observed numerosity effects emerge from the model in the qualitatively correct direction regardless of its parameter values (e.g., as reported in Fig S2). This approach follows from our central aim, which is to explain the neurocomputational principles of the number sense rather than produce a detailed model with specific parameters values fit to data. Our aim was to show that the correct qualitative behaviors naturally emerge from these principles without requiring specific parameter values (and more importantly, to show how these behaviors emerge from these principles).

Jacob, L. P., Potter, K. W., & Huber, D. E. (2021). A neural habituation account of the negative compatibility effect. Journal of Experimental Psychology: General, 150(12), 2567.

Among the tiny predicted effects (visually indistinguishable bar graphs) is the connectedness effect. But this is in fact large, up to 20%. I would say they fail here, by predicting only 6%. And I would say this is to be expected, as the illusion relies on higher-order properties (grouping), which would not immediately result from normalization. Furthermore, the illusion varies with individual personality traits (Pomè et al, JAD, 2021). The fact that it works with very thin lines suggests that it is not the physical energy of the lines that normalizes, but the perceptual grouping effect. I would either drop it, or give it as an example of where the predictions are in the right direction, but clearly fall short quantitatively. No shame in saying that they cannot explain everything with low-level mechanisms. A future revised model could incorporate grouping phenomena.

Thank you for the suggestion. We agree that trying to explain the connectedness illusion with center-surround filters is not ideal. As the reviewer says, the main driver of the connectedness illusion is likely to be groupings of dots. The current model captures groupings of dots, but it does so in a circularly symmetric way, which is not ideal for capturing the oblong groupings (barbells) that are likely to play a role in the connectedness illusion. It is probably because of this mismatch (between the shape of the groupings and shape of the filters) that the model produces a smaller magnitude connectedness illusion. If the model included a subsequent convolution layer in which the filters were oriented lines of different sizes, it would likely produce a larger connectedness illusion. Following the reviewer’s suggestion, we have placed the connectedness illusion in the supplementary materials and only refer to this in the future directions section of the discussion, writing:

“Another line of possible future work concerns divisive normalization in higher cortical levels involving neurons with more complex receptive fields. While the current normalization model with center-surround filters successfully explained visual illusions caused by regularity, grouping, and heterogeneity, other numerosity phenomena such as topological invariants and statistical pairing (He et al., 2015; Zhao and Yu, 2016) may require the action of neurons with receptive fields that are more complex than center-surround filters. For example, another well-known visual illusion is the effect of connectedness, whereby an array with dots connected pairwise with thin lines is underestimated (by up to 20%) compared to the same array without the lines connected (Franconeri et al., 2009). This underestimation effect likely arises from barbell-shaped pairwise groupings of dots, rather than the circularly symmetric groupings of dots that are captured with center-surround filters. Nonetheless, a small magnitude (6%) connectedness illusion emerges with center-surround filters (Fig. S10). Augmenting the current model with a subsequent convolution layer containing oriented line filters and oriented normalization neighborhoods of different sizes might increase the predicted magnitude of the illusion.”

In short, I like the model very much, but think the manuscript could be packaged better. Bring out the large effects more, especially those that have never been explained previously (like adaptation). And try to be more quantitative.

Thank you. We now highlight the novel computational demonstrations of adaptation to a greater degree and—as also suggested by Reviewer 1—provide more quantitative reports of the illusory effects that the model naturally produces.

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

In this study we reveal that, in both mice and humans, the metabolic benefits of caloric restriction (CR) are sex- and age-dependent. Through a systematic review of the literature, we show that sex differences have been largely overlooked by previous CR research, a finding that Reviewer 1 highlights as “an important point”. Our results have critical implications for understanding the fundamental biology linking diet and health outcomes, as well as translational strategies to leverage the therapeutic benefits of CR in humans.

We thank the reviewers for their helpful appraisal of our manuscript, which Reviewer 2 highlights as “a very well written paper”. Reviewer 1 emphasised the translational relevance of our findings and commented on the “systematic” nature of our study. They noted that it “was well performed”, ”is a valuable and important contribution to the field”, and “will elicit great interest in the scientific and public readership.” Indeed, the importance of sex as a biological variable is the focus of a September 2022 news feature in Nature (https://www.nature.com/articles/d41586-022-02919-x), underscoring the timeliness and relevance of our findings. Our response to the reviewers comments is outlined below, including the changes we have incorporated in a revised version of our manuscript.

Reviewer 1 – Major Comments:

__A) The clinical part is definitely the weak spot in the study. I don't think that the data should be omitted, but the authors should be very careful in interpreting the data. Obvious limitations apply to this part, which need to be more directly addressed in the abstract and discussion. It feels like the data from the small-scale clinical trial is exaggerated. __The clinical study was conducted by Prof Alex Johnstone’s group at the Rowett Institute of Nutrition and Health, University of Aberdeen. Her group are experts in the study of dietary interventions for weight loss. The study was conducted to a high standard and therefore we have the utmost confidence in the conclusions drawn from our analysis of this data.

As we discuss in response to the reviewer’s other points below, the clinical study was primarily designed to address other outcomes and we analysed the data retrospectively to investigate if sex and age affect CR-induced weight and fat loss. This explains some of the limitations that the reviewer mentions, e.g. the relatively low numbers of younger males, and the focus on overweight and obese subjects. As requested, we have now addressed these limitations as follows:

1. Updated the abstract to clarify that the data are from overweight and obese subjects.
2. Updated the results to emphasise that we did a retrospective analysis of CR in overweight and obese subjects (lines 396-398).
3. Performed an additional ANCOVA analysis to test if baseline adiposity or BMI contribute to the sex differences in body mass, fat mass or fat-free mass (new Supplementary Figure 11); see Reviewer 1 Major Point D below.
4. Updated the ‘Limitations’ section of the Discussion to highlight the retrospective nature of the human study (lines 746-748).
5. Updated the Methods to again clarify the retrospective nature of the analysis (lines 884-885). __B) It is important to mention in the abstract and the discussion that the human data came from obese participants. This might well influence the findings from human data. __The human subjects were overweight or obese; this was previously stated in the methods section (line 885) and in the discussion (lines 509-511). To further clarify this, we now also mention it in the Abstract (lines 52-53) and have reiterated it in the Discussion (line 744). Importantly, the fact that humans still show age-dependent sex differences in fat loss, even when overweight and obese, supports our conclusion that this age effect in mice is not simply a consequence of aged mice being fatter than younger mice. We refer to this as the ‘baseline adiposity’ hypothesis (lines 500-518 of the Discussion). In response to point D below, we have also analysed if the loss of fat mass or fat-free mass is influenced by adiposity or BMI at baseline (pre-CR). Our analyses show that neither of these parameters explain the sex differences in loss of fat mass or fat-free mass (see new Supplementary Figure 11).

__C) It is very important to calculate the % calorie restriction of the human participants achieved throughout the CR study. This is crucial information to compare it to other studies. __We have updated the Methods (lines 906-909) to explain the basis for the weight loss diet, as follows: “Participants had their basal energy requirements determined and each participant was then fed an individualised diet with a caloric content equivalent to 100% of their resting metabolic rate (Table 3). This approach was taken to standardise the diet to account for individual energy requirements and energy restriction.” We have also updated Table 3 to show the caloric intake for males and females. Note that RMR accounts for ~60-70% of total daily energy expenditure (TDEE) in adults (Martin et al., 2022), so the diet in our study would give a daily caloric deficit of around 30-40% from baseline TDEE.

__D) Since there is quite a wide range in the BMIs of the participants, can the authors also stratify against BMI? __We have done this against both baseline BMI and against baseline fat mass (the latter to further test the ‘baseline adiposity’ hypothesis). We present this data in an updated Supplementary Figure 11. We find that, in males but not in females, baseline BMI or fat mass are significantly associated with the changes in fat mass or fat-free mass: surprisingly, individuals with higher baseline fat mass or BMI show less fat loss and a greater loss of fat-free mass during CR. Importantly, males and females do not significantly differ in the relationships between baseline fat mass (or BMI) and loss of fat mass or fat-free mass. This further supports our conclusion that the sex differences in fat loss are unrelated to differences in baseline adiposity. We report this in lines 409-411 of the Results and lines 513-515 of the Discussion.

__E) There is no mention of any pre-study registration online of the clinical part (e.g. _gov_). Was this done? __This study was done before pre-registration was a requirement for clinical trials. We retrospectively analysed the study data to investigate if sex and/or age influence the outcomes. In the updated manuscript we now state this on lines 884-885 of the Methods, as well as in the Results (line 396) and Discussion (lines 746-748).

__F) In the methods section the authors write "Participants were informed that the study was funded by an external commercial sponsor...". This is important information, and this is not mentioned anywhere else in the paper. Can the authors clarify this point? A commercial sponsor would, in my view, qualify for a conflict of interest that needs to be mentioned. __We have updated the Declaration of Interests section to clarify this as follows: “The human weight loss study was funded by a food retailer; however, the company had no role in the data analysis, interpretation or conclusions presented in this paper.”

__G) How did the authors determine the group sizes for the clinical part? I have some doubts about the sub-group sizes. It would be valuable information if the authors had a statistical analysis plan prior conducting the study. It appears a bit, like the sub-groups were chosen at random, to match findings of the mouse data. Otherwise, there should have been a better allocation within the sub-groups (especially age). __We agree that larger group sizes would have been preferable. This limitation reflects that the study was not originally designed to test age and sex effects on CR outcomes, but instead was analysed retrospectively to investigate the impact of these variables. As mentioned above, we have updated the text of the manuscript to highlight the retrospective nature of the analyses. In the Discussion, under ‘Limitations’, we also highlight the fact that relatively few younger subjects are included in the human study (lines 744-745).

__H) *There's a big problem with the age stratification of the male participants in the clinical data. If I'm correct there are only 5 males 45 groupings.

__I) The applied protocol for CR in mice is known to provoke long fasting phases and probably elicits some effects through fasting alone, rather than the caloric deficit. There are some papers out addressing this (e.g. by deCabo, Lamming). The authors should not dismiss this fact and at least address it in their discussion. Also, given this fact, it would be thoughtful to include a database-search - not only regarding CR - but also regarding various types of intermittent fasting protocols in humans and animal studies (similar to what the authors did in the supplemental figure). __We agree on the importance of highlighting recent studies demonstrating that prolonged daily fasting contributes to the outcomes of typical ‘single-ration’ CR protocols. We have added a new paragraph to the Discussion to address this (Lines 710-719).

Regarding the second point, we feel that including a new literature search that addresses not only CR, but also intermittent fasting, is beyond the scope of the current manuscript. However, this is a very good idea and would be worth addressing in a future standalone review article. We have also updated our source data to include all data from our literature reviews, to help if other researchers wish to analyse according to fasting duration or other variables.

__J) Did the authors monitor the eating time of the mice? __We have since done this in new cohorts of mice fed using the same CR protocol. We find that the mice consume their food within 2-3 hours, consistent with other CR studies. We have now mentioned this in the Methods section (lines 867-868).

__K) While CR certainly has a lot of health benefits in rodents and humans, it should be advised to raise the cautious note that it may not be beneficial for everyone in the general population. For some groups of people and in some cases (e.g. infectious diseases, pregnancy) even CR with adequate nutritional intake of micro/macronutrients might be disadvantageous. This should be mentioned clearly, as the topic gets more and more "hyped" in public media and online. __We now highlight this important point in the opening paragraph of the introduction (lines 65-67).

__L) There is no indication of how the authors dealt with missing data. Statistically this can be very important, especially in cases with a low number of data points. __In the Methods section we previously explained (lines 846-848) that “Mice were excluded from the final analysis only if there were confounding technical issues or pathologies discovered at necropsy.” No data had to be excluded from our human study and we have now stated this in the Methods (lines 897-898). For analyses involving paired or repeated-measures data (e.g. time courses of body mass or blood glucose), if data points were missing or had to be excluded for some mice then we used mixed models for the statistical analysis. We have now updated this information in the ‘Statistical analysis’ section of the Methods (lines 1047-1048). Because of the large numbers of mice used in our studies, analyses remain sufficiently well powered even if some data points were missing or had to be excluded.

__M) Key data from qPCR should be followed up by western blots or other means. If this was done and there was no effect, the authors should report this. Also, is there any evidence or the possibility to support these findings regarding pck1 and ppara in human samples? __As requested, we will next use Western Blotting to assess the expression of proteins encoded by the transcripts that show sex and/or diet differences within the liver (Fig. 6A). These data will be reported in our fully revised manuscript.

Regarding effects of CR on PCK1 and PPARA expression in human liver samples, no human CR studies have taken liver biopsies for downstream molecular analysis. Recent studies of the GTEx database confirm that hepatic gene expression in humans is highly sexually dimorphic (Oliva et al., 2020). We checked PCK1 and PPARA in the GTEx database and found that, in the liver, each of these transcripts is expressed more highly in females than in males (https://www.gtexportal.org/home/gene/PCK1 & https://www.gtexportal.org/home/gene/PPARA). While this is the opposite to what we observe in our ad libitum mice (Fig. 6A), it demonstrates that sex differences in these genes’ hepatic expression do occur in humans. The effect of CR on their hepatic expression, and whether this differs between males and females, remains to be addressed.

N): I think it would be very valuable to analyse the sex-differences in lipolysis directly in fat tissues. The authors concentrated on differences in hepatic mRNA profiles, but there's an obvious possibility and gap in their story. ____We agree that this would be informative. In the Discussion we cite previous research identifying sex differences in adipose lipolysis and lipogenesis and explain how this fits with our findings (lines 567-574). Since submitting our manuscript, we have begun experiments to investigate sex differences in the effects of CR on lipid metabolism and molecular pathways in adipose tissue. However, these analyses are extensive and ongoing, so we feel strongly that attempting to include them in our present paper would not only substantially delay publication, but also overload what is already a very extensive paper. Therefore, we plan to report our findings in future publications.

__O) Given the relatively low n and sometimes small effect sizes I fear that some of their findings won't be reproduced by other labs. Were the (mouse) data collected all at once in one cohort or did the authors pool data from different cohorts/repeats? __We presume the reviewer means ‘relatively high n’, as most of our mouse analyses used large group sizes. The mouse data were pooled from across multiple cohorts, with ANOVA confirming that the same sex-dependent CR effects were observed within each cohort. This reproducibility across multiple cohorts is a clear strength of our study because it demonstrates the robustness of our findings. Importantly, the sex differences in fat loss, weight loss and glucose homeostasis were still observed in our much-smaller cohort of evening-fed mice (Fig. S5-S6) (n = 5-6), demonstrating that large sample sizes are not needed for other researchers to detect these effects.

Reviewer 1 – Minor comments:

__a) The discussion is very extensive, and I suggest compressing the information presented there to make it more easily readable. __We have removed some text that was more speculative, such as the paragraph discussing a possible role for ERalpha. We have also revised wording elsewhere to state things more succinctly. However, given the scope of our study we feel we cannot substantially cut down the Discussion without compromising the interpretation of our findings. We note the Reviewer two’s comment that “This is a very well written paper” and feel that attempting to compress the extensive information in the Discussion would compromise, rather than help, the readability.

__b) There is some confusion present in the literature regarding the nomenclature of CR/fasting interventions. Recently some reviews have summarized the different forms (e.g. Longo Nature Aging, Hofer Embo Mol Med, ...) and the authors should address this briefly. Especially the applied CR intervention in ____mice overlaps with intermittent fasting. __We have updated the Discussion (lines 710-719) to explain how our single-ration CR protocol also incurs a prolonged intermittent fast, and how this fast per se may contribute to metabolic effects.

c): The order of the subpanels in Figure 9 (and other figures where B is below A and so on) is confusing. Please rearrange or indicate in a visual way which panels belong to each other.

We disagree that the order of subpanels is confusing: the panels are clearly labelled, and we find it most logical to have the absolute values shown in the top row (panels A, C and E), with the corresponding graphs of fold changes shown beneath each of these (panels B, D and F). This allows the reader to quickly compare the absolute vs fold-change data for each readout. If we had panels A-C on the top row and D-F on the second row, then the connection between graphs 9C and 9D would be less clear and comparable.

d): Did the authors also measure cardiovascular (e.g. blood pressure) parameters? There is some evidence out there that there is an age/sex dependency during fasting/CR. This would be a nice add-on to the rather small clinical data here.

We did measure various cardiovascular parameters for our mice but find, unlike for the metabolic outcomes, these generally don’t show sex or age differences. In our human study we measured blood pressure and heart rate before starting CR and at weeks 3 and 4 post-CR. For this response to reviewers we have summarized these human data in Figure R1. The data show that CR decreases blood pressure and heart rate in males and females (Figs. R1A-E). In the younger age group (We have decided to not include these data in the current study because we feel it is already extensive and is focused on metabolic outcomes. We instead plan to report the cardiovascular outcomes (from both humans and mice) in a separate paper.

__e) What was the decision basis for stratifying the human data into 45 years? __We used 45 years as the cutoff point because this is the age when, in women, oestrogen levels begin to decline (this point was stated in lines 491-492 of the Discussion, and we now reiterate it in lines 414-415 of the Results).

__f) The part on aging starting in Figure 7 comes quite surprising and it is not clearly linked to the data before. A suggestion here would be to smooth the transition in the text and the authors could again perform a literature search regarding age-of-onset for CR/fasting interventions in mice and humans. __We have added a sentence to smooth the transition to these studies (lines 363-364). We had previously done a literature search to identify the age of onset of CR interventions in mice and humans. We summarise the findings of this search in lines 452-470 and 484-495 of the Discussion. We have also updated the source data so that it includes the our review of the CR literature, allowing other researchers to interrogate this data.

g) At the first mention of HOMA and Matsuda indices, the effect direction should be put into physiological context.

We now mention this in lines 231-232 of the Results.

h) There is no mention of how the PCA analyses were conducted.

We have updated the Methods to explain that the PCA analyses were done using R. We have updated the source data to include the outputs from these analyses, as well as the underlying code. These data and code are now available here https://doi.org/10.7488/ds/3758.

i) Were the mice aged in-house in the authors' facility or bought pre-aged from a vendor? Is it known how they were raised? If bought pre-aged, were female and male animals comparable?

We bred and aged all mice in house. Males and females were littermates from across several cohorts. Therefore, there are no concerns about lack of comparability resulting from environmental differences.

j) Very minor note: I think that "focussed" has become very rarely used, even in British English. I don't know about the journal's language standards, but I would switch to the much more common "focused".

We have updated to ‘focused’ as requested.

k) Figure 6B/F (PCAs) should indicate the % difference of each dimension.

We have updated the figures to show the % variance accounted for by each principal component. We have also updated the figure legend to specify this.

l) Limitations section: Maybe tone down on "world-leading mass spec facility". This sounds like an excuse and this statement is unsupported and doesn't add anything valuable to the section. Other limitations would include the low n, as mentioned above and the mono-centric fashion of the mouse and human experiments.

We have addressed these points as follows:

• Toned down the description of our mass spec facility (they are renowned for expertise in steroid hormone analysis, so we our original text was intended to highlight that our facility are not novices for this).
• Regarding the low n for some of the human groups, we now highlight this on lines 744-745 of the Discussion.
• We have added a new paragraph to the Discussion (lines 710-719) explaining the limitations of our CR protocol, i.e. that includes elements of both CR and intermittent fasting. Reviewer 2:

__Point 1: This is a very well written paper. __We thank the reviewer for this kind comment.

__Point 2: Since the authors fed the animals in the morning, this is likely the reason for energy expenditure to be different in the CR vs ad lib groups. Although the authors do study the effects of night v day feeding and saw no change in the outcomes regarding weight, this fact I think should be mentioned somewhere. Also, figure 4A is expressed a W while all the other graphs are in kJ. I think it would be nice to see it all consistent. __Regarding the first point, we agree that time of feeding can influence when energy expenditure is altered, but most studies show that CR decreases overall energy expenditure regardless of time of feeding. For example, Dionne et al studied the effects of CR on energy expenditure, administering the CR diet during the night phase (Dionne et al., 2016). They found that CR mice have lower energy expenditure in the day but not in the night (Figure 3C in their paper), which is the opposite to our findings (Figure 4C). However, total energy expenditure in their study remains decreased with CR. This goes against the reviewer’s suggestion that feeding the animals in the morning “is likely the reason for energy expenditure to be different in the CR vs ad lib groups”. We have updated our manuscript (Lines 576-581) to clarify this.

Regarding the second point, we have updated Figure 4A to express the data in kJ (showing the average kJ, per hour, at each time point). The figure legend has been updated to reflect this.

__Point 3: For all the graphs, can you make the CR groups bold and not filled as it is hard to see the lighter colours. __We have updated the graphs so that the CR groups are represented by solid lines, rather than dashed lines.

__Point 4: I know many investigators use them, but I am not sure how relevant HOMA-IR and the Matsuda index are in mice since they were specifically designed for humans. __The issue of whether it is ‘correct’ to use HOMA-IR and/or Matsuda index in mice is often debated in the metabolism field. Importantly, we are not using the absolute values for HOMA-IR or Matsuda in the same way that they are used in humans; instead, we are comparing the relative values between groups because these are still physiologically meaningful. We discussed this with Dr Sam Virtue, an expert in mouse metabolic phenotyping (Virtue and Vidal-Puig, 2021), who agrees on their usefulness in this way.

__Point 5: Something also to note is the fact that all the glucose uptake data is under basal conditions. Just because there are no differences in the basal state does not mean that there are no differences after a meal/during an insulin stimulation. I think that this needs to be discussed and the muscle and fat not completely discounted as a player in the differences seen. __We agree that CR can enhance insulin-stimulated glucose uptake but our OGTT data suggest that it is effects on fasting glucose, rather than insulin-stimulated glucose uptake, that contribute to the sex differences we observe. We have now updated the Discussion (lines 608-613) as follows, “CR enhances insulin-stimulated glucose uptake (82) and it is possible that this effect differs between the sexes. However, our second relevant finding is that, during an OGTT, CR decreases the tAUC but not the iAUC, highlighting decreases in fasting glucose, rather than insulin-stimulated glucose disposal, as the main driver of the improvements in glucose tolerance.”

References cited in Response to Reviewers:

Dionne, D.A., Skovso, S., Templeman, N.M., Clee, S.M., and Johnson, J.D. (2016). Caloric Restriction Paradoxically Increases Adiposity in Mice With Genetically Reduced Insulin. Endocrinology 157, 2724-2734. 10.1210/en.2016-1102.

Martin, A., Fox, D., Murphy, C.A., Hofmann, H., and Koehler, K. (2022). Tissue losses and metabolic adaptations both contribute to the reduction in resting metabolic rate following weight loss. Int. J. Obes. 46, 1168-1175. 10.1038/s41366-022-01090-7.

Oliva, M., Muñoz-Aguirre, M., Kim-Hellmuth, S., Wucher, V., Gewirtz, A.D.H., Cotter, D.J., Parsana, P., Kasela, S., Balliu, B., Viñuela, A., et al. (2020). The impact of sex on gene expression across human tissues. Science 369, eaba3066. 10.1126/science.aba3066.

Virtue, S., and Vidal-Puig, A. (2021). GTTs and ITTs in mice: simple tests, complex answers. Nat Metab 3, 883-886. 10.1038/s42255-021-00414-7.

Author Response:

Reviewer #1 (Public Review):

In this manuscript, the authors leverage novel computational tools to detect, classify and extract information underlying sharp-wave ripples, and synchronous events related to memory. They validate the applicability of their method to several datasets and compare it with a filtering method. In summary, they found that their convolutional neural network detection captures more events than the commonly used filter method. This particular capability of capturing additional events which traditional methods don't detect is very powerful and could open important new avenues worth further investigation. The manuscript in general will be very useful for the community as it will increase the attention towards new tools that can be used to solve ongoing questions in hippocampal physiology.

We thank the reviewer for the constructive comments and appreciation of the work.

Additional minor points that could improve the interpretation of this work are listed below:

• Spectral methods could also be used to capture the variability of events if used properly or run several times through a dataset. I think adjusting the statements where the authors compare CNN with traditional filter detections could be useful as it can be misleading to state otherwise.

We thank the reviewer for this suggestion. We would like to emphasize that we do not advocate at all for disusing filters. We feel that a combination of methods is required to improve our understanding of the complex electrophysiological processes underlying SWR. We have adjusted the text as suggested. In particular, a) we removed the misleading sentence from the abstract, and instead declared the need for new automatic detection strategies; b) we edited the introduction similarly, and clarified the need for improved online applications.

• The authors show that their novel method is able to detect "physiological relevant processes" but no further analysis is provided to show that this is indeed the case. I suggest adjusting the statement to "the method is able to detect new processes (or events)".

We have corrected text as suggested. In particular, we declare that “The new method, in combination with community tagging efforts and optimized filter, could potentially facilitate discovery and interpretation of the complex neurophysiological processes underlying SWR.” (page 12).

• In Fig.1 the authors show how they tune the parameters that work best for their CNN method and from there they compare it with a filter method. In order to offer a more fair comparison analogous tuning of the filter parameters should be tested alongside to show that filters can also be tuned to improve the detection of "ground truth" data.

Thank you for this comment. As explained before, see below the results of the parameter study for the filter in the very same sessions used for training the CNN. The parameters chosen (100- 300Hz band, order 2) provided maximal performance in the test set. Therefore, both methods are similarly optimized along training. This is now included (page 4): “In order to compare CNN performance against spectral methods, we implemented a Butterworth filter, which parameters were optimized using the same training set (Fig.1-figure supplement 1D).”

• Showing a manual score of the performance of their CNN method detection with false positive and false negative flags (and plots) would be clarifying in order to get an idea of the type of events that the method is able to detect and fails to detect.

We have added information of the categories of False Positives for both the CNN and the filter in the new Fig.4F. We have also prepared an executable figure to show examples and to facilitate understanding how the CNN works. See new Fig.5 and executable notebook https://colab.research.google.com/github/PridaLab/cnn-ripple-executable-figure/blob/main/cnn-ripple-false-positive-examples.ipynb

• In fig 2E the authors show the differences between CNN with different precision and the filter method, while the performance is better the trends are extremely similar and the numbers are very close for all comparisons (except for the recall where the filter clearly performs worse than CNN).

This refers to the external dataset (Grosmark and Buzsaki 2016), which is now in the new Fig.3E. To address this point and to improve statistical report, we have added more data resulting in 5 sessions from 2 rats. Data confirm better performance of CNN model versus the filter. The purpose of this figure is to show the effect of the definition of the ground truth on the performance by different methods, and also the proper performance of the CNN on external datasets without retraining. Please, note that in Grosmark and Buzsaki, SWR detection was conditioned on the

coincidence of both population synchrony and LFP definition thus providing a “partial ground truth” (i.e. SWR without population firing were not annotated in the dataset).

• The authors acknowledge that various forms of SWRs not consistent with their common definition could be captured by their method. But theoretically, it could also be the case that, due to the spectral continuum of the LFP signals, noisy features of the LFP could also be passed as "relevant events"? Discussing this point in the manuscript could help with the context of where the method might be applied in the future.

As suggested, we have mentioned this point in the revised version. In particular: “While we cannot discard noisy detection from a continuum of LFP activity, our categorization suggest they may reflect processes underlying buildup of population events (de la Prida et al., 2006). In addition, the ability of CA3 inputs to bring about gamma oscillations and multi-unit firing associated with sharp-waves is already recognized (Sullivan et al., 2011), and variability of the ripple power can be related with different cortical subnetworks (Abadchi et al., 2020; Ramirez- Villegas et al., 2015). Since the power spectral level operationally defines the detection of SWR, part of this microcircuit intrinsic variability may be escaping analysis when using spectral filters” (page 16).

• In fig. 5 the authors claim that there are striking differences in firing rate and timings of pyramidal cells when comparing events detected in different layers (compare to SP layer). This is not very clear from the figure as the plots 5G and 5H show that the main differences are when compare with SO and SLM.

We apologize for generating confusion. We meant that the analysis was performed by comparing properties of SWR detected at SO, SR and SLM using z- values scored by SWR detected at SP only). We clarified this point in the revised version: “We found larger sinks and sources for SWR that can be detected at SLM and SR versus those detected at SO (Fig.7G; z-scored by mean values of SWR detected at SP only).” (page 14).

• Could the above differences be related to the fact that the performance of the CNN could have different percentages of false-positive when applied to different layers?

The rate of FP is similar/different across layers: 0.52 ± 0.21 for SO, 0.50 ± 0.21 for SR and 0.46 ± 0.19 for SLM. This is now mentioned in the text: “No difference in the rate of False Positives between SO (0.52 ± 0.21), SR (0.50 ± 0.21) and SLM (0.46 ± 0.19) can account for this effect.” (page 12)

Alternatively, could the variability be related to the occurrence (and detection) of similar events in neighboring spectral bands (i.e., gamma events)? Discussion of this point in the manuscript would be helpful for the readers.

We have discussed this point: “While we cannot discard noisy detection from a continuum of LFP activity, our categorization suggest they may reflect processes underlying buildup of population events (de la Prida et al., 2006). In addition, the ability of CA3 inputs to bring about gamma oscillations and multi-unit firing associated with sharp-waves is already recognized (Sullivan et al., 2011), and variability of the ripple power can be related with different cortical subnetworks (Abadchi et al., 2020; Ramirez-Villegas et al., 2015).” (Page 16)

Overall, I think the method is interesting and could be very useful to detect more nuance within hippocampal LFPs and offer new insights into the underlying mechanisms of hippocampal firing and how they organize in various forms of network events related to memory.

We thank the reviewer for constructive comments and appreciation of the value of our work.

Reviewer #2 (Public Review):

Navas-Olive et al. provide a new computational approach that implements convolutional neural networks (CNNs) for detecting and characterizing hippocampal sharp-wave ripples (SWRs). SWRs have been identified as important neural signatures of memory consolidation and retrieval, and there is therefore interest in developing new computational approaches to identify and characterize them. The authors demonstrate that their network model is able to learn to identify SWRs by showing that, following the network training phase, performance on test data is good. Performance of the network varied by the human expert whose tagging was used to train it, but when experts' tags were combined, performance of the network improved, showing it benefits from multiple input. When the network trained on one dataset is applied to data from different experimental conditions, performance was substantially lower, though the authors suggest that this reflected erroneous annotation of the data, and once corrected performance improved. The authors go on to analyze the LFP patterns that nodes in the network develop preferences for and compare the network's performance on SWRs and non-SWRs, both providing insight and validation about the network's function. Finally, the authors apply the model to dense Neuropixels data and confirmed that SWR detection was best in the CA1 cell layer but could also be detected at more distant locations.

The key strengths of the manuscript lay in a convincing demonstration that a computational model that does not explicitly look for oscillations in specific frequency bands can nevertheless learn to detect them from tagged examples. This provides insight into the capabilities and applications of convolutional neural networks. The manuscript is generally clearly written and the analyses appear to have been carefully done.

We thank the reviewer for the summary and for highlighting the strengths of our work.

While the work is informative about the capabilities of CNNs, the potential of its application for neuroscience research is considerably less convincing. As the authors state in the introduction, there are two potential key benefits that their model could provide (for neuroscience research): 1. improved detection of SWRs and 2. providing additional insight into the nature of SWRs, relative to existing approaches. To this end, the authors compare the performance of the CNN to that of a Butterworth filter. However, there are a number of major issues that limit the support for the authors' claims:

Please, see below the answers to specific questions, which we hope clarify the validity of our approach

• Putting aside the question of whether the comparison between the CNN and the filter is fair (see below), it is unclear if even as is, the performance of the CNN is better than a simple filter. The authors argue for this based on the data in Fig. 1F-I. However, the main result appears to be that the CNN is less sensitive to changes in the threshold, not that it does better at reasonable thresholds.

This comment now refers to the new Fig.2A (offline detection) and Fig.2C,D (online detection). Starting from offline detection, yes, the CNN is less sensitive than the filter and that has major consequences both offline and online. For the filter to reach it best performance, the threshold has to be tuned which is a time-consuming process. Importantly, this is only doable when you know the ground truth. In practical terms, most lab run a semi-automatic detection approach where they first detect events and then they are manually validated. The fact that the filter is more sensible to thresholds makes this process very tedious. Instead, the CNN is more stable.

In trying to be fair, we also tested the performance of the CNN and the filter at their best performance (i.e. looking for the threshold f¡providing the best matching with the ground truth). This is shown at Fig.3A. There are no differences between methods indicating the CNN meet the gold standard provided the filter is optimized. Note again this is only possible if you know the ground truth because optimization is based in looking for the best threshold per session.

Importantly, both methods reach their best performance at the expert’s limit (gray band in Fig.3A,B). They cannot be better than the individual ground truth. This is why we advocate for community tagging collaborations to consolidate sharp-wave ripple definitions.

Moreover, the mean performance of the filter across thresholds appears dramatically dampened by its performance on particularly poor thresholds (Fig. F, I, weak traces). How realistic these poorly tested thresholds are is unclear. The single direct statistical test of difference in performance is presented in Fig. 1H but it is unclear if there is a real difference there as graphically it appears that animals and sessions from those animals were treated as independent samples (and comparing only animal averages or only sessions clearly do not show a significant difference).

Please, note this refers to online detection. We are not sure to understand the comment on whether the thresholds are realistic. To clarify, we detect SWR online using thresholds we similarly optimize for the filter and the CNN over the course of the experiment. This is reported in Fig.2C as both, per session and per animals, reaching statistical differences (we added more experiments to increase statistical power). Since, online defined thresholds may still not been the best, we then annotated these data and run an additional posthoc offline optimization analysis which is presented in Fig.2D. We hope this is now more clear in the revised version.

Finally, the authors show in Fig. 2A that for the best threshold the CNN does not do better than the filter. Together, these results suggest that the CNN does not generally outperform the filter in detecting SWRs, but only that it is less sensitive to usage of extreme thresholds.

We hope this is now clarified. See our response to your first bullet point

Indeed, I am not convinced that a non-spectral method could even theoretically do better than a spectral method to detect events that are defined by their spectrum, assuming all other aspects are optimized (such as combining data from different channels and threshold setting)

As can be seen in the responses to the editor synthesis, we have optimized the filter parameter similarly (new Fig.1-supp-1D) and there is no improvement by using more channels (see below). In any case, we would like to emphasize that we do not advocate at all for disusing filters. We feel that a combination of methods is required to improve our understanding of the complex electrophysiological processes underlying SWR.

• The CNN network is trained on data from 8 channels but it appears that the compared filter is run on a single channel only. This is explicitly stated for the online SWR detection and presumably, that is the case for the offline as well. This unfair comparison raises the possibility that whatever improved performance the CNN may have may be due to considerably richer input and not due to the CNN model itself. The authors state that a filter on the data from a single channel is the standard, but many studies use various "consensus" heuristics, e.g. in which elevated ripple power is required to be detected on multiple channels simultaneously, which considerably improves detection reliability. Even if this weren't the case, because the CNN learns how to weight each channel, to argue that better performance is due to the nature of the CNN it must be compared to an algorithm that similarly learns to optimize these weights on filtered data across the same number of channels. It is very likely that if this were done, the filter approach would outperform the CNN as its performance with a single channel is comparable.

We appreciate this comment. Using one channel to detect SWR is very common for offline detection followed by manual curation. In some cases, a second channel is used either to veto spurious detections (using a non-ripple channel) or to confirm detection (using a second ripple channel and/or a sharp-wave) (Fernandez-Ruiz et al., 2019). Many others use detection of population firing together with the filter to identify replay (such as in Grosmark and Buzsaki 2019, where ripples were conditioned on the coincidence of both population firing and LFP detected ripples). To address this comment, we compared performance using different combinations of channels, from the standard detection at the SP layer (pyr) up to 4 and 8 channels around SP using the consensus heuristics. As can be seen filter performance is consistent across configurations and using 8 channels is not improving detection. We clarify this in the revised version: ”We found no effect of the number of channels used for the filter (1, 4 and 8 channels), and chose that with the higher ripple power” (see caption of Fig.1-supp-1D).

• Related to the point above, for the proposed CNN model to be a useful tool in the neuroscience field it needs to be amenable to the kind of data and computational resources that are common in the field. As the network requires 8 channels situated in close proximity, the network would not be relevant for numerous studies that use fewer or spaced channels. Further, the filter approach does not require training and it is unclear how generalizable the current CNN model is without additional network training (see below). Together, these points raise the concern that even if the CNN performance is better than a filter approach, it would not be usable by a wide audience.

Thank you for this comment. To handle with different input channel configurations, we have developed an interpolation approach, which transform any data into 8-channel inputs. We are currently applying the CNN without re-training to data from several labs using different electrode number and configurations, including tetrodes, linear silicon probes and wires. Results confirm performance of the CNN. Since we cannot disclose these third-party data here, we have looked for a new dataset from our own lab to illustrate the case. See below results from 16ch silicon probes (100 um inter-electrode separation), where the CNN performed better than the filter (F1: p=0.0169; Precision, p=0.0110; 7 sessions, from 3 mice). We found that the performance of the CNN depends on the laminar LFP profile, as Neuropixels data illustrate.

• A key point is whether the CNN generalizes well across new datasets as the authors suggest. When the model trained on mouse data was applied to rat data from Grosmark and Buzsaki, 2016, precision was low. The authors state that "Hence, we evaluated all False Positive predictions and found that many of them were actually unannotated SWR (839 events), meaning that precision was actually higher". How were these events judged as SWRs? Was the test data reannotated?

We apologize for not explaining this better in the original version. We choose Grosmark and Buzsaki 2016 because it provides an “incomplete ground truth”, since (citing their Methods) “Ripple events were conditioned on the coincidence of both population synchrony events, and LFP detected ripples”. This means there are LFP ripples not included in their GT. This dataset provides a very good example of how the experimental goal (examining replay and thus relying in population firing plus LFP definitions) may limit the ground truth.

Please, note we use the external dataset for validation purposes only. The CNN model was applied without retraining, so it also helps to exemplify generalization. Consistent with a partial ground truth, the CNN and the filter recalled most of the annotated events, but precision was low. By manually validating False Positive detections, we re-annotated the external dataset and both the CNN and the filter increased precision.

To make the case clearer, we now include more sessions to increase the data size and test for statistical effects (Fig.3E). We also changed the example to show more cases of re-annotated events (Fig.3D). We have clarified the text: “In that work, SWR detection was conditioned on the coincidence of both population synchrony and LFP definition, thus providing a “partial ground truth” (i.e. SWR without population firing were not annotated in the dataset).” (see page 7).

• The argument that the network improves with data from multiple experts while the filter does not requires further support. While Fig. 1B shows that the CNN improves performance when the experts' data is combined and the filter doesn't, the final performance on the consolidated data does not appear better in the CNN. This suggests that performance of the CNN when trained on data from single experts was lower to start with.

This comment refers to the new Fig.3B. We apologize for not have had included a between- method comparison in the original version. To address this, we now include a one-way ANOVA analysis for the effect of the type of the ground truth on each method, and an independent one- way ANOVA for the effect of the method in the consolidated ground truth. To increase statistical power we have added more data. We also detected some mistake with duplicated data in the original figure, which was corrected. Importantly, the rationale behind experts’ consolidated data is that there is about 70% consistency between experts and so many SWR remain not annotated in the individual ground truths. These are typically some ambiguous events, which may generate discussion between experts, such as sharp-wave with population firing and few ripple cycles. Since the CNN is better in detecting them, this is the reason supporting they improve performance when data from multiple experts are integrated.

Further, regardless of the point in the bullet point above, the data in Fig. 1E does not convincingly show that the CNN improves while the filter doesn't as there are only 3 data points per comparison and no effect on F1.

Fig.1E shows an example, so we guess the reviewer refers to the new Fig.2C, which show data on online operation, where we originally reported the analysis per session and per animal separately with only 3 mice. We have run more experiments to increase the data size and test for statistical effects (8 sessions, 5 mice; per sessions p=0.0047; per mice p=0.033; t-test). This is now corrected in the text and Fig.1C, caption. Please, note that a posthoc offline evaluation of these online sessions confirmed better performance of the CNN versus the filter, for all normalized thresholds (Fig.2D).

• Apart from the points above regarding the ability of the network to detect SWRs, the insight into the nature of SWRs that the authors suggest can be achieved with CNNs is limited. For example, the data in Fig. 3 is a nice analysis of what the components of the CNN learn to identify, but the claim that "some predictions not consistent with the current definition of SWR may identify different forms of population firing and oscillatory activities associated to sharp-waves" is not thoroughly supported. The data in Fig. 4 is convincing in showing that the network better identifies SWRs than non-SWRs, but again the insight is about the network rather than about SWRs.

In the revised version, have now include validation of all false positives detected by the CNN and the filter (Fig.4F). To facilitate the reader examining examples of True Positive and False Positive detection we also include a new figure (Fig.5), which comes with the executable code (see page 9). We also include comparisons of the features of TP events detected by both methods (Fig.2B), where is shown that SWR events detected by the CNN exhibited features more similar to those of the ground truth (GT), than those detected by the filter. We feel the entire manuscript provides support to these claims.

Finally, the application of the model on Neuropixels data also nicely demonstrates the applicability of the model on this kind of data but does not provide new insight regarding SWRs.

We respectfully disagree. Please, note that application to ultra-dense Neuropixels not only apply the model to an entirely new dataset without retraining, but it shows that some SWR with larger sinks and sources can be actually detected at input layers (SO, SR and SLM). Importantly, those events result in different firing dynamics providing mechanistic support for heterogeneous behavior underlying, for instance, replay.

In summary, the authors have constructed an elegant new computational tool and convincingly shown its validity in detecting SWRs and applicability to different kinds of data. Unfortunately, I am not convinced that the model convincingly achieves either of its stated goals: exceeding the performance of SWR detection or providing new insights about SWRs as compared to considerably simpler and more accessible current methods.

We thank you again for your constructive comments. We hope you are now convinced on the value of the new method in light to the new added data.

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

1. General Statements

We thank the reviewer for stating that “The detailed analysis uses many state of the art techniques to address the role of ROR1 and is of great interest to a large audience including basic researchers in the field of cancer biology and oncologists in the clinic” and we appreciate the reviewer’s constructive suggestions. We have substantially revised our manuscript and plan to perform new experiments based on these valuable comments.

1. Description of the planned revisions

Three main points: (1) The importance of AURKB as a downstream effector of ROR1 [Reviewer #1: major #2] Based on these suggestions, we plan to perform a colony formation assay using AURKB-overexpressing cells with ROR1-knockdown. We will clarify this point in the revised manuscript.

(2) The link between ROR1 expression and YAP/BRD4 [Reviewer #1: major #5 and Reviewer #3: major #1] Based on the suggestion, we plan to perform the luciferase reporter assay. We will clearly describe this experiment in the revised manuscript.

(3) Single-cell analysis using other models to validate tumor heterogeneity [Reviewer #2: major #1 and Reviewer #3: major #2] Based on your suggestion, we plan to analyze primary human tumors (public data: for example, GSE155698, CRA001160) and examine PDO#1 xenografts (in-house data). We will clearly state this information in the revised manuscript.

For the two minor points suggested by Reviewer #2, we plan to (1) reanalyze TCGA data. (2) perform the organoid or colony formation assay to validate that the siRNA model functionally recapitulates the ROR1low vs. ROR1high phenotype.

Please see the “Authors’ responses to the reviewers' comments” for more details.

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

As suggested by the reviewer, we have substantially revised our manuscript, and the changes are shown in red. • Reviewer #1: major comments #2, #3, #4, and #5; minor comments #1 and #2 • Reviewer #2: major comments #2, #3, and #4; minor comments #2, #3, #4, #8, and #10 • Reviewer #3: minor comments #1 and #2

Please see the “Authors’ responses to the reviewers' comments” for more details.

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

Authors’ responses to the reviewers' comments

Reviewer #1

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

In this manuscript the authors analyzed the role of ROR1 in pancreatic cancer progression and metastasis. They found that ROR1 expression is specifically increased in an partial EMT cell cluster upon scRNA-Seq of tumor cells derived from an orthotopic mouse PDAC model. Moreover, the ROR1 high population in tumors specifies cells with high proliferation and tumor initiation capacities, increased metastatic propensity and chemoresistance, since knockdown of ROR1 shows reduction of these features in vivo. By comparing transcriptomes from several in vivo models the authors identified that ROR1 acts through AURKB and that its expression is regulated by an upstream enhancer that is bound by YAP/TAZ and BRD4 complexes. With this study the authors identified a new targetable pathway that promotes tumor progression and metastasis in PDAC. The detailed analysis uses many state of the art techniques to address the role of ROR1 and is of great interest to a large audience including basic researchers in the field of cancer biology and oncologists in the clinic. However, some of the findings are a bit preliminary and the drawn conclusions are not sufficiently supported by the experimental data. Moreover, some findings seem a bit out of context and do not really help to bring the story forward. At other instances experimental details are missing to mechanistically demonstrate the role of ROR1. In particular it remains elusive how ROR1 is regulated, i.e. which signaling events are crucial to generate ROR1 high vs. low cells. I listed my specific comments below.

[Response] We thank the reviewer for stating that “The detailed analysis uses many state of the art techniques to address the role of ROR1 and is of great interest to a large audience including basic researchers in the field of cancer biology and oncologists in the clinic” and we appreciate the reviewer’s constructive suggestions. We have substantially revised our manuscript and plan to perform new experiments based on these valuable comments.

1. The authors' initial finding is that in the partial EMT cluster ROR1, but also other RTKs (out of 56) are specifically increased. What about the other RTKs? Why was ROR1 chosen to analyze more thoroughly?

[Response 1] We are thankful for the reviewer’s suggestion to clarify why ROR1 was selected. (1) Seven candidate genes (EPHA4, EPHA7, ERBB4, FGFR1, JAK3, LYN, and ROR1) were chosen as surface markers in the partial EMT cluster. (2) The genes were sorted in order of high expression. (3) ROR1 is reported to promote metastasis in breast cancer (Cui et al, 2013). The induction of metastasis is one of the functions of tumor-initiating cells. FGFR1 is already known to enhance the CSC-like phenotype in non-small cell lung cancer (Ji et al, 2016). (4) The antibody against ROR1 was marketed as available for cell sorting using FACS. Therefore, we focused on ROR1 as a potential new marker for tumor-initiating cells with a partial EMT signature.

References Cui B, Zhang S, Chen L, Yu J, Widhopf GF 2nd, Fecteau JF, Rassenti LZ, Kipps TJ. Targeting ROR1 inhibits epithelial-mesenchymal transition and metastasis. Cancer Res. 2013 Jun 15;73(12):3649-60. doi: 10.1158/0008-5472.CAN-12-3832. PMID: 23771907; PMCID: PMC3832210. Ji W, Yu Y, Li Z, Wang G, Li F, Xia W, Lu S. FGFR1 promotes the stem cell-like phenotype of FGFR1-amplified non-small cell lung cancer cells through the Hedgehog pathway. Oncotarget. 2016 Mar 22;7(12):15118-34. doi: 10.18632/oncotarget.7701. PMID: 26936993; PMCID: PMC4924774.

1. The finding of AURKB as crucial target of ROR1 is very weak and needs more in-depth analyses. It is not clear why AURKB was chosen over the other candidates. Is AURKB expression directly regulated by ROR1? Are the two genes directly linked? Can ROR1 deficiency be compensated by AURKB overexpression? Especially the decrease in AURKB protein level in Fig. 4K is not very convincing to account for the different phenotypes in ROR1 high and low cells. Is AURKB and ROR1 expression correlated in TCGA samples (like Fig. 8B)? In Fig. 4L the readout was changed from colony numbers to colony diameter. If AURKB is the crucial player downstream of ROR1, then colony formation efficiency should be affected at first. This needs to be shown. The statement in lines 223,224 that AURKB is a direct downstream target of ROR1 was not shown!

[Response 2-1: changed] We thank the reviewer for noting this issue. We have performed additional experiments to assess the hypothesis that AURKB is a crucial downstream target of ROR1. ROR1-knockdown not only suppressed AKT phosphorylation (Supplemental Figure 9A) but also decreased c-Myc protein levels and the expression of c-Myc target genes (CDK4, CCND1, CDK2, and CCNE1), leading to a reduction in RB phosphorylation (new Supplemental Figure 9B and 9C). Based on these results, ROR1 regulates c-Myc expression through AKT signaling, leading to the activation of the E2F network (new Supplemental Figure 9D). We added some figures and descriptions to the preliminary revision manuscript (new Supplemental Figure 9B–9D, lines 357–363, lines 649–651).

[Response 2-2: the planned revisions] We also plan to perform new experiments with a colony formation assay to determine whether ROR1 deficiency is compensated by AURKB overexpression. We agree that this experiment will confirm that AURKB is an important downstream target of ROR1 in PDAC proliferation.

[Response 2-3] In TCGA-PAAD dataset, AURKB expression was not correlated with ROR1 expression. Since the ROR1high cluster is a minor population in the tumor, a downstream analysis of specific clusters with results from a bulk study such as this TCGA dataset is difficult to perform.

[Response 2-4: changed] We have added a new graph of organoid formation efficiency (new Figure 4L) and changed some descriptions in the preliminary revision manuscript (line 227).

1. Fig. 4 A-E: The ROR1 KD was induced in vitro but not continued in vitro. The transient KD has a strong impact on tumor forming capacity, even though recovery of expression is likely within the first days in vivo. This is very interesting and underscores the role of ROR1 in tumor initiation and presumably independent of differences in proliferation. Would the results be different, if the DOX treatment would start with injection of the cells and continued in vivo? Is then tumor initiation not affected and maybe only tumor growth?

[Response 3: changed] We apologize for the confusing description in the original manuscript. In Fig. 4A–E, we used PDAC cells with stable expression of doxycycline-inducible shROR1. ROR1-knockdown was maintained in vivo by adding doxycycline to the drinking water. Continuous ROR1-knockdown suppressed tumor growth (Fig. 4C–E). Several statements we made were more ambiguous than intended, and we have adjusted the text and the figures for clarity in the preliminary revision manuscript (new Figure 4A and B, lines 203–204).

1. In Fig. 5 the authors show that ROR1 is highly expressed in tumors after gemcitabine treatment and conclude that the ROR1 high cells are a resistant population. However, this statement is too strong, since gemcitabine treatment could also lead to an upregulation of ROR1 in "low" cells during acquisition of chemoresistence. Together with our knowledge on the role of EMT in driving therapy resistance and therapy-mediated induction of EMT, such a scenario is equally likely. Similarly, the statement in lines 370-372 is not supported by experimental evidence.

[Response 4: changed] We appreciate the reviewer’s critical comments. As suggested, we have not clearly determined whether (1) the ROR1high cells survived gemcitabine treatment and/or (2) the ROR1low cells increased ROR1 expression upon exposure to this treatment. We have carefully changed some descriptions in the preliminary revision manuscript (lines 241–242, 382–383).

1. In order to understand how ROR1 is regulated, the authors use ATAC-Seq and cut and run and identified a putative upstream enhancer element (Fig. 7). Although this element increases the activity of the promoter fragment in a reporter construct, the experiments do not help to understand how ROR1 activity is increased specifically in the "high" cells. Are peaks of YAP1 and BRD4 also changed between hi/lo cells? Is YAP OE and KD (BRD4 OE and KD) or the use of the inhibotor JQ1 altering the activity of the reporter constructs (i.e. only of the enhancer-promoter combination but not of the promoter only construct)? This would help to strengthen a direct link between ROR1, YAP and BRD4. Is YAP activity different in ROR1 high vs. low cells?

[Response 5-1: changed] We thank the reviewer for this important comment. We have shown differences in chromatin accessibility and histone modification of the ROR1 enhancer between ROR1high and ROR1low cells using ATAC-seq and CUT&RUN assays (Fig. 7B). Very few ROR1high/low cells are present in xenograft. We were not successful in experiments examining the binding of YAP and BRD4 to enhancers in ROR1high/low cells because of the technical limitations in the ChIP and CUT&RUN assays. Instead, we used public data to examine YAP and BRD4 occupancy at the ROR1 enhancer region of cell lines with low ROR1 expression. In T-47D and MCF7 cells (breast cancer cells, low ROR1 expression), YAP and BRD4 did not bind to the ROR1 enhancer region (new Figure 8D and 8I). We have added figures and some descriptions to the preliminary revision manuscript (new Figure 8D and 8I, lines 304–309, line 768).

[Response 5-2: the planned revisions] We plan to perform new experiments with the reporter assay you suggested. We agree that this experiment will help strengthen the direct link between ROR1, YAP and BRD4.

[Response 5-3] As shown in Figure 8C, GSEA revealed that ROR1high cells in both S2-VP10 xenografts and PDO#1 xenografts expressed higher levels of YAP-regulated genes than ROR1low cells in these xenografts. We have added a description of this result as follows: “Thus, ROR1high cells have higher YAP activity than ROR1low cells.” (lines 304–305).

1. In Fig. 8A the authors identified 202 antigens that match the H3 monomethylation / acetylation pattern. How was YAP etc. chosen?

[Response 6] We apologize for the poor description in the original manuscript. We chose YAP and BRD4 based on the following criteria: (1) these antigens are expressed in S2-VP10 cells and PDO#1 and (2) bind to the ROR1 enhancer region (based on an analysis of public data).

Minor: 1. Fig. 2D,E: What is actually shown here? Is there an overlap between the genes that define ROR1 high vs. low cells in both approaches? The gene list should be provided.

[Response: changed] We apologize for the poor description in the original manuscript. We have added this information to the preliminary revision manuscript (new Supplemental Table 3).

1. Fig. 3G: I suggest to include the images of the tumors from the ROR1 low cells in the main figure as well.

[Response: changed] We appreciate the reviewer’s suggestion. We have moved this information from the supplementary information to the main figure in the preliminary revision manuscript (new Figure 3G, lines 186–189).

Reviewer #1 (Significance (Required)):

PDAC is a very aggressive desease with very low 5-year survival rates. Understanding of the pathobiology is of keen interest. The findings of the authors are of high significance and extremely relevant as they provide a mechanism that can also be targeted by specific drug combinations, i.e. standard care gemcitabine with specific ROR1 inhibition. The findings are of great interest to a large audience including basic researchers in the field of cancer biology and oncologists in the clinic.

[Response] We greatly appreciate the reviewer’s comments.

Reviewer #2

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

In this work Yamazaki and colleagues performed single cell RNA sequencing of one xenograft tumor formed by the S2-VP10 PDAC cell line to explore PDAC intratumor heterogeneity. Using this model they identified ROR1 as heterogeneously expressed in neoplastic cells. Using further in vivo and in vitro models they show that ROR1high cells have higher tumor initiation capacity than ROR1low. By histone and ATAC-seq analyses, they identify a ROR1 enhancer upstream the promoter and show that YAP and BRD4 bind to this genomic region and that BRD4 inhibition by JQ1 reduces ROR1 expression and organoid formation. The data, figures and methods are nicely and clearly presented.

[Response] We thank the reviewer for stating that “The data, figures and methods are nicely and clearly presented”, and we appreciate the reviewer’s constructive suggestions. We have substantially revised our manuscript and plan to perform new experiments based on these valuable comments.

Major comments

1. The authors use one xenograft tumor as starting model and all conclusions are derived from the data generated with this model. To support the existence of identifie heterogeneity in the PDAC neoplastic compartment, I would strongly suggest to validate the existence of the partial EMT population and the ROR1 heterogeneity in single cell data bases generated from primary human tumors.

[Response 1: the planned revisions] We thank the reviewer for the positive suggestion. We plan to perform a new analysis of available public single-cell data from human PDAC tumors. In addition, we also launched a single-cell analysis of PDO#1 xenografts.

1. In Fig. 3G, it is mentioned that tumors grown from ROR1high cells recapitulate the original PDOx histology thus suggesting that ROR1high cells in the tumor are the actual TICs. ROR1low cells could also grow tumors, just with lower incidence. Are these tumors any different to the ROR1high derived ones? Is it just a lower tumor initiation capacity (TIC) or they can not recapitulate the tumor as the ROR1high cell? Can they also give rise to differentiated progeny cells? This should appear in the main text and not only in the discussion. I would suggest to move panel 3G to supplementary figure.

[Response 2: changed] We thank the reviewer for noting this issue and apologize for the confusing description in the original manuscript. ROR1low cells generated tumors at a low frequency, and these tumors showed a hierarchical histology mimicking the original tumor. As suggested, we have added this information to the main text (new Figure 3G, lines 186–189).

1. In line 160 you mention that known CSC markers such as CD44, PROM1 and DCLK1 are not differentially expressed between ROR1 high and low populations. Then, in figure 3H,I you analyze the expression of CD44v6 together with ROR1. I would try to put this information together in the text, or at least in fig. 3 start with something like "we had seen that both ROR1high and low express CD44, however...". In any case, I feel that the experiment with CD44 could be obviated (or at least moved to supplementary), as it brings the question of weather this is also true for DCLK1 or CD133.

[Response 3: changed] We appreciate and agree with the reviewer's comment on this point. Accordingly, we have moved this figure to the supplementary information and changed the description (new Supplemental Figure 5C and 5D, lines 191–196).

1. JQ1 has been described to inhibit PDAC growth by downregulation of MYC. To unequivocally link the effect of JQ1 in the downregulation of ROR1 (Fig. 8M) as discussed in the text it would be important to exclude that other mechanisms such as MYC downregulation are taking place. For example, does JQ1 treatment of ROR1low cells also reduce their colony formation capacity (in an experiment such as the one in fig. 3C). Or does ROR1 re-expression in Fig. 8M rescue the JQ1 effect? These or other experiments could help to establish a stronger link between (BRD4/JQ1) and ROR1.

[Response 4: changed] We thank the reviewer for this important comment. As mentioned in the response to Reviewer #1-major comment #2, we newly found that ROR1 regulates c-Myc expression through AKT signaling, leading to the activation of the E2F network (new Supplemental Figures 9B–9D, lines 357–363).

Minor comments 1. The data are nicely presented (text and figures) and the conclusions are clear. My suggestion to make the story more "catchy" at the beginning would be, if possible, to start from the observation done in primary human data and then move to the PDX model to explore ROR1 as a TIC marker in PDAC. For this, you could use available public single cell data of human PDAC tumors. If this doesn't work (it is of course possible that by unsupervised analysis you don't get the same clusters as in the PDX with the partial EMT cluster popping up), it would be nice if some primary tumor data came early in the story (currently the first figure showing heterogeneity in primary samples is in supplem fig. 4A).

[Response: the planned revisions] We thank the reviewer for these excellent comments. As suggested, we plan to perform several new analyses (please see the previous comment for details: Reviewer #2-major comment #1).

1. It is not clear if the xenografts were subcutaneous or orthotopic. It would be good to include this information in the main text (line 102) and the methods so that the reader knows what is the exact model that has been used.

[Response: changed] We thank the reviewer for this comment and apologize for the poor description in the original manuscript. As suggested, we have added this information to the preliminary revision manuscript (line 101).

1. In Fig. 2F and 2G I would highlight the EMT pathway to help the reader.

[Response: changed] We thank the reviewer for this comment. As suggested, we have changed the relevant figures in the preliminary revision manuscript (new Figure 2F and 2G).

1. In Supp Fig 4B it would be nice to have an amplified view of the staining as in panel C of the same figure.

[Response: changed] We thank the reviewer for this comment. As suggested, we have added high-magnification images of the staining in the preliminary revision manuscript (new Supplemental Figure 4A and 4B).

1. In the same figure (Fig. 4A-D) ROR1 shows an apical staining pattern that doesn't seem to resemble the staining in patient samples. I am not an expert in pathology evaluation but I would recommend a pathologist to give her/his opinion. Possibly, during the PDX process, few cells from the original patient tumor are selected giving a different staining pattern.

[Response] We appreciate the reviewer's comment on this point. Dr. Ito, a coauthor of this paper, is a pathologist. We have changed some images of staining in patient samples (new Supplemental Figure 4A). We agree that ROR1 shows an apical staining pattern in PDX samples. However, some sites show similar apical staining patterns in patient samples (Patient #2 and Patient #4 in the new Supplemental Figure 4A). We propose that PDX mimics the original patient tissue because it has heterogeneity of ROR1 expression and morphological features indicative of a luminal structure.

1. In the analyses of TCGA data, be aware that only 150 from the original dataset are actual PDAC tumors. The dataset contains otherwise data from cell lines, PDX, normal tissue, etc that should be removed for a proper analysis (see DOI: 10.3390/cancers11010126)

[Response: the planned revisions] We thank the reviewer for the careful review of this issue. We are currently reconsidering with the pathologist whether the samples are appropriate based on TCGA data (diagnosis and pathology sections) and the paper you presented. The current data (Figures 3A, 4J, and 8B) were analyzed for samples excluding cell lines, PDX, and normal tissue in the TCGA-PAAD dataset.

1. Does ROR1 correlate with RFS? This would nicely fit with the concept of TIC and metastasis.

[Response] We thank the reviewer for noting this issue. Unfortunately, no correlation was observed between ROR1 expression and RFS.

1. Line 219: ROR1 is not "depleted" in the lines as it is a downregulation model. "ROR1-downregulated" would be more correct.

[Response: changed] We thank the reviewer for this suggestion and agree with your comment. We have corrected this term accordingly in the preliminary revision manuscript (line 223).

1. It would be good to have a supplem figure showing that siROR1 cells show reduction organoid formation, to validate that the siRNA model functionally recapitulates the ROR1low vs high phenotype.

[Response: the planned revisions] We thank the reviewer for this suggestion. We plan to perform a colony formation assay.

1. Some of the supplemental figures are only referred in the discussion although they appear earlier than other in the main text. This is a bit confusing when going through the figures.

[Response] We apologize for the poor description in the original manuscript. We have adjusted the order of the supplemental figures in the preliminary revision manuscript.

CROSS-CONSULTATION COMMENTS I agree with the importance of addressing points 2 (link to AURKB), 4 (selection vs acquisition), 5 (mechanism in high vs low cells) raised by Reviewer 1, and the comments from Reviewer 3. I think that the study of other RTKs (point 1 from Reviewer 1) is not the focus of the story. It would be nice if the authors can comment on why they chose ROR1 but the fact that are other differentially expressed genes does not exclude the validity of the current story. I fell that the in vivo sustained KD experiment (point 3 from Reviewer 1) although interesting, it is not mandatory for a revision of this manuscript in case the adaptation of the animal protocol represents a long process. The experiment provided already in the current version is the best approach to address the role of ROR1 at the early initiation phase.

[Response] We thank the reviewer for these positive comments. As suggested, we have substantially revised our manuscript.

Reviewer #2 (Significance (Required)):

Significance: This is a neat and interesting work with potential implications for the clinical field of pancreatic cancer as the authors identified a new subpopulation with enhanced tumor initiating cell capacity. However, the use of JQ1 for pancreatic cancer has been previously discussed mainly linked to MYC inhibition, but also to stromal reprogramming or DNA damage induction. I missed some discussion in this regard in the discussion section. What is adding the work to the field of JQ1 treatment in PDAC? IN a way, how do the authors foresee that the discovery of ROR1high cells and the regulation of ROR1 by BRD4 and YAP will be beneficial when considering JQ1 in the clinics? Maybe by stratifying patients? Or by following ROR1 upregulation upon initial chemotherapy? These questions are just suggestions. In general, some discussion to put the work into the context of previous works using JQ1 in PDAC would be nice.

[Response: changed] We thank the reviewer for this comment. As you suggested, we have added a description of the proposed use of JQ1 and BRD4 inhibitors in ROR1high PDAC treatment to the Discussion section (lines 412–416).

I believe that this work would be interesting not only to the pancreatic cancer community but also to a more general public working on cancer and/or stemmness as it touches several interesting points in that regard that can be applicable to other systems. My own work is focused on pancreatic cancer, patient heterogeneity and stromal interactions. I am not an expert on histone or ATACseq analyses.

[Response] We greatly appreciate the reviewer’s comments.

Reviewer #3

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

Summary Yamazaki et al investigate partial EMT in pancreatic cancer and provide data that ROR1 marks pancreatic tumor cells that are capable of initiating tumors. The authors exploit scRNAseq of pancreatic tumor xenografts to identify a cluster of cells showing a partial EMT phenotype. The found 7 RTKs expressed more highly in this partial EMT cluster and focus their attention on ROR1, an 'orphan' receptor that has been implicated in WNT signaling and EMT previously. Validation experiments using ROR1-high vs low cells support that ROR1 expression correlates with EMT, poor outcome in human PDA patients, tumor forming and colony forming capacity. They also show that ROR1 high cells form tumors that recapitulate parental tumor histology. The authors show that ROR1 expression is associated with EF2 transcription factor activity, elevated expression of multiple targets including AURKB. Pharmacologic inhibition of AURKB reduces colony formation and genetic loss of ROR1 combined with chemotherapy (gemcitabine) has potent anti-tumor activity in vivo. The authors show that ROR1 expression is elevated in metastatic lesions and identify a novel enhancer element that putatively drives ROR1 expression in tumor cells. They provide evidence that this element is engaged by YAP/BRD4 and show that BRD4 inhibition reduces tumor cell colony formation. The manuscript is a solid combination of techniques with adequate controls and statistics.

[Response] We thank the reviewer for stating that “The manuscript is a solid combination of techniques with adequate controls and statistics”, and we appreciate the reviewer’s constructive suggestions. We have substantially revised our manuscript and plan to perform new experiments based on these valuable comments.

Major Comments: The overall conclusion that ROR1 expression marks a subset of pancreatic cancer cells that have the ability to initiate tumors is supported by the data provided. The correlative data are strong and the demonstration that loss of ROR1 reduces colony formation, reduces metastatic lesions and enhances the efficacy of chemotherapy are compelling. Additionally, the demonstration that ROR1 expression is elevated in metastatic lesions is consistent with many other drivers/markers of EMT in pancreatic cancer.

The conclusion that ROR1 expression is driven by YAP/BRD4 is interesting and provides important mechanistic depth to the study. However, this conclusion could be strengthened by use of a suitable rescue experiment. For instance does overexpression of ROR1 rescue the effect of BRD4 inhibition or loss of YAP?

[Response 1: the planned revisions] We thank the reviewer for this comment. We completely agree with the reviewer’s suggestion. However, the suggested examination to determine whether overexpression of ROR1 rescues the effect of BRD4 inhibition or loss of YAP may not be suitable because BRD4 and YAP act as transcriptional coregulators of various target genes. Instead, as mentioned in response to Reviewer #1-major comments 5-2, we plan to perform new experiments using a reporter assay.

A challenge with the data presented in Figure 1, the scRNA-seq data that lead them to ROR1, is that it is not stated how many tumors are used to generate the scRNA-seq data and the overall number of tumor cells analyzed is relatively low (993). The authors should provide the number of tumors used for the initial scRNA-seq. A general concern with any scRNA-seq data is batch effect, this is mitigated to a degree by the follow on studies that provide functional validation of ROR1 in multiple cell lines.

[Response 2: changed and the planned revisions] We appreciate the reviewer’s comments. As suggested, we have added this information to the preliminary revision manuscript (line 104). In addition, as mentioned in response to Reviewer #2 major comment #1, we plan to perform a new single-cell analysis of PDO xenografts (in-house data) and human PDAC tumors (available public data).

The data and methods are provided in an adequate manner. Reproduction of the experiments is likely. The authors use multiple cell lines and tools that are generally available. The authors note a limitation of the study is that only human tumor xenografts were exploited.

[Response] We thank the reviewer for the positive comment.

Minor comments: Figure 1E and text page 9. The text identifies MERB3 as a gene that marks the partial EMT cluster, I believe this is a type and the gene should actually be MSRB3.

[Response: changed] We apologize for the typo. We have corrected this error accordingly (line 114).

Please provide the dose of gemcitabine in the legend for figure 5

[Response: changed] We apologize for the poor description in the original manuscript. We have added this information.

CROSS-CONSULTATION COMMENTS I think the comments from Referee #2 are pretty reasonable - have no additions

Reviewer #3 (Significance (Required)):

Intratumor heterogeneity is a major challenge for the treatment of many cancers, including pancreatic cancer. The data provided support that ROR1 marks a subset of cancer cells in pancreatic tumors that have the capacity to drive intratumor heterogeneity. If supported these data have the potential to drive significant impact. Identification of a marker and a targetable pathway that supports tumor initiation in pancreatic cancer has the potential to nominate companion therapies that enhance the efficacy of standard of care approaches. Further, identification of a pathway that drives partial EMT in pancreatic cancer provides a substantial increase in baseline knowledge of intratumor heterogeneity.

These data would be broadly interesting to scientists interested in the tumor microenvironment, metastasis, therapy resistance and tumor progression. In addition, oncologists focused on drug development and combinatorial therapy will find this manuscript of interest.

[Response] We greatly appreciate the reviewer’s comments.

This sentence is extremely important. Not only do we tend to associate popularity with quality, but also with credibility. I see time and time again Tik Toks and videos on Twitter, Instagram, and Facebook will go viral of someone going on a rant about some issues and making very bold claims without siting anything. In return, as the video grows in popularity, people will quote and cite the video as a credible source. However, a person's opinion that has becoming well-liked does not equate truth or fact. I will also see quality information go unnoticed as it lacks audience engagement. I think of times where I come across a Tik Tok that I like but it has almost no likes or comments. I first I find this odd. However, then I realize that the algorithm is testing this video to see if people will like it and if it gets likes, then it will show it on more feeds and then more feeds. However I will easily like a video that already has hundreds of thousands of likes because I think "why not?", when I could be adding to the spread of misinformation. Bias is inevitable and something we may never be able to fully avoid. However, I think the awareness of your own bias and knowing that you are bias is already helpful enough. Unfortunately too many claim they have no bias and only base on fact, which we all know is not true.

I agree with this claim and think that while society and science may suppress the ability for us to imagine fairy tales, I think they have a much larger impact that just speaking to our imagination. Instead, they speak to what we believe as people and to what we want in life. While fairy tales may often occur in mythical lands with mythical creatures, I think the reason they appeal so much to people is because they portray a world that is less stressful and more like the world they knew in their childhood, not because people necessarily want to ride on unicorns.

I think this an interesting light to see fairytales in. It almost reminds me of the notion of "taking what you need" and our brains allowing us to focus on what resonates with us and taking out the morals that we can particularly relate to.

Context: Lindsey Graham had just proposed a bill for a nationwide abortion ban after 15 weeks of pregnancy.

McConnell's position seems to be one that choice about abolition is an option, but one which is reserved for white men of power over others. This is painful because that choice is being left to people without any of the information and nuance about specific circumstances versus the pregnant women themselves potentially in consultation with their doctors who have broad specific training and experience in the topics and issues at hand. Why are these leaders attempting to make decisions based on possibilities rather than realities, particularly when they've not properly studied or are generally aware of any of the realities?

If this is McConnell's true position, then why not punt the decision and choices down to the people directly impacted? And isn't this a long running tenet of the Republican Party to allow greater individual freedoms? Isn't their broad philosophy: individual > state government > national government? (At least with respect to internal, domestic matters; in international matters the opposite relationships seem to dominate.)

tl;dr:<br /> Mitch McConnell believes in choice, just not in your choice.

Here's the actual audio from a similar NPR story:<br /> https://ondemand.npr.org/anon.npr-mp3/npr/me/2022/09/20220914_me_gop_sen_lindsey_graham_introduces_15-week_abortion_ban_in_the_senate.mp3#t=206

McConnell is also practicing the Republican party game of "do as I say and not as I do" on Graham directly. He's practicing this sort of hypocrisy because as leadership, he's desperately worried that this move will decimate the Republican Party in the midterm elections.

There's also another reading of McConnell's statement. Viewed as a statement from leadership, there's a form of omerta or silent threat being communicated here to the general Republican Party membership: you better fall in line on the party line here because otherwise we run the risk of losing power. He's saying he's leaving it up to them individually, but in reality, as the owner of the purse strings, he's not.

Thesis:<br /> The broadest distinction between American political parties right now seems to be that the Republican Party wants to practice fascistic forms of "power over" while the Democratic Party wants to practice more democratic forms of "power with".

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Reply to the reviewers

This is a revision plan, the manuscript has not been modified yet as it is being transferred to a journal.

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

This study proposes (and uses) an elegant model of bacteria evolution to study how division of labor can emerge through the interaction between non-random mutations (occurring at some specific ``fragile' genomic sites) and genome architecture. The study is very interesting and the results are convincing. My main concerns are about the presentation of the model and results. Although I am confident about the results, some elements should be clarified for a better understanding and for a correct interpretation of the results. Two points in particular (detailed below as major comments) require clarification.

Major comments:

• the notion of telomere/centromere is used all throughout the paper but I think it is used in a misleading way. First, it seems that here there is only one telomere (but this is actually a detail of the model). More importantly, as long as I know, it is well known that in S. coelicolor the sequence degenerates more rapidly when getting closer to the telomeres (but telomeres are defined independently from this property). But here, the notion of telomere is precisely directly determined by its mutational instability (respectively, the centromere is defined by its stability). Although this is reasonable given the objective of the model, it forbid the use of sentences like "we observed that the genome of the evolved colony founded had two distinct regions: a telomeric [...] and a centromeric [...]" (line 234) or "When bacteria divide, mutations induced at fragile sites lead to the deletion of the part of the genome distal to them, causing large telometic deletions" (line 239 - this is not a result but a hidden description of the model) as this distinction between the two regions is not an outcome of the simulation but rather given a priori as a coded property of the fragile sites that all lead to deletions on the same -- called telomeric -- side (of course, formally if the genome contains no fragile site, there is no distinction but still). Please clarify this in the main text and in the methods. *

Authors response (AR, in the following): we agree with the reviewer that the directionality of the deletions determines centromere and telomere in our model (and the reviewer is correct that we only consider one arm of the chromosome). We will explicitly state both in the main text and in the methods that the model does not include any explicit centromeric and telomeric structure, and that the polarity of the genetic information (and thus centromere and telomere) depends on the choice of directionality of the deletions.

- In most part of the paper (methods, results, figures, sup mat...) antibiotics are considered to have a concentration (or a high/low production) but at least twice in the text (lines 165 and 488) it is said that only the presence/absence of antibiotics is modelled. I was not able to understand how the continuous values are transformed into presence/absence (is there a threshold?) but more importantly, I strongly suspect that this choice has a strong influence on the outcome. For instance, with a diffusion radius equals to 10, it means that an antibiotics producing cell is able to protect 2*\pi*10=~60 replicating cells. Hence, one could conjecture that the fraction of antibiotic-producing mutants should a little more than 2%... which is what is observed by the authors. So (1) please clarify this point (2) discuss (or experiments) the consequences of this choice on the conclusion.

AR: the reviewer is correct that antibiotics are modelled as presence/absence – this was done for computational efficiency. However, the probability that a bacterium deposits an antibiotic at a site within the deposition radius is a continuous number, as it depends on the number of antibiotic genes and growth genes. We will make this clear in the main text and in the methods.

Secondly, we show the effect of varying the deposition radius for the evolutionary dynamics in Supplementary Section S17. We will make this clear in the main text. For the area covered by different radius of antibiotic deposition, please see below.

* Minor comments: - line 262: "We conclude that genome architecture is a key prerequisit for the maintenance of mutation-driven division of labor". Given the model hypotheses you cannot be so affirmative (it is a key prerequisit... in this model!) *

AR: we will modify the statement as suggested. * *

- line 286: "cannot" is probably too strong. It has not been observed...

AR: we will modify the statement as suggested.

- line 288 and following: you seem to consider that there is "selection for diversity". Given the large number of possible antibiotics and given that cells are "automatically" resistant to the antibiotics they produce, could it be simply drift? There is a clear selection pressure to limit the number of growth-promoting genes but no such pressure exist for antibiotics. Hence their number could simply drift (note that figs 2 and SF1 both use a log scale; random variations due to drift could be hidden by the log. Fig. SF2 does use a log scale and shows a dynamics that---to my eyes---claims for drift rather than for selection of diversity).

AR: we agree with the reviewer that drift might contribute to the overall antibiotic diversity. This might be especially true for the antibiotic genes residing downstream of the fragile sites, which have low probability of expression in the wild-type (because of the many growth genes) and are deleted in the mutants. Duplications, deletions and modifications of these genes are effectively neutral, and are therefore likley subject to drift. We will include this discussion in the main text. However, bacteria are highly susceptible to the diverse antibiotics produced by other colonies (i.e. those produced – largely – by the mutants). These antibiotics and their diversity drives colony invasion and is thus selective. The overall number and diversity of antibiotics is therefore, at least in part, under selection.

- line 340: "ends" should be "end" when discussing the model - line 345: "a telomeric region" should be "telomeric regions" when discussing the bacteria - line 359: "S. ambofaciens" should be italic - line 365: same for "Streptomyces"

AR: we will modify the statement as suggested (and thank the reviewer for carefully reading the text).

- line 245 states that colonies begin clonally but methods (lines 434-438) don't support this. Colonies don't begin clonally but they begin without antibiotic-producing spores (see also line 618)

AR: we agree with the reviewer that colonies are not specifically initialised as clonal. We will modify the sentence as: By this process colonies eventually evolve to become functionally differentiated throughout the growth cycle.

- line 442: "their" should be "its" - line 446: "hotspot for recombination" no, for "deletion" - line 449: please remove brackets around the reference.

AR: we will modify the statement as suggested.

- line 458: if I understood it correctly, there is no explicit competition in the model. Competition simply comes from the asynchronous replication. Am I true? Could you clarify that point?

AR: The reviewer is correct that through asynchronous updating only one focal lattice site is update at a time. However, if a site is empty, the bacteria surrounding it are competing based on their replication rate kreplication. Dividing by the neighbourhood size (eta) simply ensures that a bacterium surrounded by a completely empty neighborhood replicates on average alpha_g times (alpha_g being the max growth rate). We will mention this in the methods.

- line 490: "the antibiotic deposited is chosen randomly and uniformly among them". This is not fully clear. I suppose the bacteria is still resistant to all the antibiotics it \it{can} produce?

AR: Yes. This is mentioned in the methods section “Replication”.

- figure SF1: please use the same scales as in figure 2 such that the two plots can be easily compared

AR: we will modify the x-axis to include the number of growth cycles.

- section S3 and figure SF4: What is to be understood from the figure is not clear to me. Seems that WTs win only if generalists produce less AB or replicate slower (?) Is it true?

AR: The reviewer is correct. In other words: when the artificial generalist has the same replication rate and the same antibiotic production rate as the WT, then the competition experiment ends with a near draw (the generalist still wins, but slowly). This means that the fitness cost associated to division of labor, i.e. to having two cell types doing the same work as one generalist – is small.

We will include this description in the section.

The figure is unfortunately complicated by the fact that we do not know a-priori how high the effective antibiotic production rate is (because antibiotics are spatially distributed by the stochastically generated mutants) – and so we had to make a large parameter screen to figure out the parameter values for which the competition experiment made most sense.

- I found it very difficult to draw conclusion from section S4, S5 and S6. These experiments should be analyzed with the help of mathematical analyses of the equations. Moreover, the understanding of these results are rendered difficult due to the lack of clarity regarding the discrete (or not) nature of the antibiotic production/action/diffusion

AR: We hope that we have clarified the distinction between antibiotic production rate and antibiotic presence/absence in the lattice.

The model is not amenable to analytical tractability, which makes it difficult to make exact statements based on the equations that govern it. However, we can check that the model is robust, and identify regions of parameter space where the model behaves in a qualitatively similar way to main text results.

Sections S4, S5 and S6 are essentially parameter screens to verify that the model reproduces the results reported in the main text for a broad range of parameters. The primary conclusion that can be drawn is that the model is robust to parameter changes.

Section S4 explores the model robustness to changes in two key parameters of the model: the antibiotic inhibition due to growth genes beta_g and the parameter h_g, which is the number of growth genes that produces half-maximum growth rate. Section S5 further analyses the relation between these parameters, and how they together determine the strength of the trade-off. Section S6, finally, shows that a strong trade-off is not a necessary requirement for evolution of division of labor as the division also depends (in a counterintuitive way) on the parameter alpha_g, the maximum antibiotic production rate.

We will include and expand these summarizing statements in each section, to make clear what each section achieves.

- S7 and fig SF9. It is unclear to me why the fraction of mutants decrease along time elapsed in the cycle. Please explain.

AR: The reason is that not all mutants are born with the same number of antibiotic genes (Fig. 3A). A mutant with fewer antibiotic genes might be susceptible to some of the antibiotics produced by another mutant, and could be killed by these antibiotics. Once a mutant is killed in the inner colony, a wt will replicate to fill the spot, and likely a wt offspring will take that site rather than another mutant. Thus there is a decline in overall mutant population.

We will include this discussion in Section S7.

- Figure SF14: what are the tin lines? if they correspond to the five repeats, how can it be that the bold line be the median?

AR: we realise that the caption should be clearer. Each of the five lines (both bold and thin) in each pane represents the median number of genetic elements over time. The bold line just highlights one randomly chosen simulation (the same for each genetic element), to better guide the eye.

We will clarify the caption of the figure.

- S13 and figure SF15: given that AB concentration is ON/OFF, is this result really surprising? This also questions about the accumulation of AB genes in the original model. Although the authors regularly claim that this is due to selection for diversity, drift could also be at play (see above)

AR: As mentioned above, we agree with the reviewer and we will mention that drift may co-determine antibiotic gene accumulation.

- S17: for radius 1, 2 and 3, the aliasing is likely to be strong. Hence, the results cannot be interpreted with this sole information. Please give e.g. how many cells are "protected" for each radius (e.g. for r_{alpha}=1, this value can vary between 1 and 9!)

AR: for radius=1, 2, 3, 5 ,8, 10 the area covered by antibiotic production is respectively 5 ,13, 29, 81, 197, 317. We will include this information in the figure.

- L742: "matching the antibiotic bitstring with the bitstring of the antibiotic". True and actually elegant but simpler formulation could ease the reading...

AR: We will change the sentence as follows: “Both antibiotics and antibiotic genes are characterised by a bitstring, which determines their type. Antibiotic resistance in the model is determined by matching these two strings.”

- lines 746-751 and figure SF21: There again, could it be a consequence of the AB ON/OFF diffusion model?

AR: we agree with the reviewer that a continuous diffusion model could affect resistance to antibiotics. We expect that the main effect will come from some antibiotics antibiotics having different concentrations. For instance, we could have a situation in which many deleterious antibiotics are produced in small amount, but have a compounding effect on the susceptible bacterium. This finer model of antibiotic production, diffusion and killing was not included in the model to limit the computational load.

- S18-S19-S20: what should the reader understand from these results? Please better comment the figures.

AR: we agree that figures in Section S18,19 and 20 could have more descriptive captions. Sections S18, 19 and 20 are parameter screen to check that the model is robust to changes in the mutation rates affecting fragile sites activation and de-novo formation. The primary result of Section S18 is that that division of labor evolves over a broad range of fragile site activation rates and de-novo fragile site formation rates (and even when these parameters are decreased by one order of magnitude).

Section S19 shows how these combination of parameters result in quantitative changes in genome composition.

Section S20 shows that the de-novo fragile site formation rate can be zero: as long as the system is initialised genomes that can divide labor, the fragile sites will persist even though no new ones are generated.* *

• CROSS-CONSULTATION COMMENTS Sorry about the confusion about the computation of the number of cells protected by a single AB-producing cell. Of course it is of the order 10*\pi^2 !!! The global argument still holds but the number of cells protected is of course larger than 60 (note that, due to aliasing at the periphery the exact number of cells in the protected area is difficult to determine). *

Author response: We hope the clarifications mentioned above answer the reviewer’s comment.

* Reviewer #1 (Significance (Required)):

First, an very importantly, I must say that I am no familiar with the biological model (Streptomyces coelicolor). So I am not fully able to judge the biological significance of this research (i.e. whether the way division of labor is achieved here enlights---or not---the biology of this bacteria). However, on the computational side, the model and the results (as they are summarized in the conclusion) are very interesting on their own and deserve publication.

Remark: a lots of supplementary results are added to the paper that are not not fully explained or analysed. Please, better discuss all these results and their significance. *

AR: we will extensively check and add detail to the supplementary material, ensuring that results are fully explained (see also response to reviewer 1).*

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

The manuscript "Evolution of genome fragility enables microbial division of labor" presents a model of genetically-based division of labour in bacterial colonies. It is postulated that two essential processes, growth and the important for elimination of competitors production of antibiotics, are poorly compatible in a single cell. The beneficial for a colony cell specialization is assumed to be determined only by genetic differences that appear via deletions of growth- promoting loci. These deletions and production of various antibiotics are mediated by a rather elaborate genetic architecture, which includes position-sensitive "fragile" sites, mutable antibiotic and growth-promoting genes. The model produces rather predictable results that under sufficiently strong incompatibility between growth and antibiotic production, the long-term evolution results in formation of mosaic of colonies, each specialized in production of its specific set of antibiotics. Such production is facilitated by evolving rapidly mutable genomes that constantly generate non-reproducing antibiotic-pumping cells.

The model appears very thoroughly developed and analyzed, and all major conclusion are intuitively appealing. Overall, the manuscript reads as a well-written quantitative proof of the principle of genetically-based division of labour between bacterial cells. The only part of the model that I'm a bit sceptical about is the unwarranted complexity of the genetic architecture. Unless the introduction of "fragile" sites and the directional ordering of genes is strongly justified by empirical data, a simpler and more clear assumption about mutational incapacitation of growth genes would suffice to reproduce the predicted phenomenology. So adding such empirical evidence would boost the relevance of the genetical part of the model. In the present form, all observed adaptations are inevitable simply because the expected division of labour will not evolve without each of them due to the design of the model. *

AR: We agree with the reviewer that a simpler model with a predetermined effect of mutations, such as to incapacitate the growth genes, would suffice to reproduce the phenomenology of the mutation-driven division of labor observed in Streptomyces. Adding the complexity of a genome architecture introduces one more hypothesis: that genome fragility can evolve to organize the division of labor. This hypothesis, supported by the results presented here, can be tested experimentally.

However, there is already some empirical support for our modelling choices: 1) mutation rates along the genome of Streptomyces are highly heterogeneous, 2) the genetic content is partitioned along the chromosome so that some genes are preferentially located in the mutationally quiet centromere, and others are in the mutationally active (sub)telomeric regions, 3) some cis genetic elements in Steptomyces’ genomes readily recombine to produce large-scale duplications and deletions (which we heavily simplified in the model as deletion-inducing fragile sites).

We will extend the introduction to include the references for the empirical support to our model.

* A couple of minor comments...

217 This is achieved when fewer growth-promoting genes are required to inhibit antibiotic 218 production (i.e. lower βg). Shouldn't it be "larger \beta_g"? *

AR: yes. Thanks for catching this!

* Whether in the main text or Supplementary materials, it woud help to add a complete population dynamics equation with all gain and loss terms. *

AR: we agree with the reviewer that it would be interesting to obtain a comprehensive population dynamics equation that captures the spatial dynamics of replication, mutation, and antibiotic production, causing colony formation and between-colony competition. However, deriving such equation would be a very big effort in itself, and we suspect that it would not be analytically tractable. Because of this, we prefer the “procedural” model description we gave – which also mirrors the model implementation (see github repository at github.com/escolizzi/strepto2).

* Strikingly, we find the opposite: division of labor evolves when 224 bacteria produce fewer overall antibiotics (lower αa), under shallow trade-off conditions 225 (hgβg = 5; see Suppl. Section S6).

I don't see why it is"striking". It seems perfectly explicable that a smaller \alpha requires more dedication to antibiotic production, thus favouring specialization. *

* *AR: we agree that we have not conveyed why we found this result surprising. We have set the trade-off shallow enough (h_g beta_g =5) that the generalist wins when alpha_g =1. In addition, lowering alpha_a makes the benefit of creating a mutant smaller, because a highly specialised mutant with zero growth genes makes fewer antibiotics. A generalist is proportionally less affected. Intuitively, we have compunded two benefits for the generalist.

But division of labor evolves, outcompeting the generalist – which surprised us.

We will modify the paragraph to better explain what we expected, and we will tone down the wording, removing the word “strikingly”.

*Reviewer #2 (Significance (Required)):

Due to my relative lack of familiarity with the literature on evolution of genetically-based division of labour, I would rather not comment on the degree of innovation of the manuscript.

The text is well written and is accessible to a wide readership, so it could be recommended to a general biological or evolutionary journal.

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

Summary: In this manuscript the authors explore the co-evolution of genomic architecture and division of labour in antibiotic production, in a model inspired by the bacterium Streptomyces coelicolor. In the model a genetic trade-off is implemented where the having a large number of growths promoting genes (and thus fast growth) leads to a low production of antibiotics. On the other hand, having fewer growth promoting genes allows for a higher production of antibiotics. This trade-off selects for a division of labour, where one sub population specializes in antibiotic production and another sub population specializes in reproduction. This division of labour is achieved by evolving the genome structure, so that growth promoting genes are clustered together, separated from the rest of the genome by several fragile sites (sites that allow for large deletions). This allows a single mutational event to delete a large number of growth-promoting genes, which creates a cell, lacking growth genes and that thus has a high antibiotic production (cell specializing in antibiotic production). In other words, the genome structure evolves to shape evolvability, so as to allow cells with a high growth rate to rapidly and repeatably evolve/mutate into cells with a high antibiotic production. This creates a division of labour where a part of the population specializes in growth/reproduction and another part specializes in antibiotics production. This model provides a tangible mechanism to explain a similar division of labour observed in S. coelicolor. This mechanism also fits well with the large deletions observed in antibiotic-hyperproducing S. coelicolor cells, which are also repeatably generated during colony growth.

Major comments: -Line 69, It would be good to give a bit more information here on the (number of) different types of antibiotics produced by S. coelicolor, to help the reader understand some of the modelling choices later on, such as allowing for the evolution of a large number (16 or higher if I understand correctly) of different antibiotics and a cell automatically being resistant to all antibiotics it produces (instead of having separate resistance genes). *

AR: we agree with the reviewer that adding this information would put the model more in focus. The total number of antibiotics that can be produced by the genus Streptomyces has been estimated to be of the order of 100000 (ten to the fifth, [Watve et al., 2001]). Although we use S. Coelicolor as reference model organism for our computational model, we simulate long-term evolutionary dynamics that diversify the antibiotic repertoire. Each antibiotic is represented by a 16 bits string, meaning that there are 2^16 (= 65536) possible antibiotics in the system – consistent with the number of possible antibiotics in the genus.

This being said, our model genomes evolve to have many more antibiotic genes than typical Streptomyces. Each species in the genus has up to 30 biosynthetic gene clusters [Genilloud, O. (2014)], a fraction of which make antibiotics. We discuss this discrepancy and propose solutions for this in the Discussion (also see below).

Regarding the possibility of separating antibiotic resistance from antibiotic synthesis: we (and most literature on the eco-evolutionary dynamics of antibiotic-producing bacteria) simplified antibiotic production as depending on individual “antibiotic biosynthetic genes”. In reality several genes in a cluster must be expressed to synthesize an antibiotic. A typical biosynthetic gene cluster also encodes resistance genes for the cognate antibiotic, to prevent cell suicide [Mak et al., 2014] – hence antibiotic genes providing resistance in the model. This being said, Streptomyces genomes also host resistance genes to antibiotics for which they have no biosynthetic pathway themselves, including efflux pumps that give some nonspecific resistance [Nag et al 2021].

Modelling antibiotic synthesis in more detail would allow to make a better model of antibiotic evolution, as well as to enrich the social dynamics of the model – because “cheaters” could evolve that are resistant but do not contribute to the antibiotics in the colony. These questions are certainly interesing, but would further complexify the model. They are exciting venues for future model expansions.

We will include the literature mentioned above in the introduction, and use these references to better motivate the model.

* -Lines 127-129 It is mentioned here fragile sites in the genome might represent transposable elements or long inverted repeats. Would both of these types of fragile sites behave the same? Has it been shown that both transposable elements and long inverted repeats can lead to large deletions from a linear chromosome? It would be nice to have a bit more background on how fragile sites might work or what they might look like in an empirical context. I am a bit unsure on this, but depending on their exact empirical nature, should fragile sites not also lead to increased rates of gene duplication near themselves? *

AR: we see that we have not made a clear connection between the introduction, where we introduce the mutational dynamics of Streptomyces, and the methods, where we introduce fragile sites.

Briefly, both duplications and deletions occur in Streptomyces, as well as circularization of the linear chromosome, conjugation, etc. [Hoff et al 2018,Tidjani et al 2019]. However, the outcome of all these mutations is biased towards deletion [hoff et al 2018, Zhang et al, 2020, Zhang et al, 2022]. There are many mechanisms involved in producing these mutations, forming the mutational hotspots, handling DNA breaks, and in the horizontal transfer of genetic material [Tidjani et al 2019; Lorentzi et al, 2021]. As the reviewer suggests – they do not behave all in the same way. To construct the model, we simplified all these mutational mechanisms into one genetic element, the “fragile site”, and assumed that they are solely responsible for the chromosomal-scale mutations that produce deletions.

We will add this information to the introduction (see also response to reviewer 2), and refer to it in the methods.

* -Line 160 As alluded to before, given the introduction provided, two assumptions come about here (lines 160-166) that lack a bit of justification/background/context. First, why does one allow the evolution of such a relatively large number of antibiotics? A bit more empirical in the introduction background would go a long way to making this assumption seem more justified. As far as I can see the genomic architecture leading to division of labour is only demonstrated for values of v that are 6 (i.e. 64 antibiotics) or above. Perhaps it is because I lack empirical background here, but this still seems to be a relatively large antibiotic space. Does the model also work with v=2? Perhaps it would be good to show a simulation with v=2 in supplementary material S16 as well. *

AR: Hopefully the previous comment on the number of possible antibiotics also clarifies this point.

We will carry out a simulation with v=2.

* -Line 166 The assumption is made that if a bacterium produces a certain antibiotic, it is automatically resistant to this antibiotic. Now it could be that this assumption is empirically rooted, in which case it would be good to allude to this empirical justification. I wonder how would the results be impacted if the resistance genes were separated from the antibiotic production genes? (I do not think additional simulations are in any way necessary on this point, but some more context/thoughts on this matter would be helpful, perhaps near lines 306-309) *

AR: Please see response to major comment on the possibility of separating antibiotic resistance from antibiotic synthesis. We will add the discussion there in the Discussion session.

* -Figure 1 In the subscript it becomes evident that the probability of large deletions due to fragile sites is much higher (10 fold) than single gene duplications, it seems to me this should be the other way around, single gene duplications and deletions could be much more probable than fragile site induced large deletions. Would the model still produce the same results if the values for mu-d and mu-f were switched around? (Again, I do not think additional simulations are per se required, some justification for this assumption would already be plenty). *

AR: We chose these parameter values because, empirically, large scale chromosomal rearrangements (deletions) occur more frequently than single gene duplication/deletion in Streptomyces – as they are the primary mechanism for Streptomyces development and division of labor. We now mention this in the caption of Fig. 1.

Still, would we expect results to be affected if mu_d > mu_f? We do not think so, for the following reason: mu_d and mu_f are per-gene probabilities, so the genomic probability of duplication/deletion and of fragile site activation will depend on the evolved number of genes.

in Fig. 5 we show that mu_f can be decreased by more than one order of magnitude and results do not change qualitatively. To compensate for a smaller per-gene deletion rate (mu_f), the evolved number of fragile sites per genome becomes larger (Suppl. Section S19, Fig. SF23). A similar compensatory increase of fragile sites could happen if duplications and deletions rate per gene were larger.

* Minor comments: -Line 36, perhaps replace "must" with " can" as there are other ways to achieve a division of labour that do not hinge on genomic architecture such as those listed in the next sentence. This sentence seems at odds with the next one, which lists ways to achieve cell differentiation that do not per se completely rely on genomic architecture such as gene regulation. Maybe consider moving this sentence to be on line 40 (after "...organized at the genome level remains unclear") *

AR: we will modify the text as suggested by the reviewer

* -Line 48, perhaps remove "disposable" as there is no particular reason the somatic tissue is disposable, furthermore it invokes the disposable soma theory of aging which is not relevant here *

AR: we will remove “disposable”.

* -Line 147-148 Why these particular relationships, as a reader I do not understand how these functions were constructed and how they might influence the results, a bit more justification might be helpful. Perhaps later on (results/discussion) also address what might happen if you were to use different functions? *

AR: we agree that these functions could use a little more explanation. The probability of replication is a function that increases with the number of growth genes. We assume that the function saturates, as growth cannot be arbitrarily large even if the genome hosts many growth genes. So we need at least two parameters: one for the maximum growth rate (alpha_g), and another that controls the curvature of the function (h_g). A simple choice is a Hill function, but other saturating functions would likely work just as well (e.g. an exponential function with a form alpha_g*(1-exp(-g/h_g)). Similarly, antibiotic synthesis inhibition from growth genes should tend to zero for larger numbers of growth genes, hence the exponential (but we expect that a hyperbolic form e.g 1/(1+g/beta_g) would work just the same).

As this discussion is rather technical, we will include it in the methods section.

* -I am clearly biased on this matter, since I work on evolvability. So, the authors should feel free to ignore this comment. Regardless, I think the authors have shown a wonderful example of the evolution of evolvability. Perhaps it would be nice to add a little bit of an evolvability angle in the discussion. In particular thinking about how fragile sites shape evolvability. *

AR: we agree with the reviewer that the work is a clear form of evolution of evolvability. We now explicitly mention this in the discussion.

* -Lines 404-411 It is great to see that the authors consider the wider applicability of their findings. It would be nice to add something here about the broader applicability in bacteria. As a large number of bacteria have circular chromosomes, how would these findings be impacted if circular chromosomes were at play? (I suspect they would largely still work in the same way, but keen to hear what the authors think). Referring to the work of Yona et al. 2012 on transient chromosomal duplications in yeast due to heat stress might also be good here, to show the more general applicability of the authors findings, this is another example where genomic architecture shapes evolvability. Yona AH, Manor YS, Herbst RH, Romano GH, Mitchell A, Kupiec M, Pilpel Y, Dahan O. Chromosomal duplication is a transient evolutionary solution to stress. Proc Natl Acad Sci U S A. 2012 Dec 18;109(51):21010-5. doi: 10.1073/pnas.1211150109. Epub 2012 Nov 29. PMID: 23197825; PMCID: PMC3529009. *

AR: Bacteria show many forms of targeted mutational dynamics (we do already mention CRISPR and HGT). It recently came to our attention that many bacterial and archea genomes host so-called Diversity-Generating Retroelements (DGR) [Macadangdang et al, 2022]. DGRs accelerate microbial evolution at specific sites and generate functional diversity. We will include this reference in the discussion.

We thank the reviewer for pointing us to the work on chromosomal duplication in yeast – we will also incorporate this “dramatic” form of duplication in the discussion.

* -Lines 412 -419 I agree with the authors that in practice the cells specializing in antibiotic production look somewhat like soma, however I would consider not using this term here as strictly speaking the antibiotics producing cells can still reproduce (be it at an extremely low rate, which leads to their loss). *

AR: We tone down both mentions of soma, as follows: “This gives rise to a division of labor driven by mutation, reminiscent of the division between germ and soma in multicellular eukaryotes.”

And, in the last sentence, we write: “...mutant cells *effectively* function as soma by enhancing...”

- Lines 434-438 If I understand correctly authors did not explicitly model the sporulation process (instead selecting random cells from the end of a cycle). I think this is a very good modelling choice that should not be changed; however, I do wonder how the results would be affected if sporulation was more explicitly modelled (for example by adding genes for sporulation, creating a 3 way trade-off between growth, sporulation and antibiotic production). Perhaps something that could be mentioned in the discussion.

AR: we agree with the reviewer that more complex evolutionary problem could be implemented in the system, e.g. through a gene type required for sporulation. They would likely have interesting outcomes. For instance, some bacteria may decide never to sporulate, while others could enhance their antibiotic resistance by turning into spores. Moreover, including additional functions together with an evolvable gene regulation could better capture the developmental dynamics observed through the life cycle of Streptomyces.

* I hope this review is of some use and helps the improvement of this manuscript. *

* Yours sincerely,

Timo van Eldijk

Reviewer #3 (Significance (Required)):

Significance: This study provides a clear conceptual advance by showing and studying how genome structure can evolve to create a division of labor. Thereby mechanistically explaining the division of labor in antibiotic production observed in S. coelicolor. It seems evident to me that whilst this study mainly focuses on S. coelicolor, the mechanism likely plays an important role in microbial evolution in general. Though others have previously theoretically explored such mechanisms, this study provides the first exploration modelled closely after an empirical system and hence provides a significant advance. In a more general sense, the evolution of genome architecture likely governs evolvability not just in microbes but in all life on earth. Therefore, I believe that this paper would be interesting for a general audience interested evolution. It would be of particular interest to those studying microbial evolution. My expertise lies in evolutionary biology, theoretical biology, microbial evolution and palaeontology. *

This specific area of the Code of Ethics is relevant to a situation that I have experienced in my field work for various reasons. First off, I encountered a situation when working with a client, with my supervisor present, that required me to keep this value and ethical principle in mind. In this instance, the client was expressing to me the way her family dynamic is run based on her cultural views. Although, my family dynamic may not be run the same way as my client's, in correspondence with the ethical principle of respecting her inherent dignity and worth, I used this information to be mindful of her individual differences and understand how this might play a role in her situation. This is an example of treating my client not only with respect but seeing how culturally her family operates their household. Furthermore, I could identify the emotional impact expressing this situation had on my client so I handled her feelings with empathy and compassion, as requested of us as social workers. As the session progressed, the client expressed difficulty in fully finding her drive when completing tasks in her daily routine. Due to this, I prepared self motivating activities and strategies for the client to visualize ways to push herself to complete these tasks. Specifically, we worked together on a time management chart where the client mapped out her schedule. In this, I was attempting to promote her socially responsible self-determination. By helping the client find her inner drive, I was attempting to instill in her that she was not only contributing to her personal self-determination but also to the social community around her as well. I hoped that helping her visualize this, it would make her want to act in positive ways and see how her inner self is a wonderful piece to the puzzle of her community around her. Moreover, this falls into enhancing her capacity and opportunity to change. My client expressed to my supervisor and I that it was difficult for her to pinpoint exactly what areas she wanted to work on and she felt like she needed assistance with. Through our discussion of her strengths, her interests, her hobbies, and especially her goals, I took note that the client began to open up more as the discussion went on and find her way of speaking on her struggles. This is a relevant example of respecting the dignity and worth of my client because in acknowledging her struggles, I am showing that I am not only a listener to her but a guide to her as well that seeks to help her find her inner desires. I constantly used phrases like, "I can absolutely see how you feel that way.." as a means of reassuring the client she is heard and respected. In my discussion with my client, the piece of the Code of Ethics that states, "seek to enhance clients capacity and opportunity to change" I think is especially relevant. This is because in my session with the client I aimed to have her strive to seek out just how much potential she truly possesses through conservation and self-reflection activities I provided. By giving my client strategies towards positive change, I felt as though this was representative of my client being able to pinpoint her needs while also understanding just the positive strategies she can use to meet those needs. In terms of the opportunity to change, in our session we talked a lot on this change towards a positive mindset and the ways she can do that on her own time as well, which I think also falls into this ethical principle.

Author Response

Reviewer #1 (Public Review):

1-1. I do have some concerns that the differences in network clustering reported in Fig 6 may be due to noise and I think the comparisons against the HCP parcellation could be more robust. Specifically, with regard to the network clustering in Fig 6. The authors use a clustering algorithm (which is not explained) to cluster the parcels into different functional networks. They achieve this by estimating the mean time series for each parcel in each individual, which they then correlate between the n regions, to generate an nxn connectivity matrix. This they then binarise, before averaging across individuals within an age group. It strikes me that binarising before averaging will artificially reduce connections for which only a subset of individuals are set to zero. Therefore averaging should really occur before binarising. Then I think the stability of these clusters should be explored by creating random repeat and generation groups (as done for the original parcells) or just by bootstrapping the process. I would be interested to see whether after all this the observation that the posterior frontoparietal expands to include the parahippocampal gryus from 3-6 months and then disappears at 9 months - remains.

We thank the reviewer for this insightful comment on our clustering process. For the step of “binarizing before averaging”, we followed the method proposed by Yeo et al (1). In this method, all correlation matrices are binarized according to the individual-specific thresholds. Specifically, each individual-specific threshold is determined according to the percentile, and only 10% of connections are kept and set to 1, while all other connections are set to 0. Yeo et al. (1) explained their motivation for doing so as “the binarization of the correlation matrix leads to significantly better clustering results, although the algorithm appears robust to the particular choice of the threshold”. We consider that the possible reason is that the binarization of connectivity in each individual offers a certain level of normalization so that each subject can contribute the same number of connections. If averaging occurs before binarizing, the actual connectivity contributed by different subjects would be different, which leads to bias. Meanwhile, we tested the stability of ‘binarizing first’ and ‘averaging first’, and the result is shown in Fig. R1 below. This figure suggests a similar conclusion as (1), where binarizing first before averaging leads to better clustering stability. We added the motivation of binarizing before averaging in the revised manuscript between line 577 and line 581.

Fig. R1. The comparison of clustering stability of different methods. The red line refers to the clustering stability when binarizing the correlation matrices first and then averaging the matrices across individuals, while the blue line refers to the clustering stability when averaging the correlation matrices across individuals first and then binarizing the average matrix.

For the final clustering results, we performed our clustering method using bootstrapping 100 times, and the final result is a majority voting of each parcel. The comparison of these two results is shown in Fig. R2. Overall, we do observe good repeatability between these two results. However, we also observed that some parcels show different patterns between the two results, especially for those parcels that are spatially located around the boundaries of networks or the medial wall. The pattern of the observation that “the posterior frontoparietal expands to include the parahippocampal gyrus from 3-6 months and then disappears at 9 months – remains” was not repeated in the bootstrapped results. These results might suggest that the clustering method is quite robust, the discovered patterns are relatively stable, and the differences between our original results and bootstrapping results might be caused by noises or inter-subject variabilities.

Fig. R2. Top panel: the network clustering results using all data in the original manuscript. Bottom panel: the network clustering results using majority voting through 100 times of bootstrapping. Black circles and red arrows point to the parahippocampal gyrus, which was included in the posterior frontoparietal network, and is not well repeated in the bootstrapped results. (M: months)

1-2. Then with regard to the comparison against the HCP parcellation, this is only qualitative. The authors should see whether the comparison is quantitatively better relative to the null clusterings that they produce.

Thank you for this great suggestion! As suggested, we added this quantitative comparison using the Hausdorff distance. Similar to the comparison in parcel variance and homogeneity, the 1,000 null parcellations were created by randomly rotating our parcellation with small angles on the spherical surface 1,000 times. We compared our parcellation and the null parcellations by accordingly evaluating their Hausdorff distances to some specific areas of the HCP parcellation on the spherical space, including Brodmann's area 2, 3b, 4+3a, 44+45, V1, and MT+MST. The results are listed in Figure 4. From the results, we can observe that our parcellation generally shows statistically much lower Hausdorff distances to the HCP parcellation, suggesting that our parcellation generates parcel borders that are closer to HCP parcellations compared to the null parcellations.

However, we noticed very few null parcellations that show smaller Hausdorff distances compared to our parcellation. A possible reason comes from our surface registration process with the HCP template purely based on cortical folding, without using functional gradient density maps, which are not available in the HCP template. As a result, this does not ensure high-quality functional alignment between our infant data and the HCP space, thus inevitably increasing the Hausdorff distance between our parcellation and the HCP parcellation.

1-3. … not all individuals appear (from Fig 8) to be acquired exactly at the desired timepoints, so maybe the authors might comment on why they decided not to apply any kernel weighted or smoothing to their averaging? Pg. 8 'and parcel numbers show slight changes that follow a multi-peak fluctuation, with inflection ages of 9 and 18 months' explain - the parcels per age group vary - with age with peaks at 9 and 18 - could this be due to differences in the subject numbers, or the subjects that were scanned at that point?

We do agree with the reviewer that subjects are not scanned at similar time points. This is designed in the data acquisition protocol to seamlessly cover the early postnatal stage so that we will have a quasi-continuous observation of the dynamic early brain development.

We didn’t apply kernel weighted average or smoothing when generating the parcellation, as we would like each scan to contribute equally, and each parcellation map could be representative of the cohort of the covered age, instead of only part of them. Meanwhile, our final ‘age-common parcellation’ could be representative of all subjects from birth to 2 years of age. However, we do agree that the parcellation map that is only designed for the use of a specific age, e.g., 1-year-olds, kernel weighted average, or even a more restricted age range could be a more appropriate solution.

For the parcel number that likely shows fluctuations with subject numbers, we added an experiment, where we randomly selected 100 scans by considering the minimum scan number in each age group using bootstrapping and repeated this process 100 times. The average parcel number of each age is reported in the following Table R1. We didn’t observe strong changes in parcel numbers when reducing scan numbers, which further demonstrates that our parcel numbers do not show a strong relation to subject numbers. However, the parcel number does not increase greatly from 18M to 24M in the bootstrapping results, so we modified the statement in the manuscript about the parcel number to ‘… all parcel numbers fall between 461 to 493 per hemisphere, where the parcel number attains a maximum at around 9 months and then reduces slightly and remains relatively stable afterward. …’, which can be found between line 121 and line 122.

1-4. I also have some residual concerns over the number of parcels reported, specifically as to whether all of this represents fine-grained functional organisation, or whether some of it represents noise. The number of parcels reported is very high. While Glasser et al 2016 reports 360 as a lower bound, it seems unlikely that the number of parcels estimated by that method would greatly exceed 400. This would align with the previous work of Van Essen et al (which the authors cite as 53) which suggests a high bound of 400 regions. While accepting Eickhoff's argument that a more modular view of parcellation might be appropriate, these are infants with underdeveloped brain function.

We thank the reviewer for this insightful comment. We agree that there might be noises for some of the parcels, as noises exist in each step, such as data acquisition, image processing, surface reconstruction, and registration, especially considering functional MRI is noisier than structural MRI. Though our experiments show that our parcellation is fine-grained and is suitable for the study of the infant brain functional development, it is hard to directly quantitatively validate as there is no ground truth available.

Despite these, we are still motivated to create fine-grained parcellations, as with the increase of bigger and higher resolution imaging data and advanced computational methods, parcellations with more fine-grained regions are desired for downstream analyses, especially considering the hierarchical nature of the brain organization (2). And the main reason that our method generates much finer parcellation maps, is that both our registration and parcellation process is based on the functional gradient density, which characterizes a fine-grained feature map based on fMRI. This leads to both better inter-subject alignment in functional boundaries and finer region partitions. This strategy is different from Glasser et al (3), which jointly considers multimodal information for defining parcel boundaries, thus parcels revealed purely by functional MRI might be ignored in the HCP parcellation. We hope our parcellation framework can be a useful reference for this research direction. We added this discussion in the revised manuscript between line 268 and line 271.

For the parcel number, even without performing surface registration based on fine-grained functional features, recent adult fMRI-based parcellations greatly increased parcel numbers, such as up to 1,000 parcels in Schaefer et al. (4), 518 parcels in Peng et al. (5), and 1,600 parcels in Zhao et al. (6). For infants, we do agree that the infant functional connectivity might not be as strong as in adults. However, there are opinions (7-9) that the basic units of functional organization are likely to present in infant brains, and brain functional development gradually shapes the brain networks. Therefore, the functional parcel units in infants could be possibly on a comparable scale to adults. Even so, we do agree that more research needs to be performed on larger datasets for better evaluations. We added this discussion in the revised manuscript between line 275 and line 280.

1-5. Further comparisons across different subjects based on small parcels increases the chances of downstream analyses incorporating image registration noise, since as Glasser et al 2016 noted, there are many examples of topographic variation, which diffeomorphic registration cannot match. Therefore averaging across individuals would likely lose this granularity. I'm not sure how to test this beyond showing that the networks work well for downstream analyses but I think these issues should be discussed.

We agree with the reviewer that averaging across individuals inevitably brings some registration errors to the parcellation, especially for regions with high topographic variation across subjects, which would lead to loss of granularity in these regions. We believe this is an important issue that exists in most methods on group-level parcellations, and the eventual solution might be individualized parcellation, which will be our future work. We added this discussion in the revised manuscript between line 288 and line 292.

We also agree with the reviewer that downstream analyses are important evaluations for parcellations. We provided a beta version of our parcellation with 602 parcels (10) to our colleagues, and they tested our parcellation in the task of infant individual recognition across ages using functional connectivity, to explore infant functional connectome fingerprinting (10). We compared the performance of different parcellations with 602 ROIs (our beta version), 360 ROIs (HCP MMP parcellation (3)), and 68 ROIs (FreeSurfer parcellation (11)). The results (Fig. R3) show that our parcellation with a higher parcellation number yields better accuracy compared to other parcellations. We added a description of this downstream application in the discussion between line 284 and line 287.

Fig. R3. The comparison of different parcellations for infant individual recognition across age based on functional connectivity (figure source: Hu et al. (10)). The parcellation with 602 ROIs is the beta version of our parcellation, 360 ROIs stands for HCP MMP parcellation (3) and 68 ROIs stands for the FreeSurfer parcellation (11). This downstream task shows that a higher parcellation number does lead to better accuracy in the application.

1-6. Finally, I feel the methods lack clarity in some areas and that many key references are missing. In general I don't think that key methods should be described only through references to other papers. And there are many references, particular to FSL papers, that are missing.

We thank the reviewer for this great suggestion. We added related references for FLIRT, FSL, MCFLIRT, and TOPUP For the alignment to the HCP 32k_LR space, we first aligned all subjects to the fsaverage space using spherical demons, and then used part of the HCP pipeline (12) to map the surface from the fsaverage space to HCP 164k_LR space, and downsampled to 32k_LR space. We modified this citation by referencing the HCP pipeline by Glasser et al. (12) instead and detailed this registration process in the revised manuscript between line 434 to line 440 in the revised manuscript and as below:

“… The population-mean surface maps were mapped to the HCP 164k ‘fs_LR’ space using the deformation field that deforms the ‘fsaverage’ space to the ‘fs_LR’ space released by Van Essen et al. (13), which was obtained by landmark-based registration. By concatenating the three deformation fields of steps 1, 3, and 4, we directly warped all cortical surfaces from individual scan spaces to the HCP 164k_LR space and then resampled them to 32k_LR using the HCP pipeline (12), thus establishing vertex-to-vertex correspondences across individuals and ages …”

Reviewer #2 (Public Review):

2-1. Diminishing enthusiasm is the lack of focus in the result section, the frequent use of jargon, and figures that are often difficult to interpret. If those issues are addressed, the proposed atlas could have a high impact in the field especially as it is aligned with the template of the Human Connectome Project.

We’d like to thank Reviewer #2 for the appreciation of our atlas. According to the reviewer’s suggestion, we went through the manuscript again by focusing on correcting the use of jargon, clarity in the result section, as well as figures and figure captions. We hope our corrections can help explain our work to a broader community. Our revisions are accordingly detailed in the following. Meanwhile, our parcellation maps have been aligned with the templates in HCP and FreeSurfer and made available via NITRC at: https://www.nitrc.org/projects/infantsurfatlas/.

References

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2. S. B. Eickhoff, R. T. Constable, B. T. Yeo, Topographic organization of the cerebral cortex and brain cartography. NeuroImage 170, 332-347 (2018).

3. M. F. Glasser, T. S. Coalson, E. C. Robinson, C. D. Hacker, J. Harwell, E. Yacoub, K. Ugurbil, J. Andersson, C. F. Beckmann, M. Jenkinson, S. M. Smith, D. C. Van Essen, A multi-modal parcellation of human cerebral cortex. Nature 536, 171-178 (2016).

4. A. Schaefer, R. Kong, E. M. Gordon, T. O. Laumann, X.-N. Zuo, A. J. Holmes, S. B. Eickhoff, B. T. J. C. C. Yeo, Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. 28, 3095-3114 (2018).

5. L. Peng, Z. Luo, L.-L. Zeng, C. Hou, H. Shen, Z. Zhou, D. Hu, Parcellating the human brain using resting-state dynamic functional connectivity. Cerebral Cortex, (2022).

6. J. Zhao, C. Tang, J. Nie, Functional parcellation of individual cerebral cortex based on functional mri. Neuroinformatics 18, 295-306 (2020).

7. W. Gao, S. Alcauter, J. K. Smith, J. H. Gilmore, W. Lin, Development of human brain cortical network architecture during infancy. Brain Structure and Function 220, 1173-1186 (2015).

8. W. Gao, H. Zhu, K. S. Giovanello, J. K. Smith, D. Shen, J. H. Gilmore, W. J. P. o. t. N. A. o. S. Lin, Evidence on the emergence of the brain's default network from 2-week-old to 2-year-old healthy pediatric subjects. 106, 6790-6795 (2009).

9. K. Keunen, S. J. Counsell, M. J. J. N. Benders, The emergence of functional architecture during early brain development. 160, 2-14 (2017).

10. D. Hu, F. Wang, H. Zhang, Z. Wu, Z. Zhou, G. Li, L. Wang, W. Lin, G. Li, U. U. B. C. P. Consortium, Existence of Functional Connectome Fingerprint during Infancy and Its Stability over Months. Journal of Neuroscience 42, 377-389 (2022).

11. R. S. Desikan, F. Ségonne, B. Fischl, B. T. Quinn, B. C. Dickerson, D. Blacker, R. L. Buckner, A. M. Dale, R. P. Maguire, B. T. Hyman, An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968-980 (2006).

12. M. F. Glasser, S. N. Sotiropoulos, J. A. Wilson, T. S. Coalson, B. Fischl, J. L. Andersson, J. Xu, S. Jbabdi, M. Webster, J. R. Polimeni, The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage 80, 105-124 (2013).

I like the way the article connects a tree with ourselves. Our outer selves are usually what people can see physically, the trunk is more internal thoughts of whatever you may have going on or how certain things in life have affected one, and then the roots are the more inner, mental type of thinking. This makes me think about how angry people are angry for their own respected reasons, why others can be more sensitive, and a different group might be more outgoing and laid back. It really makes you think about how everyone lives a different life, and how we all have different things going on that eventually make us unique in our own ways but also the same.

Its is crazy to think all of this had to be done to get to where we are today when it comes to technology.

Author Response

Reviewer #1 (Public Review):

Anopheles is an important disease vector and the efforts to characterize the extent of genetic variation in the system are welcome. In this piece, the authors propose a Variational Autoencoders method to assign species boundaries in a large sample of Anopheles mosquitoes using a panel of 62 nuclear amplicons. Overall, the method performs well as it can assign samples to an acceptable granularity. The main advantage of the method is that it takes reduced representation genome sampling which should cut costs in genotyping. The authors do not compare the effectiveness of their amplicon panel with other approaches to do reduced representation sequencing, or the computational method with other previously published methods. Additionally, the manuscript does not clearly state what is the importance of species assignments and the findings/method are -by definition- limited to a single biological system.

It is important to draw the reviewer’s attention to the fact that this is a two part approach – the reviewer seems to have overlooked the Nearest Neighbour component of the work. The approach is not solely a VAE – the VAE only comes into play at the species complex level. The higher level assignments are done using NN approaches.

The manuscript has three main limitations. First, there is no explicit test of the performance of ANOSPP compared to other methods of low-dimensional sampling. While the authors state that the ANOSPP panel will lead to genotyping for low cost (justifiably so), there is no direct comparison to other low-representation methods (e.g., RAD-Seq, MSG).

The key advantage of ANOSPP is that it works on the entire Anopheles genus; while the other suggested sequencing methods are more applicable to a group of specimens of the same or closely related species. The purpose of the panel is to do species identification for the whole genus; so it really is an alternative to the current methods of species identification, which commonly consists of morphological identification of the species complex, followed by complex-specific PCR amplification of a single species-diagnostic locus. The only other species identification method for Anopheles that is not limited to a single species complex, that we are aware of, is a mass spectrometry approach (Nabet et al. Malar J, 2021); however, they only investigate three different species and reach a classification accuracy of at most 67.5%.

The main advantage of ANOSPP over other reduced representation sequencing methods, like MSG and RAD-Seq, is that it is specifically designed to work for the entire Anopheles genus to support genus-wide species identification. In a genus comprising an estimated 100 million years of divergence, a sequencing approach that relies on restriction enzymes is likely to introduce a lot of variability in which parts of the genome are sequenced for different species. Moreover, both MSG and RAD-Seq typically map the reads to a reference genome; any choice of reference genome will likely introduce considerable bias when dealing with such diverged species. In general, the sequence data generated by those sequencing methods require more complicated and labour intensive processing. And lastly, the costs per sample for library preparation and sequencing are substantially lower with ANOSPP than with MSG and RAD-Seq: for library prep <1 USD (ANOSPP) versus 5 USD (RAD-Seq) (Meek and Larson, Mol Ecol Resour, 2019) and with 768 samples (ANOSPP), 384 samples (MSG; Andolfatto et al, Genome Res., 2011) and 96 samples (RAD-Seq; Meek and Larson, Mol Ecol Resour, 2019) per run.

Second, and on a related vein, the authors present NNoVAE as a novel solution to determine species boundaries in Anopheles. Perusing the very references the authors cite, it is clear that VAEs have been used before to delimit species boundaries which diminishes the novelty of the approach on its own.

The VAE is only a part of the method presented in this manuscript. We believe a substantial amount of the value of NNoVAE lies in its ability to perform assignments for the entire Anopheles genus comprising over 100 MY of divergence - the closest analogous approach would be COI or ITS2 DNA barcoding, neither of which is robust for species complexes. Using NNoVAE, samples are first assigned to their relevant groups, and in many cases to their species, by the Nearest Neighbour method. Only those samples that are identified by the Nearest Neighbour method as members of the An. gambiae complex and cannot be unambiguously assigned to a single species, are passed through the VAE assignment method.

Indeed, in (Derkarabetian et al, Mol Phylogenet Evol, 2019) VAEs are used to delimit species boundaries in an arachnid genus. However, this study works with ultra conserved elements, incorporating a total of 76kB of sequence, which is much more data than the approximately 10kB we get for all amplicons combined. Moreover, a crucial difference is that the referenced work uses SNP calls, based on alignment to one of their sequenced samples, as input for the VAE, where our VAE takes k-mer based inputs. This is also an important consideration in working with a large number of highly diverged species.

Perhaps more importantly, the manuscript does not present a comparison with other methods of species delimitation (SPEDEStem, UML -this approach is cited in the paper though-), or even of assessment of population differentiation, such as STRUCTURE, ADMIXTURE, or ASTRAL concordance factors (to mention a few among many). The absence of this comparative framework makes it unclear how this method compares to other tools already available.

NNoVAE is primarily a method for species assignment rather than for species delimitation. SPEDEStem addresses the question whether different groups of samples are separate species or not; different groups can be defined by e.g. described races, described subspecies, different morphotypes or different collection locations. The aim of ANOSPP and NNoVAE is to remove the necessity of any prior sorting of samples into groups – all that needs to be known is that the sample is an Anopheline. This avoids the issues associated with morphological identification and single marker molecular barcodes. So to perform species assignment with SPEDEStem, we’d have to run many replicates, each time asking whether a single sample is of the same species as one of the species represented in our reference database. For example, for the 2218 samples presented in the case studies, we would have to run SPEDEStem more than 130,000 times, to check for each of these samples whether they are any of the 62 species represented in the reference dataset NNv1.

However, we agree that it would be good to check that the species-groups in the reference database, NNv1, are indeed supported as separate species. We attempted to run SPEDEStem, but the web browser no longer exists, and we were not able to install the command line application, which runs on Python 2. Moreover, the example files provided in the tutorial are not complete. Therefore, we were unable to even carry out this basic comparison.

UML (unsupervised machine learning) approaches comprise quite a wide range of methods, including VAE. We have conducted a comparison between the VAE assignments and assignments based on UMAP, for the discussion see below and page 20 in the manuscript and newly added supplementary information section 4.

As requested by the reviewer, we have compared our assignment approach to ADMIXTURE on the Anopheles gambiae complex training set (see Supplementary information section 5). It is a good sanity check to compare the structure revealed by ADMIXTURE to the structure revealed by the VAE. We found that ADMIXTURE does not satisfyingly differentiate between the species in the complex that are only represented by a handful of samples, while the VAE suffers much less from the differences in group sizes in the training set. Moreover, we want to point out that ADMIXTURE is a tool for assessing population differentiation, not for species assignment. To use it as an assignment method, there are two options: either infer the allele frequencies in the ancestral populations from the training set and use those to compute the maximum likelihood of ancestry frequencies for the test set; or run ADMIXTURE on the training and test sets combined and use the labels from the training set to label ancestral populations. A major drawback from the former approach is that it is tricky to discover cryptic taxa or outliers in the test set; while with the second approach we create a dependency of the training set results on the test set it is combined with during the run. But more importantly, ADMIXTURE performs worse than the VAE on the An. gambiae complex training set by itself; and identifies only two to three different groups among the five diverged species (An. melas, An. merus, An. quadriannulatus, An. bwambae and An. fontenillei). For more information, see page 20 in the manuscript and newly added supplementary information section 5

One important use case of our method is to identify interesting samples, e.g. potential hybrids or cryptic taxa, for subsequent whole genome sequencing. After selection and whole genome sequencing of interesting samples detected by ANOSPP+NNoVAE, ADMIXTURE may be useful as one of the tools to investigate such samples.

A final concern is less methodological and more related to the biology of the system. I am curious about the possibility of ascertainment bias induced by the amplicon panel. In particular, the authors conclusively demonstrate they can do species assignment with species that are already known. Nonetheless, there is the possibility of unsampled species and/or cryptic species. This later issue is brought up in passing the 'Gambiae complex classifier datasets' section but I think the possibility deserves a formal treatment. This is particularly important because the system shows such high levels of hybridization that the possibility of speciation by admixture is not trivial.

We appreciate the reviewer’s concern regarding ascertainment bias in the amplicon panel. The targets have been selected based on multiple sequence alignments of all Anopheles reference genomes at the time (Makunin et al. Mol Ecol Resour, 2022). Using sequenced species from four different subgenera, the species span a considerable amount of evolutionary time in the Anopheles genus. For all species we have since tested the panel on, we find that at least half of the targets get amplified.

We share the reviewer’s concern regarding species which are not (yet) represented in the reference database. This is one of the main advantages of the Nearest Neighbour method: it works on three levels of increasing granularity. So for samples that cannot be assigned at species level, we are often able to identify the group of species from the reference database it is closest to. In particular, the situation of a test sample whose species is not represented in the reference database, is mimicked in the drop-out experiment by the species-groups which contain only one sample. On page 16 in the manuscript, we explain how NNoVAE deals with such samples and we show that in the majority of cases NNoVAE assigns the sample to a group of closely related species rather than misclassifying it more specifically to the wrong species.

In summary, the main limitation of the manuscript is that the authors do not really elaborate on the need for this method. The manuscript does show that the method is feasible but it is not forthcoming on why this is of importance, especially when there is the possibility of generating full genome sequences.

ANOSPP and NNoVAE are specifically designed for high throughput accurate species identification across the entire Anopheles genus – WGS is important to address many questions, but is complete overkill for doing species identification. ANOSPP costs only a small fraction of whole genome sequencing, which makes it possible to monitor mosquito populations at much larger scale (e.g., in partnership with our vector biologist collaborators in Africa, we have already generated ANOSPP data for approximately 10,000 mosquitoes and will be running 500,000 over the next few years). Moreover, for most analyses using whole genome sequencing, a reference genome of a sufficiently similar species is required. While we are in a position of privilege having reference genomes for more than 20 species in Anopheles, we have a long way to go before we have 100s of reference genomes covering the true diversity of the genus.

NNoVAE can also be used to select interesting samples (e.g. species that have not been through the panel before, divergent populations, potential hybrids), which can be submitted for whole genome sequencing subsequently.

Since Anopheles is arguably one of the most important insects to characterize genetically, the ANOSPP panel is certainly important but I am not completely sure the method of species assignment is novel or groundbreaking .

Reviewer #2 (Public Review):

The medically important mosquito genus Anopheles contains many species that are difficult or impossible to distinguish morphologically, even for trained entomologists. Building on prior work on amplicon sequencing, Boddé et al. present a novel set of tools for in silico identification of anopheline mosquitoes. Briefly, they decompose haplotypes generated with amplicon sequencing into kmers to facilitate the process of finding similar sequences; then, using the closest sequence or sequences ("nearest neighbors") to a target, they predict taxonomic identity by the frequency of the neighbor sequences in all groups present in a reference database. In the An. gambiae species complex, which is well-known for its historical and ongoing introgression between closely-related species, this approach cannot distinguish species. Therefore, they also apply a deep learning method, variational autoencoders, to predict species identity. The nearest neighbor method achieves high accuracy for species outside the gambiae complex, and the variational autoencoder method achieves high accuracy for species within the complex.

The main strength of this method (along with the associated methods in the paper on which this work builds) is its ability to speed up the identification of anopheline mosquitoes, therefore facilitating larger sample sizes for a wide breadth of questions in vector biology and beyond. This technique has the added advantage over many existing molecular identification protocols of being non-destructive. This high-throughput identification protocol that relies on a relatively straightforward amplicon sequencing procedure may be especially useful for the understudied species outside the well-resourced gambiae complex.

An additional and intriguing strength of this method is that, when a species label cannot be predicted, some basic taxonomic predictions may still be made in some cases. Indeed, even in the case of known species, the authors find possible cryptic variation within An. hyrcanus and An. nili, demonstrating how useful this new tool can be.

The main weakness of this method is that, as the authors note, accuracy is dependent on the quality and breadth of the reference database (which in turn relies on the expertise of entomologists). A substantial portion of the current reference database, NNv1, comes from one species complex, An. gambiae. This is reasonable given the complex's medical importance and long history of study; however, for that same reason, robust molecular and computational tools for identifying species in this complex already exist. The deep learning portion of this manuscript is a valuable development that can eventually be applied to other species complexes, but building up a sufficient database of specimens is non-trivial. For that reason, the nearest neighbor method may be the more immediately impactful portion of this paper; however, its usefulness will depend on good sampling and coverage outside the gambiae complex.

Another potential caveat of this method is its portability. It is not clear from either the manuscript or the code repository how easy it would be for other researchers to use this method, and whether they would need to regenerate the reference database themselves. The authors clearly have expansive and immediate plans for this workflow; however, as many researchers will read this manuscript with an eye towards using these methods themselves, clarifying this point would be valuable.

This is an important point; currently the amplicon panel is only run on specialised robots, but we are working to adapt the protocol so that it can be run in any basic molecular lab. We have now clarified this in the conclusion. Furthermore, there is never a need to regenerate the reference databases – this is fully publicly available at github.com/mariloubodde/NNoVAE and version controlled. As we obtain ANOSPP data from additional samples, representing new species or new within-species diversity, we will add these to the reference database and create an updated openly available version.

The authors present data suggesting that their method is highly accurate in most of the species or groups tested. While the usefulness of this method will depend on the reference database, two points ameliorate this potential concern: it is already accurate on a wide breadth of species, including the understudied ones outside the An. gambiae complex; additionally, even when a specific species identification cannot be made, the specimen may be able to be placed in a higher taxonomic group.

Overall, these new methods offer an additional avenue for identifying anopheline species; given their high-throughput nature, they will be most useful to researchers doing bulk collections or surveillance, especially where multiple morphologically similar species are common. These methods have the potential to speed up vector surveillance and the generation of many new insights into anopheline biology, genetics, and phylogeny.

stylez--3fKJu styles--3sKVw "> #I_have "other ideas" that are related to our concentration here; and I really think

someone in a position like yours would benefit greatly from working on the branch of crypto related to "free communioation." I want to build an open social network ["protocol"] that combines "what email, facebook, reddit and ... wikipedia to enable " commenting on anything" the light of ...

"hey ma, where did all the online newspaper comments disappear to?"

I know what has to go into it, I'm looking at things like hypothes.is, tableland.xyz and ... https://lnkd.in/gNbBAewt ... and I think it would be simple to put something together that will intrigue people; i know the software and infrastructure can offer us a bulletproof check on censorship that we need now more than ever before in history; and I'm having trouble figuring out why more people aren't interested in helping me ensure that we have a safe happy future free from "un america n th i ngz" like "no newspaper" and no recourse against insurance and credit fraud/problems; which is what I'm staring at in full blown disbelief.</textarea></div></div></label></div><div class="styles--2mJeY"><div class="styles--YBb-N styles--3IYUq"><div -- The importance of seeing that it's an open "DeFi-inspir[ing/edu]" protocol that will work with existing service and interfaces like LinkedIn and Facebook and Mastodon and diasp.org is ... without question a necessary part of understanding the vision. I think we will see great leaps and bounds in interface design that make the "second small step" Dissenter/Unity and #hypothes eze ... have almost brought to the forefront of the "right venue." https://web.hypothes.is/sponsors/ Seeing #hypothesisontableland and having it work is the first "glaringly bright flash" that will ensure that we never again watch commenting and sites like discus and reddit and facebook turn from the light of social "what's on fire?" to the darkness of shadow ... "throttling" of the presentation of the world changing that somehow has missed our tongues and hopefully not our eyes.<br /> Hopefully once we start talking and getting more involved it will be clear how easy it was and is to make the world a better place just by ... "dropping in your two cents" or BTC as the case may be. --

We've got to get serious about caring "of things like ourselves" for the truth and health and happiness that some of us probably take for granted as I do;

still you can see me smiling when i know full well it's a little early for that--maybe you can help shift the timeline.

I think it's interesting that Bush associates the memex with something the size of a desk. This may be due to the fact that he naturally assumed that something this powerful and containing this much information would need to be housed in a large apparatus. This is obviously not the case in modern day as we see smart devices hundreds of times smaller than a desk with all of the capabilities that Bush lists.

During the time of World War II many scientific innovations were made. One view that many may not think about everyday while interacting with information. Moreover, its a view that is one of the most important as storing knowledge and accessibility of it. This in fact has opened the doors for the current digital age and provides a perspective on how far we have come and the amount of knowledge we have accessible to us currently.

This is why we use methods such as partitioning and iterating in order to see the fractions as fractions that can be divided or multiplied, instead of seeing the numerator and denominator of a fraction as separate whole numbers. This can be very confusing for students to think about this way.

iterating is a process that makes improper fractions a lot more understandable. Using iterating, we can see that we have 3 equal parts of 1/2.

Why is this important in this history of psychology?

"The present work will, I venture to think, prove that I both saw at the time the value and scope of the law which I had discovered, and have since been able to apply it to some purpose in a few original lines of investigation. But here my claims cease. I have felt all my life, and I still feel, the most sincere satisfaction that Mr. Darwin had been at work long before me, and that it was not left for me to attempt to write 'The Origin of Species.' I have long since measured my own strength, and know well that it would be quite unequal to that task. Far abler men than myself may confess that they have not that untiring patience in accumulating and that wonderful skill in using large masses of facts of the most varied kinds, -- that wide and accurate physiological knowledge, -- that acuteness in devising, and skill in carrying out, experiments, and that admirable style of composition, at once clear, persuasive, and judicial, -- qualities which, in their harmonious combination, mark out Mr. Darwin as the man, perhaps of all men now living, best fitted for the great work he has undertaken and accomplished." This comes from the Classics in the History of Psychology Limits of Natural Selection By Chauncey Wright (1870). This shows us the importamce of the limits including in theories like this one. Natural selection indicates that the strongest will be the ones that will survive and there for will be the ones that will be able to have offsprings and make their generation endure. But thjis has a limit due to the sexual selection because it shows that the natural selection can not be impossed to people in any way or form. I see this working in psychology in a very big way because now that we are in a generation that is so ruled out by the social media this concept wants to persist and endure no matter what. I can see natural selecetion slowly decreasing amd really another type of selection evolving with the next future generations.

Angela Cruz Cubero (Christian Cruz Cubero)

The importance of seeing that it's an open "DeFi-inspir[ing/edu]" protocol that will work with existing service and interfaces like LinkedIn and Facebook and Mastodon and diasp.org is ... without question a necessary part of understanding the vision. I think we will see great leaps and bounds in interface design that make the "second small step" Dissenter/Unity and #hypothes eze ... have almost brought to the forefront of the "right venue."

https://web.hypothes.is/sponsors/

Seeing #hypothesisontableland and having it work is the first "glaringly bright flash" that will ensure that we never again watch commenting and sites like discus and reddit and facebook turn from the light of social "what's on fire?" to the darkness of shadow ... "throttling" of the presentation of the world changing that somehow has missed our tongues and hopefully not our eyes.

Hopefully once we start talking and getting more involved it will be clear how easy it was and is to make the world a better place just by ... "dropping in your two cents" or BTC as the case may be.

The importance of seeing that it's an open "DeFi-inspir[ing/edu]" protocol that will work with existing service and interfaces like LinkedIn and Facebook and Mastodon and diasp.org is ... without question a necessary part of understanding the vision. I think we will see great leaps and bounds in interface design that make the "second small step" Dissenter/Unity and #hypothes eze ... have almost brought to the forefront of the "right venue."

https://web.hypothes.is/sponsors/

Seeing #hypothesisontableland and having it work is the first "glaringly bright flash" that will ensure that we never again watch commenting and sites like discus and reddit and facebook turn from the light of social "what's on fire?" to the darkness of shadow ... "throttling" of the presentation of the world changing that somehow has missed our tongues and hopefully not our eyes.

Hopefully once we start talking and getting more involved it will be clear how easy it was and is to make the world a better place just by ... "dropping in your two cents" or BTC as the case may be.

The importance of seeing that it's an open "DeFi-inspir[ing/edu]" protocol that will work with existing service and interfaces like LinkedIn and Facebook and Mastodon and diasp.org is ... without question a necessary part of understanding the vision. I think we will see great leaps and bounds in interface design that make the "second small step" Dissenter/Unity and #hypothes eze ... have almost brought to the forefront of the "right venue."

https://web.hypothes.is/sponsors/

Seeing #hypothesisontableland and having it work is the first "glaringly bright flash" that will ensure that we never again watch commenting and sites like discus and reddit and facebook turn from the light of social "what's on fire?" to the darkness of shadow ... "throttling" of the presentation of the world changing that somehow has missed our tongues and hopefully not our eyes.

Hopefully once we start talking and getting more involved it will be clear how easy it was and is to make the world a better place just by ... "dropping in your two cents" or BTC as the case may be.

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

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

Summary: GlmS, the glucosamine-6-phosphate synthetase in E. coli and related bacteria, is essential, required for synthesis of both peptidoglycan and LPS. It is regulated at various levels, including positive regulation of GlmS translation by the Hfq-binding sRNA GlmZ. GlmZ activation of translation is regulated, indirectly, by the levels of GlcN6P, the product of GlmS. The components of the sensing and regulatory cascade have previously been defined, via genetics, biochemical and molecular biology studies. GlmZ is cleaved by Rnase E, becoming inactive, when GlcN6P levels are high, dependent upon the binding of GlmZ to RapZ. RapZ has been found to directly sense GlcN6P levels; another regulatory RNA, GlmY, also binds RapZ in the absence of GlcN6P, protecting GlmZ from RapZ-mediated processing. The authors of this manuscript performed cryoEM to study the structure of two important complexes in this sensing cascade, RapZ/GlmZ and RapZ/GlmZ/RNase E-NTD, with the aim of clarifying how the RNA binding protein RapZ causes the cleavage of sRNA GlmZ by RNaseE. Some of the predictions for critical residues in the RapZ/GlmZ binary complex structure were investigated by mutagenesis RapZ to define essential resiudes for GlmZ cleavage; the results are consistent with the structure.

Major comments:

• Are the key conclusions convincing? 1) Given that this is basically a structural paper, the major questions would be whether the cryoEM reconstructions are accurate (appear to be consistent with general expectations) and whether there is clear evidence to support the physiological relevance of the structure. The tests of function are of two sorts: a) Effect of RapZ mutants in Fig. 3b-d. These tests show loss of RapZ function with various alleles, likely consistent with model (but as noted below, very difficult for the reader to identify on the structures in 3a). The implication is that these will interfere with GlmZ binding. Possibly direct tests of a couple of these mutants for GlmZ binding (or pull down of GlmZ from in vivo expressed protein) would further support the model. I note that the text says T248A was unaffected in cleavage, but seems to be much reduced in Fig. 3b, even if fusion activity is good.

Our reply. We have made further tests of the mutations for GlmZ binding. Using electrophoretic mobility shift assays, we observe reduced GlmZ binding affinities for RapZ mutants K170A, H190A, C247A, T248A (figure below). We also tested the activity of RapZ variant with 4 substitutions at the proposed RapZ/NTD interface (right lanes in figure below).

We followed the recommendation of the reviewer and performed co-purification experiments (“pull-down”) using StrepTactin affinity chromatography and Strep-tagged RapZ variants as baits. Eluates were assessed for RapZ protein content and co-eluting GlmZ and processed GlmZ* sRNAs using Northern blotting. These new results, which have been incorporated in Fig. S7c, show that all tested RapZ variants except for the wild-type protein are not capable to pull-down GlmZ or GlmZ* in cell extracts. This includes the RapZ-T248A variant, which as noted by the referee is nonetheless still capable to decrease full-length GlmZ to some extent, albeit processed GlmZ* is hardly detectable (Fig. 3b, lanes 23, 24). To address this issue further, we purified the RapZ-T248A variant and some additional variants for comparison and performed EMSA. Globally, the EMSAs confirm the co-purification experiments, i.e. they demonstrate strongly reduced GlmZ binding activity for most tested RapZ variants, but also show that the RapZ-T248 variant kept some residual binding activity. This may explain the weak signal for processed GlmZ in the Northern blot (Fig. 3b) as processed GlmZ* likely binds to RapZ for stabilization. Similar effects were previously seen for the RapZquad and the RapZ 1-279 variants in Durica-Mitic et al. 2020 (Fig. 5). Accordingly, we also changed our wording concerning the RapZ-T248A variant in the text. We have not incorporated the EMSA figure into the updated manuscript.

b) The ternary complex was tested primarily by the BACTH assay of some RapZ mutants (Fig. S11), that show a reduced interaction. This is not a particularly convincing assay for a number of reasons: 1) the effects are relatively modest (2x down, in an assay that is probably not very linear with interaction, 2) some with reduced interaction (S239A, T248A) had good activity (at least all those with full interaction seem to be functional); 3) Ternary complex suggests that RapZ mediates this interaction; this could be tested by deleting glmZ (and maybe glmY as well) from this BACTH strain. 4) the authors suggest that there are also important protein-protein interactions, based on some observed interactions, and support this with similarly difficult to interpret BACTH data from a previous paper for Rnase E-RapZ interaction. Here, too, that is not the most compelling data (is this interaction RNA-independent?).

Our reply: Previous work already indicated that formation of the ternary complex involves multiple interactions – direct protein-protein contacts but also indirect interactions mediated by sRNA GlmZ. For instance, in vitro pull-down signals (RapZ = prey; RNase E = bait) become reduced but not abolished when RNA-free protein preparations are used (Durica-Mitic et al., 2020; Fig. 2E). BACTH signals are reduced 2-fold when using RNase E and RapZ variants that are strongly impaired in their RNA-binding capabilities, respectively (Durica-Mitic et al., 2020; Fig. 2C). As the BACTH assays and in vitro pull-down approaches yield similar trends, we suggest that BACTH experiments represent a useful approach to clarify the questions under study.

Point b1: To demonstrate that removal of multiple interactions is required to disrupt the ternary complex, we combined substitutions of residues making contact to the sRNA as well as residues directly contacting RNase E. According to the structure of the ternary complex presented here, residues T161, Y240, N271 and Q273 in RapZ are proposed to contact RNase E directly. Upon substitution of these four latter residues, resulting in the RapZ variant named RapZ-4 subst., the BACTH signal decreases two-fold – similar to what is observed for the RapZ variants that carry Ala substitutions of residues involved in sRNA-binding, such as H190 or R253. Importantly, when the latter two substitutions are introduced into the RapZ-4 subst. variant – either alone or in combination, the BACTH signal is reduced to almost back-ground levels. These results are in agreement with the features of the ternary complex proposed here and also with data obtained previously: They show that protein-protein and protein-RNA contacts must be concomitantly removed to disrupt the complex completely. We integrated the latter data as Fig. S7a in the revised manuscript and discuss the data at the appropriate positions in the text.

Point b2: In our opinion, the data reporting regulatory activity of the individual RapZ variants (Fig. 3 b-d) correlate well with the BACTH data (Fig. S7a): RapZ variants carrying substitutions of residues I175 and N236 retain regulatory activity and concomitantly a high RNase E interaction potential indistinguishable from the wild-type is observed. In contrast, RapZ variants carrying substitutions affecting sRNA-binding, i.e. H190A, C247A, C247S, T248D, G249W, R253A loose activity completely and concomitantly show a 2-fold decrease in the BACTH signal. The remaining BACTH signal is explained by the remaining (protein-protein) contacts as discussed above (point b1). Therefore, these variants are likely uncapable to present GlmZ in a correct manner to RNase E even though interaction is retained to some degree.

Only the RapZ mutants with exchanges H171A, S239A and T248A do not follow either of these two scenarios: albeit they exhibit reduced interaction with RNase E according to BACTH, they retain the ability to regulate the chromosomal glmS’-lacZ fusion, at least when produced from a plasmid (Fig. 3d). However, inspection of the GlmZ Northern blot signals (Fig. 3b) reveals that full-length GlmZ is decreased as expected, but that processed GlmZ* becomes either not visible or is much reduced when compared to wild-type RapZ. This explains by a reduced sRNA binding affinity, as pointed out above (point 1a), which also provides a rationale for the decreased BACTH signal.

Point b3: We agree that deletion of glmZ in the BACTH strain would be an ideal approach to dissect the contributions of protein-protein and sRNA-protein mediated interactions for formation of the ternary complex in vivo. Unfortunately, construction of the strain is not straight-forward. In our hands, the BACTH reporter strain BTH101 is not amenable to chromosomal manipulations by using engineered recombination tools such as the phage lambda-derived Red system. This may be explained by regulatory elements used by the l Red system that depend on cAMP, which cannot be synthesized in this strain.

__Point b4: __We have addressed this query in the response to point b1.

• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? Possibly the importance of RNAse E-RapZ direct interaction, without further proof that this actually is needed for function.

__Our reply: __We partially addressed this issue already in our response to point b1. Additionally, we also tested activity of the RapZ-4 subst variant that lacks the residues making direct contact to RNase E in our structure (Fig. 3b-d, last two lanes/columns). The results that are now described in the last paragraph of the results section show that this variant retains regulatory activity. Interestingly, the level of processed GlmZ* is strongly reduced in this case, similar to what is observed with the RapZ-S239A and RapZ-T248A variants discussed above. Therefore, these direct protein-protein contacts might have a role for GlmZ* decay in a manner that remains to be addressed.

• 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. As noted above, further tests of RapZ mutants for RNA binding would be useful; if this has been done previously, needs to be presented.

Our reply.

This has been addressed in the response above.

Are there Rnase E residues that would be predicted by the model to be critical for the RapZ or GlmZ interaction but are not otherwise needed for activity? Would these disrupt either the BACTH results or activity in vivo?

Our reply.

Please see response to this point above.

• 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, they are. They are generally extrapolations from what is already in the paper or in previous studies by these groups.
• Are the data and the methods presented in such a way that they can be reproduced? Yes.
• Are the experiments adequately replicated and statistical analysis adequate? Yes.

Minor comments: - Specific experimental issues that are easily addressable. None noted. - Are prior studies referenced appropriately? Yes, they are. However, the paper could more clearly outline what is already known at the level of interactions of the molecules under study here.

Our reply. We have changed the text to better introduce information from previous studies: interprotomer contacts, properties of the isolated RapZ domains, conclusions from the truncation analyses, requirements for interaction for RNase E and for sRNA-binding, stabilization of processed GlmZ through RapZ binding (Göpel et al., 2013; Gonzalez et al 2017; Durica-Mitic and Görke, 2019; Durica-Mitic et al., 2020).

• Are the text and figures clear and accurate?
• In a number of places, the text and figure order/numbers are not correct. See Fig. S1 (p. 4), S2 (legends vs. figure panels).

Our reply. We have corrected these in the revised text.

Better labeling in many figures is needed. Clarify what is shown in Fig. S2d, and make the labels readable. Label the particle types in S3. Use schematics more (as in Fig. 4 and S8) to make it easier for reader to follow structure (for Fig. 2, for instance). It is very difficult to discern RapZ tetramer here. Fig. 3a, it is very difficult to see the residue numbers on the structures. Clearly identify the fructokinase-like domains. Label lanes in Fig. 3b, c, d. Indicate active site for RNase E. in Fig. 4, in schematic at least.

Our reply. We have also corrected these in the revised text.

• Do you have suggestions that would help the authors improve the presentation of their data and conclusions? Overall, clarify and highlight better how the structures here fit with what is already known about important sequences/regions of RapZ, GlmZ, and Rnase E, maybe color-coding parts of GlmZ shown to be important for RapZ recognition, etc.

Our reply. We have added a sequence alignment for RapZ in the supplementary materials section, indicating important residues (Fig. S12).

Page 12, the second last row. Text after 'In this model...' can be simplified or removed because it is just a hypothesis.

Our reply. We have simplified the text.

Our reply:

We believe that the discussion section should also give room for novel ideas and hypotheses. Therefore, we wish to keep the paragraph.

Reviewer #1 (Significance (Required)):

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field. Rnase E is a major essential endonuclease in bacteria such as E. coli. How accessory proteins lead to its recognition and cleavage of regulatory RNAs such as GlmZ is not well understood at the structural level, and these structures provide important insight into that process. In addition, the GlmZ/RapZ regulatory circuit plays an important role in bacterial growth and pathogenesis, and understanding it at this level of detail will certainly open up possibilities for targeting this process in the future.

• Place the work in the context of the existing literature (provide references, where appropriate). The components that go into the current structures have been studied previously, with publications on RapZ structure, analysis of critical regions within the GlmZ RNA, and demonstration of the domain of Rnase E involved in interactions with RapZ (Durica-Mitic et al, 2020; Khan et al, 2020, Gonzalez et al, 2017, among others), exactly how these fit together has not been known. Other RNA binding proteins that affect degradation have been reported, but are not fully understood, and ways in which the ribonuclease binds complex RNAs is not fully understood either.

• State what audience might be interested in and influenced by the reported findings. This work should be of broad interested to the field of RNA-based regulation and RNA degradation, with particular interest for those working on these processes in bacteria.

• 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. Our expertise is in RNA-based regulation and microbial genetics; we are not able to critically evaluate the cryoEM analysis itself.

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

Summary:

Islam et al present their characterization of the E. coli RapZ-GlmZ-RNase E ternary complex in this manuscript under review. In E. coli, the RNA binding protein RapZ facilitates cleavage of GlmZ sRNA by RNase E when intracellular concentrations of GlcN-6P are high; when GlcN-6P levels are low RapZ is titrated by GlmY sRNA and GlmZ sRNA promotes an increase in the translation and stability of the mRNA encoding GlcN-6P synthase, GlmS. Via Cryo-EM, the authors of this manuscript solve the structure of the binary RapZ:GlmZ (Fig. 2) and ternary RapZ:GlmZ:RNase Y (Fig. 4) complexes. Based on the apparent RapZ-sRNA binding sites in the solved structure of the binary complex, the authors make substitutions in residues suspected to be involved in RNA binding and measure the impact of these substitutions on cleavage of GlmZ and GlmZ-mediated activation of GlmS expression (Fig. 3). The authors find that some of the residues predicted to be involved in RNA binding based on their structural studies are also important for the cleavage of GlmZ, presumably by RNase E. Finally, the authors show via bacterial two-hybrid assays that some residues of RapZ necessary for GlmZ cleavage are also important for its interaction with RNase E (Fig. S11). I would suggest that the authors measure co-immunoprecipitation of GlmZ with tagged-RapZ with or without substitutions in the proposed RNA binding residues to resolve this issue. Alternatively, EMSAs could be performed.

Our reply. Please see the response above to reviewer 1. We have included results from EMSAs with selected RapZ mutants and for multiple mutations in the BACTH analysis.

Major comments:

Overall, the structural studies our impressive and provide considerable insight into the recognition of substrates by RapZ and RNase E. Given the dearth of solved structures of RNAs with their cognate RNA binding proteins, these results are very significant.

A limitation in this work is the lack of experiments directly testing whether or not the residues of RapZ that appear to be important for its interaction with the GlmZ sRNA in the authors' Cryo-EM structures actually have a significant role in RNA binding. In lieu of measuring GlmZ binding by RapZ, the authors measure GlmZ cleavage in strains expressing RapZ or particular variants harboring substitutions in residues that appear to play a role in sRNA binding (Fig. 3b); however, it is impossible for the authors to determine whether impairment of GlmZ cleavage by RNase E in their assays is due to lack of GlmZ binding to RapZ, extraordinarily tight binding of GlmZ to RapZ, changes in the orientation of GlmZ bound to RapZ, or conformational changes in RapZ that lead to disruption of direct RapZ-RNase E contacts. The lack of this empirical data supporting their structural studies becomes more salient as the authors attempt to test whether RapZ binding of GlmZ is important for its interaction with RNase E via a bacterial two-hybrid assay. Since the authors have not directly examined the importance of particular RapZ residues on GlmZ binding, the authors' interpretation of their results from these assays is very speculative.

Our reply: Reviewer 1 raised a similar point to which we replied above. The role of candidate residues in RapZ for binding GlmZ has been addressed by more direct assays (Pull-down/EMSA).

The authors state on page 7 that "the interaction of RapZ:GlmZ with RNase E does not involve conformational rearrangement of either RapZ or GlmZ". However, the arrangement of SLII relative to SLI appears different between the RapZ:GlmZ and RapZ:GlmZ:RNase E structures presented. Additionally, SLII appears entirely bound by RapZ in the binary complex (Fig. 2b), whereas in the structure of the ternary complex, SLII appears less associated with RapZ (Fig. S4b). A supplementary figure showing side-by-side the structure of GlmZ bound to RapZ solved in the presence or absence of RNase E may make clear whether any differences that exist in the conformation of RapZ and GlmZ between the binary and ternary complex structures.

Our reply: In the revised manuscript, we have included a supplementary figure showing side-by-side comparisons of the structures.

Minor comments: Figure S1 legend. Change "inactivate" to "inactive" or "inactivated"

Figure S2 legend. The description for "(d)" is for S2c and the text for "(c)" refers to the image in S2d.

Figure legend S5a and S9a. If resolution in the key is in angstroms, then it should be indicated.

Our reply: We have now corrected the above points in the revised text.

Figure 1. The model appears to indicate that the apo-form of RapZ binds GlmZ and GlmY, whereas the GlcN-6P bound form does not. Moreover, in the discussion, the authors indicate that GlcN-6P interferes with GlmZ binding to RapZ. How does RapZ bind and cleave GlmZ when GlcN-6P levels are high, if GlcN-6P interferes with GlmZ binding? It would be useful for the authors to address this conundrum in their discussion.

Our reply. We thank the reviewer for pointing out this paradox. Our unpublished work indicates that RapZ may have phosphatase activity for GlcN6P, and we added a comment to this in the discussion section.

Fig. S3B and C. While panels in Fig. S3B and C seemed well aligned, numbering of lanes would provide additional clarity.

We will provide lane numbers, accordingly.

Many bacterial species including Bacillus subtilis, Streptococcus pyogenes, and Clostridium botulinum have RapZ homologs that bear a tyrosine instead of a histidine residue at the position corresponding to H190 in E. coli RapZ. Would you expect this change to reduce GlmZ binding by RapZ or lead to change in RNA specificity based on your structural data? This may be useful to discuss in the manuscript.

We believe that the is more behind this question. Likely, the referee (by inspecting a RapZ sequence alignment) realized that almost all residues proposed to be involved in binding GlmZ are also conserved in RapZ homologs in Gram-positive bacteria, unless His190 and His171, which are replaced by tyrosines in some of these species. However, no RNA-binding activity has been reported for the Gram-positive RapZ homologs. If true, the question arises what is making the difference here? In principle, this could be due to the lacking histidine residues, which are replaced by tyrosines in Gram-positive RapZs. Alternatively, we consider that the positively charged residues at the far C-terminus (K270, K281, R282, K283), which were identified previously to be required for sRNA binding (Göpel et al., 2013; Durica-Mitic et al., 2020), and which could not be resolved in the current structures, are additionally required to obtain RNA-binding activity.

Fig. S10. It is confusing to me that the yellow chain in the structure of RNase E is labeled as the DNase I-domain in the apo structure, whereas in the structure with RprA or GlmZ bound, this colored region is labeled as the 5' sensing domain.

We have changed the figure to make it clearer.

On page 12, the authors appear to indicate that their structural studies of the RapZ-GlmZ-RNase E ternary complex could be informative with regards to how KH domain proteins in Gram-positive bacteria could present their substrates to RNase E. First of all, these bacteria lack RNase E and instead encode an evolutionarily distinct endoribonuclease (RNase Y). Secondly, I think that it is overreaching to state that these structural studies will inform us on how KH domain proteins such as KhpA/KhpB, which may or may not have a chaperoning function akin to Hfq in Gram-positive bacteria, present substrates to RNase Y. Regardless, if this statement is to remain, the authors should make clear that is RNase Y and not RNase E that they are referring to.

We have changed the text to make clear that a different RNase is employed in this case.

Reviewer #2 (Significance (Required)):

In my opinion, the significance of this work is in the achievement of high-resolution structures of the complexes of the RNA binding protein RapZ and the endoribonuclease RNase Y with RNA substrate bound. There are very few structures solved of RNA binding proteins or RNases with their cognate substrates. This is likely due to the difficult in obtaining high resolution data for the bound RNA that may have a large degree of flexibility or many alternative conformations. More structures like this are needed to advance our understanding of RNA-protein interactions.

I believe that these findings would not only be of great interest to those that study small regulatory RNAs, such as myself, but also others more generally interested in RNA binding proteins, RNases, or protein-RNA interactions.

Field of expertise: small regulatory RNAs, RNA chaperones, RNases

**Referees cross-commenting**

1. I agree with Reviewer #1 that the results of the bacterial two-hybrid assay would be more informative, if the authors tested the impact of deletion of glmZ on the ability of the wild type and mutant RapZ proteins to interact with RNase Y by this assay.

As both reviewer #1 and I indicated, I think that it would be useful for the authors to directly assess the effect of key substitutions in RapZ on GlmY binding by a more direct measure of interaction, e.g., CoIP or EMSA.

I do think that it would be nice at some point for the authors to actually provide evidence that GlcN6P binds to the site that they predict as reviewer 3 suggested but this may be be beyond the scope of this manuscript and may be better addressed in another manuscript in which the authors solve the structure of RapZ with GlcN6P bound. In the meantime, the authors could limit their speculation.

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

Summary: The biogenesis of the bacterial cell envelope relies on glucosamine-6-phosphate (GlcN6P), which is mediated by GlmZ and the sRNA-binding protein RapZ. GlmZ stimulates translation of the GlcN6P synthetase. When the levels of the GlcN6P are sufficiently high, RapZ will presents GlmZ to the endoribonuclease RNase E for cleavage and thereby silencing synthesis of the GlcN6P synthetase. However, how RapZ recruit RNase E to GlmZ for degradation is still unsolved. This paper reports the cryoEM structure of the binary complex of RapZ: GlmZ and the ternary complex of the RNase E catalytic domain (RNase E-NTD), RapZ and GlmZ. RapZ interacts with SLI and SLII of GlmZ through complementarity in shape and electrostatic charge to the phosphodiester backbone of the sRNA and presents the sRNA by alignning its SSR comprising the cleavage site into the RNase E active center. This paper suggests a general RNase E recognition pathway for complex substrates, which will help to understand the mechanisms that other RNA chaperones such as Hfq might work in an analogous assembly to present base-paired sRNA/mRNA pairs for cleavage. In total, this is an excellent work. I will support the publication of it until these following points are presented.

Major comments: 1. It was mentioned on Page 5 that "Sulphate and malonate ions were previously seen at these positions in crystal structures of apo RapZ" and pn Page 11 that " Interestingly, the phosphate groups of the RNA backbone occupy positions in RapZ that were previously observed to bind sulphate or malonate ions in the crystal structure of apo-RapZ, suggesting that this pocket could be the binding site for a charged metabolite such as GlcN6P". Is there any following experiments to investigate it further? If possible, I suggest the author to confirm that weather RapZ has the binding activity with GlcN6P or not.

Binding of GlcN6P by the RapZ-CTD was demonstrated previously by SPR as well as by metabolomics of metabolites copurifying with RapZ (Khan et al., 2020), although evidence that the “sulphate/malonate binding sites” in RapZ also bind GlcN6P is still lacking. Crystallization of RapZ+GlcN6P is not straight forward as bound GlcN6P is apparently hydrolyzed over time.

"The kinase-like N-terminal domain of RapZ (NTD) makes only a few interactions with the RNA, and the path of the RNA does not encounter the Walker A or B motifs (Figure 2b). It is possible that this domain could act as an allosteric switch, whereby the binding of an as yet unknown ligand triggers quaternary structural changes that affect RapZ functions." Is there any more structural information supporting it? If the domain act as an allosteric switch, is it possible to make some deletion or substitution to test it?

The properties of the separated NTD and CTD of RapZ were assessed in previous work.

Is there any results to compare the binding affinity of GlmY and GlmZ with RapZ?

Affinities were determined previously using complimentary techniques:

Göpel et al., 2013/EMSA: KD GlmY ~ 30 nM; KD GlmZ ~ 75 nM

Gonzalez et al., 2017/biolayer interferometry: ~ 50 nM for both GlmY/GlmZ (full-length)

Minor comments: 1. Page 8, is it "stabilised" or "stabilized", please check it.

We have changed the spelling to “stabilized”.

The legends for Figure S2 c and d are reversed.

This has now been corrected.

It was suggested to show the RNA molecules in Figure S1a.

We have changed the figure to include single-stranded RNA substrate.

Reviewer #3 (Significance (Required)):

This paper suggests a general RNase E recognition pathway for complex substrates, which will help to understand the mechanisms that other RNA chaperones such as Hfq might work in an analogous assembly to present base-paired sRNA/mRNA pairs for cleavage. In total, this is an excellent work.

This paragraph, and reading in general, makes me think about the positives and negatives of learning almost solely with computers during the midst of the pandemic we are in. Computers are a technological device I can’t imagine not being able to use in my high school and college careers. When reflecting on my past zoom classes I have taken, I do believe that it is a valid point to bring up the statement that suggests that the technology only benefits students if the teaching is implemented in a well thought and beneficial way. When we made the sift to online school it was not only an adjustment for students, but teachers as well. This class, and material within, really makes me think about how the sudden switch to the use of technology to learn has impacted my college experience. I can definitely pick out certain classes that gave me more meaningful experiences due to the way that the teacher was able to utilize the technology at hand. Lastly, it makes me think about what educational technology may look like in school settings when I plan to become a teacher myself in the next 5+ years.

I found this section interesting as I believe its a tactic used more toward those in high school / college students. Growing up, I think children needed more 'step-by step / how to' instruction when it came to our education as we were learning things we've never encountered prior but as you get older and move onto more advanced studies I believe that we're more so relating and understanding concepts to build onto base knowledge we already have. So although I do find this tactic valuable currently at this age, I don't know how beneficial this would be to younger kids as they may not even be able to accomplish their task if they lack the instruction they need when doing their assignment online without an educator in their direct presence.

Author Response

Reviewer #1 (Public Review):

Huang et al. sought to study the cellular origin of Tuft cells and the molecular mechanisms that govern their specification in severe lung injury. First the authors show ectopic emergence of Tuft cells in airways and distal parenchyma following different injuries. The authors also used lineage tracing models and uncovered that p63-expressing cells and to some extent Scgb1a1-lineaged labeled cells contribute to tuft cells after injury. Further, the authors modulated multiple pathways and claim that Notch inhibition blocks tuft cells whereas Wnt inhibition enhances Tuft cell development in basal cell cultures. Finally, the authors used Trpm5 and Pou2f3 knock-out models to claim that tuft cells are indispensable for alveolar regeneration.

In summary, the findings described in this manuscript are somewhat preliminary. The claim that the cellular origin of Tuft cells in influenza infection was not determined is incorrect. Current data from pathway modulation is preliminary and this requires genetic modulation to support their claims.

We thank the reviewer for the comments and we have performed extensive experiments to address the reviewer’s comments. In the revised manuscript we provide additional data including genetic modulation findings to support our model.

Major comments:

1) The abstract sounds incomplete and does not cover all key aspects of this manuscript. Currently, it is mainly focusing on the cellular origin of Tuft cells and the role of Wnt and notch signaling. However, it completely omits the findings from Trpm5 and Pou2f3 knock-out mice. In fact, the title of the manuscript highlights the indispensable nature of tuft cells in alveolar regeneration.

We have modified the abstract and title accordingly.

2) In lines 93-94, the authors state that "It is also unknown what cells generate these tuft cells.....". This statement is incorrect. Rane et al., 2019 used the same p63-creER mouse line and demonstrated that all tuft cells that ectopically emerge following H1N1 infection originate from p63+ lineage labeled basal cells. Therefore, this claim is not new.

We thank the reviewer’s comment. Although Rane et al. reported the p63-expressing lineage-negative epithelial stem/progenitor cells (LNEPs) could contribute to the ectopic tuft cells after PR8 virus infection, it is still not clear whether the p63+ cells immediately give rise to tuft cells or though EBCs. Thus, we performed TMX injection after PR8 infection, different from Rane et al (Rane et al., 2019). who performed Tmx injection before viral infection to indicate the ectopic tuft cells are derived from EBCs, as shown in revised Figure 2.

3) Lines 152-153 state that "21.0% +/- 2.0 % tuft cells within EBCs are labeled with tdT when examined at 30 dpi...". It is not clear what the authors meant here ("within EBC's")? And also, the same sentence states that "......suggesting that club cell-derived EBCs generate a portion of tuft cells....". In this experiment, the authors used club cell lineage tracing mouse lines. So, how do the authors know that the club cell lineage-derived tuft cells came through intermediate EBC population? Current data do not show evidence for this claim. Is it possible that club cells can directly generate tuft cells?

We apologize for the confusion and revised the text accordingly. Here, “within EBCs” means within the “pods” area where p63+ basal cells are ectopically present. The sentence is revised as “21.0% +/- 2.0 % tuft cells that are ectopically present in the parenchyma are labeled by tdT. Notably, these lineage labeled tuft cells were co-localized with EBCs.” We don’t know whether the club cell lineage-derived tuft cells transit through intermediate EBCs and that is why we use “suggest”. It is also possible that club cells can directly generate tuft cells. To avoid the confusion, we delete the sentence.

4) Based on the data from Fig-3A, the authors claim that treatment with C59 significantly enhances tuft cell development in ALI cultures. Porcupine is known to facilitate Wnt secretion. So, which cells are producing Wnt in these cultures? It is important to determine which cells are producing Wnt and also which Wnt? Further, based on DBZ treatments, it appears that active Notch signaling is necessary to induce Tuft cell fate in basal cells. Where are Notch ligands expressed in these tissues? Is Notch active only in a small subset of basal cells (and hence generate rate tuft cells)? This is one of the key findings in this manuscript. Therefore, it is important to determine the expression pattern of Wnt and Notch pathway components.

We thank the reviewer’s interesting questions and agree the importance of identifying the specific ligands and receptors for relevant Wnt and Notch signaling during tuft cell derivation. That being said, we think the topic is beyond the scope of this study which is focused on the role of tuft cells in alveolar regeneration. The point is well taken and we will investigate the topic in our future study.

5) How do the authors explain different phenotypes observed in Trpm5 knockout and Pou2f3 mutants? Is it possible that Trpm5 knockout mice have a subset of tuft cells and that they might be something to do with the phenotypic discrepancy between two mutant models?

Again we thank the reviewer for the interesting question. As discussed in the discussion section, Trpm5 is also reported to be expressed in B lymphocytes (Sakaguchi et al., 2020). It is possible that loss of Trpm5 modulates the inflammatory responses following viral infection, which may contribute to improved alveolar regeneration. However, it is also possible that Trpm5-/- mice keep a subset of tuft cells that facilitate lung regeneration as suggested by the reviewer.

6) One of the key findings in this manuscript is that Wnt and Notch signaling play a role in Tuft cell specification. All current experiments are based on pharmacological modulation. These need to be substantiated using genetic gain loss of function models.

We have performed the genetic studies.

Reviewer #2 (Public Review):

In this manuscript, the authors describe the ectopic differentiation of tuft cells that were derived from lineage-tagged p63+ cells post influenza virus infection. These tuft cells do not appear to proliferate or give rise to other lineages. They then claim that Wnt inhibitors increase the number of tuft cells while inhibiting Notch signaling decreases the number of tuft cells within Krt5+ pods after infection in vitro and in vivo. The authors further show that genetic deletion of Trpm5 in p63+ cells post-infection results in an increase in AT2 and AT1 cells in p63 lineage-tagged cells compared to control. Lastly, they demonstrate that depletion of tuft cells caused by genetic deletion of Pou2f3 in p63+ cells has no effect on the expansion or resolution of Krt5+ pods after infection, implying that tuft cells play no functional role in this process.

Overall, in vivo and in vitro phenotypes of tuft cells and alveolar cells are clear, but the lack of detailed cellular characterization and molecular mechanisms underlying the cellular events limits the value of this study.

We thank the reviewer for the comments and acknowledging that our findings are clear. In the revised manuscript we provide more detailed characterization and genetic evidence to elucidate the role of tuft cells in lung regeneration.

1) Origin of tuft cells: Although the authors showed the emergence of ectopic tuft cells derived from labelled p63+ cells after infection, it cannot be ruled out that pre-existing p63+Krt5- intrapulmonary progenitors, as previously reported, can also contribute to tuft cell expansion (Rane et al. 2019; by labelling p63+ cells prior to infection, they showed that the majority of ectopic tuft cells are derived from p63+ cells after viral infection). It would be more informative if the authors show the differentiation of tuft cells derived from p63+Krt5+ cells by tracing Krt5+ cells after infection, which will tell us whether ectopic tuft cells are differentiated from ectopic basal cells within Krt5+ pods induced by virus infection.

We thank the reviewer for the helpful suggestion. We have performed the experiment accordingly.

2) Mechanisms of tuft cell differentiation: The authors tried to determine which signaling pathways regulate the differentiation of tuft cells from p63+ cells following infection. Although Wnt/Notch inhibitors affected the number of tuft cells derived from p63+ labelled cells, it remains unclear whether these signals directly modulate differentiation fate. The authors claimed that Wnt inhibition promotes tuft cell differentiation from ectopic basal cells. However, in Fig 3B, Wnt inhibition appears to trigger the expansion of p63+Krt5+ pod cells, resulting in increased tuft cell differentiation rather than directly enhancing tuft cell differentiation. Further, in Fig 3D, Notch inhibition appears to reduce p63+Krt5+ pod cells, resulting in decreased tuft cell differentiation. Importantly, a previous study has reported that Notch signalling is critical for Krt5+ pod expansion following influenza infection (Vaughan et al. 2015; Xi et al. 2017). Notch inhibition reduced Krt5+ pod expansion and induced their differentiation into Sftpc+ AT2 cells. In order to address the direct effect of Wnt/Notch signaling in the differentiation process of tuft cells from EBCs, the authors should provide a more detailed characterization of cellular composition (Krt5+ basal cells, club cells, ciliated cells, AT2 and AT1 cells, etc.) and activity (proliferation) within the pods with/without inhibitors/activators.

Again we thank the reviewer for the insightful suggestions. We agree that it will be interesting to further address the direct effect of Wnt/Notch signaling in the differentiation process of tuft cells from EBCs. In this revised manuscript we added new findings of EBC differentiation into tuft cells in mice with genetic deletion of Rbpjk.

3) Impact of Trpm5 deletion in p63+ cells: It is interesting that Trpm5 deletion promotes the expansion of AT2 and AT1 cells derived from labelled p63+ cells following infection. It would be informative to check whether Trpm5 regulates Hif1a and/or Notch activity which has been reported to induce AT2 differentiation from ectopic basal cells (Xi et al. 2017). Although the authors stated that there was no discernible reduction in the size of Krt5+ pods in mutant mice, it would be interesting to investigate the relationship between AT2/AT1 cell retaining pods and the severity of injury (e.g. large Krt5+ pods retain more/less AT2/AT1 cells compared to small pods. What about other cell types, such as club and goblet cells, in Trpm5 mutant pods? Again, it cannot be ruled out that pre-existing p63+Krt5- intrapulmonary progenitor cells can directly convert into AT2/AT1 cells upon Trpm5 deletion rather than p63+Krt5+ cells induced by infection.

We thank the reviewer for the comments and suggestions. Our new data using KRT5-CreER mouse line confirmed that pod cells (Krt5+) do not contribute to AT2/AT1 cells, consistent with previous studies (Kanegai et al., 2016; Vaughan et al., 2015). Our data also show that p63-CreER lineage labeled AT2/AT1 cells are separated from pod cell area, suggesting pod cells and these AT2/AT1 cells are generated from different cell of origin. We also checked the Notch activity in pod cells in Trpm5-/- mice, and some pod cell-derived cells are Hes1 positive, whereas some are Hes1 negative (RLFigure 1). As indicated in discussion we think that AT2/AT1 cells are possibly derived from pre-existing AT2 cells that transiently express p63 after PR8 infection. It will be interesting to test whether Trpm5 regulates Hif1a in this population (p63+,Krt5-), and this will be our next plan.

RLFigure 1. Representative area staining in Trpm5-/- mice at 30 dpi. Area 1: Notch signaling is active (Hes1+, arrows) in pod cells following viral infection. Area 2: pod cells exhibit reduced Notch activities. Note few Hes1+ cells in pods (arrows). Scale bar: 50 µm.

4) Ectopic tuft cells in COVID-19 lungs: The previous study by the authors' group revealed the presence of ectopic tuft cells in COVID-19 patient samples (Melms et al. 2021). There appears to be no additional information in this manuscript.

In Melms et al., Nature, 2021 (Melms et al., 2021), we showed tuft cell expansion in COVID-19 lungs but not the potential origin of tuft cells. In this manuscript we show some cells co-expressing POU2F3 and KRT5, suggesting a pod-to-tuft cell differentiation.

5) Quantification information and method: Overall, the quantification method should be clarified throughout the manuscript. Further, in the method section, the authors stated that the production of various airway epithelial cell types was counted and quantified on at least 5 "random" fields of view. However, virus infection causes spatially heterogeneous injury, resulting in a difficult to measure "blind test". The authors should address how they dealt with this issue.

We clarified that quantification method as suggested. For the in vitro cell culture assays on the signaling pathways, we took pictures from at least five random fields of view for quantification. For lung sections, we tile-scanned the lung sections including at least three lung lobes and performed quantification.

Reviewer #3 (Public Review):

In this manuscript Huang et al. study how the lung regenerates after severe injury due to viral infection. They focus on how tuft cells may affect regeneration of the lung by ectopic basal cells and come to the conclusion that they are not required. The manuscript is intriguing but also very puzzling. The authors claim they are specifically targeting ectopic basal progenitor cells and show that they can regenerate the alveolar epithelium in the lung following severe injury. However, it is not clear that the p63-CreERT2 line the authors are using only labels ectopic basal cells. The question is what is a basal cell? Is an ectopic basal progenitor cell only defined by Trp63 expression?

The accompanying manuscript by Barr et al. uses a Krt5-CreERT2 line to target ectopic basal cells and using that tool the authors do not see a signification contribution of ectopic basal cells towards alveolar epithelial regeneration. As such the claim that ectopic basal cell progenitors drive alveolar epithelial regeneration is not well-founded.

We appreciate the reviewer for the positive comments and agreeing that our findings are interesting.

The title itself is also not very informative and is a bit misleading. That being said I think the manuscript is still very interesting and can likely easily be improved through a better validation of which cells the p63-CreERT2 tool is targeting.

We have revised the title accordingly and performed extensive experiments to address the reviewer’s concerns.

I, therefore, suggest the following experiments.

1) Please analyze which cells p63-CreERT2 labels immediately after PR8 and tamoxifen treatment. Are all the tdTomato labeled cells also Krt5 and p63 positive or are some alveolar epithelial cells or other airway cell types also labeled?

We thank the reviewer for the question. To answer the reviewer’s question, we performed PR8 infection (250 pfu) on three Trp63-CreERT2;R26tdT mice and TMX treatment at days 5 and 7 post viral infection. We didn't perform TMX injection immediately as the mice were sick at a few days post infection. The lung samples were collected at 14 dpi. We observed that tdT+ cells are present in the airways (rebuttal letter RLFigure 2A, B), and it appears that the lineage labeled cells (tdT+) include club cells (CC10+) that are underlined by tdT+Krt5+ basal cells (RLFigure 2C). We think that these labeled basal cells give rise to club cells. However, we also noticed that rare club cells and ciliated cells (FoxJ1+) are labeled by tdT in the areas absent of surrounding tdT+ basal cells (RLFigure 2D). Moreover, a minor population of tdT+ SPC+ cells are present in the terminal airways that were disrupted by viral infection (RLFigure 2E and D). We did not see any pods formed in this experiment and we did not observe any tdT+ cells in the intact alveoli (uninjured area).

RLFigure 2. Trp63-CreERT2 lineage labeled cells in the airways but not alveoli when Tamoxifen was induced at day 5 and 7 after PR8 H1N1 viral infection. Trp63-CreERT2;R26-tdT mice were infected with PR8 at 250 pfu and Tmx were delivered at a dose of 0.25 mg/g bodyweight by oral gavage. Lung samples were collected and analyzed at 14 dpi. Stained antibodies are as indicated. Scale bar: 100 µm.

2) Please also show if p63-CreERT2 labels any cells in the adult lung parenchyma in the absence of injury after tamoxifen treatment.

Dr. Wellington Cardoso’s group demonstrated that Trp63-CreERT2 only labels very few cells in the airways but not the lung parenchyma in the absence of injury after tamoxifen treatment (Yang et al., 2018). Dr. Ying Yang has revisited the data and she did not observe any labeling in the lung parenchyma (n = 2).

3) Please analyze if p63-CreERT2 labels any cells with tdTomato in the absence of injury or after PR8 infection but without tamoxifen treatment.

We performed the experiment and didn't observe any labeled cells in the lung parenchyma without Tamoxifen treatment (n = 4).

4) Please analyze when after PR8 infection do the first p63-CreERT2 labeled tdTomato positive alveolar epithelial cells appear.

We administered tamoxifen at day 5 and 7 after PR8 infection and harvested lung tissues at day 14. As shown in Figure 1, we observed a few tdT+ SPC+ cells in the terminal airways that are disrupted by viral infection. Notably, we did not observe any lineage labeled cells in the intact alveoli (uninjured) in this experiment..

5) A clonal analysis of p63-CreERT2 labeled cells using a confetti reporter might also help interpret the origin of p63-CreERT2 labeled cells.

We thank the reviewer for the suggestion. Our new data demonstrate that a rare population of SPC+tdT+ cells are present in the disrupted terminal airways of Trp63-CreERT2;R26tdT mice. Our data in the original manuscript and the new data suggest that the initial SPC+;tdT+ cells are rare because we have to administrate multiple doses of Tamoxifen to label them. Given the less labeling efficiency of confetti than R26tdT mice, it is possible we will not be able to label these SPC+ cells. Moreover, our original manuscript clearly shows individual clones of SPC+tdT+ cells in the regenerated lung, and they do not seem to compose of multiple clones. Therefore we think that use of confetti mice may not add new information..

6) Lastly could the authors compare the single-cell RNAseq transcription profile of p63-CREERT2 labeled cells immediately after PR8 and tamoxifen treatment and also at 60dpi. A pseudotime analysis and trajectory interference analysis could help elucidate the identity of p63-CreERT2 labeled cells that are actually not ectopic basal progenitor cells.

We appreciated the reviewer’s suggestion and agree that single cell RNA sequencing with pseudotime analysis can provide further information regarding the origin of the lineage labeled alveolar cells of Trp63-CreERT2;R26tdT mice. That said, our new data clearly show that KRT5-CreER lineage labeled cells do not give rise to AT1/2 cells as previously described (Kanegai et al., 2016; Vaughan et al., 2015), suggesting that the ectopic basal progenitor cells do not generate alveolar cells. By contrast, Trp63-CreERT2 lineage labeled cells do give rise to AECs, suggesting that this p63+ cell population capable of generating AECs are different from Krt5+ ectopic basal progenitor cells. Our single cell core has an extremely long waiting list due to the pandemic and we hope that our new findings are enough to address the reviewer’s concern without the need of single cell analysis..

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Reply to the reviewers

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

Spinal cord injury (SCI) is a damage to the spinal cord, that causes temporary or permanent changes in its function. While in mammals the regeneration process are very limited zebrafish are able to repair the spinal cord. Based on the hypothesis, that the vascular response might affect the regeneration capacity, the paper by Ribeiro et al addresses the structure and injury response of the spinal cord vasculature. As the growth of zebrafish larvae and juveniles depends a lot on the individual response to the environment, the authors first established comparable body measurement parameters (other than age) and observed the natural spinal cord vascularization process, starting from 6mm body length of the animals. Using transgenic lines the authors describe the formation and patterning of endothelial cells and pericytes up to 9mm length, when a more developed vascular network was present. They observe the processes of vascular regeneration after a contusion based SCI model at different time points (days post injury (dpi)) and in correlation with glial and axonal regrowth, also observing BSCB barrier integrity, angiogenesis, pericyte recruitment and the dependence on Vegf signaling.

The study is interesting and novel, vascular structures in the zebrafish adult spinal cord have not been reported yet and neither has the vascular response to SCI. Currently the study remains very descriptive, although the authors tried to add functional data, by inhibiting Vegf signaling.

Major points for revision: The authors fail to establish whether there is any relationship between spinal cord regeneration and vessel regeneration. While I do very well understand the challenges and limitations the authors should put more effort into functional analyses.

For example: the authors address EC proliferation as a marker for angiogenesis, but do not analyse whether or how much EC proliferation is required for revascularization and regeneration. Pharmacological inhibition of proliferation should be possible and used. From a vascular point of view it would also be interesting whether there is a differential influence of tip or stalk cell proliferation.

Although we agree that it would be interesting to inhibit EC proliferation to assess its role in spinal cord regeneration, the use of proliferation inhibiting drugs would likely have a widespread effect on the lesioned spinal cord, since many cell types proliferate in response to injury. Therefore, a pharmacological approach would not allow us to dissect the specific role of endothelial proliferation.

The same is true for pericyte recruitment: the role of pericytes for the vascular repair or the spinal cord regeneration is not clear. The authors could use use mutants with impaired pericyte development or e.g. nitroreductase mediated ablation of pericytes.

These experiments have been performed in larvae by Tsata et al. (2021). Although it would be interesting to repeat in adults, we believe that these experiments are beyond the focus of our study.

The statements regarding the role of Vegf are too bold. The problem lies in the limitations of assessing the efficiency of Vegf inhibition. The heatshock promotor has been shown to induce transcription for up to 4 hours, depending on the efficiency of heatshock. There are no data on the stability of dnVegfaa protein. Likewise the pharmacological inhibition could be far from complete. A full inhibition of Vegf signaling is expected to stop vessel growth or angiogenesis. While it is a sign of good practice, that the authors combined a genetic model with a pharmacological one, both leave the same unresolved issue. However if we believe a very limites requirement of Vegf-signaling, it would be interesting to look for other signaling pathways, like cxcl, IL, or FGF to regulates regenerative angiogenesis.

We agree that our data does not allow us assess the level of inhibition of the Vegf pathway. Since we are unable to confirm this at the moment, we will be excluding the Vegf inhibition data and make this a descriptive study.

Minor issues

The correlation with spinal cord repair could be stated more clearly throughout the manuscript. For the uninformed reader it is less clear when exactly the spinal cord is functional again.

We will include in Fig. 3 a plot of the swimming capacity in contusion-injured fish until 90 dpi and will explain in the text how the vascular response correlates with the functional recovery.

While I find the model in figure 8 very helpful, it gives 5 to 30 days, for the neuronal regeneration. Maybe a more detailed timeline of EC regeneration and remodeling correlating with neuronal repair would help.

We will update the model in Fig. 8 with a more detailed timeline and a better description of structures important for regeneration (glial bridge, axonal regrowth).

In line with that in figure 4 it is not clear whether the images of different time points are indeed one individual animal at the different time points or representative animals for the stage (also figure 4 lacks panel labels, in my copy I can see A, K and L, but no other letters).

We will detail in the figure legend that the images are of different animals that are representative for each stage.

For understanding the (re)vascularization, the direction of blood flow might be helpful.

We will perform an additional experiment to characterise the direction of blood flow in uninjured fish. For this we will use juvenile fish with a body size of 7-9 mm, in which we expect to be able to perform live imaging. We will use a lighsheet microscope to image circulating cells in the spinal cord blood vessels in fish with labelled thrombocytes (Tg(-6.0itga2b:EGFP); Lin et al., 2005) and endothelial cells (Tg(kdrl:ras-mCherry)). These transgenic lines are already available in our fish facility. Even though the vascular network has not yet reached its mature stage at these body sizes, we expect to have enough intraspinal vessels to describe the blood flow circuit.

Especially for the connection between spinal cord regeneration and vessel regeneration. Does blood flow regulate vessel pruning after 14 dpi?

Although we agree with the reviewer that it would be interesting to understand how blood flow direction is reestablished in repaired vessels and how blood flow levels correlate with vessel remodelling and pruning, this would be difficult to assess in this system. This could be examined using live imaging, but this technique is challenging in adult zebrafish and has only been carried out in more superficial organs than the spinal cord, such as skin (Castranova et al., 2022) and superficial brain structures (Barbosa et al., 2015; Castranova et al., 2021). In addition, SC-injured fish are more sensitive to external conditions and would probably not survive the long-term/repeated anaesthesia required for imaging.

This analysis could be performed in fixed samples, for example using the the Golgi complex position in relation to the endothelial nuclei as a proxy for blood flow direction (Kwon et al., 2016), however: (1) this would require a new transgenic line (Tg(fli1a: B4GALT1-mCherry)) that would take time to import and establish in the lab; (2) the identification of regressing vessels is not straightforward in fixed samples and is usually studied in very well established vascular models, such as the mouse retina and zebrafish ISVs (Franco et al., 2015).

For these reasons, we will not address this question by reviewer 1.

The combined Vegfaa DN and PTK treatment data looks like it could be inhibiting endothelial cell proliferation (Figure7I).However, Supplementary Figure 8B shows endothelial proliferation does not change. Does it mean the number of endothelial cells is same but the volume of endothelial cells decrees?

We will not be addressing the changes in endothelial density in the presence of dn-vegfaa and PTK787, since we will be removing the figures related to Vegf inhibition.

There are also some remaining grammatical errors, for example (but NOT limited to) line 133 to 135.

We will review grammatical errors in the text.

As a personal interest I think evaluating the role of Notch in the SCI model would also be very interesting, especially with regard to the vasculature, however that might be out of the scope of the manuscript.

We agree that Notch signalling may be a player during spinal cord revascularisation. However, mutants for dll4 (the Notch ligand involved in angiogenesis) die between 7-14 dpf and cannot be used for this study. In addition, the use of Notch-inhibiting drugs would likely have pleiotropic effects, since the Notch pathway is also involved in other aspects of spinal cord regeneration, namely in the regulation of regenerative neurogenesis (Dias et al., 2012). To our knowledge, tools that allow the endothelial-specific inhibition of the Notch pathway have not been developed, and therefore we will not be able to address this question.

Reviewer #1 (Significance (Required)):

The study is partially descriptive, but very novel as the aspects of vascularisation in a spinal cord injury model have not been described before. If the major revisions regarding functionality are addressed fully, I would wholeheartedly recommend publication and expect an interest for a broad audience. The presented images and their analyses are of very high quality, and therefore also enhance the impact of the study.

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

The study by Ribeiro et al. investigates the formation of new blood vessels after spinal cord injury in adult zebrafish. The authors initially characterize the extend of spinal cord vascularization during the development of juvenile zebrafish and investigate the association of pericytes with the newly forming vasculature. They then injure the spinal cord and describe the subsequent regeneration of blood vessels. They perform assays to analyze the functionality of the newly forming blood vessels and show that initially blood vessels are leaky. Through EdU labelling the authors show that endothelial cells proliferate. Pericytes similarly increased in numbers. Lastly, the authors inhibited VEGF signaling, which only mildly affected vascular regeneration.

Together, this manuscript describes the re-vascularization of the regenerating spinal cord in adult zebrafish and addresses how blood vessels mature during this process through pericyte recruitment and decrease in leakiness. The manuscript provides some interesting initial insights into spinal cord vascularization, but is mainly descriptive and unfortunately remains superficial in this regard, as specified below:

1. The authors only refer to "blood vessels" without specifying the type of blood vessels they observe (are these veins, arteries, capillaries)? A wealth of markers and transgenic zebrafish lines are available to better characterize spinal cord vessels. This is not necessary in case the authors solely refer to "blood vessels" as they do, but it greatly limits the insights into spinal cord vascularization. For instance, Wild et al. (2017) showed that new vessels apparently sprout from veins in the spinal cord. Is this also true during regeneration?

We will perform RNA in situ hybridisation using probes for arterial and venous markers. We will assay the expression of arterial markers (dll4, dlc, flt1 and efnb2a) and venous markers (flt4 and ephb4a) in uninjured spinal cord (to characterise vessel identity in homeostasis) and in 3 and 7 dpi spinal cords (to investigate the identify of angiogenic vessels during regeneration).

1. The authors state that their characterization revealed a "stereotypic organization of blood vessels". However, the organization does not appear to be stereotypic (as I understand this term as looking the same in each fish) at all. Can the authors compare e.g. 3 or 5 wildtype fish and extract features that all fish share and those that differ between fish? This would greatly enhance our understanding of the vascular variability within the wildtype population.

We will provide an additional figure comparing the spinal cord vasculature in different fish.

1. The authors show an interesting metameric organization of the vasculature with regions of high vascularization interspersed with sparsely vascularized areas. Are there any morphological landmarks that would precipitate these differences?

We will acquire light sheet images of adult spinal cords without removing the vertebrae. This will allow us to determine if the metameric organisation is correlated with the vertebral distribution.

Can the authors check whether they induce a lesion in a highly or poorly vascularized area? This might greatly influence the degree of re-vascularization.

We always perform the spinal cord injury in the region between neural arches (Dietrich et al., 2021). Once we determine how the vasculature is organised in relation to the vertebrae, we will be able to determine if the lesions are performed in a region of high or low vascularisation.

1. The same superficial characterization unfortunately also applies to the cell population the authors refer to as "pericytes". Traditionally, pericytes are characterized as being associated with capillaries and sharing a basement membrane with the endothelium. Is this the case here?

We will further characterise the association between Tg(pdgfrß:citrine)-positive cells and blood vessels using an anti-laminin antibody (#L9393, Sigma) to label the basement membrane. Preliminary results recently acquired indicate that Tg(pdgfrß:citrine)-positive perivascular cells and endothelial cells are both enveloped by the basement membrane, supporting the identity of Tg(pdgfrß:citrine)-positive cells as pericytes. Moreover, pericytes are generally described as solitary mural cells associated with small diameter blood vessels (the type of distribution we observe for Tg(pdgfrß:citrine)-positive cells), whereas vascular smooth muscle cells (vSMCs) form concentric layers around larger blood vessels (a distribution we do not detect with this transgene) (Hellström et al., 1999). For these reasons we believe that this transgene is labelling pericytes. We will explain more clearly in the text how the morphology, localisation and density of Tg(pdgfrß:citrine)-positive cells suggests these cells are pericytes.

In addition, pdgfrb is hardly specific for pericytes, as it also labels a multitude of other cell types (refer to e.g. Tsata et al. (2021)).

The different cell types labelled by the pdgfrb reporter line used in the Tsata et al., 2021 paper were identified not by the use of different cell markers, but by their localisation: perivascular cells (the same cell type that we also detect), myoseptal cells (which we would not expect to detect, since we are only analysing the spinal cord tissue and not the adjacent muscle) and floor plate cells (a reporter distribution that the authors show is lost after 3 dpf and is not present in the adult spinal cord). Moreover, the Tsata et al., 2021 paper also includes a supplementary figure (S1, panel N) showing a restricted perivascular pdgfrb:GFP distribution in the wholemount adult spinal cord, in agreement with our characterisation. By their morphology and density, these perivascular cells are likely pericytes, as argued above.

It is also not clear why the transgenic pdgfrb line the authors use only labels cells next to blood vessels. Tsata et al. show a much broader labelling. The authors need to validate their transgenic line using in situ hybridization showing where pdgfrb is being expressed endogenously and how this overlaps with the fluorescent protein expression of the pdgfrb transgenic line.

We will perform ISH for pdgfrb to confirm if the Tg(pdgfrß:citrine) reporter reproduces the endogenous expression in the uninjured spinal cord and at 3 and 7dpi. The 3-7 dpi period is approximately equivalent to the 1-2 days post-lesion in larvae and, if the non-perivascular pdgfrb:GFP cells observed in the larval spinal cord are present in the adult, we expect to detect them by ISH during this phase of regeneration.

There are also several transgenic lines available that allow for the distinction between smooth muscle cells and pericytes (e.g. Shih,..., Lawson, Development 2021 and Whitesell,..., Childs, Plos ONE 2014). As for the vasculature, this more detailed characterization is not necessary in case the authors refer to the cells as "cells labelled by the pdgfrb transgene and reside next to endothelial cells". However, this would not be reflective of the level of detail currently present in the field.

As we explain above, the morphology and density of the pdgfrb:Citrine-positive cells suggests that these cells are pericytes and not smooth muscle cells (SMCs). To confirm this we will compare the expression of pdgfrb with markers of SMCs (i.e, 𝛼-smooth muscle actin and desmin) using immunohistochemistry and/or ISH.

The reviewer also suggests the characterisation of pericyte subtypes using the lines described by Shih et al., 2021. Although this would be interesting, we do not consider it is essential for our study. It would be very demanding to import the reporter lines and it is not certain that these subtypes are present in the spinal cord.

1. The authors state that "New blood vessels rapidly attracted pericytes, formed through proliferation and possibly migration of existing pericytes". This statement is not supported by the data, as the authors do not perform lineage tracing of pre-existing pericytes. The authors need to specifically label existing pericytes and then follow whether these pre-labelled cells can be found on newly forming blood vessels. Tsata et al. provide some evidence for this in zebrafish larvae, but they also conclude that pdgfrb expressing tenocytes contribute to new mural cells.

We will reformulate the sentence to clarify that we detect pericyte proliferation, but pdgfrb-lineage tracing would be needed to provide evidence that existing pericytes contribute to the generation of mural cells associated to new blood vessels. However, we will not perform the lineage tracing experiment for the revision, as we are unable to currently import this line.

1. The findings that new blood vessel growth only marginally depended on VEGFA signaling is striking. However, it might also point towards an inefficient inhibition of VEGFA signaling. In particular, other publications, for instance Cattin et al. 2015 have shown that inhibiting VEGFA signaling prevents new blood vessel growth during peripheral nerve regeneration in mouse. It will therefore be important that the authors demonstrate that their approach leads to successful inhibition of VEGFA signaling. VEGFAB mutants appear to be homozygous viable and important for spinal cord vascularization (Matsuoka et al., 2017). In addition, heterozygous VEGFAA mutants already have some vascular phenotypes, but are also viable. Can the authors combine these mutants with their inhibitor treatments to achieve a greater reduction in VEGFA signaling?

Since we are unable to confirm the level of inhibition of the Vegf pathway and we are unable to import the suggested lines at the moment, we will be excluding the Vegf inhibition data.

Reviewer #2 (Significance (Required)):

Together, this publication is the first to describe to some extend the regenerating vasculature after spinal cord injury in adult zebrafish. However, both the vascular and regeneration fields are much more advanced than what the authors cover. Both blood vessels and perivascular cells can be characterized in much more detail, as outlined above. Also, studies on nerve regeneration and its dependence on the vasculature, e.g. during peripheral nerve regeneration in mouse have been carried out with a wealth of functional data available. Therefore, the impact of the present study in its current form will be limited. I am an expert on zebrafish blood vessel development.

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

Summary: Provide a short summary of the findings and key conclusions (including methodology and model system(s) where appropriate).

Ribeiro et al. described vascular development in the spinal cord from larval to adult stages in zebrafish, and found the dependence of vessel length on body-size. Then, the authors depicted the vascular regeneration process after spinal cord injury (SCI), which includes initial vascularization, angiogenesis, pericyte recruitment, and blood-spinal cord barrier establishment. Although the molecules or signaling pathways that drive the re-vascularization remain unidentified, this study illustrates the cellular processes of spinal cord vascular development and regeneration from the descriptive level, which may facilitate further understandings of mechanisms underlying vascular regeneration in the spinal cord.

Major comments: - Are the key conclusions convincing? The descriptions of spinal cord vascularization during development and vascular regeneration after SCI are convincing. However, inhibition of Vegfaa and Vegfr2 is nearly ineffective. The author might not conclude that the Vegfr2 signaling plays any role.

Since we are unable to confirm the level of inhibition of the Vegf pathway, we will be excluding the Vegf inhibition data.

Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

Major comments: 1) In Figure 3, the exact injured site on the spinal cord is not clear. Please include a schematic illustration of full spinal cord to show where is the injured site. Are all the injury experiments in this study done at the same site? If not, is there any site difference regarding the regenerative capability.

We will include a scheme of the injury site in the spinal cord in Fig.3. All the injury were performed in the same position and this will be clarified in the methods.

2) Figure 2E showed a segmented pattern of spinal cord vasculature. Is this pattern correlated with the position of vertebra?

We will acquire light sheet images of adult spinal cords without removing the vertebrae. This will allow us to determine if the metameric organisation is correlated with the vertebral distribution.

3) In Figure 3, during vascular regeneration after SCI, the author only showed partial regeneration at 30 dpi. Why not show the stage of complete regeneration? At that stage, how about the behaviors of the regenerated animals?

We will add an additional timepoint (90 dpi) to the characterisation of the revascularisation. Moreover, we will include in Fig. 3 a plot of the swimming capacity in contusion-injured fish until 90 dpi and will explain in the text how the vascular response correlates with the functional recovery.

4) Only EdU data is not sufficient to conclude that new vessels come from proliferation of remaining endothelial cells. For example, these new vessels might come from transdifferentiation of lymphatic vessels, or immune cells, or glial cells, in the meantime proliferate. This could also explain why the inhibition of Vegfr2 signaling is ineffective on new vessel formation. Cre/loxP-mediated lineage tracings need to be performed to exactly identify where these new vessels originate.

We will clarify in the text that while the detection of endothelial proliferation suggests existing endothelial cells contribute to new vessels, we cannot exclude that other cell types also give rise to endothelial cells. However, regarding the transdifferentiation of immune and glial cells into endothelial cells, to our knowledge few examples have been described in the literature and generally associated with cancers or in in vitro conditions (Fernandez Pujol et al., 2000; Li et al., 2011; Soda et al., 2011). For this reason we do not expect this rare process to occur during spinal cord repair.

A cell type that has been associated with transdifferentiation into ECs are lymphatic cells (Das et al., 2022). However, we have analysed the expression of a lymphatic marker (Tg(lyve1b:DsRed)) and were only able to detect very few lyve1b:DsRed-positive cells before or after injury, suggesting that any possible lymphatic contribution would likely be very limited. We plan to include these data in the revised submission.

5) To confirm the Tg(hsp70l:dn-vegfaa) did work in this study, the authors need a positive control. For example, the effects on vasculogenesis or angiogenesis during embryonic development after heat shock. If the transgene works, the vascular development at early stages should be blocked (Marín-Juez et al., 2016).

We will be removing the vegf inhibition data, therefore we will not address this question.

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 suggested experiments are realistic in terms of time and resources.

• Are the data and the methods presented in such a way that they can be reproduced? In the method, the author should describe how to identify the Tg(hsp70l:dn-vegfaa) in more details, because there is no fluorescence before and after heat shock.

We will be removing the vegf inhibition data, therefore we will not address this question.

Are the experiments adequately replicated and statistical analysis adequate? Yes.

Minor comments: - Specific experimental issues that are easily addressable. In Figure 6, from 30 dpi to 90 dpi, the number of pericytes decreased. Did these pericytes undergo apoptosis from 30 dpi on?

We have not investigated pericyte apoptosis during vessel remodelling. However, this experiment would require the acquisition of long-term samples (between 60 and 90 dpi) and we would prefer not to address this question.

Are prior studies referenced appropriately? Yes.

• Are the text and figures clear and accurate? Please clearly labeled the injured region in Figure 6.

We will identify more clearly the site of the injury in Fig.6.

• Do you have suggestions that would help the authors improve the presentation of their data and conclusions? The number of proliferating ECs at 3 dpi is more than those at 5 dpi (Figure 5G). But the number of total EdU+ cells at 3 dpi is less than those at 5 dpi (Figure 5A-D). These data are consistent with Figure S3, which showed ECs were the leading cell type to enter the lesioned site, then were the axons and glial cells at later stages. Please explain and discuss whether the regeneration of other cell types is dependent on the accomplishment of vascular regeneration.

As the reviewer points out, our data suggest that endothelial cells display an earlier peak of proliferation than spinal cord cells in general and colonise the lesioned tissue before new axons and glial cells. Although these observations could point to a role for ECs in the regeneration of other cell types, we would need to inhibit vascular repair to assess this possibility, which we were unable to do using Vegf inhibition. In our discussion we already mention some possible roles for ECs in stem cell proliferation, neurogenesis and axonal regrowth, but can expand this discussion if necessary.

Reviewer #3 (Significance (Required)):

• Describe the nature and significance of the advance (e.g. conceptual, technical, clinical) for the field.

Although this study has characterized the development and regeneration of spinal cord vasculature in details, the significance of the advance needs to be improved due to lack of mechanisms. Obviously Vegfa is not essential for the vascular regeneration after SCI. It is better for the authors to identify one or two factors required for this process, in addition to identify cell origins of new vessels. With those, the significance of this study will be improved because the cell origins and required factors will provide potential therapeutic targets after SCI.

• Place the work in the context of the existing literature (provide references, where appropriate).

• State what audience might be interested in and influenced by the reported findings. The audience includes people who are interested in vascular development and regeneration, and spinal cord clinicians.

• Define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. My field of expertise includes brain vascular regeneration, digestive organ development and regeneration. This study reported spinal cord vascular development and regeneration, which fit my expertise.

Micah Redding -> Christian Transhumanist Association · oSorpetdsnftf0t30m86 1 u2c10ag1g6mi4ffaial25il81h5 h u7gc4t6571t · A few years ago, a friend called his young daughter over, and said to her, “Did you know that Micah thinks we’re all going to become immortal cyborgs, and that’s how we’ll usher in the kingdom of heaven?” I laughed. That wasn’t something I had said, but I think it was his attempt at “reading between the lines”. Is that what I think? The question raises other questions. Could the relationship between God and humanity in the person of Christ be described as “cyborg”? What about the spiritual body of 1 Corinthians 15? Some people have indeed described things this way. After all, it is the “bizarre and unnatural” juxtaposition of God’s spirit and human flesh that saves us and transforms us into constituents of the kingdom of heaven. This combination remains just as repulsive to many people as a robotic eye or an exoskeleton. But I think most people would probably be wondering about something else: Is it our technology that ultimately saves us? I think this question reflects confusion around cause and effect, faith and works, salvation and renewal. In the book of Revelation, we see a glorious city, descending from heaven to earth. This city is organic, human, and technological—a “cyborg city” just like our cities today. All the glory of the nations is brought into it—every good thing created or discovered has a place there. And this city is actively producing new and unprecedented means to heal and renew the outside world. I think this is a picture of what Paul calls “the body of Christ”, the community and ecosystem that Paul says will one day fill the universe. Salvation means being included in this community, both now and into the indefinite future. As part of this community, we are bound into a network of relationships that continually renews and sustains our life. As part of this community, we have gifts and works to do (Eph 2:10), which are the works of creation and healing that renew the world. Salvation consists of relationships, and relationships are always expressed in gifts and acts and works of creation. For several years now, I’ve been arguing that we can’t make sense of Genesis 1-2 (let alone Hebrews 2, 1 Cor 15, or Romans 8;) unless we understand science and technology to be part of these God-given works of creation and renewal. So is it our technology that saves us? No, rather our technology is a gift, a work, a byproduct of salvation overflowing into renewal and creation.

Becoming immortal cyborgs won’t save us—but being saved may turn us into immortal cyborgs.

Author Response

Reviewer #2 (Public Review):

Suggestions to improve the paper:

Major Issues

1) I do not think that the introduction accurately reflects the state of the field with respect to single cell omics and nerve injury. The CCI model is different than the SNI model, which has been used in most previous studies, in terms of the nature of the injury, and the resolution of pain after the injury. I do not think it is accurate to claim that the CCI model is somehow more relevant clinically, because both models are just that. It is also not really true that co-mingling, un-injured neurons have not been profiled before. The Renthal paper did this, but using a different model. There is value in what the authors have done here, but they can state it more clearly in the introduction. In particular, most published studies have only used male mice, so the sex differences aspect of this work is important. In that regard, the authors did not cite any of the growing literature on sex differences in neuropathic pain mechanisms.

We revised the introduction and discussion to address the comments. Specifically, we revised the related information about animal models (Page 4-5). Although Renthal et al. examined co-mingling, “un-injured” neurons using a sciatic crush injury model, they did not find cell-type specific changes in uninjured neurons. The reason for this is unclear, but we speculate that it may be partially due to differences in the techniques (e.g., tissue processing, cell sorting, sequencing depth) and animal models (CCI versus crush injury). Compared to sciatic CCI induced by loose ligation of the sciatic nerve, crush injury would injure most nerve fibers (~50% of L3-5 DRG neurons are axotomized in this model). Therefore, the remaining “uninjured’ neurons for sequencing may be much less than that in the CCI model. In addition, we used Pirt-EGFPf mice to establish a highly efficient purification approach to enrich neurons for scRNA-seq and therefore largely increased the number of genes detected in DRG neurons. Comparatively, the neuronal selectivity and number of genes detected were lower in the previous study, which may have resulted in fewer DEGs and decreased ability to detect aforementioned changes. We include a brief discussion (Page 24).

We appreciate the reviewer’s good suggestion, and cited sex differences studies in neuropathic pain mechanisms (Pages 5, 25). Although our findings suggest that peripheral neuronal mechanisms may also underlie sexual dimorphisms in neuropathic pain, Renthal et al. reported no differences in subtype distributions or injury-induced transcriptional changes between males and females after sciatic nerve crush injury (Renthal et al., 2020). We also discussed the differences between current findings and previous work and also emphasized the sex differences aspect of this work in the discussion (Page 25).

2) I am curious about the choice to only use samples from 7 days after CCI. One of the advantages of the CCI model is that pain resolves at about 35-60 days, depending on how the ligations are done, and this allows one to look at how transcriptional programs change in DRG neurons after pain resolves. This would give some new insight, at least in comparison to the very comprehensive profiling done in the sciatic nerve crush model by Renthal and colleagues.

We thank the reviewer for this comment. We provided the rationale for day 7 post-CCI (Page 22). It is the time point when neuropathic pain-like behavior is fully developed in most animals, and the post-injury time point examined in many previous studies. The reviewer is correct, an advantage of the CCI model is that pain resolves at about 35-60 days. Although meaningful, it was not our intention to conduct a time course study to fully characterize time-dependent transcriptional changes using scRNA-seq, which is costly and requires a great effort for data analysis, etc., and is beyond the scope of the current study. We will address this in a future study, and provided a brief discussion (Page 22).

3) An alternative interpretation of the ATF3 expression is that the dissociation protocol causes this upregulation. ATF3 induction may be rapid and could occur due to the technique the authors chose to use. This could be acknowledged.

We agree and acknowledged this in our original discussion (Page 22).

4) I think the authors are a bit over-confident in their call of "injured" and "un-injured" neurons based on Sprr1a expression. This is really the only grounds for calling these neurons injured or uninjured. The fact is that the CCI model does not provide a clear way to determine injured and uninjured neurons contributing to neuropathic pain. This is an advantage of the SNL model, as shown in many classic papers from the Chung lab.

We included a brief discussion about Sprr1a (Page 22). Although Atf3 is a classic marker of injured neurons in some previous studies, a recent study suggested that Sprr1a may be a better standard to define “injured” neurons (Nguyen et al., 2017). Although injured and uninjured neurons can be readily separated in the SNL model, they are mostly from different DRGs, but not intermingled in the same DRG. Since glia-neuron interaction and neuron-neuron interaction may occur between cells within the same DRG after injury, these interactions may profoundly affect neuronal excitability and gene expression. Accordingly, we choose the CCI model for the current study to determine whether injured and uninjured neurons contribute to neuropathic pain. We included a brief discussion (Page 5, 23, 24).

5) There are now two papers on human DRG neurons that are available. One was recently published in eLife, and the other is available on Biorxiv, and has been there since Feb 2021. I expected the authors to make some comparisons of cell types that are changing in CCI with populations that are found in humans. Would similar effects be expected? Are these cell types represented in the human DRG?

Study of human DRG is important, and recent studies elegantly characterized neurochemical and physiological properties. Previous findings have suggested some notable difference between human and rodent DRGs. Importantly, many markers and methods used for classifying subpopulations of rodent DRG neurons do not apply well to human DRG neurons. In addition, data from human DRG came from patients with different etiologies, but not due to peripheral nerve injury as in the animal study. Due to these differences, we feel that it is difficult to make direct compassion of cell types that are changing in CCI with corresponding human DRG neurons.

Minor Issues

1) Does the 40 um cell strainer eliminate some larger diameter cells from the analysis?

We think this is unlikely, as large-diameter cells such as NF1 and NF2 clusters were also observed in our dataset. Importantly, we examined the cell strainer by washing it out inversely and did not find single cells. In addition, all subtypes identified in other studies were also found in our study. Nevertheless, an underrepresentation of the amount of NF neurons may be a result of the fact that not all NF neurons are GFP-positive in Pirt-EGFPf mice. In Pirt-EGFPf mice, expression of the knockin EGFPf was under the control of the endogenous Pirt promoter. Anti-GFP antibody staining revealed that GFP is widely expressed in 83.9% of all neurons. However, Pirt-negative neurons are mainly NF200+ and have large-diameter cell bodies. In addition, compared to small neurons, large neurons are also easier to lose during FACS sorting. We included a brief discussion of this potential limitation, as the NF population may be underrepresented in our sample set (Page 21).

Author Response

Reviewer #2 (Public Review):

Zhong et al conducted a scRNA-seq analysis to uncover the features in multiple myeloma (MM) based on the Revised International Staging System (R-ISS) stage. They contributed 11 scRNA-seq datasets, including 9 MM samples and 2 healthy BMMC. And validated their findings using the deconvolution method in large cohorts.

In addition, the newly identified and validated a subset of GZMA+ cytotoxic multiple myeloma cells. The experiments were nicely conducted and the datasets generated in this study might benefit many other studies. Major comments:

1) Several studies on scRNA-seq in MM have been reported, but different from that reported in this study. The authors might discuss the insight gained from their study.

Thanks for your comments. Several studies on scRNA-seq in MM have been disclosed some heterogeneity of MM. For example, Jang JS et al identified the molecular pathways during MM progression (MGUS, SMM, NDMM, and RRMM) [Blood Cancer J. 2019 Jan 3;9(1):2.]. Jean Fan et al devised a computational approach called HoneyBADGER to identify copy number variation and loss of heterozygosity in individual cells from single-cell RNA-sequencing data [Genome Res. 2018 Aug; 28(8):1217-1227.]. These studies verified the high heterogeneities existed in MM. But the specific the mechanism was not clear. Furthermore, these studies didn’t specify the heterogeneity among different stages in R-ISS staging system, which has been an international wide used prognostic stratification system. Therefore, we focused on the specific cluster, marker, and cross-talk pattern among the three stages of MM to reveal the potential mechanism of heterogeneity.

2) The author claimed Proliferating plasma cells were increased in EBV-positive MM patients. It would be interesting to examine the abundance of EBV RNA levels in the scRNA-seq datasets. Several tools, such as viral-track or PathogenTrack, might be used to conduct such analysis.

Thanks for the reviewer’s great suggestions and comments. According to your suggestion, we used PathogenTrack to identify pathogens in MM patients and added this analysis results in the file ‘Data for reviewers-1(PathogenTrack).xlsx’. However, the algorithm did not identify EBV reads in the scRNA-seq datasets. In order to verify our conclusion, we collected more MM patients’ samples and examined EBV, MKI67, and PCNA. Our result showed that EBV positive samples had significantly higher MKI67 and PCNA expression, compared with EBV negative samples on Lines 193 to 195, Page 6 (in Figure 5B and 5C).

3) Methods used for deconvolution are missing.

We thank the reviewer’s comments and suggestions. In our study, we didn’t use an analytical tool named CIBERSORT, thus we didn’t use deconvolution either in the manuscript. It may cause you a misunderstanding because of our unclear description.

Reviewer #3 (Public Review):

The authors constructed a single-cell transcriptome atlas of bone marrow in normal and R-ISS-staged MM patients. A group of malignant PC populations with high proliferation capability (proliferating PCs) was identified. Some intercellular ligand receptors and potential immunotargets such as SIRPA-CD47 and TIGIT-NECTIN3 were discovered by cell-cell communication. A small set of GZMA+ cytotoxic PCs was reported and validated using public data.

For scRNA-seq data analysis, the authors did QC and filtering and removed low quality cells, including some doublets and followed by batch effect correction. Malignant PC populations were identified using the copy number analysis tool "inferCNV".

The authors have done lots of analysis. But I think the results can be improved if they can do more analyses. I would recommend to 1) analyze doublets; 2) remove cell cycle effect; 3) GO and pathway analysis for genes with copy number change; 4) do cell-cell communication with more cell type/clusters.

Thanks for your suggestion and comment.

1) We applied Scrublet to computationally infer and remove doublets in each sample individually, with an expected doublet rate of 0.06 and default parameters used otherwise. The doublet score threshold was set by visual inspection of the histogram in combination with automatic detection. Information about this description was added to material and methods section as ‘We applied Scrublet [74] to computationally infer and remove doublets in each sample individually, with an expected doublet rate of 0.06 and default parameters used otherwise. The doublet score threshold was set by visual inspection of the histogram in combination with automatic detection.’ accordingly in Lines 731-734, Page 27.

2) As we focused on the differences in proliferative capacity of myeloma cells, the cell cycle could reflect the difference well. Therefore, the cell cycle data was provided accordingly. Information about this description was added into main text as ‘Next, we analysed the cell cycle of six PC clusters, and distinguished them from other clusters, PCs in cluster 6 (PCC6) were presumably enriched in G2/M stage (Figure. 3B)’ in Lines 142-144, Page 5.

3) We have analyzed the GO and pathway analysis for genes with copy number changes, and provided the file ‘Data for reviewers-2 and 3 (InferCNV for PCC4 and PCC6)’. Based on this, we found that oxidative phosphorylation was the most significant enriched pathways for PCC4 and PCC6, respectively. Cell-cell communication with more cell type/clusters was provided with the supplementary data in the file ‘Data for reviewers-3 (Overall T cells interaction ligand-receptor pairs dotplot, Overall T cells interaction ligand-receptor, Overall T cells interaction map)’.

Data analysis of public data was sufficient to prove the small set of GZMA+ cytotoxic PCs. More data analysis or wet experiment proof is required.

Thanks for your suggestion. The subset of cytotoxic PCs was identified in this study. These PCs exhibited NKG7 and GZMA. Furthermore, NKG7 showed the higher expression level than NKG7. Therefore, we validated it using Multi-parameter Flow Cytometry (MFC) and Immunofluorescence in MM samples. We identified a new subset of NKG7+ cytotoxic PCs and found that the percentage of NKG7+ PCs displayed obvious diversities among stage I, II and III groups. Information about this description was added in the main text as ‘In another MM single-cell dataset focusing on PC heterogeneity of symptomatic and asymptomatic myeloma (dataset GSE117156) [19], one cluster, C21, exclusively expressing NKG7 corresponded to PC18 in our dataset (Fig 2C-2D). In GSE117156 of all 42 samples, the cell proportion varied from 0% to 30.95% of all PCs, with an average percentage of 4.28% (Figure. 2E).Next, immunofluorescence confirmed the expression of NKG7 in cytoplasm of PCs (CD138 positive) from patients with MM (Figure. 2F). Finally, twenty MM patients (stage I: three patients, stage II: 10 patients and stage III: seven patients) were enrolled for multi-parameter flow cytometric (MFC) analysis. The results showed that the percentage of NKG7+ PCs displayed obvious diversities among stage I, II and III groups (Figure. 2G and Figure. S2). The average percentage of NKG7+ population was 2.73% in stage I, 8.89% in stage II and 0.58% in stage III (Figure. 2G and Figure. S3). In summary, we characterized a NKG7+ PC population (PC18), which may provide a novel perspective for the cytotherapy of MM.’ in Figure 2 and S3 and Lines 118-130, Page 4-5.

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Reply to the reviewers

Reviewer #1.

Reviewer #1 summary:

In this manuscript by Lu et al., the authors describe some CRISPR screens and protein-protein interaction screens to identify novel regulators of wild-type p53 and mutant p53 function and stability. Besides generating a wealth of data, they discover FBXO42-CCDC6 as positive regulators of the some p53 hot-spot mutants, including R273H mutant p53, but not of all p53 mutants tested and also not of wild-type, indicating selectivity. Furthermore, the found C16orf72(TAPR1) as a negative regulator of p53 stability.

Mechanistically, the authors claim a direct interaction between FBXO42 and CCDC6 and p53, but the importance of these interactions has not been shown. On the other hand the authors suggest that the FBXO42/CCDC6 regulate p53 via destabilization of USP28, but also the mechanism has not been worked out. For C16orf72, they show that it interacts with USP7, but no relevance of this interaction is shown either.

Response: We sincerely thank the reviewer for the constructive and thorough review. We have incorporated most of the suggestions into our planned revision, with our major focus on the molecular mechanistic follow-up.

Reviewer #1, major points.

1. One very important point for me is that the authors do not show the levels of expression of p53 in the p53-mClover stable cell lines. It is known that overexpressed p53 is usualy more stable than endogenous levels of wt-p53. Therefore, I think it is necessary that the authors show the levels of the p53-mClover fusion proteins in the stably transduced cell lines compared to endogenous p53 levels in the parental RPE1 cells and also compared to the endogenous levels of R273H mutant in the PANC-1 cells.

Response: We fully agree that the levels of overexpressed p53s are often more than the endogenous ones, due in part to increased expression and stability. In designing the reporter, we first tried to avoid the stabilisation of p53-GFP due to GFP aggregation by using the monomeric mClover-variant. Further, we titrated the WT and R273H clones (similar to our recent work in PMID: 35439056), to select clones with p53 levels closer to endogenous protein, and exhibiting high dynamic response to Nutlin-3a treatment.

In the revised submission, we will include Western blotting comparing the levels of p53-mClover (WT and R273H) expression to the endogenous p53s in RPE1 (WT) and PANC1 (R273H) cell lines, in the presence or absence of Nutlin-3a.

Also the functionality of the wild-type p53-mClover fusion is questionable, at least not shown. One would expect that the overexpression of a functional wt-p53 in p53-KO cells will affect the survival of the RPE1 cells. In Figure 5A the authors show that depletion of MDM2 or C16ORF72 is toxic for the RPE1 cells in a p53-dependent manner, indicating that elevated levels of p53 cannot be handled by these cells. So, experiment(s) showing that the wt-p53/mClover fusion is functional is needed.

Response: We agree that it will be an important point to benchmark the reporter design. The ectopically expressed WTp53 is often observed to have reduced functionality compared to the endogenous WTp53. The WTp53-reporter line behaves similarly to the RPE1 line (p53-proficient), where both chemical (e.g. Nutlin) or genetic perturbation (e.g. depletion of MDM2/C16orf72) would be toxic in a p53-depedent manner. In line with this data, we have observed that the WTp53-reporter line is able to induce a p53 response as demonstrated by induction of p53-target genes such as p21, which is not observed in p53 null RPE cells, albeit the p21 induction is not as dramatic as in RPE1 cells with endogenous WTp53. Together, these data indicate that our WTp53-reporter is functional albeit with a somewhat reduced activity.

In the revised submission, we will better demonstrate the functionality of the WTp53-mClover fusion by probing WTp53 target (e.g. p21), in the presence and absence of Nutlin. This is also performed as a part of the experiment addressing Point #1 above.

A second important point is that the 'verification' of the hits from the screens is only done in one cancer cell line, PANC-1, with mutant p53. I would have like to see at least one other cell line with another p53 mutant endogenously expressed that is also regulated by FBXO42/CCDC6.

Response: we will include validation of the hits (FBXO42, CCDC6) in other 1-2 tumour lines with confirmed R273H endogenous mutation (e.g. MB-MDA-468, etc).

For many of the p53-mutants, a bimodal expression is observed. In the FBXO42- and CCDC6-depleted cells, the equilibrium shifts towards more negative cells but the levels in the two populations itself don’t change (while for example for USP28 depletion also the right peak shifts further up, Fig S4E). Is there any correlation with the cell cycle and p53 expression? And can the authors exclude that FBXO42 and CCDC6 are involved in cell cycle progression and hereby influence p53 indirectly (by combining PI staining with Clover-p53 for example).

Response: we have indeed observed that the “bimodal” levels in the reporters of several mutants, which are also observed in other studies probing the endogenous p53 level (PMID: 29653964); while the population equilibrium shifts, the location of each peak (as a proxy of the level of p53s) are more stable.

Regarding the relation between p53-level and cell cycle stage, indeed, both the authors in the paper above and we have probed this possibility, but were unable to establish a direct connection.

In the revised submission, we will add flow cytometry analysis of the p53-mClover level, and the cell cycle position using Hoechst 33342 (live-cell permeable DNA staining).

The authors claim that the FBXO42-CCDC6 axis regulates stability specifically some p53-mutants, including R273H-mutant, in a manner involving USP28. But USP28 regulates all forms of p53, not just some mutants version. How can the authors reconcile this apparent contradiction?

Response: we thank the reviewer for this critical observation. From our screen (Supplemental Table 1A), we have indeed noticed a pronounced effects (|Z score| >=3) of FBXO42 on R273H and R248Q stability, and a marginal effect on wild-type p53. Similarly, USP28 had pronounced effects on R273H and R248Q and WTp53.

In the discussion of the paper, we noted that USP28 was shown to regulate p53 levels through distinct mechanisms:

‘USP28 was originally implicated as a protective deubiquitinating enzyme counteracting the proteasomal degradation of p53, TP53BP1, CHCK2, and additional proteins68-71. USP28 regulates wild-type p53 via TP53BP1-dependent and -independent mechanisms. Concordantly, our data shows that USP28 and TP53BP1 are strong positive regulators of wild-type p53. However, while USP28 was also a strong hit in the mutant R273H p53 screen, TP53BP1 was not, indicating that the effects we see upon loss of USP28 on R273H p53 are independent of TP53BP1.’

Together, this indicates that the R273H-mutant is regulated by a FBXO42-CCDC6-USP28 axis while wild-type p53 is regulated mainly via a USP28-TP53BP1 axis. We will attempt to address and discuss it in the revision.

On a similar note, the authors show that FBXO42 and CCDC6 interact with p53, but not USP28. Do FBXO42 and CCDC6 interact with each other and with USP28? And is the interaction with p53 specific for the R273H version? This part of the mechanism is very poorly defined and the Co-IPs are not very convincing or relevant for the proposed model.

Response: This comment will be more extensively addressed in the revision. We have indeed observed the interaction between FBXO42 and CCDC6 (via BioID and APMS); however, we failed to recover USP28 as an interactor of either FBXO42 or CCDC6. The interaction between CCDC6/FBXO42 is not specific to R273H; although we were able to IP endogenous R273H with CCDC6 in PANC-1 line, the WTp53 (as in HEK 293 TRex BioID line) was also picked up in the BioID preys of CCDC6/FBXO42. In addition, we have new data to show that FBXO42 directly interacts with WTp53.

In the revised submission, we will improve the molecular underpinning of the FBXO42-CCDC6-USP28-p53 axis we propose. We will specifically address the following.

(1.1.) Biochemically, further support that CCDC6 and FBXO42 regulate p53 via regulating USP28 stability: We will address this by established biochemical assays, e.g. cycloheximide-chase/MG132 experiment. While USP28 is an established WTp53 regulator, little is known about the mechanism, and the “upstream” regulation of USP28; we will attempt to fill this gap:

(1.2.) And to an unbiased systematic approach, how R273H interactome changes upon the loss of CCDC6 or FBXO42.

We will perform R273H-BioID upon loss of CCDC6 and FBXO42 and USP28.

(1.3.) Furthermore, we will specifically exam the interaction of USP28-p53R273H with or without the genetic perturbation of FBXO42/CCDC6.

Through these efforts, we hope to gain further mechanistic insights into this regulatory axis, but hope that the editors and reviewers will agree that a fully annotated mechanistic understanding is probably beyond the scope of this paper.

Reviewer #1, minor points.

The mechanisms of p53 regulation may vary greatly in different cell lines. Can the authors discuss why they choose to do the screen with different mutants, rather than with different cell lines expressing these same mutant endogenously?

Response: While it is certainly very interesting to assess how WT and mutant p53 is regulated in different cell lines, such an approach is confounded by the ‘genetic make-up’ of the respective tested cell lines. For example, TP53BP1 might be a regulator in one cell line but not in another for the simple reason that the later cell line harbors a TP53BP1 deletion or mutation or expression levels. In addition, while working with endogenous p53 mutations certainly has many advantages, comparing different mutants in different cell lines is again very much confounded by the ‘genetic make-up’ of the respective tested cell lines.

Our focus was slightly different, and we wanted to set out and specifically ask what the difference between p53 hotspot mutations are. Are they all the same or are there differences and importantly, are there differences between mutants and WT p53 and this can only be achieved when working in the same cellular background. In designing the screen, we have thus tried to optimise the inclusion of different hotspot mutants in an isogenic screening system. As such, we first depleted the endogenous WTp53 to minimise its interference and built the current isogenic system in the non-transformed RPE1 (“normal”) line.

However, as discussed above, we agree that the screen results will be validated in more cell lines carrying respective endogenous mutants.

Figure 1: Typo in the legends : Nultin ipv Nutlin

Response: We apologise for the typos. This is addressed in the current submission, along with improved figure legends to improve readability.

Figure 1b,1c : Show basal and Nutlin-3 induced MDM2 levels and in the overexpression cell lines; if WT-p53 is functional, MDM2 levels should be higher in WT-transduced cells compared to control or mt-p53 expressing cells.

Response: In the revised submission, we will include Western blotting probing MDM2 levels (antibody permitting); this is a part of the experiment proposed for Points 1 and 2.

Authors should explain which they name USP7 a negative regulator of p53, since it is supposed to de-ubiquitinate p53?!

Response: The effects of USP7 on WTp53 have indeed been difficult to elucidate (by Prof. Vogelstein PMID: 15118411, and PMID: 15058298, and seemingly opposite by Prof. Gu Wei, PMID: 15053880, and PMID: 11923872). However, consistent with Prof. Vogelstein group, the inhibition of USP7 (either by inhibitor or genetically via CRISPR in our studies), has resulted in elevated p53 level.

Figure 2E: the effect of MG132 on p53 seems to be very minimal on this Western blot; it would need quantification to be convincing...Quality of the blot is also not great.The fact that in control cells the levels of p53 R273H are not affected by MG132 treatment fits with Suppl Figure 2E, indicating that the proteasome has no effect on p53 R273H.

Response: We indeed noticed that while the proteasome pathway is largely implicated in the WTp53 screen, it has much reduced effects on R273H. Interestingly, the treatment of MG 132 also has limited effects using PANC-1 line (with endogenous R273H). We will repeat this experiment and provide quantifications and modify the text accordingly.

Suppl figure 3b, 3c, 3d:

Somehow, I have the feeling that the results from the western blots and the FACS do not match fully, although not all the time-points are shown in the various experiments.

For example, the FACS analysis (3b) suggests that in control-transduced cells after 16hr p53 is still increased. However, that is not clear at all in the Western blot (3c)

Is Suppl Figure 3d the quantification of 3c experiment? If so, in the blot also the 24 hrs should be shown.

The blot shown in Suppl Figure 3c suggests that CCDC6 expression increased upon irradiation. Do the authors agree with that? Would that explain why depletion of CCDC6 has more effect upon irradiation?

Suppl Figure S3E: if I am right, this is essentially the same type of experiment as shown in figure 2e, but analysis of p53-expression by Western blot. In that blot no real effect of MG132 on p53 levels could be seen. But here, in the FACS analysis, MG132 clearly increases the p53-Clover fusion levels; for me again that Western blot and FACS data do not neccesarily match.

Response: We apologise for the confusion. In the revised submission, we will improve the figure legends for better readability. Furthermore, in anticipation to the multiple cell lines involved in the revision, we will also clarify the cell lines in the figure.

With regards to the difference between the flow cytometry and WB data, we have generally observed the flow cytometry bimodal shifting to be more sensitive than the WB, e.g. a 50% shift in population (FACS) is reflected by a 15% reduction in WB (which may be partially explained as WB is a measurement across the cell population and FACS determines the p53-GFP levels of every cell and thus the shift of cells between peaks). Similarly, we noticed flow-cytometry based quantification by antibody staining the endogenous p53 yielded similar sensitivity (PMID: 29653964). As such, we will ensure the validation of hits is performed in two modes. For WB experiment, we will do so in two cell lines carrying the endogenous mutants as suggested by Reviewers #1 and 2.

Figure 3B: In the CCDC6 IP a very small amount of p53 can be found. I don't know how much input lysate compared to amount of lysate for IP is used, but the percentage of p53 found interacting with CCDC6 seems so marginal that is difficult to explain the effect of KO of CCDC6 in PANC1 cells.

And, the authors called it a 'reciprocal IP' (Suppl Figure 4a) after transfection of V5-tagged CCDC6 into PANC1 cells, but it actually is the same type of IP. Did the authors try to IP p53 and blot for CCDC6? That would be a reciprocal IP.

Response: We apologise for the confusion. In the revised submission, we will specify the portion of the lysates used for pre-IP (5% lysate) and IP (1 mg). As for the IP, we will also include the true reciprocal IP (IP p53, and blot for CCDC6).

Figure 3H: how can authors explain that basal levels of USP28 in control and CCDC6-KO cells transfected with control plasmid are more or less the same and not reduced in the CCDC6-KO cells?

Response: We will provide a better blot and quantification for this observation. In the current Fig 3H, the CCDC6-KO lane is slightly overladed as seen by the H3 loading control.

Figure 3I: Essentially the whole blot here is of low quality; especially the FBXO42 blot; is deletion of USP28 increasing FBXO42 protein levels, or is it just the quality of the blot? All in all it seems that FBXO42 is very low expressed in the used cell lines.

Response: We apologise for the confusion. In the revised submission, we will repeat and try to include higher quality WB, with more optimised condition for using the FBXO42 antibody.

FBXO42 messenger level is readily detected using qRT.

Figure 4B: I find it a bit surprising that USP7 is also found in the synthetic viability screen, since it has been shown that USP7 has many more essential targets and KO of p53 only partially rescues the development of USP7-KO mouse embryo's.

Response: We thank the reviewer for this critical observation. While the double p53-USP7 knockout line is viable, we acknowledge that it is amongst the top scored hits due to the large differential viabilities between WT and p53-null lines. In the revised submission, we will further clarify the screen analysis and the associated interpretation.

Figure 5: the authors nowhere show the efficacy of the guides targeting c16orf72. A Western blot showing the expression and the reduction upon expressing the guide-RNAs is essential.

Response: We thank the Reviewer for this suggestion. The efficacy of each guide has been verified using ICE (at the genomic level), and in the revised submission, we will include this critical information as part of the Figure S2F.

Figure 5E: First, here probably parental RPE1 cells have been used, but that is not stated. Second, the authors state 'only a slight increase in p53 levels upon siHUWE1'; I would say none compared to scrambled.

I know HUWE1 is a very huge protein, but the blot of HUWE1 is not convincing. I seem to be able to conclude that siMDM2 and siUSP7 reduces HUWE1 levels?

Response: We apologise for the confusion. In the revised submission, we will be specific of the cell line information on the figure, to improve the readability.

We agree with the reviewers that assessment of large protein by WB is often difficult but given that this band almost completely disappears upon HUWE1 knock-down, strongly argues that we are indeed assessing the endogenous HUWE1. We also agree that it is an interesting observation that the levels of HUWE1 seem to be slightly reduced upon knock-down of MDM2 and USP7. We will repeat this experiments and provide quantitative data for HUWE1 and p53. Of note, in the screen, HUWE1 also scored as a negative regulator of wt-p53 and did not quite reach statistical significance for the p53 mutants.

Regarding the relationship between C16orf72 and HUWE1, a newly published work (PMID: 35776542) seems to suggest that siHUWE1 has resulted in an increased C16orf72 level (termed HAPSTR1 in the paper), while siC16orf72 seemed to have no effect on HUWE1 level, although the stability of such a large protein by WB is often difficult to conclude.

Figure 5F, in relation to figure 5D. Here the author overexpress both c16orf72 and USP7, and find an interaction. The implication of that is not clear. If they want to make point of this interaction, they should have looked at endogenous proteins.

Response: We acknowledge the many concerns associated with coIP with ectopically, and especially overexpressed proteins in large quantity. In the revised submission, we will attempt to perform endogenous-based IP experiment (antibody permitting).

It is worrying that USP7 apparently was not one of the hits in the Mass-spec experiment of which results are shown in Figure 5D. Also in that experiment c16orf72 was overexpressed, and USP7 is very highly expressed in essentially all cell lines, so do the authors have an explanation?

Response: We indeed acknowledge this discrepancy. In the revised submission, we will attempt the coIP/IP using endogenous proteins (antibody permitting, or at least using endogenous target for one of the two partners). We also acknowledge that the limitation associated with the APMS for the detection of interactors.

Suppl. figure 5D is missing

Response: We apologise for the confusion. The Figure S5D was inconveniently placed at the top of the figure panel due to space limitation. In the revised submission, we will address this as a part of the overall readability improvement.

Reviewer #1, Significance.

The topic of the paper is of high interest given the relevance of p53 and its gain-of-function mutants in oncology, and the screens are well executed and clearly presented. In terms of novelty, FBXO42 has been linked to p53-degradation before, and c16orf72 was recently shown to be able to destabilize p53. However, the link between CCDC6 and p53 is novel and of interest, since they are both substrates of USP7 and are both regulators of the cell cycle.

We think the manuscript has potential to add something to the field, but would benefit greatly from a better understanding of the molecular underpinnings of their newly described mechanisms, as well as the conditions in which the mechanism is active.

Therefore, it might be advisable to shorten the manuscript, and go more in-depth in finding the mechanisms of regulation.

Response: We sincerely thank the reviewer for all the constructive critiques. We will incorporate them in to our revision.

Reviewer #2.

Reviewer #2 summary:

The paper describes several genome-wide CRISPR screens designed to identify regulators of p53 stability. The authors use a system in which p53 levels are marked by mClover expression, using RFP expression to normalise for gene expression changes.

Reviewer #2, major points.

1. The bimodal distribution of p53 expression levels in some reporter cell lines (G245S, R248Q, R248W and R273H) hampers the implementation of a robust readout and makes correct interpretation of the results challenging. While it is possible that the bimodal distribution indicates dynamic changes in p53 levels within one population, it also seems possible that a subclone of these cells have acquired additional alterations affecting p53 stability, and that the authors are screening a mixed population of two intrinsically different cell populations. This would make it difficult to interpret the results of the screen in these cell lines and may be a challenge when trying to identify something that has not already been highlighted on depmap.

Response: We thank the reviewer for this critical observation. We strongly believe that this bimodal distribution is actually an inherent property of the p53 mutants in these cells for the following reasons: (1) The observation of the similar bimodal appearance in cell lines harbouring corresponding endogenous mutant p53s (PMID: 29653964) suggest that these two populations are of biological significance. (2) We have established 5-10 clonal lines each from the G245S, R248Q, R248W and R273H p53 reporter line and all of them exhibit a bimodal distribution, making it very unlikely that these populations are all through stochastic outgrowth of sub-populations with spontaneous mutations/alterations. (3) The bimodal distribution is stable over several months to years in culture. If it were a spontaneous mutations giving rise to a clone with higher mutant p53 levels, we would likely expect that over time this clone takes over the population. (4) We observed that such a pool of bimodal cells could be “synchronised” (e.g. by Nutlin, or MDM2 knockout) to one population, and later return to and repopulate the other (e.g. Nutlin washoff, Figure 1B). (5) When we sort out a single cells from the upper or the lower peak, expand them, we obtain again populations of cells with the same bimodal distribution, indicating that this is a dynamic process. Thus, we believe that these two populations were rather intrinsic, such that a cell in the population may assume both states.

We also acknowledge the difficulties of screening using a bimodal population; however, we took advantage of these “bimodal” mutants and using FACS assessed the state of a single cell in relation to a genetic perturbation. Each guide has an equal chance of entering a cell that belongs to one of the two populations. If a gene knock-out really affects p53 levels, the cells with the respective guides enrich in one and deplete in the other population and the analysis comparing the guide abundances from these two peaks ensures the experiment are being perfectly internally controlled.

While many of the top scored hits from the resulting screens are known regulators, it is critical that we validate our hits in an independent system, such as the cell lines harbouring endogenous p53 mutations, echoed by both Reviewers #1 and 2.

The coverage of the sgRNA library (200x) is rather low for a negative selection screen, where a coverage of 500x would be more desirable. The FDR threshold is also rather lenient, a more stringent FDR threshold would seem more appropriate and shorten the list of potential hits.

Response: We thank the reviewer for this constructive suggestion. A higher coverage, along with a more stringent FDR, will ensure an even stronger confidence for the remaining individual hits. The present reporter-based enrichment screen and the synthetical viability drop-out screen used four guides per gene, and with 200x coverage for each guide.

In determining the coverage, we tried to reference recent successful screenings and apply earlier titration result for the 200x coverage (e.g. PMID: 26627737, PMID: 33465779, and reviewed in Nat Rev Methods Primers 2, 8 (2022). https://doi.org/10.1038/s43586-021-00093-4). While the threshold of FDR was often arbitrary, we fully agree that a more stringent FDR, which results in shortened hits list, may further boost the confidence of the hits, though also at the cost of losing potential hits due to collateral effects (e.g. guide efficiency).

We agree with this reviewer that a higher FDR, esp. at the hits that result in p53 stabilization, would make sense as any gene whose loss causes cellular or genotoxic stress, would likely lead at least in part to p53 stabilization. In the revised submission, we will adjust the FDR accordingly.

Although the study is focused on the regulation of p53 stability, there are no experiments to show that any of the manipulations alter the ubiquitination or degradation (half-life) of p53. The rescue of expression by proteasome inhibition is very modest (Figure 2E), suggesting the loss of expression may not be a reflection of degradation. A role for endogenous FBXO42 and C16orf72 in regulating the ubiquitination and half-life of endogenous p53 should be confirmed

Response: We thank the reviewer for this suggestion. In the revised submission, we will monitor the ubiquitination status and also degradation (cycloheximide-chase) experiments for R273H cells, with or without the genetic alteration of CCDC6/FBXO42/C16orf72.

Many p53 mutants are used for the initial screens, but very little validation is carried out to show that the apparent differences in factors regulating their stability persists in cells naturally expressing these mutants. For example, FBXO42 is identified as a protein required to maintain the stability of R273H, 248W and R248Q, but not R175H, G245S and R337H. While the authors show an association of CCDC6 and p53 in PANC1 cells (expressing 273H), it would be important to show a panel of R273H, 248W and R248Q expressing tumor cells and the response of p53 to FBXO42 and CCDC6 depletion, compared to similar experiments in a panel of R175H, G245S and R337H expressing tumor cells. Again, it would be important to show that any changes in protein levels are due to changes in protein stability.

Response: We thank the reviewer for this suggestion. In the revised submission, we will include validations in more cell lines carrying endogenous mutant p53s, with a focus on the R273H mutant. We will also try to involve a line with an endogenous p53 mutation that does not respond to FBXO42/CCDC6 alteration.

The potential hits should also be tested in wild type p53 expressing cells to confirm the specificity to mutant p53s.

Response: In the revised submission, we will include WB for WT lines (e.g. RPE1) upon genetic alteration of CCDC6 and FBXO42. This was already performed for C16orf72 (Figure 6D).

(6A) The role of C16orf72 in restraining p53 activity has been reported previously, as has the interaction with HUWE1 (including a new publication PMID: 35776542). The authors suggest an interaction between C16orf72 and USP7, although this should be shown with endogenous proteins. The relative importance of USP7 and HUWE1 binding is not explored. (6B) The effect of C16orf72 overexpression in promoting mammary tumors is impressive, although maybe the more interesting question is whether inhibition of C16orf72 expression can limit tumor development in this system.

Response to 6A: we are excited about the independent observations by other group(s) confirming similar results! As a part of our improvement for mechanistic work-up, in the revised submission, we will attempt to address, whether C16orf72’ regulation of p53 is dependent on USP7 and/or HUWE1, or other known E3s, such as MDM2.

(1) Whether the interaction of C16orf72 and HUWE1 or USP7 is required for the C16orf72 regulation of p53. Specifically, for example, we will perform epistasis experiments to test USP7’ or HUWE1’ ability to rescue the p53 levels in reporters upon ∆C16orf72. Due to the toxicity/lethality in WTp53 lines induced by the loss of C16orf72, we intend to test using R273H-reporter, or RPE1-line with ∆CDKN1A (p21) that is a synthetic viable rescue for ∆*C16orf72. *

(2) In the revised submission, we will attempt to perform endogenous-based C16orf72-USP7 IP experiment (antibody permitting).

6B. The effect of C16orf72 overexpression in promoting mammary tumors is impressive, although maybe the more interesting question is whether inhibition of C16orf72 expression can limit tumor development in this system.

Response: We are also equally excited about the in vivo result supporting the idea that C16orf72 overexpression in tumour-prone mice (Pik3caH1047R) mice harbouring WTp53 may accelerate tumour formations. In the revised submission, we will further support that this effect is specific to WTp53/C16orf72, by including data of the control cohort with p53-null background (LSL-Pi3kH1047R; p53Flox/Flox).

In regard to the effects of C16orf72-depletion in controlling tumour growth - we agree that this would be a very exciting avenue. Conditional C16orf72 mice are being made at the moment and these mice will allow us to comprehensively address this question. However, it will take several more month to generate and validate this line, and then another 2 breeding rounds to generate homozygous C16orf72fl/fl; Pik3caH1047R mice. In addition, the long time required to form tumours in the control mice with WTp53 (~250 days), it becomes not feasible for us to test whether the inhibition of C16orf72 could limit the tumour development, given the revision timeline. As such we respectfully believe that this would be beyond the scope of this manuscript.

Reviewer #2, Minor comments.

Figure 1b: The nutlin concentration stated in the methods section is wrong. Should be 10 µM instead of 10 nM (correct in figure legend).

Figure 6b: y-axis label is missing.

Figure 1e/f Legend: Should be FDR 0.5.

Response: We apologise for typos. The current submission has incorporated the corrections.

Figure 1c: Include results for a mutant that is not regulated by MDM2, such as R175H. Otherwise, as a standalone experiment, this figure doesn't add much.

Response: We thank the reviewer for this suggestion. In the revised submission, we will include R175H/R337H.

Figure 1h: While an UpSet plot is an elegant way to present unique and overlapping hits between different screens, Venn diagrams might be more 'accessible' to many readers and easier to understand.

Response: We thank the reviewer for this feedback. The choice of UpSet blot was largely motivated by the different categories involved, which made the area representation and the intersection of the conventional Venn diagram no longer feasible.

In the revised submission, we will improve our figure legend for the UpSet blot, to improve the readability.

Might be worth stating that mClover is an eGFP variant and can therefore be targeted by eGFP sgRNAs so that it is easier to understand the following:

o Page 5, paragraph 1: "We used the TKOv3 sgRNA library, which contains [...] 142 control sgRNAs targeting EGFP, LacZ and luciferase"

o Page 5, paragraph 2: "As expected, sgRNAs targeting p53 and mClover were the most depleted sgRNAs, [...]

Response: We thank the reviewer for this suggestion. We believe this will also improve the readability and have incorporated this into our current submission.

Reviewer #2, Significance.

Reviewer #2 (Significance (Required)):

This is an interesting concept and the results could provide a useful resource for groups interested in the regulation of p53. The authors chose to focus on candidate genes that could have been identified by looking for the top 30 p53 co-dependent genes on depmap (C16orf72 is #24 in this list and FBXO42 is #28, most of the other genes ranking above are already known as p53 regulators). While this validates the screen, it would have been interesting if the authors had identified and validated new regulators of p53 that were not apparent from previously published work.

Response: We thank the reviewer for all the thorough and constructive comments! In relation to the DepMap dataset, we are excited that many of the top hits from our screens are indeed top WTp53-correlators/anti-correlators (e.g. MDM2, USP28)!

While the DepMap dataset used cell fitness/viability to construct the genetic relation score, this assay may not effectively rule out the many regulators that could otherwise elicit their regulation of p53 via regulating the general cell response to cell cycle, stress, etc. In our screen systems (i.e. protein stability and synthetic viability screens), we attempted to focus on the regulators of p53-stability (post-translational), and further coupled it with the synthetic viability screens to concentrate on hits that have a more direct role in p53 regulation (e.g. MDM2, C16orf72).

One other difficulty to fully couple our screens to the DepMap dataset is due to the limited cell lines harbouring endogenous mutant p53s, e.g. R337H. This may also contribute to the uniqueness of the identified R337H-reporter specific hits (where cell lines harbouring R337H have not yet been included in the DepMap dataset), e.g. several Aminoacyl tRNA synthetases (SARS, YARS, etc) were identified as R337H unique regulators and subsequently verified using different guides in the reporter line, but could not be obtained via DepMap.

We largely see this paper as a resource for the p53 field and would like to publish it as soon as possible. In fact, when we started working on C16orf72 or CCDC6/FBXO42, these hits were not known for their ability to regulate p53. We will work up several other hits, but this would be beyond the scope of this paper and the first author’s Ph.D. thesis that needs to be completed under a timeline.

Reviewer #3.

Reviewer #3 summary:

The manuscript by Lu and coworkers performed genome wide CRISPR screens to search for genes that when knocked out, lead to p53 accumulation or degradation. Wt p53 and a panel of p53 hotspot mutants were chosen as reporter for the screen. The approach reassuringly identified many previously described regulators of p53 degradation, and also found a large set of new hits that many appear to be indirectly affecting p53 level.

A key step of this approach is the follow up functional and mechanistic study of the hits. To this end, the authors chose FBXO42 as a top hit that blocks mutant p53 degradation, and C16orf72 as a top hit that promotes wt/mutant p53 degradation.

Overall the functional data for FBXO42 is disappointing. FBXO42 knockout has quite modest effect on mutant p53 level (~50% reduction). The knockout also showed some effect on p53 mRNA level (~25% reduction), making the determination of mechanism difficult. It does not appear to be a promising targeting for reducing mutant p53 level and gain of function activity in tumor cells.

We thank the reviewer for this constructive comment! We will address this in the revision, as proposed in Point #3.

The C16orf72 finding unfortunately lost some novelty because it was independently identified as a p53 regulator in a recent study using CRISPR screening (PMID: 33660365). However, the repeated identification is reassuring and the current work provides more convincing functional data, showing C16orf72 knockout increase wt p53 level, inhibits cell proliferation specifically in p53+/+ cells, and overexpression of C16orf72 reduce wt p53 level and accelerates progression of a breast tumor mouse model. Their results suggest C16orf72 is a biologically relevant regulator of p53 in cancer development. In order to provide a reasonable amount of new information and set it further apart from the published study, some biochemical analysis looking into the mechanism of C16orf72 will be helpful.

Reviewer #3 Major and Minor comments:

Specific comments:

1. There appears to be a mix up in the figure legend for Fig.1A describing line 1 and 2.

Response: We sincerely apologise for the mix up in the figure legend! In the current submission, this has been fixed.

Fig.2. Data for some p53 mutants mentioned in the text cannot be found in the main figure 2D and supplemental figure S3A.

Response: We apologise for having not included the R175H and R337H mutants in Supplemental Figure S3A. In the revised version, we will include these two mutants.

Fig.2 E-F. The effects of FBXO42 and CCDC6 KO on endogenous mutant p53 level is small (~50% decrease). Given that mutant p53 accumulates at high levels, whether a 50% decrease has meaningful effect on its gain of function activities is questionable. The knockouts also caused a ~25% decrease in p53 mRNA (FigS3F) which makes the mechanism quite difficult to investigate further.

Response: We agree with the reviewer that the current data makes it difficult to conclude the mechanism. Given the design of our reporter, we still believe that the regulations could largely be at the post-translational level. In our revised version, we plan to exam the ubiquitination status of p53 upon losses of CCDC6/FBXO42, and also monitor the p53 degradation via cycloheximide chase.

To further address whether this reduced level of mutp53 has biological impacts, we plan to test it in the tumour cell context. Given the difference in migration capability observed between PANC-1 and PANC-1-∆p53 line (e.g. PMID: 35439056), we plan to also evaluate the migration pattern of PANC-1, with the presence and absence of FBXO42/CCDC6 (controlled by similar FBXO42/CCDC6 loss in PANC-1- ∆p53 background). Furthermore, in tissue culture, although there is only marginal to no difference in cell growth rate between many mutant p53 lines (e.g. PANC-1) and their ∆p53 line, we plan to test whether a reduced serum or nutrient level could exacerbate the difference, and hence further be used to monitor the difference resulted from the loss of FBXO42/CCDC6.

Fig.3B. The IP experiment using p53 shRNA and control shRNA should be done by IP of p53 followed by CCDC6 western blot. If CCDC6 IP is used as in the figure, then a CCDC6 shRNA knockdown sample should be compared to control shRNA. The current data does not rule out the possibility that CCDC6 antibody can nonspecifically pull down some p53.

Response: We apologise for the confusion. In the revised version, we will include the proper reciprocal IP, with IP of endogenous p53 (R273H) followed by blotting of CCDC6.

Fig.3D. The in vitro pull down experiment needs specificity controls such as non affected R175H p53 core domain. The data presented would suggest that MBP-FBXO42c captured more than 1:1 molar ratio of R273H core domain, which is unusual for specific binding unless there is aggregation of p53.

Response: We thank the reviewer for this constructive comment! In the revised version, we will incorporate this, by repeating the in vitro pull-down assay including a non-p53 control protein.

To increase the impact of the current study, the authors could provide more mechanism insight on how C16orf72 regulates p53 level, which was also missing in the other published study. For example, addressing whether C16orf72 effect is dependent on MDM2. Does it cooperate with MDM2 to ubiquitinate p53. Does it promote p53 ubiquitination in the absence of MDM2, since it interacts with HUWE1. Does it act by recruiting usp7 to stabilize MDM2.

Response: we thank the reviewer for this very constructive and thorough comment! In our revised version, we will attempt these assays and incorporate them into the submission.

Together with our response to Reviewer #2, Point #6, in the revised submission, we will attempt to address if C16orf72 regulation of p53 is dependent on MDM2 or HUWE1.

(1) Whether the interaction of C16orf72 and HUWE1, or C16orf72 and USP7 is required for the C16orf72 regulation of p53. Specifically, for example, we will perform epistasis experiments to test HUWE1’ or USP7’s ability to rescue the p53 levels in reporters upon the loss of C16orf72 (∆C16orf72). Due to the toxicity/lethality in WTp53 lines induced by the loss of C16orf72, we intend to test using the R273H-reporter, or RPE1-line with ∆CDKN1A (p21) that is a synthetic viable rescue for ∆*C16orf72. *

(2) Whether C16orf72 dependent upon or cooperate with MDM2 in regulating p53.

We will first probe whether C16orf72 overexpression increased the p53 ubiquitination, and then decide whether overexpression of C16orf72 has additive effects to MDM2 overexpression in regulating p53 levels.

We previously observed that overexpressing C16orf72 could not rescue the R273H level resulted from losing MDM2 (using flow-cytometry in R273H-reporter-∆MDM2), and as such, we plan to test the C16orf72-MDM2 relation in the MDM2-proficient context.

The manuscript is in a form extremely unfriendly to review, text, figures and legends are all split up at multiple locations, the pdf figures are very sluggish to scroll.

Response: We sincerely apologise for the inconvenience. In the current submission, we have split the submission into three separate files, (1) main text, (2) main figures, and (3) supplemental figures, along with (4) supplemental tables as individual EXCELs. We will also reduce the resolution of a few images, so the overall higher resolution is retained, while still fitting into the file size limit.

Reviewer #3 (Significance (Required)):

The work is significant in identifying a functionally relevant regulator of p53 stability.

Response: we thank the reviewer again for the very constructive feedback!

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Referee #1

Evidence, reproducibility and clarity

Summary:

In this manuscript by Lu et al., the authors describe some CRISPR screens and protein-protein interaction screens to identify novel regulators of wild-type p53 and mutant p53 function and stability. Besides generating a wealth of data, they discover FBXO42-CCDC6 as positive regulators of the some p53 hot-spot mutants, including R273H mutant p53, but not of all p53 mutants tested and also not of wild-type, indicating selectivity. Furthermore, the found C16orf72(TAPR1) as a negative regulator of p53 stability. Mechanistically, the authors claim a direct interaction between FBXO42 and CCDC6 and p53, but the importance of these interactions has not been shown. On the other hand the authors suggest that the FBXO42/CCDC6 regulate p53 via destabilization of USP28, but also the mechanism has not been worked out. For c16orf72, they show that it interacts with USP7, but no relevance of this interaction is shown either.

Major points

One very important point for me is that the authors do not show the levels of expression of p53 in the p53-mClover stable cell lines. It is known that overexpressed p53 is usualy more stable than endogenous levels of wt-p53. Therefore, I think it is necessary that the authors show the levels of the p53-mClover fusion proteins in the stably transduced cell lines compared to endogenous p53 levels in the parental RPE1 cells and also compared to the endogenous levels of R273H mutant in the PANC-1 cells.

Also the functionality of the wild-type p53-mClover fusion is questionable, at least not shown. One would expect that the overexpression of a functional wt-p53 in p53-KO cells will affect the survival of the RPE1 cells. In Figure 5A the authors show that depletion of MDM2 or C16ORF72 is toxic for the RPE1 cells in a p53-dependent manner, indicating that elevated levels of p53 cannot be handled by these cells. So, experiment(s) showing that the wt-p53/mClover fusion is functional is needed.

A second important point is that the 'verification' of the hits from the screens is only done in one cancer cell line, PANC-1, with mutant p53. I would have like to see at least one other cell line with another p53 mutant endogenously expressed that is also regulated by FBXO42/CCDC6.

For many of the p53-mutants, a bimodal expression is observed. In the FBXO42- and CCDC6-depleted cells, the equilibrium shifts towards more negative cells but the levels in the two populations itself don't change (while for example for USP28 depletion also the right peak shifts further up, Fig S4E). Is there any correlation with the cell cycle and p53 expression? And can the authors exclude that FBXO42 and CCDC6 are involved in cell cycle progression and hereby influence p53 indirectly (by combining PI staining with Clover-p53 for example).

• The authors claim that the FBXO42-CCDC6 axis regulates stability specifically some p53-mutants, including R273H-mutant, in a manner involving USP28. But USP28 regulates all forms of p53, not just some mutants version. How can the authors reconcile this apparent contradiction?

On a similar note, the authors show that FBXO42 and CCDC6 interact with p53, but not USP28. Do FBXO42 and CCDC6 interact with each other and with USP28? And is the interaction with p53 specific for the R273H version? This part of the mechanism is very poorly defined and the Co-IPs are not very convincing or relevant for the proposed model.

Minor points

The mechanisms of p53 regulation may vary greatly in different cell lines. Can the authors discuss why they choose to do the screen with different mutants, rather than with different cell lines expressing these same mutant endogenously? .

Figure 1: Typo in the legends : Nultin ipv Nutlin

Figure 1b,1c : Show basal and Nutlin-3 induced MDM2 levels and in the overexpression cell lines; if WT-p53 is functional, MDM2 levels should be higher in WT-transduced cells compared to control or mt-p53 expressing cells. Authors should explain which they name USP7 a negative regulator of p53, since it is supposed to de-ubiquitinate p53?!

Figure 2E: the effect of MG132 on p53 seems to be very minimal on this Western blot; it would need quantification to be convincing...Quality of the blot is also not great. The fact that in control cells the levels of p53 R273H are not affected by MG132 treatment fits with Suppl Figure 2E, indicating that the proteasome has no effect on p53 R273H.

Suppl figure 3b, 3c, 3d:

Somehow, I have the feeling that the results from the western blots and the FACS do not match fully, although not all the time-points are shown in the various experiments. For example, the FACS analysis (3b) suggests that in control-transduced cells after 16 hr p53 is still increased. However, that is not clear at all in theWestern blot (3c) Is Suppl Figure 3d the quantification of 3c experiment? If so, in the blot also the 24 hrs should be shown. The blot shown in Suppl Figure 3c suggests that CCDC6 expression increased upon irradiation. Do the authors agree with that? Would that explain why depletion of CCDC6 has more effect upon irradiation? Suppl Figure S3E: if I am right, this is essentially the same type of experiment as shown in figure 2e, but analysis of p53-expression by Western blot. In that blot no real effect of MG132 on p53 levels could be seen. But here, in the FACS analysis, MG132 clearly increases the p53-Clover fusion levels; for me again that Western blot and FACS data do not neccesarily match.

Figure 3B: In the CCDC6 IP a very small amount of p53 can be found. I don't know how much input lysate compared to amount of lysate for IP is used, but the percentage of p53 found interacting with CCDC6 seems so marginal that is is difficult to explain the effect of KO of CCDC6 in PANC1 cells. And, the authors called it a 'reciprocal IP' (Suppl Figure 4a) after transfection of V5-tagged CCDC6 into PANC1 cells,but it actually is the same type of IP. Did the authors try to IP p53 and blot for CCDC6? That would be a reciprocal IP.

Figure 3H: how can authors explain that basal levels of USP28 in control and CCDC6-KO cells transfected with control plasmid are more or less the same and not reduced in the CCDC6-KO cells?

Figure 3I: Essentially the whole blot here is of low quality; especially the FBXO42 blot; is deletion of USP28 increasing FBXO42 protein levels, or is it just the quality of the blot? All in all it seems that FBXO42 is very low expressed in the used cell lines.

Figure 4B: I find it a bit surprising that USP7 is also found in the synthetic viability screen, since it has been shown that USP7 has many more essential targets and KO of p53 only partially rescues the development of USP7-KO mouse embryo's.

Figure 5: the authors nowhere show the efficacy of the guides targeting c16orf72. A Western blot showing the expression and the reduction upon expressing the guide-RNAs is essential. Figure 5E: First, here probably parental RPE1 cells have been used, but that is not stated. Second, the authors state 'only a slight increase in p53 levels upon siHUWE1'; I would say none compared to scrambled. I know HUWE1 is a very huge protein, but the blot of HUWE1 is not convincing. I seem to be able to conclude that siMDM2 and siUSP7 reduces HUWE1 levels? Figure 5F, in relation to figure 5D. Here the author overexpress both c16orf72 and USP7, and find an interaction. The implication of that is not clear. If they want to make point of this interaction, they should have looked at endogenous proteins. It is worrying that USP7 apparently was not one of the hits in de Mass-spec experiment of which results are shown in Figure 5D. Also in that experiment c16orf72was overexpressed, and USP7 is very highly expressed in essentially all cell lines, so do the authors have an explanation?

Suppl. figure 5D is missing

Referees cross-commenting

I agree essentially with all comments of Reviewer #2. Especially the major points 3 and 4. The use of more cell lines expressing endogenous mutant p53 is very important. In addition, I can agree with almost all comments of Reviewer #3. The effects especially of FBXO42 ablation are rather minimal, so relevance is questionable.

Significance

Nature and Significance

Compare to existing literature

The topic of the paper is of high interest given the relevance of p53 and its gain-of-function mutants in oncology, and the screens are well executed and clearly presented. In terms of novelty, FBXO42 has been linked to p53-degradation before, and c16orf72 was recently shown to be able to destabilize p53. However, the link between CCDC6 and p53 is novel and of interest, since they are both substrates of USP7 and are both regulators of the cell cycle.

We think the manuscript has potential to add something to the field, but would benefit greatly from a better understanding of the molecular underpinnings of their newly described mechanisms, as well as the conditions in which the mechanism is active.

Therefore, it might be advisable to shorten the manuscript, and go more in-depth in finding the mechanisms of regulation.

Watson (1907) is pointing out one of the greatest ways to learn about human/animal behavior. This is done through observation. In 1907 this may not be as clear and simple due to the fact that psychology was not something relevant during those times.

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Reply to the reviewers

Manuscript number: RC-2021-01204R

Corresponding author(s): Alexander, Aulehla

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

*The paper by Miyazawa and colleagues addresses a key question: How is changed metabolic activity sensed and to induce changes in developmental programs. In recent years, there is more and more indication that metabolism is not only a dull workhorse synthesizing the building blocks for new cells and providing chemical energy, but that metabolic activity itself has also a regulatory role. How this precisely works is largely unknown and even also unexplored in higher cells. From early insights obtained in microbes, it seems that certain metabolites - possibly reflecting metabolic activity (i.e. flux) - could be metabolic signals that feedback into cellular regulation. *

*The current paper takes this idea now to developmental processes, where the authors found that the glycolytic metabolite fructose-1,6-bisphosphate is a flux-dependent signal that interferes with developmental processes. This is a very exciting finding, as it indicates that this metabolite not only has a regulatory function in microbes but also in mouse during mesoderm development. *

*Answering the question how such a flux-dependent metabolite mechanistically interferes with the developmental processes is an enormously difficult. Compared to other mechanistic studies, where deleting genes, modifying genes, and changing protein expressions will usually do the trick, here, perturbing metabolite levels is extremely challenging, particularly if such perturbations need to be carried out in a way that nothing else is perturbed. Researchers, who are not overly familiar with metabolism, usually underestimate the difficulty with targeted and insightful perturbation of metabolism. *

*To this end, the authors of this paper need to be congratulated for a very well carried out study with very solid data, and excellent control experiments. The authors open up a new path towards understanding how embryo mesoderm development is regulated by metabolic activity. In particular, they show that that glycolytic flux, FBP and important developmental phenotypes as well as protein localization changes are linked. As normal with a complex metabolism-based story as this one, there is always more that could be done. Yet, the results are highly important to be reported now such that the field as a whole can build on these interesting results and to explore the exciting path further that has been opened by the authors. Thus, I strongly recommend publishing these findings: The data generated by the authors are accompanied by the required control experiments. The conclusions drawn are very solid. I do not have any major concerns but just a number of minor suggestions that the authors could consider in a revised version of the manuscript. *

*Minor: *

1. • At the end of the introduction, the authors stated their original goal. As it is phrased, it is unclear whether this goal has been obtained or not. They might want to consider replacing the last introductory sentence by a sentence stating what the reader can find in this paper.*

1. We agree with the reviewer and have rephrased accordingly (line 112–117):

“In this study, our goal was therefore to first determine in vivo sentinel metabolites during mouse embryo PSM development. We then combined genetic, metabolomic and proteomic approaches to investigate how altered glycolytic flux and metabolite levels impact developmental signaling and patterning processes.”

• Data from Fig 3: If you plot the lactate secretion vs the FBP levels of the controls and the overexpression experiment, would the control and the overexpression data lie on one line (maybe if combined with the data shown in Fig 1A)?*

2. As the reviewer suggested, it is of great interest to check whether lactate secretion and FBP levels show a similar correlation in control and cytoPfkfb3 embryos, considering that cytoPfkfb3 overexpression lifts the upper limit of glycolytic capacity and FBP levels (revised Figure 3B, 3E). As the reviewer suggested, we plotted FBP levels against lactate secretion and fitted a linear regression line onto control samples (please see the Figure R1 below). The new plot shows that lactate secretion and FBP levels in cytoPfkfb3 embryos lie on the linear regression line derived from wild-type samples, highlighting that a correlation between lactate secretion and FBP levels is maintained even in cytoPfkfb3 embryos. We now included this new plot in the revised Figure S4C and modified the text accordingly (line 474-477):

“In addition, FBP levels showed a linear correlation with lactate secretion in control explants, and such a correlation was maintained even in cytoPfkfb3 explants (Figure S4C).”

Figure R1. Correlation between lactate secretion and FBP levels in PSM explants. Linear regression line (a grey line) was derived from the data of control samples cultured in 0.5–25 mM glucose (black circles; from Figure 1A and 3E). The data from cytoPfkfb3 embryos cultured in 2.0–10 mM glucose (from Figure 3B and 3E) are shown as red rectangles.

• Maybe the authors could attempt an experiment like the following one: Chose the strongest phenotype observed and test a combination of overexpressing cytoPfkfb3 and reducing extracellular glucose level at the same time? *

3. We agree this suggested experiment is important to show that the phenotype in cytoPfkfb3 embryos is indeed dependent on glycolytic flux and have already addressed this specific point in our manuscript, see results in Figure 4B and 5A in our original manuscript. The results show that the phenotypes in cytoPfkfb3 explants, i.e. reduction in somite formation and downregulation of Msgn mRNA expression occur in a glucose dose-dependent manner. Since in this embryonic context, we show that glucose concentration impacts glycolytic flux (see increased lactate production upon glucose titration in Figure 3B), our findings support the conclusion that the effect of cytoPfkfb3 overexpression is flux-dependent and not due to the overexpression per se. Based on the reviewer's feedback, we have modified the text to clarify and highlight this critical point (line 339–345):

“Combined, these results show that cytoPfkfb3 overexpression results in reduced segment formation, arrest of the segmentation clock oscillations and downregulation of Wnt signaling, in a glucose-dose dependent manner. As glucose concentration impacts, in turn, glycolytic flux (Figure 1A, 3B), these findings suggest that these phenotypes are flux-dependent and are not a mere result of cytoPfkfb3 overexpression.”

• Can the proteomics experiments shown in Fig. 6 be repeated with high and low extracellular glucose? High glucose should yield high FBP levels and one would then expect to see the same as with the experiment where at 2 mM glucose 20 mM extracellular FBP were added. Is this the case? *

4. We agree with the reviewer that based on the findings, one would expect the phenotype, i.e. in this case translocation of proteins, to correlate with FBP levels. Two of our results are of note in this regard.

First, our data indicates that in order to see the effect on protein localization, high levels of FBP have to be reached. Accordingly, we find that Pfkl becomes depleted from the nuclear-cytoskeletal fraction in cytoPfkfb3 explants when cultured in 10 mM glucose but not (visibly) in 2.0 mM glucose (Figure 7D). Corresponding to this, FBP levels in cytoPfkfb3 explants show a significant increase (about 3-fold) from 2.0 to 10 mM glucose conditions (revised Figure 3E).

Second, in control samples, FBP levels saturate in high glucose conditions. FBP levels in control samples do not further increase when glucose concentration is increased from 10mM to 25mM, and thus it does not become as high as in cytoPfkfb3 embryos cultured in 10 mM glucose (revised Figure 3E).

Therefore, in order to reveal the translocation, it requires an experimental strategy that leads to significantly increased FBP levels, such as in cytoPfkfb3 explants with high glucose condition, or alternatively, direct supplementation of FBP.

As also pointed out by the other reviewers, we are experimentally generating controlled conditions that exceed the physiological range which the embryo is exposed to. Accordingly, our data does not constitute evidence that under physiological conditions an alteration of protein localization in response to change in glycolytic flux and FBP levels occurs, at a smaller scale.

We regard our approach as a first step to reveal potential mechanisms and so far hidden possible responses to changes in metabolic flux. In order to see minor changes in translocation upon small changes in glycolytic-flux/FBP levels, more quantitative approaches, such as live-imaging of tagged proteins, will need to be developed. We hence decided to include these discussion in our revised manuscript (line 657-666):

“Of note, the translocation of proteins was observed only when high levels of FBP were reached upon direct FBP supplementation or cytoPfkfb3 overexpression with high glucose (Figure 6, 7). Future studies hence need to investigate whether flux-dependent change in protein localization occurs upon moderate and more physiological changes in glycolytic-flux/FBP levels. To this end, the development of more quantitative approaches, such as live-imaging of tagged enzymes and the development of metabolite biosensors, are needed.”

• While the authors quantified proteins in different compartments, I was wondering whether they also looked for whole-embryo protein expression changes? *

5. We have not done protein expression analysis using whole embryos, or other isolated tissues in this study. This is indeed a potentially interesting future experimental comparison.

• Throughout the manuscript, the authors state the glucose levels or cytoPfkfb3 changes the glycolytic flux. While I tend to agree with this, it is important to note that the authors have not directly measured glycolytic flux, but use the amount of accumulated lactate as a proxy. I think it is important to add this disclaimer at important points in the manuscript, such that readers are aware of this point. *

6. We fully agree with the reviewer and now have added the following sentence in the first result section to make this point clearer to the reader (line 126-128):

"Throughout this study, we used quantification of secreted lactate as a proxy for glycolytic flux due to the inability to directly measure flux in embryonic tissues."

Another aspect for changing FBP levels could be connected on what was found in yeast, where the FBP levels were found to oscillate with the cell cycle (https://pubmed.ncbi.nlm.nih.gov/31885198/). Could this be connected with the pattern formation here?

7. This is indeed an interesting aspect to discuss; in the absence of experimental evidence connecting the observed pattern formation and cell cycle (though some classic work had suggested its existence) we have decided to omit the discussion of this potential link.

• Line 606: The mentioned review article also covers yeast. As such, maybe the authors should replace the term "bacteria" with "microbes"? *

8. We modified our manuscript accordingly.

Reviewer #1 (Significance (Required)):

**Referees cross-commenting**

As I mentioned in my comment, targeted metabolic perturbations are extremely difficult. Perturbing a metabolite level without at the same time perturbing the flux through this pathways is difficult (of not impossible). Also, the opposite is the case.

I am not sure whether experiments as the one suggested by reviewer 2 (comment 1) will really lead to results from which further conclusions can be drawn. Furthermore, there does not need to be a linear correlation between the extracellular glucose concentration and metabolic flux/FBP levels (as my reviewer colleague implies). Thus, I am not sure whether doing this experiment makes sense, or would lead to strengthened conclusions.

Reviewer 2 also states "The lack of proven mechanism for the activity of FBP might restrict the real general impact of this work." I agree that we do not know the downstream targets of FBP, but finding them would likely require many years of additional work. Such work will not be initiated if this paper is not published, and it would be a pity if it would be further delayed. I feel that the evidence is strong enough that FBP has an important role and with this paper published, it will motivate others to look for the downstream targets.

Reviewer 3 makes the point: "Given that FBP levels are highly correlated with extracellular glucose levels (which impact glycolytic flux )(TeSlaa and Teitell, 2014) the authors should elaborate on why progressive increase in extracellular glucose does not affect PSM patterning, in the same way that increasing FBP levels does. " Here, I feel my reviewer colleague might be overlooking that in biochemistry molecular interactions typically reach a saturation at some point. The correlation between extracellular glucose and glycolytic flux has likely only a range where these two measures linearly correlate. Similarily, the correlation between glycolytic flxu and FBP likely also exists only within a certain range, and finally FBP levels and the downstream targets likely also only linearly interact within bounds. Thus, the absence of a correlation at "extremes" does by no mean mean that what the authors propose is incorrect. In fact, it just shows what you expect from biomolecular interactions that there a limits to linear correlations.

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

*Summary. *

*The work described in this paper first searches for potential sentinel metabolites of glycolytic flux, focusing on the process of somitogenesis during mouse embryonic development. By measuring the levels of different metabolites in the presomitic mesoderm (PSM) of E10.5 mouse embryos cultured in the presence of three different glucose concentrations, the authors identify 14 metabolites whose concentration rises with increasing glucose concentration in the culture medium. Among them, they selected fructose 1,6-bisphosphate (FBP) for further analyses, as it showed the highest linear correlation with extracellular glucose concentrations. They then show that addition of FBP to the incubation medium of cultured embryo tails interfere with somitogenesis and tail extension in a concentration-dependent fashion. In addition, they show that this effect is exacerbated when extracellular glucose levels are increased. By analyzing specific targets of Wnt and Fgf signaling, the authors also show that addition of FBP down-regulates both signaling pathways in the PSM. They then use a genetic trick (ubiquitous overexpression of cytoPfkfb3) to increase FBP levels by allosteric activation of Pfk (the enzyme that produces FBP) in developing embryos. When tails from these transgenic embryos were cultured in vitro and exposed to various glucose concentrations somitogenesis was affected in a way resembling the effects of FBP on cultured tails from wild type embryos. The authors then go on to determine the subcellular localization of different proteins in tails incubated in the presence of various FBP concentrations to identify that some enzymes involved in the glycolytic pathway (and they specifically focus on Pfkl and Aldoa) are excluded from nuclear fractions at high FBP concentrations. The authors conclude that FBP functions as a flux-signaling metabolite connecting glycolysis and PSM patterning, potentially through modulating subcellular protein localization. *

*Major comments *

*I think that in general the work described in this manuscript has been performed to the highest technical standards. However, I do not think that I can agree with the authors' conclusions (that FBP connects glycolysis with PSM patterning and that subcellular localization of glycolytic enzymes play a role in this process), which in my opinion go way beyond what can be proven by the data provided. *

*1- Explants incubated with external glucose concentrations up to 25 mM have no obvious defects on somitogenesis or on the segmentation clock as determined by LuVeLu cycling activity. Under these conditions, explants are expected to contain very high FBP levels if this metabolite keeps its linear relationship with external glucose (in this work it was not measured beyond 10 mM glucose in the medium, where FBP concentration was already very high). This contrasts with the phenotypes observed upon exogenous supplementation of FBP, which affects somitogenesis already at 2 mM glucose. These latter results are at odds not only with the lack of phenotypic alterations under high glucose conditions, but also with the observation that exogenous addition of fructose 6-phosphate (F6P), the substrate of Pfk enzymes to generate FBP, does not alter somitogenesis. The authors take the absence of effects by incubation with F6P as a control of the specificity of FBP. However, as F6P is the natural substrate of Pfk, it is possible that supplementation of F6P also leads to an increase of FBP but in a way closer to a physiological condition. Therefore, I find it essential to determine FBP levels in tails incubated in the presence of increasing amounts of F6P, as if it increases FBP levels, similarly to what the authors described for the tails incubated with increasing glucose concentrations, it will have important implications to the interpretation of the work presented in this manuscript. *

9. We agree with the reviewer and to directly address this central point, we have performed an extended, additional experiment, collecting 375 embryos to quantify FBP levels under five conditions with three biological replicates.

There are two major results that we highlight here: First, we found that addition of F6P did not lead to increased FBP levels compared to control samples cultured in 10 mM glucose, which is in stark contrast to cytoPfkfb3 embryos cultured in 10 mM glucose (revised Figure 3E). Second, while increasing glucose concentration is mirrored by elevated FBP levels as we reported, we find clear evidence of saturation above a concentration of 10mM glucose: increasing glucose to 25mM does not increase FBP levels further (revised Figure 3E).

This saturation effect seen in glucose titration, but also the absence of elevated FBP upon F6P addition, might be expected outcomes because, as also the reviewer 1 pointed out in the response, Pfk is commonly considered to be a rate-limiting enzyme in the glycolytic pathway. We now have the direct experimental data supporting this hypothesis and thank the reviewers to have initiated this additional (very involved..) experiment.

This new data allows us to conclude more firmly on the correlation between FBP levels and phenotype: at high FBP levels, which are seen in cytoPfkfb3 samples, we observe PSM patterning defects. These high levels are not reached even at 25mM glucose or upon F6P addition, due to the saturation at the level of PFK enzymatic step. Hence, while glucose titration does elevate FBP significantly until this saturation, FBP levels are not as high as in cytoPfkfb3 samples. As a correlative finding, we see that only those conditions with very high FBP levels, or the direct addition of high levels of FBP, cause the arrest of segmentation clock activity. At moderately elevated FBP levels, observed in control explants with high glucose or in cytoPfkfb3 explants with low glucose, clock activity continues and we find a quantitative effect at the level of gene expression, i.e. Wnt signaling target downregulation (Figure S3, 5A).

The new data has been included in the revised manuscript and the text has been adjusted accordingly:

• (Result Part, line 245–254) "Consistently, we found that cytoPfkfb3 overexpression lifted the upper limit of FBP levels in PSM cells (Figure 3E, S4B, S4C). In control explants, FBP levels did not increase further when glucose concentration was increased from 10 mM to 25 mM. It was also the case when control explants were cultured in 20 mM of F6P (Figure 3E). These results indicate that the Pfk reaction carries a (rate-)limiting role for glycolytic flux and FBP levels, and that cytoPfkfb3 overexpression hinders the flux-regulation function of Pfk."

• (Discussion Part, line 551–573) “Our findings suggest that flux-regulation at the level of Pfk is critical to keep FBP steady state levels within a range compatible with proper PSM patterning and segmentation. In agreement with such a rate-limiting function for Pfk, we found in glucose titration experiments that FBP levels saturated and did not further increase at glucose levels above 10 mM (Figure 3E). Along similar lines, the supplementation of high concentrations of the Pfk substrate F6P did not result in a significant increase of FBP levels, again compatible with a rate-limiting function at the level of Pfk (Figure 3E). The upper limit of glycolytic flux and FBP levels can be experimentally increased by cytoPfkfb3 overexpression (Figure 3B, 3E). We interpret the data as evidence that cytoPfkfb3 overexpression compromises the flux-control function of Pfk and hence much higher FBP (and secreted lactate) levels are reached. Such a drastic increase in glycolytic flux and FBP levels correlates with a severe PSM patterning phenotype (Figure 4), which resembles the phenotype induced by supplementation of high dose of FBP (Figure 2). Our results in mouse embryos hence provides evidence that flux regulation by Pfk, an evolutionary conserved role present from bacteria to humans, serves to maintain FBP levels below a critical threshold.”

*The main difference between the experiments involving FBP supplementation and those involving high glucose concentrations or exogenous F6P addition is that in the later two cases increase in FBP would be restricted to the tissue(s) expressing Pfk, whereas upon FBP supplementation this metabolite would hit any tissue, regardless of whether or not it would ever be physiologically exposed to this molecule. In the case of the PSM, this might be relevant because it has been shown that there is a gradient of glycolysis, being high at the caudal tip and becoming lower at more anterior regions of the PSM, most likely mirroring the distribution of Pfk activity. Exogenous administration of FBP would flatten the gradient, which could lead to alterations in PSM patterning, whereas glucose (and eventually F6P) would not as they would increase FBP locally in the area where it is normally activated, keeping the natural gradient. *

*On the basis of these arguments, to which extent does FBP connect glycolysis and somitogenesis under physiological conditions? *

10. First, we would like to clarify that while indeed glycolytic activity is graded along the PSM, as other and we reported previously (reported in Bulusu et al., 2017 and Oginuma et al., 2017), the baseline expression of the entire glycolytic machinery (from glucose transport to lactate production) is very high, in all PSM cells. Hence, we see that cells all along the entire PSM have very active glycolysis, the posterior PSM being even more active.

For this and related reasons, our interpretation about the difference seen between glucose titration/F6P addition on one side, and FBP addition/cytoPfkfb3 addition on the other side, is based on the role of Pfk in controlling either flux levels or dynamics in all PSM cells.

Hence, while we agree that we generate experimental conditions that allow FBP levels to surpass those found in control embryos, we would like to highlight the fact that even moderate changes in flux does result in very robust functional consequences on gene expression (Figure S3, 5), as we show in this work.

We can currently not fully address the first point raised, i.e. the role of graded flux/graded metabolite levels, due to the experimental limitations. Such a study requires, for instance, the generation of metabolite biosensor reporter lines in order to be able to monitor these changes dynamically, in space and time.

*ESSENTIAL ADDITIONAL EXPERIMENT related to point #1: Measure FBP from PSM explants incubated under various exogenous concentrations of F6P. *

11. We have performed this suggested experiment, which required the collection of n=375 embryos cultured under the various conditions and analysis by LC-MS to quantify metabolites. The outcome was indeed very informative (please refer to our response #9).

*ANOTHER EXPERIMENT THAT COULD BE INFORMATIVE: measure FBP levels in PSM incubated under different glucose concentrations but instead of using the whole PSM together, dividing the PSM in posterior, medium and anterior parts (similarly to what was done in Oginuma et al, 2017, reference in the manuscript) to see if there is a gradient in FBP activation. *

12. While in principle we agree that this experiment could be informative, we consider the proposed experiment beyond the scope of this work and technically very challenging (although possible). With a similar motivation, the development of metabolite biosensors is an alternative route that we are pursuing for future studies (for the detail, please refer to our response #10).

*2- A similar argument could be presented for the results with the cytoPfkfb3 transgenics, as they are based on global artificial overactivation of Pfk, in addition to other possible effects of the ectopic activity of cytoPfkfb3, which were not controlled. Also, while the phenotypic alterations in the PSM in vitro, most particularly in the experiments involving incubation of the tails, are rather strong, the reported effects on somitogenesis in vivo are minor, also questioning the contribution of the in vitro conditions to the final phenotypic effects observed throughout the manuscript. *

13. First of all, we would like to emphasize that the phenotype seen in cytoPfkfb3 embryos, i.e. the reduction of segmentation and downregulation of Wnt-target gene expression, occurs in a glucose dose dependent manner (Figure 4B and 5A). Hence, it is not the overexpression of cytoPfkfb3 per se that can account for the effects seen. But rather, increased glycolytic flux caused by the combination of transgene expression with high glucose results in functional consequences.

In addition, ‘other possible effects’ that the reviewer is referring to should be evident in all transgenic embryos, irrespective of glucose dose. To the contrary, transgenic embryos cultured in low glucose conditions appear unaltered to control embryos.

Second, we agree that we need to distinguish between strong phenotypes, visible at the level of clock arrest, and milder phenotypes, visible at the level of quantitative gene expression changes. It is important to note that the moderate phenotype, i.e. the quantitative gene expression changes seen in posterior PSM, are seen upon the addition of FBP at moderate levels and upon in glucose titration within the physiological concentration range, as well as in cytoPfkfb3 embryos. We take this as evidence that the effects seen in cytoPfkfb3 transgenic embryos reflect a common response also seen under physiological conditions.

To extend this argument to the in vivo setting, we have performed additional experiments using a genetic mouse model for diabetes. As shown in our previous submission, cytoPfkfb3 transgenic animals do not exhibit a drastic in vivo phenotype when dissected at embryonic day 10.5. One interpretation of this finding is that since the cytoPfkfb3 phenotype is glucose and flux-dependent, the in vivo flux is low, reflecting low glucose concentrations described in vivo. To test the effect of increased flux in cytoPfkfb3 embryos in vivo, we therefore crossed the transgenic mice into a diabetic model called Akita, in which a point mutation in the Insulin2 gene causes high maternal glucose levels (Yoshioka et al., 1997; Wang et al., 1999). Using this experimental setup, we tested whether transgenic embryos in Akita diabetic females would manifest in vivo phenotypes.

Indeed, we found that cytoPfkfb3 transgenic embryos developing in Akita diabetic females showed significantly increased cases of neural tube closure defects (50% of cytoPfkfb3 embryos) and developmental delay (control: 38 somites vs. cytoPfkfb3: 34 somites at E10.5), defects not seen in transgenic cytoPfkfb3 embryos from control females (please refer to Figure R2 below). This dependency of the in vivo phenotype on maternal glucose conditions again highlights that the defects observed in cytoPfkfb3 embryos are not due to the expression of cytoPfkfb3 per se, but are rather directly linked to increased/unregulated glycolytic flux.

We included the new in vivo data in the revised Figure S5D-E and modified the text accordingly.

Figure R2. In vivo phenotype of cytoPfkfb3 embryos grown in diabetic Akita females. (A) The number of somites in control (Ctrl) and cytoPfkfb3 (Tg) E10.5 embryos grown in diabetic Akita females. (B) In situ hybridization of Msgn, Uncx4.1, and Shh mRNAs in Ctrl and Tg E10.5 embryos grown in diabetic Akita females (ss, somite stage; scale bar, 500 µm).

In conclusion, combining the arguments in the two previous comments, to which extent the results from the addition of FBP or from the transgenic activation of Pfk are not artefactual phenotypes without real physiological relevance?

14. In our view, two main conclusions, both in vivo and in vitro, can be drawn based on the result we obtained:

First, we find that a moderate increase in glycolytic flux, within the physiological range, leads to a quantitative and consistent change in gene expression, such as downregulation of Wnt target genes (Figure S3, 5). Such a phenotype was the result of either glucose titration or culturing cytoPfkfb3-transgenic embryos in low glucose concentration.

In these conditions, while overall PSM patterning is qualitatively normal, we do find consistent changes at quantitative level, i.e. gene expression changes, which are also mirrored by a reduced rate of segmentation (Figure 4B). A detailed analysis of the quantitative changes at the level of segmentation clock dynamics is being carried out and will be presented in a dedicated follow up study.

Second, we find that a very significant increase in FBP levels, i.e. when cytoPfkfb3 transgenic animals are cultured in high glucose conditions or when samples are cultured in high levels of FBP, PSM patterning is qualitatively altered and segmentation clock ceases to oscillate. In this case, we agree that it is not a physiological condition, as such high levels of flux and FBP are not reached in control samples which have intact flux regulation by Pfk. Nevertheless, such an experimental condition can be insightful, as it very clearly reveals the potential link between glycolysis, clock activity, PSM patterning and the Wnt signaling pathway.

It is the combination between the moderate and the more severe effects, observed both in vitro, and now also in vivo using the Akita model (see above), that we take as evidence for an intrinsic, physiological link between glycolytic activity, PSM patterning and signaling.

*3- The authors seem to give a strong functional meaning to the absence of Pfkl and Aldoa from the nuclear fraction in tails incubated with exogenous FBP, suggesting a "moonlighting" function of these enzymes under FBP regulation. In addition to the purely speculative nature of this interpretation (there is no proof for such activity or even an attempt to test it), the data provided is also difficult to interpret for various reasons. *

15. We fully agree that we do not show a functional role for either the nuclear localization of enzymes or their dynamic change in sub-cellular localization and have tried to express this clearly in the original manuscript:

• (Result Part, line 382-388) “While we have not been able to address the functional consequence of specific changes in subcellular localization, such as the nuclear depletion of Pfkl or Aldoa when glycolytic flux is increased, these results pave the way for future investigations on the mechanistic underpinning of how metabolic state is linked to cellular signaling and functions.”

• (Discussion Part, line 575-577): “While future studies will need to reveal if nuclear localization of glycolytic enzymes is linked to their moonlighting functions or metabolic compartmentalization…”

Based on this comment by the reviewer, we have further emphasised this point in the revised manuscript(line 635-639):

“While we do not have any direct functional evidence so far for a functional role of nuclear localized glycolytic enzymes, our findings do raise the question whether their subcellular compartmentalization is linked to a non-metabolic, moonlighting function.”

The protein levels in nuclear fractions are clearly much lower than those in the cytoplasm (this is best seen in the blots of Figure 6D). Does this represent similar subcellular distribution of these enzymes throughout the tissue or the different levels result from the presence of the enzymes in the nucleus of only a subset of the cells? This might be of importance to understand the possible relevance of the subcellular distribution of those enzymes. All the analyses were done on bulk tissue and, therefore, it is not possible to distinguishing between these possibilities. As the authors have antibodies for these enzymes, they could try to perform immunofluorescence analyses, which would provide spatial data.

16: We agree that a spatially resolved analysis of the subcellular localization of these various enzymes is needed. Unfortunately, the immunofluorescence experiments that we performed did not yield clear, reliable results and hence we can’t provide the answer at this time.

*In addition to this, it would be important to determine Pfkl and Aldoa subcellular localization in explants incubated with different external concentrations of glucose, which in a way reproduces better possible physiological effects (see point 1), to see if under those conditions high FBP also affects subcellular distribution of those enzymes. *

17: Please find our response under #4 (attached below), as this important point was also raised by the reviewer 1.

*(Our response #4) *

*#4. We agree with the reviewer that based on the findings, one would expect the phenotype, i.e. in this case translocation of proteins, to correlate with FBP levels. Two of our results are of note in this regard. *

*First, our data indicates that in order to see the effect on protein localization, high levels of FBP have to be reached. Accordingly, we find that Pfkl becomes depleted from the nuclear-cytoskeletal fraction in cytoPfkfb3 explants when cultured in 10 mM glucose but not (visibly) in 2.0 mM glucose (Figure 7D). Corresponding to this, FBP levels in cytoPfkfb3 explants show a significant increase (about 3-fold) from 2.0 to 10 mM glucose conditions (revised Figure 3E). *

*Second, in control samples, FBP levels saturate in high glucose conditions. FBP levels in control samples do not further increase when glucose concentration is increased from 10mM to 25mM, and thus it does not become as high as in cytoPfkfb3 embryos cultured in 10 mM glucose (revised Figure 3E). *

*Therefore, in order to reveal the translocation, it requires an experimental strategy that leads to significantly increased FBP levels, such as in cytoPfkfb3 explants with high glucose condition, or alternatively, direct supplementation of FBP. *

As also pointed out by the other reviewers, we are experimentally generating controlled conditions that exceed the physiological range which the embryo is exposed to. Accordingly, our data does not constitute evidence that under physiological conditions an alteration of protein localization in response to change in glycolytic flux and FBP levels occurs, at a smaller scale.

We regard our approach as a first step to reveal potential mechanisms and so far hidden possible responses to changes in metabolic flux. In order to see minor changes in translocation upon small changes in glycolytic-flux/FBP levels, more quantitative approaches, such as live-imaging of tagged proteins, will need to be developed. We hence decided to include these discussion in our revised manuscript (line 657-666):

“Of note, the translocation of proteins was observed only when high levels of FBP were reached upon direct FBP supplementation or cytoPfkfb3 overexpression with high glucose (Figure 6, 7). Future studies hence need to investigate whether flux-dependent change in protein localization occurs upon moderate and more physiological changes in glycolytic-flux/FBP levels. To this end, the development of more quantitative approaches, such as live-imaging of tagged enzymes and the development of metabolite biosensors, are needed.”

SUGGESTED ADDITIONAL EXPERIMENTS related to point #3:

*3a- Analysis of subcellular localization of Pfkl and Aldoa by Immunofluorescence. This analysis is not limited by the amount of biological material available, so it could be applied to different experimental conditions. *

18. We addressed this point in our response #15.

*3b- Subcellular distribution of Pfkl and Aldoa in explants exposed to different exogenous glucose concentrations. As this involves wild type embryos, it can be done following similar protocols as in figures 6 and 7 of the manuscript. *

19. We addressed this point in our response #16.

4- The results from the work presented in this manuscript would indirectly indicate a negative relationship between glycolysis and somitogenesis. This contrasts with previous reports indicating the essential role of aerobic glycolysis for the same process. There is no explanation for this apparent (and important) contradiction (the authors only comment the discrepancy between the data provided in this paper and previous reports in what concerns the relationship between glycolysis and Wnt signalling, although they also do not provide an explanation).

19. We cannot resolve this discrepancy, but now offer a more detailed discussion, also based on the additional data we obtained.

First, it is important to point out that we have performed additional experiments to substantiate this part of the work, i.e. a transcriptome analysis with control and cytoPfkfb3 explants cultured in 10 mM glucose. We decided to focus on an early time point, i.e. three-hour after incubation, in order to increase the chance to score the primary response of PSM cells upon changes in glycolytic flux. In addition, our nanostring data in Figure S3 shows that glucose titration can change the expression levels of some Wnt-targets in both directions, i.e. decreasing glucose upregulates their expressions while increasing glucose downregulates their expressions. Again, this analysis was done at short time-scales to score the immediate effect.

One possible explanation regarding the difference to Oginuma et al. could indeed be the late time point of analysis in their study, i.e. 16-hour after culture. This difference in sampling time, i.e. 3-hour vs. 16-hour after culture, is of particular importance given the dynamic nature of metabolic and signaling responses.

We have added a sentence to explain this point in more detail (line 608-617):

“This discrepancy could relate to the time point of analysis: while Oginuma et al. mainly focused on analyzing samples 16-hour after metabolic changes, we chose to score the effects of altered glycolytic flux/FBP levels already after a three-hour incubation, with the goal to capture the primary response of PSM cells. Whether the difference in sampling time underlies the observed difference is yet unknown, but both studies highlight that Wnt signaling is responsive to glycolytic flux, supporting a tight link between metabolism and PSM development.”

Minor comments.

*It was not specified the tissue used for the Western blot analyses (was it the PSM alone, the whole tails including somites, etc). This is of relevance to comment #3. *

20. PSM explants without somites were cultured for one/three-hour and were subjected to subcellular protein fractionation. This information is now included in the revised method section.

Reviewer #2 (Significance (Required)):

-The work described in this manuscript identifies FBP as a sentinel metabolite for the glycolytic flux. This, itself has the potential to be important for different processes in which differences in glycolysis makes a difference, although I do not think that this will be relevant for the developmental process on which the authors focused their study (see major comments #1 and 2). Indeed, the lethality of global transgenic cytoPfkfb3 expression (although it was not analyzed if it was during development of in postnatal stages, or the cause of this lethality) but with very minor effects on somitogenesis in vivo supports this conclusion.

21. Please see our detailed comments also based on the newly added in vivo experiments done with the Akita diabetic mouse model in our responses #9–14.

*- The potential moonlighting activity of Pfk (connected with specific subcellular localization), is an interesting idea but so far does not go beyond pure speculation. This is prone to the typical double edged effect of stimulating research in that direction but also the potential negative effect of being taken for granted without rigorous proof. *

22: We have added a statement to highlight the nature of this finding and the requirement for follow up studies both in this and other contexts. Please refer to our response #15 for the details.

• The importance of metabolism in general and glycolysis in particular for somitogenesis and axial extension has been recently reported (the relevant papers are cited in the manuscript) and therefore the work described in this manuscript extends those studies. Also, the recent observations that metabolic process can influence cell activity beyond their participation on the classical pathways in which they are involved, including processes apparently as distant as epigenetic regulation of gene activity (see for instance Tarazona and Pourquie, 2020, Dev Cell 54, 282-292), is opening new perspectives to the study of the influence of metabolism on physiological and pathological processes (championed by cancer and immunological response). It also provides a link between control mechanisms across large scale phylogeny, from procaryotes to eukaryotes.

-In principle, the potential audience for this work could be wide, as the interest in understanding the involvement of metabolism in the regulation of physiological and pathological processes has been growing over the last years. However, the lack of proven mechanism for the activity of FBP might restrict the real general impact of this work. In this regard, the suggestion that it might control some type of still unknown moonlighting activity of Pfk is so far totally speculative.

• I am a developmental biologist with strong focus on mechanisms of somitogenesis and axial extension in vertebrate embryos. There is no part of this work for which I do not feel competent to evaluate.

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

*Summary - *

*In the present manuscript, Miyazawa and colleagues explore the role of glycolytic flux on embryonic development by using presomitic mesoderm (PSM) patterning as a model. *

*First, the authors examined the steady-state levels of central carbon metabolism metabolites in PSM explants. Explants were cultured in various concentrations of glucose and subjected to gas chromatography mass spectrometry (GC-MS). These experiments allowed the identification of metabolites (such as lactate, 3PG, and FBP) that exhibit a linear correlation with glucose levels and can therefore serve as sentinel metabolites for glycolytic flux in PSM cells. Among the metabolites identified, fructose 1,6-bisphosphate (FBP) showed the strongest linear correlation with glucose levels and was used to inform the design of subsequent experiments. *

*Second, to elucidate the functional role of FBP on PSM patterning, the authors supplement the media used to culture PSM explants with various concentrations of FBP and: *

*- analyze the dynamics of Notch signaling (a critical player in mesoderm segmentation during embryogenesis) using real-time imaging of the LuVeLu reporter; *

*- assess gene expression patterns using in situ hybridization of candidate genes. *

*The authors find that supplementation with FBP, but not F6P or 3PG, impairs mesoderm segmentation and disrupts the activity of the segmentation clock in the posterior PSM. Furthermore, FBP supplementation led to the reduced expression of FGF- and WNT-target genes Dusp4 and Msgn, respectively. *

*Third, the authors generate a conditional cytoPfkfb3 transgenic mouse line in which a cytoplasmic form of the Pfkfb3 enzyme is overexpressed. Pfkfb3 can promote glycolysis, and more importantly, leads to increased levels of FBP in a glucose-dependent manner. The authors find that cytoPfkfb3 transgenic PSM explants contain higher levels of FBP and secrete lactate at higher levels when compared to control explants. Importantly, cytoPfkfb3 transgenic PSM explants exhibit impaired somite formation and reduced expression of Msgn (but not Dusp4) in a glucose-dependent manner when compared to control explants. *

Finally, the authors investigate changes in protein subcellular localization in their pharmacological and genetic models of FBP-driven glycolytic flux activation. This was prompted by previous reports on the changes in subcellular localization of glycolytic enzymes (Hu et al., 2016). To this end, the authors perform proteome-wide cell-fractionation analyses in drug-treated and cytoPfkfb3 transgenic PSM explants and find that certain glycolytic proteins exhibit altered subcellular localization in both cases (albeit in different fractions).

*Major concerns: *

*- (Re: Results from Fig. 2 and Fig. S1.) *

*o Given that FBP levels are highly correlated with extracellular glucose levels (which impact glycolytic flux )(TeSlaa and Teitell, 2014) the authors should elaborate on why progressive increase in extracellular glucose does not affect PSM patterning, in the same way that increasing FBP levels does. This is especially important given the claim that FBP is a sentinel metabolite of glycolytic flux. *

23. This important point was also addressed by the reviewer 2, so please see our responses that are also listed under #9, #10, #14 (attached below).

*(Our response #9) *

*We agree with the reviewer and to directly address this central point, we have performed an extended, additional experiment, collecting 375 embryos to quantify FBP levels under five conditions with three biological replicates. *

*There are two major results that we highlight here: First, we found that addition of F6P did not lead to increased FBP levels compared to control samples cultured in 10 mM glucose, which is in stark contrast to cytoPfkfb3 embryos cultured in 10 mM glucose (revised Figure 3E). Second, while increasing glucose concentration is mirrored by elevated FBP levels as we reported, we find clear evidence of saturation above a concentration of 10mM glucose: increasing glucose to 25mM does not increase FBP levels further (revised Figure 3E). *

This saturation effect seen in glucose titration, but also the absence of elevated FBP upon F6P addition, might be expected outcomes because, as also the reviewer 1 pointed out in the response, Pfk is commonly considered to be a rate-limiting enzyme in the glycolytic pathway. We now have the direct experimental data supporting this hypothesis and thank the reviewers to have initiated this additional (very involved..) experiment.

*This new data allows us to conclude more firmly on the correlation between FBP levels and phenotype: at high FBP levels, which are seen in cytoPfkfb3 samples, we observe PSM patterning defects. These high levels are not reached even at 25mM glucose or upon F6P addition, due to the saturation at the level of PFK enzymatic step. Hence, while glucose titration does elevate FBP significantly until this saturation, FBP levels are not as high as in cytoPfkfb3 samples. As a correlative finding, we see that only those conditions with very high FBP levels, or the direct addition of high levels of FBP, cause the arrest of segmentation clock activity. At moderately elevated FBP levels, observed in control explants with high glucose or in cytoPfkfb3 explants with low glucose, clock activity continues and we find a quantitative effect at the level of gene expression, i.e. Wnt signaling target downregulation (Figure 5A, S3). *

The new data has been included in the revised manuscript and the text has been adjusted accordingly:

- (Result Part, line 245–254) "Consistently, we found that cytoPfkfb3 overexpression lifted the upper limit of FBP levels in PSM cells (Figure 3E, S4B, S4C). In control explants, FBP levels did not increase further when glucose concentration was increased from 10 mM to 25 mM. It was also the case when control explants were cultured in 20 mM of F6P (Figure 3E). These results indicate that the Pfk reaction carries a (rate-)limiting role for glycolytic flux and FBP levels, and that cytoPfkfb3 overexpression hinders the flux-regulation function of Pfk."

- (Discussion Part, line 551–573) “Our findings suggest that flux-regulation at the level of Pfk is critical to keep FBP steady state levels within a range compatible with proper PSM patterning and segmentation. In agreement with such a rate-limiting function for Pfk, we found in glucose titration experiments that FBP levels saturated and did not further increase at glucose levels above 10 mM (Figure 3E). Along similar lines, the supplementation of high concentrations of the Pfk substrate F6P did not result in a significant increase of FBP levels, again compatible with a rate-limiting function at the level of Pfk (Figure 3E). The upper limit of glycolytic flux and FBP levels can be experimentally increased by cytoPfkfb3 overexpression (Figure 3B, 3E). We interpret the data as evidence that cytoPfkfb3 overexpression compromises the flux-control function of Pfk and hence much higher FBP (and secreted lactate) levels are reached. Such a drastic increase in glycolytic flux and FBP levels correlates with a severe PSM patterning phenotype (Figure 4), which resembles the phenotype induced by supplementation of high dose of FBP (Figure 2). Our results in mouse embryos hence provides evidence that flux regulation by Pfk, an evolutionary conserved role present from bacteria to humans, serves to maintain FBP levels below a critical threshold.”

(Our response #10)

*#10. First, we would like to clarify that while indeed glycolytic activity is graded along the PSM, as other and we reported previously (reported in Bulusu et al., 2017 and Oginuma et al., 2017), the baseline expression of the entire glycolytic machinery (from glucose transport to lactate production) is very high, in all PSM cells. Hence, we see that cells all along the entire PSM have very active glycolysis, the posterior PSM being even more active. *

*For this and related reasons, our interpretation about the difference seen between glucose titration/F6P addition on one side, and FBP addition/cytoPfkfb3 addition on the other side, is based on the role of Pfk in controlling either flux levels or dynamics in all PSM cells. *

Hence, while we agree that we generate experimental conditions that allow FBP levels to surpass those found in control embryos, we would like to highlight the fact that even moderate changes in flux does result in very robust functional consequences on gene expression (Figure S3, 5), as we show in this work.

*We can currently not fully address the first point raised, i.e. the role of graded flux/graded metabolite levels, due to the experimental limitations. Such a study requires, for instance, the generation of metabolite biosensor reporter lines in order to be able to monitor these changes dynamically, in space and time. *

(Our response #14)

*In our view, two main conclusions, both in vivo and in vitro, can be drawn based on the result we obtained: *

*First, we find that a moderate increase in glycolytic flux, within the physiological range, leads to a quantitative and consistent change in gene expression, such as downregulation of Wnt target genes (Figure S3, 5). Such a phenotype was the result of either glucose titration or culturing cytoPfkfb3-transgenic embryos in low glucose concentration. *

In these conditions, while overall PSM patterning is qualitatively normal, we do find consistent changes at quantitative level, i.e. gene expression changes, which are also mirrored by a reduced rate of segmentation (Figure 4B). A detailed analysis of the quantitative changes at the level of segmentation clock dynamics is being carried out and will be presented in a dedicated follow up study.

*Second, we find that a very significant increase in FBP levels, i.e. when cytoPfkfb3 transgenic animals are cultured in high glucose conditions or when samples are cultured in high levels of FBP, PSM patterning is qualitatively altered and segmentation clock ceases to oscillate. In this case, we agree that it is not a physiological condition, as such high levels of flux and FBP are not reached in control samples which have intact flux regulation by Pfk. Nevertheless, such an experimental condition can be insightful, as it very clearly reveals the potential link between glycolysis, clock activity, PSM patterning and the Wnt signaling pathway. *

*It is the combination between the moderate and the more severe effects, observed both in vitro, and now also in vivo using the Akita model (see above), that we take as evidence for an intrinsic, physiological link between glycolytic activity, PSM patterning and signaling. *

- (Re: Fig. 2A and Fig. 2B)

*o The authors should be consistent with the glucose concentrations for the experiments where they assess the dynamics of Notch signaling (Figure 2A) and gene expression (Figure 2B) or otherwise elaborate on why different concentrations are used for these assays. *

24: We agree that ideally the experimental parameters should be as consistent as possible. In regards to the control glucose concentration used in this study, both 0.5 mM and 2.0 mM glucose were used. It reflects that over the years, minor adjustments in the experimental protocol were made, i.e. we now use 2.0 mM glucose as standard setting for all experiments, while previously, 0.5 mM glucose was used (see Bulusu et al., 2017). This change is based on the observation of a slightly improved culture outcome, in terms of reporter gene expression. We have confirmed that the developmental outcome and also effects seen upon addition of FBP are consistent at 0.5 mM and at 2.0 mM glucose. We made a note in the methods section to explain this point (line 1082-1084):

“Basal culture condition was 0.5 mM glucose at the beginning of this study but was later switched to 2.0 mM glucose which yields a slightly improved reporter gene expression. No major difference was observed in the effects of FBP between these glucose conditions.”

*- (Re: Results from pharmacological and genetic models of increased FBP levels) *

*o The authors state that FBP-driven impairment of mesoderm segmentation is most pronounced in the undifferentiated PSM cells (in the posterior-most end of the explants) and is, therefore, unlikely to be due to a toxic effect that might otherwise affect the whole explant. While this is a reasonable assumption, it does not discount the possibility that the spatial specificity of the effect of FBP could be driven primarily by increased cell death in the posterior end of the explant. Thus, the authors should test whether cell death underlies the mesoderm patterning defects seen in PSM explants subjected to increased FBP levels. *

25. We have performed immunostaining of active caspase-3 in explants cultured for three-hour in medium containing 0.5 mM glucose and 20 mM FBP and found no difference between control and FBP-treated explants (please refer to the Figure R2 below). This qualitative result does not indicate a major effect via cell death in the tail bud region (i.e. posterior PSM) as the underlying reason for the observed phenotype. We included the new data in the revised Figure S2C and adjusted the text accordingly.

Figure R3. Immunostaining of active caspase-3 in PSM explants. Explants were cultured for three hours in the presence or absence of 20 mM of FBP. Neural tubes were outlined by white dotted lines.

*- (Re: Gene expression experiments/analyses) *

*o This study would benefit greatly from transcriptomic analysis of wt and cytoPfkfb3 transgenic PSM explants (and/or transcriptomic characterization of FBP-treated vs. control PSM explants). The candidate approach used to assess gene expression (through in situ hybridization) may not be sufficient to conclude that cytoPfkfb3 over-expression leads to the downregulation of Wnt signaling (a claim the authors make at the beginning of the manuscript). *

26. We fully agree with the reviewer’s comment. We have now performed RNA-sequencing (RNAseq) analysis using control and cytoPfkfb3 explants cultured in 10 mM glucose, importantly after three hours of incubation in order to score early effects at transcriptome level (please refer to Figure R4).

We found clear evidence that many Wnt-target genes (i.e. Axin2, Cdx4, Dact1, Dkk1, Mixl1, Msgn1, Sp5, Sp8, T) were significantly downregulated in cytoPfkfb3 explants, supporting the conclusion that Wnt signaling activity is downregulated in cytoPfkfb3 explants under high glucose condition.

Furthermore, in order to examine similarities between the effects of cytoPfkfb3 overexpression and FBP supplementation, we also performed RNAseq analysis with explants treated with high dose of FBP or F6P. FBP supplementation resulted in downregulation of Wnt target gene expression (i.e. Dact1, Dkk1, Mixl1, Lef1, Sp5, T, Tbx6), mirroring the effects seen in cytoPfkfb3 samples. Such a response was not detected in F6P-treated explants.

Combined, these new data significantly strengthen our conclusion that an increase in glycolytic flux and FBP levels leads to downregulation of Wnt signaling activity. The new data is now included in the revised Figure 5C–E and adjusted the texts accordingly.

Figure R4. Transcriptome analysis of control (Ctrl) and cytoPfkfb3 (TG) PSM explants. PSM explants were cultured for three hours under different culture conditions. (A) Effects of cytoPfkfb3 overexpression on gene expression under 10 mM glucose condition. (B, C) Effects of 20 mM FBP (B) or F6P (C) on gene expression under 2.0 mM glucose condition. Wnt-target genes that were significantly downregulated in cytoPfkfb3 or FBP/F6P-treated explants are highlighted in blue.

*- (Re: Results related to the neural tube closure defects in cytoPfkfb3 transgenic embryos) *

*o The section of the manuscript describing the neural tube closure defects in cytoPfkfb3 transgenic embryos is superficial, lacks detail, and distracts from the focus of the study. Perhaps the data and text on neural tube closure defects should be included as supplemental information. *

27: We agree with the reviewer that in the previous version, this data appeared isolated. It also connects with the point raised by the reviewer 2 about the in vivo significance of our findings. To address both these points, we have now performed additional in vivo experiments using a diabetic mouse model (Akita) to directly test the in vivo consequence of cytoPfkfb3, which interestingly links to the previous findings of neural tube defects. Please see our response #13 for the details (attached below):

(Our response #13)

*First of all, we would like to emphasize that the phenotype seen in cytoPfkfb3 embryos, i.e. the reduction of segmentation and downregulation of Wnt-target gene expression, occurs in a glucose dose dependent manner (Figure 4B and 5A). Hence, it is not the overexpression of cytoPfkfb3 per se that can account for the effects seen. But rather, increased glycolytic flux caused by the combination of transgene expression with high glucose results in functional consequences. *

In addition, ‘other possible effects’ that the reviewer is referring to should be evident in all transgenic embryos, irrespective of glucose dose. To the contrary, transgenic embryos cultured in low glucose conditions appear unaltered to control embryos.

*Second, we agree that we need to distinguish between strong phenotypes, visible at the level of clock arrest, and milder phenotypes, visible at the level of quantitative gene expression changes. It is important to note that the moderate phenotype, i.e. the quantitative gene expression changes seen in posterior PSM, are seen upon the addition of FBP at moderate levels and upon in glucose titration within the physiological concentration range, as well as in cytoPfkfb3 embryos. We take this as evidence that the effects seen in cytoPfkfb3 transgenic embryos reflect a common response also seen under physiological conditions. *

*To extend this argument to the in vivo setting, we have performed additional experiments using a genetic mouse model for diabetes. As shown in our previous submission, cytoPfkfb3 transgenic animals do not exhibit a drastic in vivo phenotype when dissected at embryonic day 10.5. One interpretation of this finding is that since the cytoPfkfb3 phenotype is glucose and flux-dependent, the in vivo flux is low, reflecting low glucose concentrations described in vivo. To test the effect of increased flux in cytoPfkfb3 embryos in vivo, we therefore crossed the transgenic mice into a diabetic model called Akita, in which a point mutation in the Insulin2 gene causes high maternal glucose levels (Yoshioka et al., 1997; Wang et al., 1999). Using this experimental setup, we tested whether transgenic embryos in Akita diabetic females would manifest in vivo phenotypes. *

Indeed, we found that cytoPfkfb3 transgenic embryos developing in Akita diabetic females showed significantly increased cases of neural tube closure defects (50% of cytoPfkfb3 embryos) and developmental delay (control: 38 somites vs. cytoPfkfb3: 34 somites at E10.5), defects not seen in transgenic cytoPfkfb3 embryos from control females (please refer to Figure R2 below). This dependency of the in vivo phenotype on maternal glucose conditions again highlights that the defects observed in cytoPfkfb3 embryos are not due to the expression of cytoPfkfb3 per se, but are rather directly linked to increased/unregulated glycolytic flux.

We included the new in vivo data in the revised Figure S5D-E and modified the text accordingly.

*Figure R2. In vivo phenotype of cytoPfkfb3 embryos grown in diabetic Akita females. (A) The number of somites in control (Ctrl) and cytoPfkfb3 (Tg) E10.5 embryos grown in diabetic Akita females. (B) In situ hybridization of Msgn, Uncx4.1, and Shh mRNAs in Ctrl and Tg E10.5 embryos grown in diabetic Akita females (ss, somite stage; scale bar, 500 µm). *

• (Re: Conclusions of the study)

o A previous study by Oginuma et al., 2020 provided strong evidence for a mechanism underlying the positive regulation of Wnt signaling by glycolysis (initiated by the elevation of intracellular pH) in the chick embryo tailbud. As mentioned in the discussion, the results of the present study are not consistent with this mode - and this contradiction is not sufficiently resolved. This is a concern, given that the evidence that cytoPfkfb3 inhibits Wnt signaling is sparse (see above).

28: This important point was also raised by the reviewer 2, please see our response as listed under #19 (attached below).

(Our response #19)

*We cannot resolve this discrepancy, but now offer a more detailed discussion, also based on the additional data we obtained. *

*First, it is important to point out that we have performed additional experiments to substantiate this part of the work, i.e. a transcriptome analysis with control and cytoPfkfb3 explants cultured in 10 mM glucose. We decided to focus on an early time point, i.e. three-hour after incubation, in order to increase the chance to score the primary response of PSM cells upon changes in glycolytic flux. In addition, our nanostring data in Figure S3 shows that glucose titration can change the expression levels of some Wnt-targets in both directions, i.e. decreasing glucose upregulates their expressions while increasing glucose downregulates their expressions. Again, this analysis was done at short time-scales to score the immediate effect. *

*One possible explanation regarding the difference to Oginuma et al. could indeed be the late time point of analysis in their study, i.e. 16-hour after culture. This difference in sampling time, i.e. 3-hour vs. 16-hour after culture, is of particular importance given the dynamic nature of metabolic and signaling responses. *

We have added a sentence to explain this point in more detail (line 608-617):

“This discrepancy could relate to the time point of analysis: while Oginuma et al. mainly focused on analyzing samples 16-hour after metabolic changes, we chose to score the effects of altered glycolytic flux/FBP levels already after a three-hour incubation, with the goal to capture the primary response of PSM cells. Whether the difference in sampling time underlies the observed difference is yet unknown, but both studies highlight that Wnt signaling is responsive to glycolytic flux, supporting a tight link between metabolism and PSM development.”

*o Another discrepancy lies in the lack of an observable phenotype when culturing mouse PSM explants at very low glucose concentrations (e.g., 0.5 mM in Fig. 2A). Oginuma et al. observed clear disruptions to embryonic elongation and somite formation at a glucose concentration equal to 0.83 mM. Would this be due to species-specific mechanisms? Furthermore, while the authors focus on sentinel metabolites (such as FBP), experiments involving direct manipulation in glycolysis could resolve some of these inconsistencies. *

29: Indeed species specific differences in the requirement for glucose are to be expected. Our extensive analysis shows that at 0.5mM glucose, segmentation and elongation proceeds (Bulusu et al., 2017).

Regarding the second point, we have outlined several strategies to directly perturb glycolysis, i.e. glucose titration (mirrored by increase in lactate secretion) and by genetic targeting of the rate-limiting enzyme, Pfk. Glucose titration in wild-type embryos corresponds to the experiment the reviewer suggested, and we again found that higher glucose (i.e. higher flux) leads to down regulation of several Wnt-target genes (Figure S3). Of note, also in cytoPfkfb3 explants the effects are glucose-dose dependent (again mirrored by increase of lactate secretion), clearly indicating that we successfully and directly controlled glycolysis.

*References - *

1. • Hu, Hai, et al. "Phosphoinositide 3-kinase regulates glycolysis through mobilization of aldolase from the actin cytoskeleton." Cell 164.3 (2016): 433-446. *
2. • TeSlaa, Tara, and Michael A. Teitell. "Techniques to monitor glycolysis." Methods in enzymology 542 (2014): 91-114. *
3. • Oginuma, Masayuki, et al. "Intracellular pH controls WNT downstream of glycolysis in amniote embryos." Nature584.7819 (2020): 98-101. * *Reviewer #3 (Significance (Required)): *

The experimental results reported in this study enhance our understanding of how cellular metabolic states regulate cellular behaviors during embryonic development. The study provides insight into how PSM elongation is controlled by morphogenetic mechanisms that are modulated by glycolytic flux. One of the strengths of the study is the use of an interdisciplinary approach that includes GC-MS, in vivo imaging and mouse transgenic lines. It should be noted that some of the conclusions of the study diverge from previous papers that examine the role of metabolism in developmental patterning (e.g., Oginuma et al., 2020).

The geography theory postulates that prosperity and poverty of a country are caused by its geography especially tropical versus temperate climate which may influence the attitude of people, the diseases that can impact health, tropical soil which is not very conducive to agriculture as well as the flora/fauna of the place. I think there is another element of the geography theory that we can evaluate is the availability of broader natural resources. Logically the country with higher natural resources should be growing faster than those with lesser. However, in their paper, “Natural resource abundance and economic growth” (published in National Bureau of Economic Research, 1995 https://www.nber.org/system/files/working_papers/w5398/w5398.pdf), Jeffery Sachs and Andrew Warner from Harvard institute of international development, conclude that natural resource-rich countries tend to grow slower than those with scare resources in their study of 97 developing countries over two decades (1970-1989). The key hypothesis validated by them was that resource-rich countries tend to focus their labor on extracting natural resources thereby leaving few resources and investments into manufacturing and value-added industries. In addition, they practice protectionist state-led development policies which lead to lower investment and hence lower growth. So in many ways, riches by themselves become a curse versus a boon.

Author Response

Reviewer #2 (Public Review):

The manuscript presents an interesting study on a timely topic (hyperacusis). The study was carried out in awake animals using modern approaches in neurosciences (calcium imaging, optogenetic). The amount of data is impressive, the study is very ambitious, and overall its quality is indisputable. However, I have some general comments and questions on some concepts that are critical for the study, and also on the interpretation of the data, in particular the behavioral data.

We appreciate Reviewer 2’s overall positive evaluation as well as their more specific critiques, which we address below.

The first point I want to mention is the concept of 'homeostatic plasticity'. I am not sure we agree on its definition. My understanding of it is that the AVERAGE of central activity will remain constant around a set point value. In case of a reduction of sensory inputs (hearing loss), the neurons' sensitivity will be enhanced in such a way that the averaged activity will be preserved. So, neural hyperactivity after partial or sensory deprivation is not 'maladaptive': it is a collateral effect, 'the price to pay' for maintaining neural activity stable around a given value. In my opinion, this point is crucial. The authors should also mention and cite the model's paper from Schaette et al.

“Homeostasis” is a term used widely in physiology to describe a negative feedback process in which an internal adjustment compensates for an external perturbation to return a given system (temperature, pH, etc.) to a set point. To the reviewer’s point, homeostatic processes – broadly defined – can work at many different biological scales including perhaps large, distributed systems like the example s/he gave of neurons throughout the central auditory pathway. By contrast, “homeostatic plasticity” is a mechanism studied by dozens of laboratories in hundreds of papers by which neurons (typically studied in cortical neurons) adjust their synaptic and intrinsic excitability to maintain their activity around a set point range. A key feature of homeostatic plasticity is that neurons “sense” deviations from their set point and initiate a compensatory process to offset this deviation. Up to this point, it seems that we are on the same page as the reviewer.

The first point of possible disagreement lies in the interpretation of how excess neural activity relates to homeostatic plasticity. The reviewer mentioned modeling papers by Schaette and Kempter (2006, 2007, 2012) on the cochlear nucleus, which are also based on homeostatic plasticity and their work is now cited in the revised text (see line 71). The reviewer is correct that there is a difference in how the term is used and interpreted, but the difference is fairly subtle. Their work and our work propose that homeostatic plasticity processes are applied within a single neuron to offset the reduced afferent input that accompanies cochlear damage. As the reviewer recalled, they describe hyperactivity as a consequence of this compensation, as we do as well. The only difference is that they and the reviewer describe hyperactivity as the byproduct of the normal, successful implementation of homeostatic plasticity, which it unequivocally is not because – by definition – homeostatic plasticity is a stabilizing process that maintains activity at a predetermined set point range.

The second point of disagreement lies in the reviewer’s statement that “neural hyperactivity after partial or sensory deprivation is not 'maladaptive': it is a collateral effect, 'the price to pay' for maintaining neural activity stable around a given value.” We disagree. Hyperactivity can be both a collateral and maladaptive effect. Hyperactivity and hypersynchrony are understood to be the basis of tinnitus, which is a maladaptive, disordered state. The reviewer’s comment implies that there is no alternative for compensating for sensory deprivation but to make cortical neurons hyperactive. We see no reason why this must be so. In fact, stabilization of activity rates after sensory deprivation has been demonstrated in hundreds of studies in the developing visual system. In the adult auditory system, activity in cortical neurons is initially depressed after injury before rebounding to exceed baseline levels (see Resnik Polley 2017 eLife, Asokan 2018 Nat Comm., Resnik Polley 2021 Neuron). It is not obligatory for cortical activity rates to pass through the set point range and continue into hyperactivity, nor is it obligatory for cortical activity rates to remain elevated above baseline many days after the injury. Additional evidence for this point comes from Figures 4, 6, and 8, which show that some cortical neurons actually do homeostatically regulate their activity back to baseline (i.e., show stable gain). This raises the intriguing question of why some neurons recover to their homeostatic activity set point while others do not. Figure 8 provides new insight into this question by showing that that their baseline response properties can account for 40% of the variability in gain stabilization after peripheral insult.

A third point of disagreement related to the reviewer’s statement that “My understanding of it is that the AVERAGE of central activity will remain constant around a set point value. In case of a reduction of sensory inputs (hearing loss), the neurons' sensitivity will be enhanced in such a way that the averaged activity will be preserved”. We agree that homeostatic plasticity processes are influenced by activity propagating through distributed neural networks. However, the biological implementation of the process is programmed into individual neurons. The activity set point is neuron-specific, the error signal that encodes a deviation from the set point is neuron-specific, and the transcriptional/translational changes deployed to stabilize the activity rate are neuron-specific. As an analogy, home climate control systems work autonomously for each house, because the sensors (thermostat) and actuators (heating/cooling) are sensitive to fluctuations in that home, not across other houses in the town. The heating and cooling systems for each house in town may be driven by a distributed, common source (e.g., a hot day) but the mechanisms that bring the ambient temperature back to the set point for each house are autonomous and reflect the particular thermostat programming for each house. The widely studied homeostatic plasticity mechanisms mentioned in our manuscript (e.g., excitatory synaptic scaling) are not sensitive to and do not target the averaged neural activity among millions of neurons distributed throughout the sensory neuroaxis.

As a final point on this statement, there is no demonstration that we are aware of that average central activity remains constant after a reduction of sensory inputs. This would require recording from many neurons across multiple stages of the sensory pathway in a single animal to show that the increased gain at later stages in the system exactly offsets the reduced responsiveness at earlier stages of the system. So, the reviewer’s definition of homeostatic plasticity is based on a general supposition about a distributed process that has never been empirically demonstrated whereas the definition we use is consistent with the mechanisms and terminology used throughout the neuroscience literature (albeit often incorrectly in the hearing loss literature).

The second point is that a lot is built on the behavioral procedure and d'. I am not convinced by the behavioral procedure (and the d') is a convincing measurement of loudness (and therefore loudness hyperacusis). So, in my opinion, the title may be changed and more importantly the entire spirit of the paper should be modified.

The reviewer’s critique as well as comments from other reviewers helped us realize that we had used the terms “hyperacusis” and “loudness” imprecisely. We think that is part of the confusion. What we have studied here is auditory hypersensitivity after sensorineural hearing loss, which may or may not be a model of why persons with hyperacusis can exhibit loudness hypersensitivity.

Once “hyperacusis” and “loudness” have been stripped away from the behavior, we contend that we have a behavioral assay for auditory hypersensitivity, which is the main point of our study. To be clear, the behavioral readout most commonly employed in the animal literature to model hyperacusis is reaction time, which has a less direct relationship to hypersensitivity than does d’. D-prime is widely used as the sensitivity index in detection behaviors. The main advantage of d’ is that it controls for differences in response bias either between subjects or after noise exposure. We used the d’ metric to show that mice can more reliably detect tone levels near their sensation threshold and can more reliably detect direct stimulation of thalamocortical projection neurons after acoustic trauma. These observations provide the framework for all of the neural measurements that follow.

On the balance, the reviewer was correct that our imprecise use of hyperacusis and loudness was confusing and contradictory. The terms “hyperacusis” and “loudness” now only appear in the manuscript to describe other published findings or to describe what our study does not address. This resulted in several small text changes throughout the manuscript as well as a direct statement about the relationship between our work, loudness, and hyperacusis on Pg. 14, Lns 448-466.

“While the findings presented here support an association between sensorineural peripheral injury, excess cortical gain, and behavioral hypersensitivity, they should not be interpreted as providing strong evidence for these factors in clinical conditions such as tinnitus or hyperacusis. Our data have nothing to say about tinnitus one way or the other, simply because we never studied a behavior that would indicate phantom sound perception. If anything, one might expect that mice experiencing a chronic phantom sound corresponding in frequency to the region of steeply sloping hearing loss would instead exhibit an increase in false alarms on high-frequency detection blocks after acoustic trauma, but this was not something we observed. Hyperacusis describes a spectrum of aversive auditory qualities including increased perceived loudness of moderate intensity sounds, a decrease in loudness tolerance, discomfort, pain, and even fear of sounds (Pienkowski et al., 2014a). The affective components of hyperacusis are more challenging to index in animals, particularly using head-fixed behaviors, though progress is being made with active avoidance paradigms in freely moving animals (Manohar et al., 2017). Our noise-induced high-frequency sensorineural hearing loss and Go-NoGo operant detection behavior were not designed to model hyperacusis. Hearing loss is not strongly associated with hyperacusis, where many individuals have normal hearing or have a pattern of mild hearing loss that does not correspond to the frequency dependence of their auditory sensitivity (Sheldrake et al., 2015). While the excess central gain and behavioral hypersensitivity we describe here may be related to the sensory component of hyperacusis, this connection is tentative because it was elicited by acoustic trauma and because the detection behavior provides a measure of stimulus salience, but not the perceptual quality of loudness, per se.”

A lot is derived/interpreted from the results, but I believe there is a lot of over-interpretation. I would suggest the authors be more cautious and moderate in their speculations and conclusions. I would reconfigure the manuscript, and simplify it.

We believe that the changes mentioned above and in the response to their specific comments below reduce over-interpretation and simplify the manuscript.

As an example of a change made to moderate the conclusions from our work, we added the following to Pg. 14, Lns 442-447

“Further, while the perceptual salience (Figure 2) and neural decoding of spared, 8kHz tones (Figure 5) were both enhanced after high-frequency sensorineural hearing loss, these measurements were not performed in the same animals (and therefore not at the same time). Definitive proof that increased cortical gain is the neural substrate for auditory hypersensitivity after hearing loss would require concurrent monitoring and manipulations of cortical activity, which would be an important goal for future experiments.”

Reviewer #3 (Public Review):

The study uses a mouse animal model of sensorineural hearing loss after sound overexposure at high frequencies that mimics ageing sensorineural hearing loss in humans. Those mice present behavioural hypersensitivity to mid-frequency tones stimuli that can be recreated with optogenetic stimulation of thalamocortical terminals in the auditory cortex. Calcium chronic imaging in pyramidal neurons in layers 2-3 of the auditory cortex shows reorganization of the tonotopic maps and changes in sound intensity coding in line with the loudness hypersensitivity showed behaviourally. After an initial state of neural diffuse hyperactivity and high correlation between cells in the auditory cortex, changes concentrate in the deafferented high-frequency edge by day 3, especially when using mid-frequency tones as sound stimuli. Those neurons can show homeostatic gain control or non-homeostatic excess gain depending on their previous baseline spontaneous activity, suggesting a specific set of cortical neurons prompt to develop hyperactivity following acoustic trauma.

This study is excellent in the combination of techniques, especially behaviour and calcium chronic imaging. Neural hyperactivity, increase in synchrony, and reorganization of the tonotopic maps in the auditory cortex following peripheral insult in the cochlea has been shown in seminal papers by Jos Eggermont or Dexter Irvine among others, although intensity level changes are a new addition. More importantly, the authors show data that suggest a close association between loudness hypersensitivity perception and an excess of cortical gain after cochlear sensorineural damage, which is the main message of the study.

The problem is that not all the high-frequency sensorineural hearing loss in humans present hyperacusis and/or tinnitus as co-morbidities, in the same manner that not all animal models of sensorineural hearing loss present combined tinnitus and/or hyperacusis. In fact, among different studies on the topic, there is a consensus that about 2/3rds or 70% of animals with hearing loss develop tinnitus too, but not all of them. A similar scenario may happen with hearing loss and hyperacusis. Therefore, we need to ask whether all the animals in this study develop hyperacusis and tinnitus with the hearing loss or not, and if not, what are the differences in the neural activity between the cases that presented only hearing loss and the cases that presented hearing loss and hyperacusis and/or tinnitus. It could be possible that the proportion of cells showing non-homeostatic excess gain were higher in those cases where tinnitus and hyperacusis were combined with hearing loss.

We thank the reviewer for her/his careful reading of the original manuscript and many helpful suggestions and critiques that have been addressed in the revision. Both Reviewer 2 and Reviewer 3 understood that we were presenting our high-frequency sensorineural hearing loss manipulation as a way to model the clinical phenomenon of hyperacusis. This was not our intent, and we regret the wording of the original manuscript communicated this point. In fact, the clinical literature shows that hyperacusis does not have a strong association with hearing loss and moreover our behavioral and neural outcome measures were not designed to index the core phenotype of hyperacusis (a spectrum of sound-evoked distress, disproportionate scaling of loudness with sound level, and sound-evoked pain). Our study addresses the neural and behavioral signatures of auditory hypersensitivity, which is an “upstream” condition that may (or may not) be related to the presentation of clinical phenomena like hyperacusis and tinnitus.

The reviewer mentions a litmus test for animal models of tinnitus, in which the utility of an animal model for tinnitus would be evaluated in part based on whether a controlled insult only produced a behavioral change suggestive of a chronic phantom percept in a fraction of animals. That may be so, but our study is clearly not modeling tinnitus and we make no claims to this effect in the original or revised manuscript. The Reviewer then goes on to say that “a similar scenario may happen with hearing loss and hyperacusis”. “May” is the operative word here because the association between sensorineural hearing loss and the clinical presentation hyperacusis is quite weak overall in human subjects but no study (that we are aware of) has attempted to document the probabilistic appearance of hyperacusis before and after acoustic trauma. So, we really don’t know whether hyperacusis has a probabilistic appearance like tinnitus or is more deterministic like cochlear threshold shift. But, again, the main point is that our experiments make no direct claim about hyperacusis one way or the other, which we now clarify and discuss throughout the revised text, as detailed below.

We do contend that our experiments allow us to study auditory hypersensitivity, though again there is no precedent or consensus in the literature for expecting auditory hypersensitivity to present probabilistically or deterministically across mice after a controlled insult. Regardless, we agree with the reviewer that it is a very good idea to provide the individual animal data to the reader. We added new panels to Figure 2C to show that an increase in the 8kHz d’ slope after noise exposure (i.e., a change > 1) was observed in 7/7 mice that underwent acoustic trauma but 1/6 mice in the sham exposure group, suggesting a deterministic, binary behavioral effect found in every mouse with noise-induced high-frequency sensorineural damage. On the other hand, within the acoustic trauma cohort, 3 mice showed marked increases in the d’ growth slope (> 2) while 4 showed more subtle changes, suggesting a more graded or probabilistic effect. By providing the individual animal data as per the Reviewer’s request, the reader can now make a more informed determination about the reliability of auditory hypersensitivity within the acoustic trauma cohort.

Regarding the relationship between the peripheral/cortical/perceptual auditory hypersensitivity we report here and the clinical conditions of tinnitus and hyperacusis, we revised the text such that the word “hyperacusis” only appears in the context of other publications and have added the following text (Pg. 14, Lns 448-466).

“While the findings presented here support an association between sensorineural peripheral injury, excess cortical gain, and behavioral hypersensitivity, they should not be interpreted as providing strong evidence for these factors in clinical conditions such as tinnitus or hyperacusis. Our data have nothing to say about tinnitus one way or the other, simply because we never studied a behavior that would indicate phantom sound perception. If anything, one might expect that mice experiencing a chronic phantom sound corresponding in frequency to the region of steeply sloping hearing loss would instead exhibit an increase in false alarms on high-frequency detection blocks after acoustic trauma, but this was not something we observed. Hyperacusis describes a spectrum of aversive auditory qualities including increased perceived loudness of moderate intensity sounds, a decrease in loudness tolerance, discomfort, pain, and even fear of sounds (Pienkowski et al., 2014a). The affective components of hyperacusis are more challenging to index in animals, particularly using head-fixed behaviors, though progress is being made with active avoidance paradigms in freely moving animals (Manohar et al., 2017). Our noise-induced high-frequency sensorineural hearing loss and Go-NoGo operant detection behavior were not designed to model hyperacusis. Hearing loss is not strongly associated with hyperacusis, where many individuals have normal hearing or have a pattern of mild hearing loss that does not correspond to the frequency dependence of their auditory sensitivity (Sheldrake et al., 2015). While the excess central gain and behavioral hypersensitivity we describe here may be related to the sensory component of hyperacusis, this connection is tentative because it was elicited by acoustic trauma and because the detection behavior provides a measure of stimulus salience, but not the perceptual quality of loudness, per se.”

Author Response

Reviewer 1 (Public Review):

To me, the strengths of the paper are predominantly in the experimental work, there's a huge amount of data generated through mutagenesis, screening, and DMS. This is likely to constitute a valuable dataset for future work.

We are grateful to the reviewer for their generous comment.

Scientifically, I think what is perhaps missing, and I don't want this to be misconstrued as a request for additional work, is a deeper analysis of the structural and dynamic molecular basis for the observations. In some ways, the ML is used to replace this and I think it doesn't do as good a job. It is clear for example that there are common mechanisms underpinning the allostery between these proteins, but they are left hanging to some degree. It should be possible to work out what these are with further biophysical analysis…. Actually testing that hypothesis experimentally/computationally would be nice (rather than relying on inference from ML).

We agree with the reviewer that this study should motivate a deeper biophysical analysis of molecular mechanisms. However, in our view, the ML portion of our work was not intended as a replacement for mechanistic analysis, nor could it serve as one. We treated ML as a hypothesis-generating tool. We hypothesized that distant homologs are likely to have similar allosteric mechanisms which may not be evident from visual analysis of DMS maps. We used ML to (a) extract underlying similarities between homologs (b) make cross predictions across homologs. In fact, the chief conclusion of our work is that while common patterns exist across homologs, the molecular details differ. ML provides tantalizing evidence to this effect. The conclusive evidence will require, as the reviewer rightly suggests, detailed experimental or molecular dynamics characterization. Along this line, we note that we have recently reported our atomistic MD analysis of allostery hotspots in TetR (JACS, 2022, 144, 10870). See ref. 41.

Changes to manuscript:<br /> “Detailed biophysical or molecular dynamics characterization will be required to further validate our conclusions(38).”

Reviewer 3 (Public Review):

However - at least in the manuscript's present form - the paper suffers from key conceptual difficulties and a lack of rigor in data analysis that substantially limits one's confidence in the authors' interpretations.

We hope the responses below address and allay the reviewer’s concerns.

A key conceptual challenge shaping the interpretation of this work lies in the definition of allostery, and allosteric hotspot. The authors define allosteric mutations as those that abrogate the response of a given aTF to a small molecule effector (inducer). Thus, the results focus on mutations that are "allosterically dead". However, this assay would seem to miss other types of allosteric mutations: for example, mutations that enhance the allosteric response to ligand would not be captured, and neither would mutations that more subtly tune the dynamic range between uninduced ("off) and induced ("on") states (without wholesale breaking the observed allostery). Prior work has even indicated the presence of TetR mutations that reverse the activity of the effector, causing it to act as a co-repressor rather than an inducer (Scholz et al (2004) PMID: 15255892). Because the work focuses only on allosterically dead mutations, it is unclear how the outcome of the experiments would change if a broader (and in our view more complete) definition of allostery were considered.

We agree with the reviewer that mutations that impact allostery manifest in many different ways. Furthermore, the effect size of these mutations runs the full gamut from subtle changes in dynamic range to drastic reversal of function. To unpack allostery further, allostery of aTF can be described, not just by the dynamic range, but by the actual basal and induced expression levels of the reporter, EC50 and Hill coefficient. Given the systemic nature of allostery, a substantial fraction of aTF mutations may have some subtle impact on one or more of these metrics. To take the reviewer’s argument one step further, one would have to accurately quantify the effect size of every single amino acid mutation on all the above properties to have a comprehensive sequence-function landscape of allostery. Needless to say, this is extremely hard! Resolution of small effect sizes is very difficult, even at high sequencing depth. To the best of our knowledge, a heroic effort approaching such comprehensive analysis has been accomplished so far only once (PMID: 3491352).

Our focus, therefore, was to screen for the strongest phenotypic impact on allostery i.e., loss of function. Mutations leading to loss of function can be relatively easily identified by cell-sorting. Because our goal was to compare hotspots across homologs, we surmised that loss of function mutations, given their strong phenotypic impact, are likely to provide the clearest evidence of whether allosteric hotspots are conserved across remote homologs.

The reviewer raised the point of activity-reversing mutations. Yes, there are activity reversing mutations in TetR. However, they represent an insignificant fraction. In the paper cited by the reviewer, there are 15 activity-reversing mutations among 4000 screened. Furthermore, the paper shows that activity-reversing in TetR requires two-tofour mutations, while our library is exclusively single amino acid substitutions. For these reasons, we did not screen for activity-reversing mutations. Nonetheless, we agree with the reviewer that screening for activity-reversing mutations across homologs would be very interesting.

The separation in fluorescence between the uninduced and induced states (the assay dynamic range, or fold induction) varies substantially amongst the four aTF homologs. Most concerningly, the fluorescence distributions for the uninduced and induced populations of the RolR single mutant library overlap almost completely (Figure 1, supplement 1), making it unclear if the authors can truly detect meaningful variation in regulation for this homolog.

Yes, the reviewer is correct that the fold induction ratio varies among the four aTF homologs. However, we note that such differences are common among natural aTFs. Depending on the native downstream gene regulated by the aTF, some aTFs show higher ligand-induced activation, and others are lower. While this is not a hard and fast rule, aTFs that regulate efflux pumps tend to have higher fold induction than those that regulate metabolic enzymes. In summary, the variation in fold induction among the four aTFs is not a flaw in experimental design nor indicates experimental inconsistency but is instead just an inherent property of protein-DNA interaction strength and the allosteric response of each aTF.

Among the four aTFs, wildtype RolR has the weakest fold induction (15-fold) which makes sorting the RolR library particularly challenging. To minimize false positives as much as possible, we require that dead mutant be present in (a) non-fluorescent cells after ligandinduction (b) non-fluorescent cells before ligand-induction (c) at least two out of the three replicates for both sorts. Additionally, for RolR specifically, we adjusted the nonfluorescent gate to be far more stringent than the other three aTFs (Fig. 1 – figure supplement 1). Furthermore, we assign residues as allosteric hotspots, not individual dead mutations. This buffers against false strong signals from stray individual dead mutations. Finally, the top interquartile range winnows them to residues showing strong consistent dead phenotype. As a result of these “safeguards” we have built in, the number of allosteric hotspots of RolR (57) is comparable to the other three aTFs (51, 53 and 48). This suggests that we are not overestimating the number of hotspots despite the weaker fold induction of RolR. We highlight in a new supplementary figure (Figure 1 – figure supplement 4) that changing the read count threshold from 5X to 10X produces near identical patterns of mutations suggesting that our results are also robust to changes in ready depth stringency.

Changes to manuscript: In response to the reviewer's comment, we have added the following sentence.

“We note that the lower fold induction (dynamic range) of RolR makes it particularly challenging to separate the dead variants from the rest.”

The methods state that "variants with at least 5 reads in both the presence and absence of ligand in at least two replicates were identified as dead". However, the use of a single threshold (5 reads) to define allosterically dead mutations across all mutations in all four homologs overlooks several important factors:

Depending on the starting number of reads for a given mutation in the population (which may differ in orders of magnitude), the observation of 5 reads in the gated nonfluorescent region might be highly significant, or not significant at all. Often this is handled by considering a relative enrichment (say in the induced vs uninduced population) rather than a flat threshold across all variants.

We regret the lack of clarity in our presentation. We wish to better explain the rationale behind our approach. First, we understand the reviewer’s point on considering relative enrichment to define a threshold. This approach works well in DMS experiments involving genetic selections, which is commonly the case, because activity scales well with selection stringency. One can then pick enrichment/depletion relative to the middle of the read count distribution as a measure of gain or loss of function.

Second, this strategy does not, in practice, work well for cell-sorting screens. While it may be tempting to think of cell sorting as comparably activity-scaled as genetic selections, in reality, the fidelity of fluorescent-activated cell sorters is much lower. Making quantitative claims of activity based on cell sorting enrichment can be risky. It is wiser to treat cell sorting results as yes/no binary i.e., does the mutation disrupt allostery or not. More importantly, the yes/no binary classification suffices for our need to identify if a certain mutation adversely impacts allosteric activity or not.

Third, the above argument does not imply that all mutations have the same effect size on allostery. They don’t. We capture the effect size on individual residues, not individual mutations, by counting the number of dead mutations at a residue position. This is an important consideration because it safeguards us from minor inconsistencies that inevitably arise from cell sorting.

Fourth, a variant to be classified as allosterically dead, it must be present both in uninduced and induced DNA-bound populations in at least two out of three replicates (four conditions total). This is a stringent criterion for selecting dead variants resulting in highly consistent regions of importance in the protein even upon varying read count thresholds. To the extent possible, we have minimized the possibility of false positive bleed-through.

Finally, two separate normalizations were performed on the total sequence reads to be able to draw a common read count threshold 1) between experimental conditions & replicates and 2) across proteins. First, total sequencing reads were normalized to 200k total across all sample conditions (presorted, -inducer, and +inducer) and replicates for each homolog, allowing comparisons within a single protein. Next, reads were normalized again to account for differences in the theoretical size of each protein’s single-mutant library, allowing for comparisons across proteins by drawing a commont readcount cutoff. For example, total sequencing reads of RolR (4,332 possible mutants) increased by 1.18x relative to MphR (3,667 possible mutants) for a total of 236k reads.

Changes to manuscript: We have provided substantial additional details in the Fluorescence-activated cell sorting and NGS preparation and analysis sections.

We also added the following in the main text.

“In other words, we use cell sorting as a binary classifier i.e., does the mutation disrupt allostery or not. We capture the effect size on individual residues, not individual mutations, by counting the number of dead mutations at a residue position. This is an important consideration because it safeguards us from minor inconsistencies that inevitably arise from cell sorting.”

Depending on the noise in the data (as captured in the nucleotide-specific q-scores) and the number of nucleotides changed relative to the WT (anywhere between 1-3 for a given amino acid mutation) one might have more or less chance of observing five reads for a given mutation simply due to sequencing noise.

All the reads considered in our analyses pass the Illumina quality threshold of Q-score ≥ 30 which as per Illumina represent “perfect reads with no errors or ambiguities”. This translates into a probability of 1 in 1000 incorrect base call or 99.9% base call accuracy.

We use chip-based oligonucleotides to build our DMS library, which allows us to prespecify the exact codon that encodes a point mutation. This means the nucleotide count and protein count are the same. The scenario referred to by the reviewer i.e., “anywhere between 1-3 for a given amino acid mutation” only applies to codon randomized or errorprone PCR library generation. We regret if the chip-based library assembly part was unclear.

Depending on the shape and separation of the induced (fluorescent) and uninduced (non-fluorescent) population distributions, one might have more or less chance of observing five reads by chance in the gated non-fluorescent region. The current single threshold does not account for variation in the dynamic range of the assay across homologs.

We have addressed the concern raised by the reviewer on fluorescent population distributions in answers to questions 10 and 11.

The reviewer makes an important point about the choice of sequencing threshold. We use the sequencing threshold to simply make a binary choice for whether a certain variant exists in the sorted population or not. We do not use the sequencing reads as to scale the activity of the variant. To address the reviewer's comment, we have included a new supplementary figure (Fig 1 – figure supplement 4) where we compare the data by adjust the threshold two levels – 5 and 10 reads. As is evident in the new figure, the fundamental pattern of allosteric hotspots and the overall data interpretation does not change.

TetR: 5x – 53 hotspots, 10x – 51 hotspots

TtgR: 5x – 51 hotspots, 10x – 51 hotspots

MphR: 5x – 48 hotspots, 10x – 48 hotspots

RolR: 5x – 57 hotspots, 10x – 60 hotspots

In other words, changing the threshold to be more or less strict may have a modest impact on the overall number of hotspots in the dataset. Still, the regions of functional importance are consistent across different thresholds. We have expanded the discussion in the manuscript to address this point.

Changes to manuscript: We have now included a new supplementary comparing hotspot data at two thresholds: Figure 1 – figure supplement 4.

We also added the following in the main text.

“To assess the robustness of our classification of hotspots, we determined the number of hotspots at two different sequencing thresholds – 5x and 10x. At 5x and 10x, the number of hotspots are – TetR: 53, 51; TtgR: 51, 51; MphR: 48, 48 and RolR: 57,60, respectively. Changing the threshold has a modest impact on the overall number of hotspots and the regions of functional importance are consistent at both thresholds”

The authors provide a brief written description of the "weighted score" used to define allosteric hotspots (see y-axis for figure 1B), but without an equation, it is not clear what was calculated. Nonetheless, understanding this weighted score seems central to their definition of allosteric hotspots.

We regret the lack of clarity in our presentation. The weighted score was used to quantify the “deadness” of every residue position in the protein. At each position in the protein, the number of mutations that inhibited activity was summed up and the ‘deadness’ of each mutation was weighted based on how many replicates is appeared to inactivate the protein. Weighted score at each residue position is given by

Where at position x in the protein, D1 is the number of mutations dead in one replicate only, D2 is the number of mutations dead in 2 replicates, D3 is the number of mutations dead in 3 replicates, and Total is the total number of variants present in the data set (based on sequencing data). Any dead mutation that is seen in only one replicate is discarded and does not contribute to the “deadness” of the residue. Mutations seen in two and three replicates contribute to the score. We have included a new supplementary figure (Fig. 1 – figure supplement 2) to give the reader a detailed heatmap of all mutations and their impact for each protein.

Changes to manuscript: The weighted scoring scheme is now described in greater detail under Materials and Methods in the “NGS preparation and analysis” section.

The authors do not provide some of the standard "controls" often used to assess deep mutational scanning data. For example, one might expect that synonymous mutations are not categorized as allosterically dead using their methods (because they should still respond to ligand) and that most nonsense mutations are also not allosterically dead (because they should no longer repress GFP under either condition). In general, it is not clear how the authors validated the assay/confirmed that it is giving the expected results.

As we state in response to question 12, we use chip-based oligonucleotides to build our DMS library, which allows us to pre-specify the exact codon that encodes a point mutation. We have no synonymous or nonsense mutations in our DMS library. Each protein mutation is encoded by a single unique codon. The only stop codon is at 3’end of the gene.

The authors performed three replicates of the experiment, but reproducibility across replicates and noise in the assay is not presented/discussed.

Changes to manuscript: A new supplementary table (Table 1) is now provided with the pairwise correlation coefficients between all replicates for each protein.

In the analysis of long-range interactions, the authors assert that "hotspot interactions are more likely to be long-range than those of non-hotspots", but this was not accompanied by a statistical test (Figure 2 - figure supplement 1).

In response to the reviewer's comment, we now include a paired t-test comparing nonhotspots and hotspots with long-range interactions in the main text.

Changes to manuscript: In all four aTFs, hotspots constituted a higher fraction of LRIs than non-hotspots (Figure 2 – figure supplement 1; P = 0.07).

Author Resonse

Reviewer #1 (Public Review):

The authors trained rats to self-initiated a trial by poking into a nose poke, and to make a sequence of 8 licks in the nose poke after a visual cue. Trials were considered valid (called "timely") only if rats waited for more than 2.5 sec after the end of the previous trial. An attempt to initiate a trial (nose poking) before the 2.5 sec criterion was regarded as "premature". The authors recorded from the dorsal striatum while rats performed in this task. The authors first show that some neurons exhibited a phasic activation around the time of port entry detected using an infrared detector ("Entry cell"), as well as port exit ("Exit cell). Some neurons showed activation at both entry and exit ("Entry and Exit cell") or between these two events ("Inside-port cell"). Fractions of neurons that fall into these four categories are roughly the same (Fig. 3C). The main conclusions drawn from this study are that (1) the activity preceding a port entry was positively correlated with the latency to initiate a trial (or "waiting time"; Fig. 4E), which appear to reflect the value upcoming reward, and that (2) in adolescent rats, the activity rose more steeply with the latency to trial initiation (Fig. 7J).

These observations are potentially interesting, in particular, the possible difference between adult and adolescent rats is intriguing. However, this study does not examine whether this brain region actually plays a role in the task. Some of the conclusions appear to be premature.

1) Previous studies have found correlations between the activity of neurons in the striatum and the latency to trial initiation (e.g. Wang et al., Nat. Neurosci., 2013) or action initiation more generally (e.g. Kunimatsu et al., eLife, 2018). In the former study, the trial initiation was self-generated, similar to the present study, and was modulated by the overall reward value (state value). In the latter study, the latency was instructed by a cue. Furthermore, there are many studies that showed correlations between striatal activity and future rewards (e.g. Samejima et al., Science, 2005; Lau and Glimcher, 2008). Many of these studies varied the value of upcoming reward (e.g. amount or probability). Although some details are different, the basic concepts have been demonstrated in previous studies.

Although there are other studies linking striatal activity to trial/action initiation and reward probability, here the striatal activity preceding the execution of a learned sequence is dependent on the internal representation of the time waited. Elapsed time is the only cue the animal has regarding the possible outcome until it is too late and the trial has already been initiated. Although a light cue then tells the rat if the timing was correct or not, providing an opportunity to stop the behavior, the behavior released during premature trials resembles very closely that observed during unrewarded timely trials. This remarkable similarity between premature trials and timely unrewarded trials allowed comparing very advantageously the effect of wait time-based modulation of anticipatory striatal activity. Moreover, we have compared striatal activity between adult and adolescent rats finding a steeper wait time-based modulation of striatal activity in adolescent animals that correlates with a more impulsive behavior in these animals.

2) The authors conclude that "in this task, the firing rate modulation preceding trial initiation discriminates between premature and timely trials and does not predict the speed, regularity, structure, value or vigor of the subsequently released action sequence". This conclusion is based on the observation that premature and timely trials did not differ in terms of kinematic parameters as measured using accelerometer. Although the result supports that the difference in activity between premature and timely cannot be explained by the kinematic variables, it does not exclude the possibility that the activity is modulated by some kinematic variables in a way orthogonal to these trial types.

While our accelerometer data do not support that differences in movement initiation time or velocity could explain the differences in striatal activity between adolescent and adult rats, we can not rule out that kinematic variables not captured by the head accelerometer recordings could explain some of the results. This is acknowledged in the main text, results section, page 8, line 180.

3) The firing rate plot shown in Figure 4D should be replotted by aligning trials by movement initiation (presumably available from accelometer or video recording). Is it possible that the activity rise similarly between trials types but the activity is cut off depending on when the animal enters the port at different latency from the movement initiation? In any case, the port entry is a little indirect measure of "trial initiation".

Unfortunately, we have not systematically obtained video recordings of the sessions and only have accelerometer recordings of a few of the animals that provided the neuronal data, which precludes replotting the data as suggested. Accelerometer recordings are available from two of adult and two adolescent rats. Latency from movement initiation to port entry do not differ between premature and timely trials at both ages. This is now reported on page 8 line 175 for adult rats, and page 15 line 341 for adolescent rats. These results appear to be at odds with the idea that decreased neuronal activity in premature trials is the result of a cut-off of the response.

4) The difference between adult and adolescent rats are not particularly big, with the data from the adolescent rats showing a noisy trace.

New data from two adolescent rats reduced the variability and confirmed the behavioral and physiological differences with adult rats. All panels from figure 7 now include the data from 5 adolescent animals instead of 3. The number of neurons analyzed in the adolescent group passed from 552 to 876. The inclusion of these new data allowed us to perform new statistical comparisons. We adjusted a logistic function to accumulated trial initiation timing data (Fig.7N) and found that the rate of accumulation is higher in adolescent rats. Importantly, this is observed not only in the part of the curve corresponding to premature responding but also during timely responding, indicating that adolescent rats' premature responding is a manifestation of a more general behavioral trait that makes them self-initiate trials faster than adults (Fig. 7N). The noisy trace of curves showing the amplitude modulation of anticipatory activity as a function of waiting time was partly due to the relatively low number of premature trials that demanded using relatively long time bins. With more data available we have been able to replot these curves using a smaller bin size for the short waiting times (Fig. 7M). We have adjusted a logistic function to these data and observed a higher rate of increase of this activity modulation in adolescent rats, paralleling the behavioral data. Moreover, we report a significant correlation between the behavioral and neurophysiological data (a steeper rate of trial initiation times curve correlates with a steeper wait modulation of anticipatory activity, Fig. 7O). These new findings are reported in the results section, from page 17 line 405 to page 18 line 417.

Reviewer #2 (Public Review):

The authors conduct an ambitious set of experiments to study how neural activity in the dorsal striatum relates to how animals can wait to perform an action sequence for reward. There are a lot of interesting studies on striatal encoding of actions/skills, and additionally evidence that striatal activity can help control response timing and time-related response selection. The authors bridge these issues here in an impressive effort. Recordings were made in the dorsal striatum on several tasks, and activity was assessed with respect to action initiation, completion, and outcome processing with respect to whether animals could wait appropriately or could not wait and responded prematurely. Conducting recordings of this sort in this task, particularly in some adolescent animals, is technically advanced. I think there is a very timely and potentially very interesting set of results here. However, I have some concerns that I hope can be addressed:

It seems like the recordings were made throughout the dorsal striatum (histology map), including some recordings near/in the DLS. Is this accurate? The manuscript is written as though only the DMS was recorded.

We acknowledge that our recordings are spread along the medial and central regions of the dorsal striatum. Although we are not sure that there is a consensus regarding the limits of the DMS and DLS, we believe that none of our recordings are clearly located within the DLS. Following your suggestion, we have modified the text and refer to the location of our recordings as “dorsal striatum”. We believe that, as there is a lot of work on the roles of the DLS and DMS in reward learning, it is still important to refer to this work in the Introduction section and to discuss our findings in its context, particularly, since we find that most task-related activity is concentrated at the beginning and end of the task as shown in several studies focused in the DLS.

If I understand correctly, the rats must lick 8 times to get the water. If this is true, one strategy is to just keep licking until the water comes. Therefore, the rats may not have learned an 8-lick action sequence. The authors should clarify this possibility, and if it is, to consider avoiding using phrases like "automatized action sequence" since no real action sequence might have been learned. In short, I am not convinced the animals have learned an action pattern rather than to just keep licking once a waiting period has elapsed.

We acknowledge that the experiments do not allow us to establish if the rats know what the exact number of licks needed is; when the skill is acquired, licking becomes highly stereotyped and the rats might as well be learning a time after which continuous licking leads to reward. We still believe that the stereotyped performance, the inability to stop the behavior when the absence of the light cue unequivocally indicates that no reward will be obtained in premature trials, and the rapid decrease of lick rate after the eighth lick was emitted and no reward was obtained, support that the behavior is automatic until the time of expected reward delivery. A representative raster plot showing lick sequences during a whole session in a trained adult rat is presented in Fig. 1I and Figure 7 – supplement 1H shows an example of the licks of an adolescent rat.

The number of subjects per group is very low. This is fine for analysis of within-animal neural activity. However, comparing the behavior between these groups of animals does not seem appropriate unless the Ns are substantially increased.

The revised version of the manuscript includes a higher number of adolescent rats from which striatal activity and behavior were recorded, which allowed us to perform a more detailed statistical analysis of the correlations between these measures. In addition, we now include new behavioral data from an independent sample of non-implanted 6 adults and 6 adolescent rats that confirms the results obtained with the implanted animals (presented in Figure 7 – supplement 4).

I found the manuscript difficult to decipher. There are many groups. If I understand correctly, there are the following:

-ITI 2.5s experiment

-ITI 5 s experiment

-ITI2.5-5s experiment

-ITI 2.5 s experiment (adolescent)

-Two accelerometer animals (unclear which experiment)

-Two animals in ITI 2.5 sec without recordings (unclear how incorporated into analyses)

Within each group, there are multiple categories of behavioral performance. This produces a large list of variables. In some parts of the results, these groups are separated and compared, but not all groups are compared in those such sections. In other sections the different groups (all or just some?) appear to be combined for analysis, but it is not clearly described. Another consequence of mixing the groups and conditions together in analysis as they do is that some of the statements in the results are very hard to follow (E.g., line 305 "...similar behavior observed in 8-lick prematurely released and timely unrewarded trials...").

To clarify the experimental groups, we now include a table (Table 1) summarizing which tasks were used and how many animals were trained in each task.

Generally, it is difficult to understand the results without first understanding the details of the different tasks, the different groups of animals, and the different epochs of comparison for neural analysis. It took me a long time to work through the methods and I am still not sure I completely understand it. On this point, some sentences are very long and should be broken up into smaller, clearer sentences. There are a lot of phrases that only someone familiar with the cited articles might understand what they mean (e.g., even one paragraph starting with line 39 includes all of the following terms: automaticity in behavior; behavioral unit or chunk; reward expectancy; reward prediction errors and trial outcomes; explore-exploit; cost-benefit; speed-accuracy tradeoffs; tolerance to delayed rewards; internal urgency states). It is very hard to follow how each of these processes are to be understood in terms of behavioral measures used to study them and how they do or do not relate to the hypothesis of the present study. The discussion similarly uses a lot of different phrases to discuss the task and neural responses in a way that makes it hard to understand exactly what the author's interpretation of the data are. Is there maybe a 'most likely' interpretation that can be stated for some of the responses?

Our main aim is to disclose the mechanisms underlying differences between adult and adolescent rats relating to impulsivity. We hope that this will become clearer in this version of the manuscript after deepening the analysis of the differences between them. We believe that our data do not allow us to unequivocally determine what is the ultimate cognitive process producing the striatal activity differences between adult and adolescent rats, i.e., differences in internal urgency states, time perception, tolerance to delayed rewards, and tried to reflect that fairly in the Discussion.

The data set is extremely rich; there are lot of data here. As a result it can be hard to understand how all of the data relate to the main hypothesis of the article. It often reads as an exploratory set of results section rather than a series of hypothesis tests.

We have tried to improve the overall clarity of the text.

Reviewer #3 (Public Review):

Cecilia-Martinez et al., implement a task that allows the study of premature versus timely actions in rats. First, they show that rats can learn this task. Next, they record the activity in the DMS showing start/stop signals in the cells recorded, next they propose that the activity detected before the release of actions sequences discriminate the premature vs the timely initiations showing a relationship between the waiting time and the activity of cells recorded, furthermore they show that it could be the expectancy of reward what could be encoded in the activity before entering the port. Last they show that adolescent rats show more premature starts than adult rats documenting a difference in activity modulation of DMS cells in the relation between waiting time and firing rate (although above the premature threshold, see comments below).

Overall the paper is well presented describing a well-developed set of experiments and deserves publication attending only minor comments.

1) I understand rats learn to execute sequences of <8licks or 8 licks, although diagrams are presented, no examples of the individual trials with 8 licks, neither distributions of bouts of these licks are presented.

Rats learn to execute a lick sequence to obtain the reward. The experiments do not allow us to establish if they know what the exact number of licks needed is; when the skill is acquired, licking becomes highly stereotyped and the rats might as well be learning a time after which continuous licking leads to reward. A representative raster plot showing lick sequences in a session in a trained adult rat is presented in Figure 1I and Figure 7 - supplement 1H shows an example of the licks of an adolescent rat.

2) Relevant to the statement: "in this task, the firing rate modulation preceding trial initiation discriminates between premature and timely trials and does not predict the speed, regularity, structure, value or vigor of the subsequently released action sequence"... It is not clear if the latency to first lick (plot 2D) and the inter-lick interval (2E) is only from the 8Lick sequences or not. If that is not the case, it is important to compare only the ones with 8Licks.

The data are from 8 lick sequences, this is now indicated in the figure legend.

3) Related to the implications of the previous statement, there seems to be a tendency for longer latency to first lick in timely vs premature trials in Figure 2D (timely-trials-Late vs premature-trials-late)? Again here it is important to compare the 8licks sequences only.

Only 8-lick sequences are compared and the two-way ANOVA showed a significant effect of the training stage without significant effects of trial timing (premature versus timely) and a non-significant interaction. The average ± SEM latencies to the first lick (of the eighth lick sequence) were 0.717 s ± 0.063 for timely trials late and 0.805 s ± 0.086 for premature trials late.

4) I could not find in the main text whether the individual points in Fig.2 (e.g. 2B-E) are individual animals. Please specify that.

In this figure panels every individual point corresponds to the mean of a session, the data correspond to 5 adult animals (2-5 sessions per animal and timing condition). Whether the data correspond to animals or sessions is now clarified in all figure legends.

5) Although very elegant the argument presented in Figure 4C and 6C, I wonder if the head acceleration may lose differences in movements outside the head in the two kinds of trials. If that is the case please acknowledge it.

We acknowledge in the main text, results section, page 8, line 180, that the accelerometer does not allow us to determine if the movements of other body parts differ between trial types.

6) Also in 4C, small separations between timely vs premature signals are seen before 0. Is there a way to know if animals in timely vs premature trials approached the entry port in the same way? This request is pertinent in order to rule out motor contribution to the differences in Figure 4A-B.

Although it is not possible to completely rule out small movement differences between premature and timely trials, no evident behavioral differences can be detected by trained observers or by analyzing video recordings taken during some sessions. The available accelerometer recordings also suggest that a similar motor pattern is displayed in premature and timely trials (Figure 4C).

7) when saying: "Similar results were obtained in rats trained with a longer waiting interval (Supplementary Figure 5)", "is hard to see the similarity in the premature range, while in the 2.5 seconds task there is a positive relationship in the 5 seconds task it is not.

Please note that a positive relationship is observed for the two bins preceding trial initiation, which are about 2.75s and 1s before port entry. The bin that seems to not fit is centered 4s before port entry (1s after exiting the port in the previous trial). Because of the longer waiting time, in the 5 s task behavior becomes less organized during the first seconds after port exit, however, the modulation of activity is still observed in the bins that are close to port entry.

8) The data showing that the waiting modulation of reward anticipation grows at a faster rate in adolescent rats is clear, however, it is not clear how it could be related to the data showing that the adolescent rats were more impulsive.

We acknowledge that the data do not provide a causal link with behavior. After adding two new adolescent rats we have been able to study in more detail the relationship between the waiting modulation of neuronal activity and the accumulation of trial initiations (depicted in figures 7M and 7N respectively) by adjusting logistic functions to the data. The new results are explained on page 17,line 384. There is a striking parallel between the growth rate of both curves, and the curves of adolescent rats are significantly steeper than those of adult rats. Moreover, there is a significant correlation between the coefficients that mark the rate of growth of the behavioral and neurophysiological data (Fig. 7O).

9) Related to the sentence: "the strength of anticipatory activity increased with the time waited before response release and was higher in the more impulsive adolescent rats"....One may expect to see a difference in the range of the premature time however the differences were observed in the range >2.5 seconds. Please explain how to reconcile this finding with the fact that the adolescent rats were more impulsive.

Please, note that the more impulsive behavior of adolescent rats (and the faster growth of the wait modulation of anticipatory activity) is observed along waiting times that exceed the 2.5s criterion wait time; we added a phrase in the Results section (page 18, lines 413) and in the Discussion section (page 19, line 443) to emphasize this point. Regarding the premature trials, a related issue was raised by reviewer #1, concern 4. The addition of new data from adolescent animals allowed us to used smaller bins to better discriminate what happens at short waiting times and included an inset in Figure 7M that allows to better appreciate what happens at these intervals.

It's interesting to see the the emotion of surprise no matter how composed, calm or worried you are, the feeling of surprise always affects everyone the same because you lose all that feeling of readiness when it hits you. On the other hand, surprises can sometimes show one's best moments as your whole body is reacting and focusing to the surprise, your reaction, thinking can also temporally be enhanced for that moment. I said in the last lecture that because we are different there are different results but i think this time for the emotion of surprise the background event and how unique it is what determines what emotion of surprise the person may feel.

This shows examples that women need change in society and their rights. I believe what this tells us is that this is what people think about women in general.

Author Response

Reviewer #2 (Public Review):

McCoy et al. has developed a new urban tree species database from existing city tree inventories. They designed procedures to collect and clean a large amount of data, i.e., more than five million trees from 63 US cities. They found that urban trees were significantly clustered by species in 93% of cities using the compiled data. They also showed that climate significantly shaped both nativity and tree diversity. Also, they identified the homogenization effect of the non-native species. The interest in patterns of urban biodiversity and its driving mechanism has been rising recently. This paper provides an important data source for addressing research questions on this topic. The finding presented by the authors exemplified its potential. Strengths Compared to the existing urban tree database, such as the one developed by Ossola et al.(Global Ecology and Biogeography 2020), the new database added information on spatial location, nativity statuses, and tree health conditions besides occurrences. The new information expands data usability and saves valuable time for researchers. The authors also make the tools available so others can use them to process their own data sets. Because of the added information, various analyses of the diversity pattern of urban trees and the potential driving mechanism could be conducted. The authors found that individual species nonrandomly clustered urban trees. This finding corroborates the existing knowledge that some common species dominate urban trees. Nevertheless, the authors showed that the dominance was apparent in the spatial dimension. The preliminary finding that the native status of a tree had no apparent impact on tree health is interesting. It can potentially contribute to the debate on native vs. exotic in urban tree species selection, which the author mentioned in the paper.

Thank you for the feedback!

Weakness

While the new database and the analysis based on it has strengths, some aspects of the concepts and data analysis need to be clarified and extended.

We appreciate these helpful comments and have made many changes in response, detailed below.

First, the authors need to define several critical concepts used in the paper, including city trees, urban forests, biodiversity, and species diversity. The authors used city trees and urban forests interchangeably throughout the paper. Nevertheless, a widely accepted definition of the urban forest is:"All woody and associated vegetation in and around dense human settlements." Konijnendijk et al. had a good discussion on the terminology used in urban forestry (Urban Forestry & Urban Greening, 2006). Similarly, biodiversity is different from species diversity. Effective species number is a diversity indicator. Therefore, it is challenging to accept conclusions being drawn on biodiversity in urban forests without clear definitions.

We appreciate these clarifications– we have clarified our terminology throughout and added these important definitions.

• “...urban forests, which are the woody and associated vegetation in and around dense human settlements (Konijnendijk et al., 2006).”

• “City tree communities, an essential component of urban forests, provide many services.”

We replaced the term “biodiversity” throughout the text where really we meant to say “tree species diversity” or just “diversity.”

Second, the tree inventories varied significantly regarding the number of records (214~720,140). The variation can be due to the actual variation of tree abundance in studied cities or incomplete inventories. Biases can be introduced into the findings when comparing these inventories without adjusting the unequal sample sizes. The authors did not detail how they dealt with this issue when conducting the analysis.

We redid all of our relevant analyses and applied Chao’s rarefaction and extrapolation techniques throughout the manuscript. The (substantial) changes are fully described above in the “Essential Revisions” section. We also copy them here.

First, we redid all of our diversity calculations applying Chao’s rarefaction and extrapolation techniques through the R package iNext. Therefore, our summary datasheet now has many new columns to include the following values for each city:

○ Effective species number:

■ Raw effective species number

■ Asymptotic estimate of effective species number with confidence interval

■ Estimate of effective species number for a given population size (37,000 trees– the median population size rounded to the nearest 1,000) with confidence interval

○ Species richness:

■ Raw species richness (number of species)

■ Asymptotic estimate of number of species with confidence interval

■ Estimate of number of species for a given population size (37,000 trees– the median population size rounded to the nearest 1,000) with confidence interval

○ The same for the native-only population of trees in each city (e.g., not just raw number of effective number of native species but also the iNext estimates and confidence intervals)

○ Whether or not each of the values above was calculated using extrapolation or interpolation

○ Sample coverage estimates

Second, we re-ran our models testing for significant correlations between species diversity in a city and other factors (including climate), where we used the extrapolated / interpolated effective species numbers from iNext. Specifically, we found the best fit model, which included the following predictors: environmental PCA1, environmental PCA1:environmental PCA2, and whether or not a city was designated as a Tree City USA. Then, we ran this model under six sensitivity conditions, varying the independent variable and/or which cities we included based on completeness of their sample. Climate was still a significant correlate of diversity.

○ first, with independent variable = effective species as calculated for a given population of 37,000 trees ("effective species for a standardized population size");

○ second, independent variable = the asymptotic estimate of the effective species number for that city as calculated using iNext;

○ third, the raw effective species number;

○ fourth, excluding cities with fewer than 10,000 trees;

○ fifth, excluding cities with <50% spatial coverage;

○ sixth, excluding cities with <0.995 sample coverage as calculated by iNext.

○ For the fourth, fifth, and sixth models, the independent variable was effective species for a standardized population size of 37,000 trees.

Third, we redid our comparisons of tree populations in parks versus those in urban areas. Parks were still more diverse than urban areas.

○ Specifically, we used iNext to calculate diversity metrics based on the smaller of the two population sizes (park vs urban) to enable fair comparison for each city.

○ We reported comparison results for (i) raw effective species number, (ii) asymptotic estimate, and (iii) estimate for a given population.

○ In doing so, we eliminated Milwaukee from the comparison (it had only 28 trees recorded as being in an urban setting).

Fourth, we redid our pairwise comparisons of tree community composition between cities in order to account for different population sizes and sampling efforts. To do so, we randomly subsampled the larger city to make its population equal to the smaller city, calculated comparison metrics, and repeated this process 50 times. We report the average comparison metrics.

Our new Methods text is copied here for your convenience:

○ “Throughout our analyses, it was necessary to control for different sample sizes (and different, but unknown, sampling efforts across cities). To do so, we relied on the rarefaction / extrapolation methods developed by Chao and colleagues (Chao et al., 2015, 2014; Chao & Jost, 2012) and implemented through the R software package iNext (Hsieh et al., 2016). In short, these methods use statistical rarefaction and/or extrapolation to generate comparable estimates of diversity across populations with different sampling efforts or population sizes, alongside confidence intervals for these diversity estimates. iNext performs these tasks for Hill numbers of orders q = 0, 1, and 2. We used two techniques in iNext to allow for comparisons across cities (and between parks and urban areas within cities). First, we generated asymptotic diversity estimates for each; second, we generated diversity estimates for a given standardized population size. For our diversity analyses, the standardized population size we used was 37,000 trees (the rounded median of all cities). For analyses of the diversity of native trees, we used a standardized population size of 10,000 trees. For comparisons of the diversity between park and urban areas in a city, we used the smaller of the two population sizes (park or urban). In all cases we also recorded confidence estimates, and plotted rarefaction/extrapolation curves.

○ To control for variation in how uniformly trees were sampled across a city’s geographic range, we developed a procedure to score each city’s spatial coverage (see section Spatial Structure below).

○ We identified the best-fitting model, and then repeated our analysis under six sensitivity conditions to control for differences in population size, sampling effort, spatial coverage, and sample coverage. Our sensitivity analyses were as follows: first, with independent variable = effective species as calculated for a given population of 37,000 trees ("effective species for a standardized population size"); second, independent variable = the asymptotic estimate of the effective species number for that city as calculated using iNext; third, the raw effective species number; fourth, excluding cities with fewer than 10,000 trees; fifth, excluding cities with <50% spatial coverage; sixth, excluding cities with <0.995 sample coverage as calculated by iNext. For the fourth, fifth, and sixth models, the independent variable was effective species for a standardized population size of 37,000 trees.”

Reviewer #3 (Public Review):

This paper's strength is in the utility of the assembled datasets and some interesting and creative proof of concept analyses. This is an amazing resource for comparative analysis. However the paper felt a little sparse in the conceptual and methodological underpinnings of the questions asked to demonstrate the utility of the analysis. Specifically, I suggest:

A) More substance in the introduction (currently only two short paragraphs) and a clear statement of research questions.

We have added text to frame our goals and hypotheses:

○ “In particular, we wanted to know whether local climatic conditions are associated with the species diversity of city tree communities, how species diversity was distributed in space within cities, and whether introduced tree species contribute to biotic homogenization among urban ecosystems.”

B) Add data on the extent to which each dataset represents a complete sample of each city's trees. I know are complete inventories, but some consist of 720 trees and cannot be a complete sample. A column in the meta data indicating effort and if there were any bias in where sampling occurred if the dataset is not complete are needed for others to use this data appropriately. For example, we know tree cover/diversity increases with wealth (which the author rightly cites). Let's say in City X, trees were only inventoried in one wealthy neighborhood. They would not be a representative sample of the city and dataset users need to be aware of this before they draw incorrect conclusions about City X where the sample was biased compared to city Y where the inventory was complete, including a sampling of all affluent and poor areas. This is also needed to support the research questions throughout the paper.

We completely agree, and have made two major changes in response.

First, we redid all of our diversity analyses after applying Chao’s rarefaction and extrapolation methods to permit comparison between populations of different sizes and sampling efforts. We added new columns to our datasheet with sample coverage estimates, asymptotic estimates of diversity, and diversity estimates for a given population size.

Second, we also examined spatial coverage in a city because of the valid concern you raised that trees may only be sampled from particular neighborhoods or areas. In short, we divided each city into grid cells, counted trees per grid cell, and calculated metrics of coverage (adjusted number of trees per grid cell, and proportion grid cells that were empty) and bias (skew, kurtosis of number trees in occupied grid cells). These factors are presented in Spatial_Coverage_Supplement.zip. AS you can see even just from a glance at the spatial coverage plots, some cities are indeed extremely biased! Therefore, we ran a sensitivity analysis where we excluded cities with <50% spatial coverage.

C) The authors chose to use effective species counts as their alpha diversity metric of choice. They explain why: "effective species counts (a measure that allows comparison between cities of different sizes)" (Ln 109). While effective species number is an excellent metric with much better behavior and attributes in linear modeling, I believe it is still strongly dependent on both city area and the number of individual trees sampled and so the above statement and all of the comparisons that flow out of it in the manuscript are currently unsupported. Just as species richness needs to be rarified or extrapolated to be compared at an equivalent # of individuals or area to be accurate so too does EFN (effective species count). Fortunately there is an R package (iNext) based on Chao's method (citation below) that makes it very easy to create effective species accumulation curves for each city by tree individuals sampled.

a. Chao, Anne, Nicholas J. Gotelli, T. C. Hsieh, Elizabeth L. Sander, K. H. Ma, Robert K. Colwell, and Aaron M. Ellison. 2014. "Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies." Ecological Monographs 84 (1): 45-67. https://doi.org/https://doi.org/10.1890/13-0133.1.

b. The standardization (rarefaction/extrapolation) of EFN or richness for # individual trees sampled needs to be made for all analyses that make claims to compare diversity metrics across cities or between groups like urban and park areas (i.e. Fig 2a,b,c; Fig 3b; Fig 5a,b, S1a, S2a, S5, Table S2)

c. If the authors have an argument for why diversity/area or diversity/sampling effort relationships do not apply for a particular question, then they should make that case instead.

We very much appreciate this suggestion. Indeed, as described above, we applied Chao’s method to all of our analyses.

D) The question posed by the Beta diversity analysis is fascinating (i.e. is it non-native species that are driving biotic homogenization across species. However, while frequency (which I assume is relative abundance but maybe it is incidence data- please define) is used to deal with different sample sizes consider whether it makes sense to include incomplete, or very small city datasets in the analysis even with frequency data. For example one city only has ~720 trees listed. If this is an incomplete dataset which seems likely, it will probably be much more differentiated (overlap less) from another city with small numbers simply due to incomplete sampling. Diversity analysis in cities always requires tradeoffs and cannot be identical to methods used in "natural" forested ecosystems, but I encourage the authors to explore this a bit. Perhaps a sensitivity analysis could help where incomplete or small sample sizes are dropped or datasets are resampled via random draw to equalize sizes? The latter would handle incomplete samples but would not deal with bias in which neighborhoods were sampled (see point B above).

Great suggestion. We redid this analysis using a random drawn approach, as you suggested, to equalize sizes. The new analysis found the same results as our old analysis, with slightly different values. The new method is described here:

○ “How similar are species compositions across cities? For N = 1953 city-city comparisons of street tree communities, we could calculate weighted measures of similarity because we had frequency data. We calculated similarity scores for the entire tree population, the naturally-occurring trees only, and the introduced trees only. We used chi-square distance metrics on species frequency data, and we controlled for different population sizes (and potentially, sampling efforts) between cities by sub-sampling the larger city 50 times to match the smaller city’s tree population size and calculating average metrics. In this manner we controlled for differences in sample size.”

E) Additional context/conceptual underpinning the clustering analysis would be great.

a. The authors state in Line 390-395:"For city trees, which are often organized along grids or the underlying street layout of a city, this method can more meaningfully cluster trees than merely calculating the meters between trees and identifying nearest neighbors (which may be close as the crow flies but separated from each other by tall buildings)."- I very much agree with this sentiment and it is biologically meaningful for animal and plant dispersal, but as written it is unclear to me how the method described in the text "knows" that a tall building or elevation or some sort of feature exists to separate clusters rather than empty space or a ball field. Please clarify.

We appreciate these comments, and we have added text and references for the interested reader. Here is the new description in full:

○ “We wanted to quantify the degree to which trees were spatially clustered by species within a city (rather than randomly arranged). To do so, we first clustered all trees within each city using hierarchical density based spatial clustering through the hdbscan library in Python (McInnes et al., 2017). HDBSCAN, unlike typical methods such as “k nearest neighbors”, takes into account the underlying spatial structure of the dataset and allows the user to modify parameters in order to find biologically meaningful clusters. For city trees, which are often organized along grids or the underlying street layout of a city, this method can more meaningfully cluster trees than merely calculating the meters between trees and identifying nearest neighbors (which may be close as the crow flies but separated from each other by tall buildings). In particular, using the Manhattan metric rather than Euclidean metrics improves clustering analysis in cities (which tend to be organized along city blocks). For further discussion of why hbdscan is preferable to other clustering metrics, see (Berba, 2020; Leland McInnes et al., 2016; McInnes et al., 2017).”

b. Would you ever expect composition to be truly random either in a city or a natural forest given environmental conditions etc.? In some sense, the ones closest to random are the most surprising. Can you dive into one to give an example of what is going on in that city?

c. It seems like there are two metrics here- the size of the cluster and then the observed/expected EFN per cluster. The latter is analyzed in this paper but is there any important information in the former? It seems like an interesting structural measurement of the city and possibly useful in its own right.

d. Are there any target levels of randomness? Could the authors suggest how this might be determined moving forward with their datasets to illustrate this for foresters?

Great points. We have given a lot of thought to your comments– these are large and interesting questions!! In the end, I think these questions fall mostly beyond the scope of this study, but we added a substantial amount of text to address your comments:

○ “Clustering by species is not necessarily a negative, nor indeed should we necessarily expect trees to be randomly arranged (see suggestions for further research in “Future Analyses” section below). Here, we take a first step toward making spatial clustering a metric of interest in city tree planning.”

○ “Researchers could also use this dataset to perform more refined analysis of clustering. For example, what is the biological significance of variation in cluster size (as determined by the hdbscan clustering algorithms)? The size and arrangement of the clusters themselves may be useful metrics. How clustered should we expect trees to be in both wild and urban settings? That is, what our are null expectations? Further, researchers could apply network theory to predict how pest species would proliferate through each of these cities (depending on the spatial arrangement of pest-sensitive trees).”

F) The statement that this dataset enables "the design of rich heterogenous ecosystems built around urban forests" (Ln 72) seems strange. To my mind this tool will enable a more nuanced evaluation of the urban forests that already exist and suggest ways to target future plantings for increased resilience to climate, pest resistance, biodiversity support etc. I don't understand what ecosystem you would build around and not in the urban forest. If this is what is meant please elaborate. For example, do you mean non-tree installations?

We agree with you and have changed the text as follows:

○ “With these tools, we may evaluate existing city tree communities with more nuance and design future plantings to maximize resistance to pests and climate change. We depend on city trees.”

This claim takes into account that those on the right wing are viewing the purchase of Twitter as a new way to push their narrative on this social media platform. However, they will still push the narrative that they are being censored. This is all a big scheme to make their followers view them as an oppressed group who are being silenced. I think it is important for platforms like this to implement free speech but we have to fact check sources and this is something I feel conservatives may not be taking into consideration.

This contributes to the imperative idea that we talked about on the first day of classes. The imperatives that we have to keep in mind do not only think about the planet and what is best in the long run, but rather the needs of humans and the lives we are currently accustomed to as well. By substituting rather than completely cutting out certain routine actions, it would still result in emissions, but significantly less than those previous to the substitution

Personally I had this view about language before, wouldn't it be nice if we all spoke the same language. As I've grown, I realized that even though it may be convenient, I think that's the beautiful part about humans and society, that we coexist despite our differences. I think it would make us more of a dystopia than a utopia if we were all the same.

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Reply to the reviewers

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

In this paper, Staneva et al describe a novel complex found at RNA PolII promoters that they term the SPARC. The manuscript focuses on defining the core components of the complex and the pivotal role of SET27 in defining its function, and role in PolII transcription. This manuscript is a logical follow on from an initial paper (Staneva et al, 2021) by the same authors where they systematically analyzed chromatin factors, and their role in both transcription start and termination. What is also very clear, is that this complex is one made of histone readers and writers which suggests its function is to change the chromatin structure around a PolII promoters. The authors show that this complex is necessary for the correct positioning of PolII and directionality of transcription.

This was a well-designed study and well written and clear manuscript that provides fascinating insight transcription control in bloodstream form parasites.

I have no major comments only a few minor ones.

1) Localisation of the different SPARC components appears to be either nuclear or nuclear and cytoplasmic. - Both SET27 and CRD1 show a nuclear and cytoplasmic localisation in the bloodstream form IFA (Supplementary Fig 1B), but only a nuclear localisation procyclic form.

Did the authors attempt C terminally tagging SET27, CRD1 to see if this resulted in a change in the pattern?

We have not tagged either protein at the C terminus, however SET27 (Tb927.9.13470) has been tagged both N- and C-terminally in procyclic form (PF) cells as part of the TrypTag project (http://tryptag.org). In both cases, SET27 localized to the nucleus, suggesting that the differences in localization we observe for SET27 depend on the life cycle stage, and not on the position of the tag. One caveat is that in the TrypTag project proteins are tagged with mNeonGreen whereas in our study proteins were tagged with YFP. Based on our images, CRD1 appears to be predominantly nuclear in both bloodstream form (BF) and PF parasites. CRD1 (Tb927.7.4540) has been tagged only N-terminally in PF cells as part of the TrypTag project where it has also been classified as mostly nuclear with only 10% of cells showing cytoplasmic localization for CRD1.

We are well aware that tags can alter the behaviour of a protein. Absolute confirmation of location will require the generation of antibodies that detect untagged proteins. However, this is a longer-term undertaking. We have added the following statement to the Results section to address the point raised:

“We tagged the proteins on their N termini to preserve 3′ UTR sequences involved in regulating mRNA stability (Clayton, 2019). We note, however, that the presence of the YFP tag and/or its position (N- or C-terminal) might affect protein expression and localization patterns”.

• The point is made that JBP2 shows a 'distinct cytoplasmic localisation' in PF cells. by this logic, the SET27 localisation in BF is also distinctly cytoplasmic and a nuclear enrichment is not clear.

Indeed the reviewer is correct - we have inadvertently over accentuated the significance of this difference in the text. We had emphasized the predominantly cytoplasmic localization of JBP2 in PF trypanosomes as potentially related to its weaker association with other (predominantly nuclear) SPARC components in the mass spectrometry experiments. The presence of SET27 in the nuclei of both BF and PF cells is confirmed by a positive ChIP signal. We have revised the manuscript text by changing “distinct cytoplasmic” to “predominantly cytoplasmic” to describe JBP2 localization in PF cells. We hope that this resolves the issue.

• Why would the localisation pattern change between life cycle stages? Surely PolII transcription should remain the same?

Although our analysis suggests that there may be some shift in SET27 and JBP2 localization between BF and PF stages, sufficient amounts of these proteins may be present in the nucleus for proper SPARC assembly and RNAPII transcription regulation in both life cycle forms. The proportion of SET27 and JBP2 proteins that localizes to the cytoplasm may have functions unrelated to transcription.

2) Several of the images in Supplementary Fig 1B seem to show foci in the nucleus (CSD1, PWWP1, CRD1). Do you see foci throughout the cell cycle or just in G1/S phase cells as shown here?

We have not systematically investigated protein localization at different cell cycle stages, so we do not have microscopy images for all proteins at all stages of the cell cycle. However, the images we did collect suggest the punctate pattern is preserved for CRD1 in the G2 phase in both BF and PF cells (see below) as we showed in Supplemental Figure S1B for cells with 1 kinetoplast and 1 nucleus (G1/S phase cells). The significance of these puncta remains to be determined.

3) In Figure 6, what does 'TE' stand for?

TE denotes transposable elements. We have added this to the figure legend.

4) The authors show this interesting link between SPARC complex and subtelomeric VSG gene silencing. -In the CRD1 ChIP or RBP1 ChIP, are there any other peaks in telomere adjacent regions in the WT cells similar to that seen on chromosome 9A? And does the sequence at this point resemble a PolII promoter?

Apart from peaks located on Chromosome 9_3A, there are other CRD1 and RPB1 ChIP peaks in chromosomal regions adjacent to telomeres in WT cells. We observed broadening of RPB1 distribution in these regions upon SET27 deletion, similar to what we show for Chromosome 9_3A. In particular, wider RPB1 distribution on Chromosome 8_5A coincides with upregulation of 10 VSG transcripts. These two loci explain most of the differentially expessed genes (DEGs) detected, but other subtelomeric regions show a similar pattern. We have added the following statement to the Results section to highlight that the phenotype shown for Chromosome 9_3A is not unique:

“We also observed a similar phenotype at other subtelomeric regions, such as Chromosome 8_5A where 10 VSGs and a gene encoding a hypothetical protein were upregulated upon SET27 deletion (Supplemental Table S3)”.

Cordon-Obras et al. (2022) have recently defined key sequence elements present at one RNAPII promoter. We searched for similar sequence motifs but failed to identify them as underlying CRD1 and RPB1 ChIP peaks, highlighting the likely sequence heterogeneity amongst trypanosome RNAPII promoters. To address this point, we have added the following sentence to the Discussion:

“Sequence-specific elements have recently been found to drive RNAPII transcription from a T. brucei promoter (Cordon-Obras et al., 2022), however, we were unable to identify similar motifs underlying CRD1 or RPB1 ChIP-seq peaks, suggesting that T. brucei promoters are perhaps heterogeneous in composition”.

-In the FLAG-CRD1 IP (Figure 3B), the VSG's seen here are not represented (as far as I can tell) in Figure 6B and C. If my reading is correct could, is this a difference in the FC cut off for what is significant in these experiments?

The VSGs detected in the FLAG-CRD1 IP from set27D/D cells are indeed different from the ones shown in Figure 6 (even after setting the same fold change cutoffs). We have highlighted this by adding the following statement to the Results section: “Gene ontology analysis of the upregulated mRNA set revealed strong enrichment for normally silent VSG genes (Figure 6B-D) which were distinct from the VSG proteins detected in the FLAG-CRD1 immunoprecipitations from set27D/D cells (Figure 3B)”.

The VSGs in the mass spectrometry experiments likely represent unspecific interactors of FLAG-CRD1. To clarify this, we have added the following statement to the Results section: ”Instead, several VSG proteins were detected as being associated with FLAG-CRD1 in set27D/D cells, though it is likely that these represent unspecific interactions”.

Reviewer #1 (Significance (Required)):

Trypanosomes are unusual in the way that they transcribe protein coding genes. Recent advances have defined the chromatin composition at the TSS and TTS, and the recent publication of a PolII promoter sequence(s) further adds to our understanding of how transcription here is regulated. Defining the SPARC complex now add to this understanding and highlights the role of potential histone readers and writers. I think that this will be of interest to the kinetoplastid community especially those working on control of gene expression.

Our lab studies gene expression and antigenic variation in T. brucei.

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

In this manuscript, the authors identify a six-membered chromatin-associated protein complex termed SPARC that localizes to Transcription Start Regions (TSRs) and co-localizes with and (directly or indirectly) interacts with RNA polymerase II subunits. Careful deletion studies of one of its components, SET27, convincingly show the functional importance of this complex for the genomic localization, accuracy, and directionality of transcription initiation. Overall, the experiments are well and logically designed and executed, the results are well presented, and the manuscript is easy to read.

There are a few minor points that would benefit from clarification and/or from a more detailed discussion:

1) The concomitant expression of many VSGs (37) in a SET27 deletion strain is remarkable and has important implications for their normally monoallelic expression. It is well established that VSG expression in wild-type T. brucei can only occur from one of ~15 subtelomeric bloodstream expression sites, which include the ESAGs. This result implies that VSG genes are also transcribed from "archival VSG sites" in the genome, not only from expression sites. Are there VSGs from the silent BESs among the upregulated VSGs? Is there precedence in the literature for the expression of VSGs from chromosomal regions besides the subtelomeric expression sites?

Our analysis of differentially expressed genes (DEGs) revealed that 43 VSG genes (37 of which are subtelomeric) and 2 ESAG genes are upregulated in the absence of SET27. Both ESAGs but none of the upregulated VSGs in set27D/D cells are annotated as located in BES regions. While it is possible that recombination events have resulted in gene rearrangements between the reference strain and our laboratory’s strain, at least some of the upregulated VSGs are likely to be transcribed from non-BES archival sites. VSG transcript upregulation from non-BES regions was also recently described by López-Escobar et al (2022).

We note that the upregulated mRNAs in set27D/D are still relatively lowly expressed (Figure 6C). This is presumably insufficient to coat the surface of T. brucei, and expression from BES sites instead may be required to achieve this. We have revised the manuscript Discussion section to make these points more clear:

“Bloodstream form trypanosomes normally express only a single VSG gene from 1 of ~15 telomere-adjacent bloodstream expression sites (BESs). In contrast, in set27D/D cells we detected upregulation of 43 VSG transcripts, none of which were annotated as located in BES regions. Recently, López-Escobar et al (2022) have also observed VSG mRNA upregulation from non-BES locations, suggesting that VSGs might sometimes be transcribed from other regions of the genome. However, the VSG transcripts we detect as upregulated in set27D/D were relatively lowly expressed (Figure 6C) and may not be translated to protein or be translated at low levels compared to a VSG transcribed from a BES site”.

2) The role of SPARC in defining transcription initiation is compelling. It's less clear to the reviewer if the observed transcriptional silencing within subtelomeric regions can also ascribed to SPARC. Have the authors considered the possibility that some components of the SPARC may be shared by other chromatin complexes, which could be responsible for the transcriptional activation of silent genes in SET27 deletion mutants?

We cannot rule out indirect effects through the participation of some SPARC components in other complexes operating independently of SPARC. Indeed, the transcriptional defect within the main body of chromosomes appears to be somewhat different from that observed at subtelomeric regions, particularly with respect to distance from SPARC. We have added a statement in the Discussion section to highlight the possibility raised by the reviewer:

“However, an alternative possibility is that transcriptional repression in subtelomeric regions is mediated by different protein complexes which share some of their subunits with SPARC, or whose activity is influenced by it”.

3) The authors mention that the observed interaction of FLAG-CRD1 with VSGs in the immunoprecipitations (Fig. 3B) is evidence for the actual expression of normally silent VSGs on the protein level. This is true, but it should be spelled out that this interaction is nevertheless likely an artifact, at least the physiological relevance of these interactions is questionable.

We agree that these are likely background associations and have added the following statement to the Results section to clarify this point:

“Instead, several VSG proteins were detected as associated with FLAG-CRD1 in set27D/D cells, though it is likely that these represent unspecific interactions”.

To avoid unnecessary confusion we have also removed the following sentence from the revised Discussion:

“The interactions of FLAG-CRD1 with VSGs in the affinity selections from set27Δ/Δ cells indicate that some of the normally silent VSG genes are also translated into proteins in the absence of SET27”.

4) "ophistokont" is misspelled in the introduction

Thanks for noticing. We have corrected it to “Opisthokonta”.

Reviewer #2 (Significance (Required)):

The manuscript by Staneva et al. addresses the fundamental regulatory mechanism of gene transcription in the protozoan parasite Trypanosoma brucei, a highly divergent eukaryotic organism that is renowned for unusual features and mechanisms in gene regulation, metabolism, and other cellular processes. While post-transcriptional regulation is prevalent and relatively well established in T. brucei, much less is known about the mechanism of transcription initiation and transcriptional control, in part due to the general paucity of well-defined conventional promoter regions in this organism (only very few have been identified thus far). In this context, the work by Staneva et al. is highly significant and represents an important contribution to the field of gene regulation and chromatin biology in T. brucei and other related kinetoplastid parasites.

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Reply to the reviewers

Reviewers 1 and 2 are very positive about our manuscript, while reviewer 3 is surprisingly critical.

However, except for the first observation, most of reviewer 3´s comments are based on incorrect interpretations of our results.

We have integrated the useful comment into our revised version, and we will discuss in the following sections why reviewer 3’s remaining criticisms should be disregarded.

Reviewer 1:

Reviewer 1 has only minor suggestions and is satisfied that we prove convincingly our claims. The reviewer also finds our results reinforce our previously proposed hypothesis that the glands and the trachea evolved from common metamerically repeated ancient primordia.

We have introduced the following changes to the text to accommodate Reviewer’s 1 minor suggestions.

Main suggestion: Insert a paragraph in discussion explaining the relevance of new insights to more basal insects that do not form a ring gland.

RESPONSE:We have introduced at the end of Discussion the following paragraph:

“Our analysis of snail activation in the CA and PG shows that these glands and the trachea share similar upstream regulators, reinforcing the hypothesis that both diverged from an ancient segmentally repeated organ. In Drosophila melanogaster the CA and the PG primordia experiment a very active migration after which they fuse to the corpora cardiaca forming the ring gland (Sanchez-Higueras and Hombria, 2016). This differs from more basal insects where the CA fuses to the corpora cardiaca but not to the PG, and from the Crustacea where the three equivalent glands are independent of each other (Chang and O'Connor, 1977; Laufer et al., 1987; Nijhout, 1994; Wigglesworth, 1954). As the mechanisms we here describe relate to the early specification of the glandular primordia in Drosophila, it will be interesting to investigate if the equivalent genes are also involved in the endocrine gland specification of more distant arthropods”.

Additional comment 1: Introduction, pg 3, a paragraph starting with "In comparison to the extensive knowledge we have of ..." - consider omitting or greatly shortening, this text breaks a flow as it is focused on tracheal development. I understand the authors' logic, but this information distracts from the main focus on CA and PG. RESPONSE:We agree that the trachea description paragraph breaks the flow of the introduction to gland development. As suggested by the reviewer, we have deleted most of the descriptive text on trachea development but left all the references so that interested readers can find the information.

Additional comment 2: Beginning of discussion, pg 11: - change 2nd sentence to: " Our results indicate that the HH and the Wnt pathways act indirectly to negatively regulate the spatial activation ..." - the following sentence, starting with "Engrailed activation off hh transcription ...." is way too long and hard to follow, consider breaking into two sentences. RESPONSE:We have changed both sentences as suggested

Additional comment 3: In Fig 4B, mx and lb segments should be labeled so this panel is consistent with labeling in 4A. RESPONSE:We have changed Fig.4B labels to be consistent with 4A

Additional comment 4: In Fig 6, reduce a font size for labels on right-hand side (A1, A2, A1+A2 proximal, etc), so that they are visualy distinct from panel labels on left-hand side (A, B, C,..).

RESPONSE:We have changed Fig.6 Font size as suggested

Reviewer 2

The reviewer is positive and agrees that the results we present in “this paper add to our understanding of how the CA and PG primordia are specified and highlights important similarities with the specification of the tracheal primordia”. The reviewer’s comments focus specially on the activation vs. maintenance of sna.

Specific Comment a): Referring to Fig 1G-J, the reviewer says: It is not clear to me from either this figure or from the text whether the initial pattern of expression of the sna-rg reporter in stage 11 embryos is WT and then disappears at stage 12, or whether it is always defective. In trying to understand the activation process, I think it would be important to know for sure whether rg enhancer activity during the initiation phase in stage 11 is WT or not.

RESPONSE: As suggested by the reviewer, we have included st11 embryos in Fig. 1 as panels G,J which illustrate that early sna-rg activation occurs normally in snaΔrgR2embryos prior to apoptosis kicking in. To make space for these images, we have taken out the st12 embryos that we had in our previous submitted version. This does not affect the manuscript’s message, as st12 phenotypes are similar to those at st13 which are presented in Fig. 1H,J.

Moreover, in this revised version, the embryos in Fig. 1G-J have also been double stained with the apoptosis marker DCP1 to highlight the cell death observed in the gland primordia of snaΔrgR2 embryos (Fig. 1G’-J’).

Specific Comment b) The authors argue that the rg deletion removes the only region driving sna expression in CA/PG. I'm not convinced that necessity necessarily implies sufficiency with respect to the requirements for rescue. While the sna-rg reporter is expressed in a pattern that seems to mimic the endogenous gene, do we know that a rg-sna transgene would fully rescue the rg deletion mutant?

RESPONSE: In our previous paper (Sanchez-Higueras 2014) we presented evidence that in sna null embryos, a Snail BAC gene lacking the sna-rg CRM can fully rescue the mesoderm phenotypes but not the ring gland ones. This proved that in the BAC transgene there was no shadow CRM capable of rescuing the gland formation in the absence of sna-rg. In the current paper we show that deleting the endogenous sna-rg CRM in the sna locus results in the absence of sna transcription from the gland primordia.

Making a sna-rg- construct expressing sna to test if this rescues the snaΔrgR2 homozygous mutants could be done, but it will delay this publication without adding much to the paper: we already know that sna-rg is sufficient to drive activation in all the CA and the PG cells (Sanchez-Higueras 2014 Fig 2J-M) and it would be expected to rescue the gland formation in snaΔrgR2 homozygous mutants.

Having said that, we have changed the wording in the manuscript to one that may be acceptable to the reviewer.

Instead of:

“These results prove that snaΔrgR2 deletes the only regulatory region driving sna expression in the CA and PG gland primordia…”

We now say:

“These results prove that the snaΔrgR2 deletes mutation inactivates the only regulatory region driving sna expression in the CA and PG gland primordia…”

Specific Comment c) is Sna required for maintaining sna expression?

RESPONSE:This experiment is relevant to the maintenance mechanism of sna expression in the ring gland, and not to its activation which is the main focus of this paper.

The search for the maintenance mechanisms is currently been followed in the laboratory and we prefer not deal with it in this paper. Providing a negative answer to this question would not be satisfactory, as we would need to search for the factors controlling sna’s maintenance.

Specific comment d) The authors show that there is an expansion in the number of sna-rg reporter expressing cells along the AP axis when upd is ectopically expressed using a sal-Gal4 driver. Though not mentioned in the text at this juncture, sal is expressed in the PG primordia, while seven-up (svp) is expressed in the CA primordia. I assume that the upd induced expansion is only observed for the PG primorida (LB) and not the CA primordia (Mx)-at least this is what the figure looks like. (…) How about svp driven upd-assuming there is a svp-Gal4 driver-does it cause an expansion of Ca but not PG.

RESPONSE: As the reviewer has noticed, there is a stronger expansion of sna-rg-GFP expression in the labial segment than in the maxillary segment. This is not due to the use of the sal-Gal4 line. We see the same effect with arm-Gal4 which drives similar expression on the maxilla and the labium. To illustrate this point, we have included two new panels (Fig.5D-E) where the ectopic expression of Upd has been induced with arm-Gal4. These embryos have been stained with anti-Sal to label the PG. This experiment shows clearly that the PG has expanded much more than the CA.

There are several reasons why expansion of the glands could be more efficient in the labium than in the maxilla. One possible reason is the temporal response to Upd activation. Upd induction by the arm-Gal4 and sal-Gal4 lines may occur after the cells in the maxilla are no longer capable of activating sna-rg but still capable of activating it in the labium. This temporal hypothesis is based on our results showing that the CA expresses more transiently the upd gene and that STAT activation lasts for longer in the labium than in the maxilla (Fig. 4A-D)].

A second possibility, that we favour, is the existence of dorso-ventral repressor genes modulating sna-rg expression intrasegmentally. Some of our results point towards the sna-rg CRM receiving repressor inputs that modulate intrasegmental spatial expression in the dorso-vental axis. When we delete the A2 distal region of the sna-rg enhancer, its expression in the labium expands ventrally (Fig. 6E,G and Sup.Fig. 4D). If a similar repressor was also modulating sna-rg in the maxilla it could be blocking its expansion. However, at this stage we have no solid data to support any of these hypotheses. As explained before for the maintenance mechanisms of sna-rg expression, our ongoing work aims to isolate and characterize further elements controlling the ring gland gene network, including these negative regulators.

In the revised manuscript we now describe the different effects of Upd ectopic activation on the expression of sna-rg in the maxilla and the labium (underlined text is new to this revised version):

“To test if generalised Upd expression in the maxilla and labium can activate sna-rg expression independently of other upstream positive or negative inputs, we induced UAS-upd with either the sal-Gal4 or the arm-Gal4 lines. We observe that, these embryos have expanded sna-rg expression along the antero-posterior axis in the maxillary and labial segments (Fig. 5C). Analysis of Sal expression, which labels the PG primordium (Sanchez-Higueras et al., 2014), shows that Upd ectopic expression induces a moderate expansion of the CA primordia while resulting a much larger increase of the PG primordium (Fig. 5D-E). This expansion occurs mostly in the anterior and posterior axis from cells where the Hh and the Wnt pathways are normally blocking sna-rg expression, while expansion is less noticeable in the dorso-ventral axis. This indicates that most of the antero-posterior intrasegmental inputs provided by the segment polarity genes converge on Upd transcription but that the dorso-ventral information is registered downstream of Upd.”

The differential response of sna-rg to Upd activation in the maxillary and labial segments is also mentioned in Fig. 5 legend. (see Continuation comment d).

* Continuation comment d) “It looks to me also like the vvl domain is expanding as well. This information should be clarified.*”

RESPONSE: Yes, ectopic upd expression also expands vvl1+2 expression. We have previously published that vvl1+2 is a direct target of JAK/STAT signalling in the trachea (Sanchez-Higueras 2019 and Sotillos et al. 2010 Dev.Biol). Although vvl1+2 expands dorsally in the Mx, those cells do not activate sna-rg dorsally. The ventral restriction of sna-rg in the maxilla is controlled by Dfd while in the labium its dorsal expression depends on Scr. We explain this in Fig.5’s figure legend where we now say (underlined text is new to this revised version):

(C) Ectopic Upd expression driven with sal-Gal4 induces ectopic sna-rg and vvl1+2 expression in the gnathal segments, which for sna-rg is more pronounced in the labium than in the maxilla. Note that in the maxillary segment Upd can induce ectopic dorsal vvl1+2 but not sna-rg expression, this is expected as Dfd only induces sna-rg ventrally in the maxilla. (D-E) sna-rg-GFP embryos stained with anti-GFP (green) and anti-Sal (red). In control embryos (D) Sal labels the PG primordium but not the CA. In arm-Gal4 embryos ectopically expressing Upd, the PG is more expanded than the CA as shown by number of cells co-expressing Sal and GFP.

Specific Comment e) The authors note a difference between CA and PG in the requirement for STAT binding sites in the enhancers. Is that related to the fact that svp is expressed in CA and sal is expressed in PG? Would driving svp expression using the sal-Gal4 driver maintain sna-rg expression.

RESPONSE: During our preliminary ongoing experiments on sna maintenance mechanisms we looked in svp mutants and did not notice a change in sna-rg expression, thus it is unlikely that Svp is responsible for the difference. As said above, we continue looking for genes involved in gland formation. Sal could be involved in the maintenance of sna in the PG, but as Sal is expressed in the maxilla and labial segments before gland formation, it is difficult to disentangle if Sal is required for sna activation or maintenance (or both).

Specific Comment f) Do svp or sal have a role in initiating sna expression when upd is present or maintaining sna expression after upd disappears? Presumably there is already published data that would answer these questions.

RESPONSE: As explained above we did not find any effect of svp on activation of sna-rg, however we find that in sal mutants the labium does not express sna-rg. This shows that sal is likely to be another positive input. As in sal mutants both trh and Ubx become ectopically expressed in the Lb (Casanova1989 Roux's archives of developmental biology 198: 137-140; Castelli-Gair 1998 IJDB42:437-444) we have done the experiment in sal trh double mutants and in sal Ubx,abdA,Abd-B mutants. In both cases we still see a failure of sna activation in the Lb reinforcing the idea that Sal is an additional positive input. However, we prefer not to add the sal experiments as they would complicate the paper which currently focuses on the similar requirement of the Wnt, Hh and JAK/STAT signalling pathways.

Reviewer 3

Reviewer is very critical. We accept some of the points raised and have modified the manuscript accordingly. However, as we detail below, the most serious criticisms are incorrect and do not affect the conclusions reached by our work.

We agree with the following comment:

“In the Dfd Scr double mutant, both the CA and PG expression of the snail-rg-GFP reporter is still there - admittedly, the gland cells look abnormal at late stages, but this reporter that is supposed to function as a proxy for gland induction is still expressed. That either means that expression of sna-rg-GFP is not a proxy or that the glands are still being specified in the absence of the Hox genes that are proposed to specify these organs. The reporter should not be expressed if these Hox genes are what specify these endocrine organs.”

RESPONSE: The reviewer has made a good observation. The expression of sna-rg-GFP is not completely absent in Dfd Scr mutant embryos (Fig. 5F in this revised version), which indicates that although the Hox genes are required to activate upd in the maxilla and labium and in their absence the gland primordia become apoptotic, there must be other positive inputs to the enhancer. However, this does not mean the Hox gene input is irrelevant for gland specification. Not only the Hox genes are required to keep normal levels of upd expression in the Mx and Lb primordia and gland viability, but previously we also showed that cephalic Hox genes influence the dorso-ventral position inside the vvl1+2 expressing cells where the sna-rg enhancer is activated: in the maxilla Dfd induces the ventral vvl1+2 expressing cells to activate sna-rg, while in the labium Scr induces the dorsal vvl1+2 cells to activate sna-rg (Sanchez Higueras 2014). The data presented in this paper indicate that the input of both Dfd and Scr over sna-rg CRM activation are indirect.

As a result of the reviewer’s criticism, we have tested if the additional positive input could be provided by Ci. In our previous submitted version, we showed that the repressor form of Ci blocks sna-rg activation. In this revised version, we have tested what is the effect of expressing the activator form of Ci. In embryos overexpressing the activator CiPKA isoform, we have observed that the expression of sna-rg and upd are expanded, indicating that Ci can provide the additional Hox-independent positive input. In the revised version we present these new results as Fig.3G and Fig. 4I. We have modified accordingly the scheme that appears in panel 3I to include this. In the main text we describe the result in the Hh regulation section where we have added:

“Although the above results indicate Ci is not absolutely required for sna-rg expression, we observed that overexpression of CiPKA, the active form of Ci, causes a non-fully penetrant expansion of sna-rg expression (Fig. 3G) suggesting the possibility that sna-rg may be responsive to Ci and to a second activator.”

… and in the “Regulation of Upd ligand expression by the Wg and Hh pathways” section

where we say:

“We also found that ectopic expression of the activator Ci protein results in a non-fully penetrant expansion of upd expression in stage 10 embryos (Fig. 4H-I).”

We have also modified the final scheme in Fig. 7 to mention that Dfd and Scr prevent the apoptosis of the gland primordia, and that there must be an additional positive input controlling upd activation besides the Hox input. However, in the figure we do not define Ci as the activating input as we would like to have additional evidence before making such claim.

To clarify that the Hox input is not absolutely required we have modified the text in several places. Where we said:

“Expression of the sna-rg reporter in the maxilla and the labium requires Dfd and Scr function …”

We now say:

“Development of the CA and PG and normal expression of the sna-rg reporter in the maxilla and the labium require Dfd and Scr function …”

We also mention this in Fig. 5 legend where we have added:

“In Dfd Scr mutant embryos (F), although the gland primordia become apoptotic, residual GFP expression indicates that there must exist Hox independent inputs activating the sna-rg enhancer.”

As a result of reviewer 3’s comment, we have noticed a further example of similarity between the gland and the trachea specification, which we have commented in the revised discussion where we added the following paragraph:

“Another interesting similarity between glands and trachea is that, although ectopic Hox gene expression can ectopically induce sna-rg and trh outside their normal domain, the lack of Hox expression does not completely abolish their endogenous expression, indicating that in both cases a second positive input can compensate for the absence of Hox mediated activation. Our results suggest that, in the glands, this redundant input could be provided by the activating Ci form (Figs. 3G and 4I), but further analysis to confirm this possibility and discard alternative sna-rg activators should be performed.”

We disagree with the following comments:

The finding that the CA and PGs form in slightly different DV positions from each other and slightly different DV positions from the trachea (based on the vvl1+2 mCherry reporter staining combined with that of the sna-rg-GFP reporter staining in Figure 5A, where staining does not overlap except where the CA cells have started to migrate over the vvl1+2 mCherry expressing cells) argues pretty strongly against the CA and PG being homologous to each other or absolutely homologous to the trachea primordia

RESPONSE: This erroneous claim was based on Fig. 5A, that showed a double stained embryo where co-expression is difficult to appreciate without separating the channels. Co-expression of these two reporter lines in the ring gland has been previously documented beyond doubt in our 2014 publication, cited throughout the manuscript, where we presented eight different panels of glands clearly co-expressing both markers at various developmental stages (Current Biology 2014 Fig.2B-I). To prevent any readers reaching the same conclusion as the reviewer, we have modified Fig. 5A to show a double stained sna-rg-GFP vvl1+2-mCherry embryo alongside with the two separate channels (panels 5A’ and A’’) to make the co-expression evident.

Although we are not including it in this manuscript, the reviewer will also be able to find images in the same 2014 Current Biology publication (Fig.3), where the ectopic activation of Dfd in the trunk leads to the activation of the sna-rg-GFP reporter in the vvl1+2 tracheal cells, proving that the glands and the trachea are formed at homologous positions.

Having made clear that sna-rg activation in both the CA and the PG occurs in vvl1+2 expressing cells, we now refute a second criticism: The reviewer is puzzled that despite the glands being formed at different dorso-ventral positions in the vvl1+2 expressing patch of cells, we claim both groups of cells are homologous to the trachea.

We are not saying that the CA are formed at homologous positions to those giving rise to the PG. What we say is that both the CA and the PG are formed at positions homologous to those giving rise to the trachea in the trunk segments.

To make this clear in the revised version, we have changed the wording of a sentence in the Introduction section that might have originated the confusion.

Instead of saying:

“First, the CA, the PG and the traqueal primordia are specified in the lateral ectoderm at homologous positions”.

Now, it reads:

“First, the CA and the PG are specified in the cephalic lateral ectoderm at homologous positions to those forming the tracheal primordia in more posterior trunk segments.”

It has been shown that each tracheal primordium (which are labelled by vvl1+2-mCherry) gives rise to different tracheal branches depending on the positions where they are specified: the dorsal cells give rise to the dorsal tracheal branches, the ventral cells to the ganglionic branches, the medial cells to the dorsal trunk etc. (for illustration see Fig.12 in Manning and Krasnow 1993). Each of these tracheal branches have a different shape and migrate to different positions. We believe that a similar positional specification occurs in the vvl1+2 cells in the maxilla and the labium. In the maxilla only the vvl1+2 ventral cells activate sna and svp (among other genes) to give rise to the CA. In the labium vvl1+2 dorsal cells activate sna, sal, phm (among other genes) to give rise to the PG. This regionalization is similar to what happens during tracheal branch specification, with the only difference that the interaction with Dfd and with Scr is what makes the positional outcome in the maxilla and the labium different (see in our Current Biology 2014 publication Fig. 3E-F and H-J). Thus, when the reviewer considers the equivalence between the CA/PG/trachea homology with that of the wing/haltere or that of the thoracic leg1/2/3 saying: “Indeed, the situation with these endocrine glands and the trachea is completely unlike the situation with the wing and haltere, wherein both structures arise from the same DV position in adjacent segments, or with legs 1, 2 and 3, which arise from the same DV position in adjacent segments”

…the reviewer should think about the coxa and the tarsi in the legs. The coxa in T1 is not homologous to the tarsi in T2 or T3, but when considering the leg structure as a whole, the coxa and the tarsi form part of the same homologous structure in T1, T2 and T3 despite being formed at different positions inside the leg primordia.

The reviewer also doubts that the activation of upd occurs in the sna-rg primordium when saying: “Likewise, the STAT10X-GFP staining does not overlap with the sna-rg-mCherry staining (I see red cells and I see green cells - there are no yellow cells). If activation of snail is through Upd activation of STAT signaling, we should see that the snail reporter expression is within the domain of STAT10X-GFP expression.”

RESPONSE: This is due to the fact that upd activation in the CA is extremely transient, leading to the loss of the x10STAT-GFP expression before the sna-rg-mCherry levels are robust enough in the maxilla. This criticism does not apply to the PG where due to upd expression lasting longer, co-expression of sna-rg-mCherry and x10STAT-GFP in panel 4B should be evident to the reviewer.

To try to sort the CA co-expression problem, we are currently repeating the experiment but instead of analysing sna-rg-mCherry activation with the RFP antibody, we will do an mcherry RNA in situ. We hope that the mcherry transcript will be detectable earlier than the protein and the co-expression will be evident.

We strongly disagree when the reviewer says: “This paper provides a strong basis for arguing that the CA and PG are induced independently of Jak/Stat signaling, whereas trachea require this signaling pathway.”

RESPONSE: When making this claim, the reviewer is ignoring a large number of experiments presented in the manuscript. If the CA and the PG are induced independently of JAK/STAT signalling:

(1) Why sna-rg expression disappears from the glands in mutants lacking the Upd ligands (Fig. 5B and 6K)?

(2) Why deleting the region containing the putative STAT binding sites in the sna-rg enhancer causes the loss of enhancer expression (Fig. 6C)?

(3) Why the smaller enhancer mentioned in point (2) recovers gland expression when adding a STAT binding site from an unrelated gene (Fig. 6G)?

(4) Why the regained expression of the construct mentioned in (3) is lost by the mutation of two bases affecting this single STAT site (Fig. 6H)?

The reviewer’s conclusion rests on giving an excessive importance to his reservations to CA co-expression in panel 4A while, surprisingly, disregarding the co-expression in the PG shown in panel 4B and all the experiments presented in Fig. 5 and Fig.6.

Reviewer 3 Minor comments: RESPONSE: Both comments have been taken into account in the revised version.

In summary, in this revised version we have answered most queries raised by reviewers 1 and 2. Moreover, reviewers 1 and 2 agree that the results presented in this manuscript reinforce the hypothesis that the CA and the PG glands and the trachea derive from the divergent evolution of a metamerically repeated homologous organ.

Reviewer 3 has made a good point that we have taken into account and has improved the revised submission.

However, reviewer 3 is wrong when concluding:

“This paper provides a strong basis for arguing that the CA, PG and trachea are not homologous structures”, and when saying: “the CA and PG are induced independently of Jak/Stat signaling, whereas trachea require this signaling pathway”.

As we argue above, these conclusions are erroneous because:

(1) Are based on the incorrect interpretation of Fig 5A and ignore previous published evidence cited throughout the manuscript.

(2) It does not take into account key experiments presented in this work, while giving too much weigh to a result that can be easily interpreted.

(3) It misinterprets the arguments justifying the positional homology between the CA/PG glands and trachea primordia.

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Referee #3

Evidence, reproducibility and clarity

Summary:

This paper focuses on the specification of two endocrine glands that form from head ectoderm, the corpora allata (CA), which forms in the maxillary segment and secretes Juvenile hormone, and the prothoracic glands (PG), which form in the labial segment and secrete Ecdysone. Secretion of both hormones results in a larval molt. Secretion of only Ecdysone induces metamorphosis, the transition of the larvae into the adult forms. Both the CA and PGs form in positions homologous to the tracheal primordia (approximately) and previous reports indicate that ectopic expression of the appropriate Hox genes can result in homeotic transformations of the glands into tracheal primordia and of tracheal primordia into glands. Using a GFP reporter construct for the snail gene as a proxy for gland specification, the authors show that CA and PG formation is regulated by two segment polarity genes: Hh and Wnt, with Hh signaling activating reporter gene expression and Wnt signaling inhibiting reporter gene expression. They also suggest that their endocrine gland GFP reporter is regulated by the two Hox proteins expressed in those segments: Dfd (maxillary) and Scr (labial) (although figure 5D,E argue against this conclusion). They presumably show that reporter gene regulation by Wnt signaling and Hh signaling is indirect and through localized transcriptional activation of the JAK/STAT signaling pathway ligand gene upd (however, the STAT reporter and the snail reporter are expressed in different cells (fig 4B) - so I'm not so convinced of this conclusion). The authors also find that the CA and PG primordia form at slightly different dorsal ventral positions and that DV positional information is controlled downstream of upd JAK/STAT signaling.

Major comments:

The paper is well written and makes for a nice story, but the corresponding data are not supportive of most of the conclusions drawn by the authors.

First, in the Dfd Scr double mutant, both the CA and PG expression of the snail-rg-GFP reporter is still there - admittedly, the gland cells look abnormal at late stages, but this reporter that is supposed to function as a proxy for gland induction is still expressed. That either means that expression of sna-rg-GFP is not a proxy or that the glands are still being specified in the absence of the Hox genes that are proposed to specify these organs. The reporter should not be expressed if these Hox genes are what specify these endocrine organs. This finding might explain why mutating the Hox consensus binding sites had no effect on expression of the smaller snail reporters.

The finding that the CA and PGs form in slightly different DV positions from each other and slightly different DV positions from the trachea (based on the vvl1+2 mCherry reporter staining combined with that of the sna-rg-GFP reporter staining in Figure 5A, where staining does not overlap except where the CA cells have started to migrate over the vvl1+2 mCherry expressing cells) argues pretty strongly against the CA and PG being homologous to each other or absolutely homologous to the trachea primordia. Likewise, the STAT10X-GFP staining does not overlap with the sna-rg-mCherry staining (I see red cells and I see green cells - there are no yellow cells). If activation of snail is through Upd activation of STAT signaling, we should see that the snail reporter expression is within the domain of STAT10X-GFP expression. This would be consistent with observing a loss of upd mRNA in the maxillary and labial segments with loss of Dfd and Scr, but not seeing a loss of the sna-rg-GFP reporter. This would also argue against the proposed homology between the glands and the trachea. Indeed, the situation with these endocrine glands and the trachea is completely unlike the situation with the wing and haltere, wherein both structures arise from the same DV position in adjacent segments, or with legs 1, 2 and 3, which arise from the same DV position in adjacent segments. This paper provides a strong basis for arguing that the CA, PG and trachea are not homologous structures and that the CA and PG are induced independently of Jak/Stat signaling, whereas trachea require this signaling pathway.

Minor comments:

Page 3: tracheal is misspelled in the first paragraph, line 3.

Page 5, end of first sentence in first full paragraph: "lethal" should be changed to "non-viable". I think the authors mean that homozygous embryos die, not that they cause the death of other life forms.

Significance

Nature of significance of advance:

I think the significant finding is that the CA, PG, and trachea are not homologous structures. But that is not what the authors are concluding. The only findings consistent with the data provided are that Wg signaling represses expression of the snail reporter and Hh signaling activates its expression (Figures 1 - 3). Most of the other conclusions do not seem to be sufficiently supported by the data.

Context of the work:

These authors have published that the CA and PG are structures specified in homologous positions to the trachea. It has already been published that CA, PG and trachea primordia express the Vvl transcription factor - although I did not go back to see how that was determined. It has already been published that ectopic expression of specific Hox genes can transform the gland primordia into trachea and vice versa (these experiments may also warrant a closer look). So, idea that CA, PG and TR arose from divergent evolution of a segmentally repeated ancient structure has been proposed.

Best target audience:

With the findings that are consistent with the story line (figures 1 - 3), Drosophila embryologists working on the formation of these glands would be interested.

My field of expertise:

Drosophila development.

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Reply to the reviewers

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

Summary: Klein and colleagues generate an ES cell model system with inducible FACT depletion to understand how loss of FACT affects gene regulation in ES cells. They find that FACT is critical for ES cell maintenance through multiple mechanisms including direct regulation of key pluripotency transcription factors (Sox2, Oct4, and Nanog), maintaining open chromatin at enhancers, and regulated enhancer RNA transcription. The paper is well-written, the experiments are generally well-controlled and appropriately interpreted and placed within the context of the field.

We appreciate the Reviewer’s support of this manuscript.

Major comments: 1. In general, the ChIP-seq and CUT&RUN data are not that similar. Although correlation seems reasonable (S2A), looking at the heatmaps in S2B/C these seem pretty different. It's not very clear if this is a case where CUT&RUN has higher specificity (and signal-to-noise, which is very clear from example tracks) or if these two methods are picking up biologically different sites. Could the authors include some overlap analysis of peaks and comment on these discrepancies. Looking at the example tracks in Figure 2B, it seems likely that prior SPT16 and SSRP1 ChIP-seq were relatively high-noise.

We have identified overlapping peaks between the two techniques, and while CUT&RUN identified substantially more peaks overall, percentage of peaks shared between datasets were relatively consistent (1-6% of total) between the individual ChIP-seq datasets and the CUT&RUN dataset (Response Figure 1). We note that the biological classes identified through all datasets were remarkably consistent (Fig. 2D), and therefore attribute the discrepancies to the greater number of reproducible peaks called from CUT&RUN data. As discussed in the paper, peak calling algorithms designed for the specific data types were used, and therefore peak calling could also contribute to differences.

Response Figure 1. ChIP-seq and CUT&RUN peak overlap. Pie chart depicting the unique and overlapping peaks called from V5-SPT16 CUT&RUN data and FACT ChIP-seq data. These data are included in the revised manuscript (as a new Figure panel 2E). Peaks must have been identified in at least two technical or biological replicates.

Are motifs described in Figure 2E CUT&RUN only, and do prior ChIP-seq experiments also identify these motifs?

The motifs shown in Figure 2E (now 2F) are indeed CUT&RUN peaks only. We were unable to confidently assign enriched motifs to the ChIP-seq datasets (the most enriched motifs were approximately p = 10-18). By analyzing all SPT16 ChIP-seq peaks, rather than only intersected SPT16 ChIP-seq peaks, we were able to identify motifs recognized by two of the top three CUT&RUN motif hits (SOX2 and OCT4/SOX2/TCF/NANOG); however, enrichment was quite poor (p = 10-3). By limiting the analysis to intergenic regions, we were able to identify strong enrichment for motifs recognized by CTCF and BORIS (p = 10-58 and 10-51, respectively). As validation, we also called motifs from peak files published as supplementary material to the original Tessarz lab manuscript but were still unable to confidently call motifs (all p > 10-7 for SPT16 peaks, p > 10-15 for SSRP1 peaks). Related to major comment 1, we suspect that the weak motif enrichment is due to high background in ChIP-seq datasets compared to CUT&RUN datasets.

The authors state that FACT depletion affects eRNA transcription and measured this using TT-seq. The analysis in Figure 3B seems to be all the different types of sites looked at together (genes, PROMPTs, etc). Is there evidence that eRNAs specifically are regulated by FACT loss.

We apologize for the confusion and have clarified that Figure 3B (now 3A) is referring to mRNAs only in the text and figure. Our analysis of eRNA regulation by FACT is predominantly contained within Fig. 4B (TT-seq from DHSs, but no histone mark overlap assessment), Supp Fig. S4 (as in Fig 4B, but at DHSs overlapping H3K27ac or H3K4me1), Fig. 5E (FACT localization to putative enhancers, defined as in S4), and Fig. 6D (ATAC-seq demonstrating loss of accessibility at putative enhancers upon FACT depletion). Based on these results, we believe there are many eRNAs specifically misregulated by FACT loss and that potential direct targets (based on change in depletion and containing FACT binding) are in Fig 5E.

Could these be compared to DHS sites that lack FACT binding to support a direct role for FACT at these sites?

We appreciate the suggestion and have performed this analysis (see Response Figure 2). Relatedly, we analyzed putative silencers, defined as DHSs marked by H3K27me3, for FACT binding and expression changes (measured by TT-seq) following FACT depletion (Supp Fig. S7). As expected, FACT does not bind these putative silencer DHSs and transcription does not markedly increase or decrease from these regions after FACT depletion. Complicating the matter, FACT binds at many DHSs, even those that did not to meet our stringent peak-calling criteria (see Response Figure 2, middle cluster).

__Response Figure 2. Overlap between FACT binding sites and gene-distal DHSs. __Individual clusters are sorted by V5-SPT16 binding. Clusters were assigned based on direct overlap between called V5-SPT16 peaks and assigned gene-distal DHSs. Overall, 17.6% of DHSs overlapped a FACT peak identified in at least one CUT&RUN replicate (8.5% of DHSs overlapped a peak present in multiple replicates).

One mechanism proposed for how FACT regulates enhancers is that it is required for maintaining a nucleosome free area, and when FACT is depleted nucleosomes invade the site (Figure 7). It wasn't clear if they compared distal DHS sites were FACT normal bound to those without FACT binding in the MNase experiments, which could help support the direct role or specificity of FACT in regulating those enhancers (or a subset of them).

We have subset the V5-SPT16 CUT&RUN peaks and distal DHSs into groups and have identified increased nucleosome occupancy after depletion at both FACT-bound and FACT-unbound DHSs suggesting both direct and indirect regulation (Fig. 6A, D). There is disruption to nucleosome arrays at non-FACT-bound DHSs (although more modest relative to the FACT bound locations), and therefore we speculate that a nucleosome remodeler is involved downstream of FACT (possibly CHD1, per recent work out of Patrick Cramer and François Robert’s labs, among others).

1. Data quality for nucleosome occupancy was a little strange (Figure 7F), where the two clones had very different MNase patterns at TSS sites. Could the authors comment on why there is such a strong difference between clones here.

We agree that the trends identified by visualizing differential MNase-seq signal near TSSs do not fully replicate; however, in examining the nondifferential MNase-seq heatmaps, we see a more expected distribution (see new Figure 7A). Per our newly-added Supp Fig. S9B, all MNase-seq replicates had a pairwise Pearson correlation value of at least 0.73 (SPT16-depleted clone 1/rep 1 vs untagged rep 3), and the vast majority of samples had pairwise correlations of above 0.85, suggesting that these discrepancies are not due to strong differences in sequencing depth or MNase-protected regions. We therefore suspect that the clonal distinctions are a result of different background occupancy of nucleosomes near the TSS, resulting in an array with increased occupancy in one clone and more generalized increased occupancy in the other clone. We also added the MNase-seq data over TSSs in a non-differential form in Fig 7A, and believe the difference between the clones is due to the differential analysis, and have commented accordingly in the revised manuscript.

More details on some of the analysis steps would be really helpful in evaluating the experiments. Specifically, was any normalization done other than depth normalization? I ask this because the baseline levels for many samples in metaplots look quite different. For example, see Figure 7B where either clone 1 has a globally elevated (at least out 2kb) ratio of nucleosome in the IAA samples relative to the EtOH, or there is some technical difference in MNase. One suggestion is to look at methods in the CSAW R package to allow TMM based normalization strategies which may help.

We appreciate the suggestion – we have expanded our explanation of normalization methodology in the paper. We initially used quartile and RPGC normalizations to attempt to mitigate technical differences in MNase-seq data. Size distribution plots did not suggest differences in MNase digestion between samples, and neither quartile/RPGC nor TMM-based normalization fully resolved this issue. Because our ATAC-seq datasets agree with the general trends identified by MNase-seq (which are consistent, despite technical differences between clones), we do not believe that the differences constitute true biological difference, but rather experimental noise.

1. I appreciated the speculation section, and the possible relationship between FACT and paused RNAPII is interesting. While further experiments may be outside the scope of this work and I am not suggesting they do them, I am wondering if others have information on locations of paused RNAPII in ESC that would allow them to test if genes with paused RNAPII have a special requirement for FACT that they could use their current data to assess.

We agree that experiments to test the relationship between paused RNAPII and FACT are an intriguing next step, and plan to dissect those in the near future.

Minor comments: 1. When describing the peaks found in the text related to Figure 2 they refer to 'nonunique' peaks. Does this mean the intersection of the independent peak calls? Could they clarify this.

We apologize for the confusion and have clarified in the text that nonunique peaks does indeed refer to the intersection of independent peak calls (now specified on manuscript page 8, line 15).

In the text they refer to H3K56ac data in S2D and I don't see that panel. The color scheme for the 1D heatmaps (Figure 5A) is tough to appreciate the differences. I'd suggest something more linear rather than this spectral one might be easier to see.

We apologize for the confusion and removed the remaining H3K56ac-related data and references in the text. We appreciate the suggestion regarding the 1D heatmap color scheme and have adjusted the colors to a linear (white à red) scheme.

For the 2D heatmaps of binding, could they include the number of elements they are looking at for each group?

We appreciate the suggestion and have included numbers of elements visualized wherever applicable in the figure panels and legends.

1. Also for 2D heatmaps, I think the scale is Log2 (IAA/EtoH), but could they confirm that and include it in the figure?

We apologize for the confusion; the only heatmaps displaying log2(IAA:EtOH) are those in Fig. 6; for those panels, we have clarified the scale in the figure and legend.

Reviewer #1 (Significance (Required)):

• The use of degrader based approaches to depleting a protein allows refined kinetic and temporal assays which I think are important. Several papers showed a rapid invasion of nucleosomes after SWI/SNF loss using these kinds of approaches and revealed surprisingly fast replacement of SWI/SNF. This paper is consistent with those models, showing that another remodeler behaves the same, suggesting there may be general requirements for active chromatin remodeling to maintain the expression of these genes. It also highlights a key gap in how specificity works to target these enzymes remains somewhat unknown.

• This work will be of interest to those studying detailed mechanisms of gene regulation. Compared to some other chromatin regulators, FACT is understudied and so this work will allow comparison between different chromatin remodeling complexes.

• My experience: chromatin, gene regulation, cancer, genomics

We appreciate the thorough review and hope that we have sufficiently addressed your concerns.

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

The authors propose that the FACT complex can regulate pluripotency factors along with their regulatory targets through non-genic locations. They find that acute depletion of FACT leads to a "reduction" in pluripotency in mouse embryonic stem cell by disrupting transcription of master regulators of pluripotency. They also show FACT depletion affected the transcription of gene distal regulatory sites, but not silencers. They also stated that SPT16 depletion resulted in both, a reduction of chromatin accessibility and increase of nucleosome occupancy over FACT bound sites.

Overall the study appears technically well executed. The use of an Auxin induced depletion system is a good model to study the acute effects of FACT depletion. However, I have a number of concerns relating to specificity and interpretation of the results that need to be addressed. We appreciate the careful review and have addressed your comments below:

Major points o Authors claimed that depletion of the FACT complex "triggers a reduction in pluripotency". As evidence supporting this statement they present images of alkaline phosphatase assays of a time course performed upon depletion of FACT. These experiments indeed show that ESCs are destabilized in the absence of SPT16. However, some key questions regarding the phenotype remain unresolved: o What is are the kinetics of expression of selected naïve pluripotency and early differentiation markers? Are differentiation markers upregulated, consistent with normal differentiation upon FACT depletion?

We appreciate the suggestion and have emphasized the decrease in pluripotency factor expression, accompanied by an increase in differentiation marker expression across all three germ layers. We graphed 7 pluripotency factors and 7 differentiation markers for each germ layer; generally speaking, pluripotency factors are decreased while differentiation markers are increased (Response Figure 4; pluripotency factors are included in the new Fig. 3B, while differentiation markers are included in the new Supp Fig. S3 F-H).

We have also performed an immunocytochemistry (ICC) timecourse, per Reviewer 3’s suggestion. This ICC timecourse allows us to orthogonally assess decreased pluripotency factor expression, to pair with the OCT4 Western blot shown in Supp Fig. S1B. These new ICC data are shown in the new Fig. 1D and included here for convenience (Response Figure 5). In addition, we have added alkaline phosphatase staining at 12 hours of depletion to Fig. 1C.

__Response Figure 4. Plots of DESeq2 analysis across experimental timecourse. __Shown are lineage markers denoting: A. Pluripotency B. Endoderm C. Mesoderm and D. Ectoderm. Generally, expression of pluripotency factors decrease over time, while differentiation markers of each lineage increase over time. These data are shown in Figure 3B and Supplemental Figure S3F-H.

__Response Figure 5. Immunocytochemistry timecourse depicting DAPI staining (left panels, blue) and OCT4 immunofluorescence (right panels, green). __Images are representative of plate-wide immunofluorescence changes.

O Is only ESC identity affected or does loss of FACT impair viability also of cells that have exited pluripotency? To address this, growth curves and/or cell cycle analysis upon FACT depletion could be performed. Alternatively, the authors could utilize surface markers to distinguish naïve pluripotent form differentiated cells in the cell cycle analysis experiments to identify a potential differential response of pluripotent and differentiated cells to FACT depletion.

We have performed a growth curve with FACT depletion as suggested; as the two points are related, we will explain further below:

o Another key question is whether it is only the metastable pluripotent state of ESCs in heterogeneous FCS/LIF conditions which is affected by FACT loss, and whether cells cultured in the more homogeneous and more robust 2i-LIF conditions can tolerate FACT removal. If that is indeed the case it would enable the authors to address one main concern I have with this manuscript, which is that it is nearly impossible to distinguish the direct effect of FACT loss from differences induced by differentiation (and maybe cell death, see comment above). This is a critical concern that needs to be addressed and discussed appropriately.

We apologize for the confusion – all original experiments for this project were performed in the presence of LIF as well as GSK and MEK inhibitors CHIR99021 and PD0325091, respectively (2i+LIF conditions). To address the reviewers question, we have now performed a timecourse growth assay under both LIF-only and 2i+LIF conditions (Response Figure 6 and new Supp Fig S1F), and as suggested by the reviewer, observe a stronger effect of FACT depletion on cell viability in LIF-alone (FACT-depletion results in ~90% death within ~24 hours, with differences in growth observed by 12 hours) than in 2i+LIF (FACT-depletion results ~80% death within 48 hours, with differences in growth observed starting around 18 hours). Overall, ES cells in LIF alone are indeed more sensitive to FACT loss, supporting our decision to perform the experiments throughout the manuscript in 2i+LIF conditions.

LIF alone LIF + 2i

Response Figure 6. __Growth assays in LIF (left) and 2i+LIF (right) conditions. __Cells were treated with either EtOH or 3-IAA and counted at the indicated times. Viability was assessed using trypan blue exclusion. Error bars indicate standard deviation for biological triplicate experiments.

o A further major concern is about the specificity of the effect of FACT depletion. The authors claim that FACT is required to maintain pluripotency. From the data presented this is unclear. FACT appears to be part of the general transcription machinery in ESCs. It appears generally associated with active promoters and active genes, according to the data in this manuscript. Whether there is any specific link to pluripotency remains to be shown. It is unclear how enrichment analyses have been performed. If they haven't been performed using a background list of genes actively transcribed in ES cells, they will obviously show enrichment of ESC specific GO categories, because ESCs express ESC specific genes robustly expressed in ESCs?

We apologize for the confusion and have updated our methods section to include more comprehensive details on our pathway enrichment analyses. We have confirmed that pluripotency-related categories are still highly enriched in FACT-regulated DEGs, even when using a background dataset of all transcribed genes, per our TT-seq datasets (baseMean ≥ 1 in DESeq2 output).

In line with this: the authors show that FACT bound loci well overlap with Oct4 bound regions. But which proportion of FACT targets loci are actually Oct4 bound too?Is FACT binding exclusive to Oct4 regulated enhancers and promoters? In other words, will FACT be recruited to all actively transcribed genes in ES cells? In that case, a specific effect on pluripotency network regulation cannot be claimed.

We appreciate the suggestion, and have added the number of OCT4/SOX2/NANOG-bound FACT peaks and vice versa in the text and legend of Fig 3E-F. We have also summarized this information in Response Table 1, below (and included these data as Table 2 in the revised manuscript).

OCT4 peaks

Sox2 Peaks

Nanog Peaks

Any of OSN

V5 Peaks

8,544

5,948

5,307

9,682

OSN Peaks

45,476

19,211

16,817

52,899

% of OSN peaks bound by FACT

18.33%

30.72%

31.40%

17.91%

% of V5 peaks bound by pluripotency factor(s)

52.41%

36.85%

32.94%

59.63%

V5-bound promoters

4,261

2,719

2,327

4,452

OSN-bound promoters

6,550

1,542

666

6,948

V5- and OSN-bound promoters

2,040

801

343

2,202

OSN-bound gene-distal peaks

38,926

17,669

16,151

45,938

V5-bound gene-distal OSN peaks

6,504

5,147

4,964

7,480

__Response Table 1. Overlapping CUT&RUN and ChIP-seq peaks shared between OCT4, SOX2, NANOG, and V5-SPT16 under various stratifications. __Shown are numbers or percentages of peaks overlapping between V5 and OSN. The last column are peaks containing any of OCT4, SOX2, and/or NANOG. The first four rows include all peaks, regardless of location, and the last five rows are broken down by promoter (as defined by an annotated mRNA) or gene-distal location (defined by a minimum of +/- 1kb from a gene).

Of the 45,865 OCT4 peaks, 3,688 are located at promoters, and 1,209 of these peaks are bound by V5-SPT16 (32.8%). Inversely, 13,228 of 42,177 gene-distal OCT4 peaks are called as SPT16-V5 peaks in at least one CUT&RUN replicate (31.36%), suggesting a relationship between OCT4 binding and FACT binding, which has long been identified with genic transcription, but has roles extending beyond gene-proximal regulation. We observe similar trends with NANOG and SOX2.

o It is disappointing that neither raw data (GEO submission set to private) nor any Supplemental Tables containing differentially expressed transcripts and ChIP or Cut and Run peaks and associated genes were made available. This strongly reduces the depth of review that can be performed.

We apologize if the reviewer token in the cover letter was not accessible. The GEO datasets (including differentially expressed transcripts, raw fastq files, and analyzed datasets) will be made public upon publication; in the meantime, the GEO entry (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE181624) can still be accessed using the previously provided reviewer token: wvkvwmwynjeffux.

o To what extent do FACT bound loci overlap with genes differentially expressed 24h after FACT depletion? This analysis would help determine the direct targets of FACS regulation.

We appreciate the suggestion. This analysis can be found in the original Figure S6, broken down by FACT-repressed (expression increased upon FACT depletion), unchanged, and FACT-stimulated (expression decreased upon FACT depletion) DESeq2 results (ordered left-to-right, respectively). Figure S6A-C shows that V5-SPT16 binding is enriched, but not exclusive to, genes with FACT-regulated expression, while Fig. S6D-F shows TT-seq data for each group, sorted by log2-fold change assigned by DESeq2.

o The paper mainly relies on NGS analysis. Therefore, it is crucial that authors show as Supplemental Material some basic QC of these data. PCA analyses to show congruency of replicates are the minimum requirement.

We appreciate the suggestion and have included a new Supp. Fig S9, with pairwise comparative Pearson correlation scatterplots and heatmaps for replicates in each dataset, in addition to the scatterplots shown for CUT&RUN and ChIP-seq data in the original Supp Fig. S2A.

o Did the authors perform any filtering for gene expression levels before analysis? Are genes in the analysis robustly expressed in at least one of the conditions?

We apologize for the confusion. Due to the sensitive nature of TT-seq and the germ layer-inconsistent pattern of cell differentiation following FACT depletion, we did not perform filtering for gene expression prior to any analyses. For the vast majority of genes analyzed, however, we are able to identify transcription via TT-seq, even in those that do not significantly change expression upon FACT depletion (see Supp Fig S6E). As discussed above, we did include a cutoff for expressed genes in our revised pathway analysis.

o Wherever p values were reported for enrichment analyses, adjusted p values should be used

We apologize for the oversight; the p values were in fact adjusted p values and have updated the text and figures to make it explicit that the adjusted p values were used wherever applicable.

o I cannot follow the logic used by the authors to explain discrepant results from Chen et al about the role of FACT in ESCs. Chen et al showed that FACT disruption by SSRP1 depletion is compatible with ESC survival and leads to ERV deregulation. The authors of the present study attribute these differences to potential FACT independent roles of SSRP1. However, I would assume that if there are indeed FACT independent roles of SSRP1, then the phenotype of SSRP1 KOs in which FACT and other processes should be dysfunctional should be even stronger than a plain FACT KO. This needs a proper and careful explanation.

We apologize that our discussion of FACT-independent roles of SSRP1 was not clear and have clarified our wording in the text (page 4, line 49 – page 5, line 4)in the revised manuscript); we intended to reconcile the results of Chen et al. 2020 with Goswami et al. 2022 and Cao et al. 2003; despite SSRP1 knockout viability in embryonic stem cells, SSRP1 knockout is lethal in mice between 5-40 weeks and general SSRP1 knockout is lethal 3.5 days post-conception (per Goswami et al. 2022). We therefore posit that the general requirement for SSRP1 may be due to distinct roles from those carried out by the FACT complex in ES cells, as discussed by Spencer et al. 1999, Zeng et al. 2002, Li et al. 2007, and Marciano et al. 2018.

We note that our findings are in agreement with papers from the Gurova lab and others in that depletion of mRNA or protein of SPT16 leads to concomitant loss of SSRP1; we therefore do not expect total SSRP1 loss to have a stronger effect than SPT16 depletion. We therefore expect, and confirmed via Western blotting (Figure 1B, Supplemental Figure 1), that depletion of SPT16 leads to loss of both FACT subunits, and therefore all FACT subunit activity, complex-dependent or -independent.

Also, did the authors observe any evidence for ERV deregulation upon acute SPT16 depletion?

We did indeed observe ERV deregulation upon SPT16 depletion. When reviewing our TT-seq datasets, 7.1% of ERVs were derepressed, while 2.4% decreased in expression upon 24h FACT depletion (mm10 ERVs sourced from gEVE, Nakagawa and Takahashi, 2016). Further, we identified increased chromatin accessibility after FACT depletion at annotated LTR elements, as shown in the table below (Response Table 2). Here we are displaying the calculated enrichment score for accessibility detected at these locations. A negative value indicates lower accessibility than expected by region size, while a positive score indicates that reads are more enriched than expected at the indicated region.

ATAC-seq enrichment score for locations losing accessibility with FACT depletion

3h

6h

12h

24h

LTR Enrichment

-1.445

-1.299

-0.917

-0.559

Intergenic Enrichment

-6.046

-4.765

-3.926

-2.972

Promoter Enrichment

3.335

2.789

2.726

2.233

ATAC-seq enrichment score for locations gaining accessibility with FACT depletion

3h

6h

12h

24h

LTR Enrichment

-1

-0.436

1.103

1.13

Intergenic Enrichment

-1

0.134

0.435

0.236

Promoter Enrichment

-1

-3.585

1.171

1.39

__Response Table 2. Changes in ATAC-seq peak enrichment for selected regions, annotated via HOMER. __At regions differentially accessible between SPT16-depleted and SPT16-undepleted samples, regions were assigned to an annotated genomic feature using HOMER annotatePeaks.pl and assigned an enrichment score based on the ratio of ATAC-seq signal to region size. Over time, LTR elements become more enriched among the ATAC-seq peaks both gaining and losing accessibility, indicating a role for FACT in maintaining LTR accessibility.

We do wish to note, however, that Lopez et al. 2016 identified SPT16-independent regulation of LEDGF/HIV-1 replication by SSRP1, and therefore cannot rule out effects on ERV dysregulation due to SSRP1 loss that accompanies SPT16 depletion.

Minor points o Figure S2A is very small and resolution is low. Page 10: "...while all four Yamanaka factors (Pou5f1, Sox2, Klf4, and Myc) and Nanog were significantly 24 reduced after 24 hours (Fig. 3A, S3A-B)". No data for myc is being shown.

We apologize for the figure resolution and have included a larger image. Because pairwise comparative scatterplots are not space-efficient, we opted to display the Pearson correlations for the datasets including more samples (TT-seq and ATAC-seq timecourses) as heatmaps in the new Supp Fig S9. We have added Myc labeling to the volcano plot (now in Fig. 3A) and included a trace of Myc expression over time to the new pluripotency factor graph in Fig. 3B.

o Are the two bands in the middle in figure 1B is supposed to be a ladder? This should be clarified.

We thank the reviewer for noticing this and apologize for the oversight.

o Figure 3C- This Figure is complicated to read. Also, information appears redundant with the Table 1, I recommend to remove this panel.

We have moved the panel to the supplement (now Supp Fig. S3A). While the information is somewhat redundant with Table 1, we chose to include the former panel 3C as a visual representation of the consistent deregulation over depletion time across transcript categories.

o Figure 6 and figure 7 could be presented in one single figure since both aspects are complementary and target related aspects.

While we thank the reviewer for this suggestion, we do not believe that the information contained in Figs. 6 and 7 can effectively be conveyed in a single figure. While both figures focus on chromatin accessibility and nucleosome occupancy, Fig. 6 is designed to address the changes in chromatin accessibility over time, while Fig.­­­ 7 is more relevant to the biological mechanism through which FACT co-regulates targets of the core pluripotency network (OCT4/SOX2/NANOG) after 24 hours of depletion.

o Are the authors certain that the effects observed are directly linked to the FACT complex in contrast to FACT independent roles of SPT16, if any exist? The experiment to address this would be to deplete SSRP1 and investigate whether the effects are identical, which would be the hypothesis to be tested.

We thank the reviewer for this suggestion. We did attempt to create additional SSRP1-AID-tagged lines; however, generating these lines proved to be technically challenging, and comparison of the FACT-dependent and -independent roles of the individual subunits is beyond the scope of this work. Further complicating the matter, SSRP1 is effectively depleted within 6 hours of 3-IAA addition in SPT16-AID lines due to the interdependence of FACT subunits. We again thank the reviewer for their suggestion and will consider this work for a future study.

Reviewer #2 (Significance (Required)):

My expertise is pluripotency and GRNs.

I would judge the significance of the study as presented as low, mainly because at this moment it remains unclear what FACT indeed does concerning regulation of pluripotency.

We respect the reviewer’s opinion and hope that our revisions have made more clear how the FACT complex prevents nonspecific differentiation from occurring, thereby maintaining pluripotency and self-renewal in embryonic stem cells. Importantly, neither untagged cells treated with 3-IAA nor tagged cells treated with vehicle display the growth defects, loss of pluripotency factor expression, increased differentiation marker expression, phenotypic evidence of differentiation, and reduced alkaline phosphatase staining that the FACT-depleted cells do, highlighting a key requirement for FACT in pluripotent cells. Beyond this, we believe the novel gene distal regulatory role we have identified for FACT presents an exciting new role for this complex in gene regulation.

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

In this manuscript, Klein, et al. addressed function of FACT complex in mouse ESCs, using cut&run, TT-seq, ATAC-seq, MNase-seq, together with Auxin-mediated FACT degradation system. The authors first reported that efficient and acute depletion of SPT16 with the Auxin-mediated degradation system resulted in over 5,000 up- and 5,000 down-regulated genes within 24 hours, including down-regulation of pluripotent gens. Then, they demonstrated that many of FACT binding sites overlap with Oct4, Sox2, Nanog binding sites by Cut&Run, and those loci increase nucleosome occupancy 24 hour after removal of FACT.

The Auxin-mediated degradation system seems to be working very well (while I would like to see an over exposed version of Western blotting), and efficient degradation might explain the different phenotypes from the previous reported phenotypes by shRNA and the chemical inhibitor, which might not deplete FACT function completely and/or might have off-target effects. The Cut&Run data also have much sharper peaks than previously reported SSRP1, SPT16 ChIP-seq data. Doing ATAC-seq, MNase-seq upon removal of FACT is excellent. WIth the excellnet degradation system, depletion of FACT resulted in loss and gain of gene expression and differentiation. However, unfortunately it was not very clear to me what was the direct consequences of FACT removal and its mechanisms, waht was consequence of differentiation.

We appreciate the kind words regarding our choice and execution of techniques and the reviewer’s time spent on this manuscript. We have made a number of changes to the manuscript in order to clarify the direct role of FACT and the consequences of FACT loss on embryonic stem cells.

Although we did not develop the blots for a longer period when we performed the Westerns, we have artificially overexposed our V5-SPT16 Western blot from Figure S1 (in Adobe Illustrator) to highlight the more subtle bands at later depletion timepoints; we hope that this helps to clarify the effectiveness of the degron system.

Response Figure 7. V5-SPT16 Western blot with adjusted exposure. We manually adjusted the entire blots’ exposures using Adobe Illustrator. L indicates ladders, and the timecourse depletion is shown above the blot.

In my opinion, doing many of the analysis 24 hours after FACT depletion, where differential expressed (coding) genes (DEGs) are >10,000 (Table 1)), is too late to understand what the direct consequences are. Seeing 214 up- and 174 down-regulated DEGs 6 hours after FACT depletion, I do agree that FCAT seems to do both suppression and activation of target genes. It could have been really interesting to investigate what % of FACT bindign sites change chromatin accesibility and nucleosome occupancy at that time point, if those loci are close to any of the up- or down-regualted DEGs.

We appreciate the suggestion and have included more information regarding the percentage of FACT binding sites with altered chromatin accessibility, as well as included some analyses to address the directness of FACT’s contribution to DEGs at all timepoints (see Supp Figs S4, S6). We would like to note that, we performed the TT-seq and ATAC-seq experiments at 0, 3, 6, 12, and 24 hours post 3-IAA treatment in order for us to explore the progressive change in both the transcriptome and chromatin accessibility, with only the MNase-seq limited to 24 hours. As originally shown in our Sankey plot in Supp Fig 4, we see a progressive change in expression for a small subset of genes over our timecourse running from 0-24 hours, with the largest effect observed at 24 hours, once the FACT protein levels are almost entirely depleted. Similarly, we see a progressive change in ATAC-seq signal over the same regions, with the strongest effects over the same regions visible at 24 hours post-depletion. Due to our observation that SPT16 is not depleted at 3 or 6 hours, with significant depletion seen at 24 hours (see Response Figure 7) and because we intended to study the FACT complex’s role in preventing differentiation, we were most interested in the effects at 24 hours of depletion, which allow us to analyze both the disruption of pluripotency factor expression and the facilitation of differentiation marker expression across all three germ layers (see Response Figure 4).

Followings are reasons of above my judgement and suggestions to improve the manuscript.

Major points 1. Figure 1. ALP staining is not very sensitive way to evaluate ESC differentiation. I recommend Immunofluorescence for pluripotency genes (NANOG and/or SOX2) and quantification. Or present changes of pluripotency genes in graphs over time course from RNA-seq data.

We appreciate the suggestions and have taken both into account. We have included a new panel in Figure 3 (new 3B) to display the changes of pluripotency factor expression over our timecourse. We have also included some data showing differentiation factors as part of a response to Reviewer 1, which can be found above (Response Figure 4). In addition, we performed immunocytochemistry to examine OCT4 abundance over a depletion timecourse and have added a 12-hour to our alkaline phosphatase assay to address the sensitivity of differentiation over time (Figure 1C, D and Response Figure 5).

1. Fig 2A, 3E, 3F. How many transcription start sites are shown here? (Throughout the manuscript, it is hard to know how many loci are shown in the heatmaps. It should be described within the figures)

We apologize for the omission and have added numbers of loci shown to relevant Figure panels throughout the paper.

It is nice to see nascent transcription high sites have high FACT binding, but can you also show actual nascent transcription of these loci as a heatmap, before and after FACT depletion? These heatmaps should be shown in a descending order of FACT Cut&Run signalling, as FACT binding is important in this manuscript.

We appreciate the suggestion and have plotted those data below (see Response Figure 8).

Response Figure 8. Nascent transcription from sites with high FACT binding. Top: TPM-normalized TT-seq signal after 12-hour treatment, oriented to mRNA strand and plotted as entire mRNA length, ± 500 bp. Data are sorted by SPT16 CUT&RUN signal over 1kb upstream of annotated TSSs. n = 1 over 22,597 rows (RefSeq Select mRNAs). Bottom: TPM-normalized TT-seq signal after 24-hour treatment, oriented to mRNA strand and plotted as entire mRNA length, ± 500 bp. Data are sorted by SPT16 CUT&RUN signal over 1kb upstream of annotated TSSs. n = 3 (mean) over 22,597 rows (RefSeq Select mRNAs).

Strong FACT binding sites have strong transcription. Is FACT really supressing transcription?

We agree that it is very difficult to disentangle FACT function due to its binding correlation with transcription; however, we see a clear trend of FACT binding at promoters that are sensitive to FACT depletion (Supp Fig. S6A/D and C/F). Intriguingly, the genes that see the greatest derepression by DESeq2 analysis are those that are directly bound by FACT (per ChIP-seq and CUT&RUN; Supplemental Figure S6A/D), while the greatest decrease in expression occurs at genes that are less bound by FACT (Supp Fig S6C/F). In our opinion, this trend lends credence to both direct repression by FACT and distal gene regulation. We note that others (e.g., Kolundzic et al. 2018) have shown direct repression of gene expression by FACT, in line with that aspect of our data.

1. Fig 3ABD. It is more important to show 3h, 6h 12 h time points. The same apply to Fig 4. What %, how many of DEGs (coding and non-coding) at each time point had FACT binding nearby in ESCs?

We agree that the early timepoints are important and have added volcano plots to the supplemental material for earlier timepoints, with genes of interest specifically annotated. We have also examined pluripotency and differentiation markers at earlier timepoints, per other reviewers’ suggestions, and have included the percentage of DEGs with nearby FACT binding in the manuscript. Specifically, 2013 replicated V5 peaks (out of 16,054; 12.54%) occurred within 1000 bp of a RefSeq Select TSS.

Timepoint

Total DEGs (up)

V5-bound DEGs (up)

Total DEGs (down)

V5-bound DEGs (down)

3h

58

16 (27.59%)

5

1 (20%)

6h

214

38 (17.76%)

174

31 (17.82%)

12h

1366

123 (9.00%)

1932

281 (14.54%)

24h

5398

431 (7.98%)

5000

663 (13.26%)

__Response Table 3. Table of DESeq2-assigned DEGs that are bound by SPT16-V5. __To be defined as V5-SPT16-bound, a DEG must have SPT16-V5 binding within 1000 bp upstream of its RefSeq-select annotated TSS.

We believe that these earliest depletion timepoints are in line with FACT-mediated gene regulation occurring distal to the regulated genes’ promoters.

Fig 3EF. Interesting data and the overlap between SPT16 binding sites and pluripotency binding sites look very strong. But it is difficult to know what % is overlapping from these figures.

We appreciate the difficulty in quantifying the overlap between pluripotency factor binding sites and FACT binding sites; we have added those data to the manuscript below Figure 3E for OCT4; for other pluripotency factors, these data can be found in Response Figure 9 and Response Table 1. Briefly, 18.33% of OCT4 ChIP-seq peaks are bound by V5-SPT16 and 52.41% of V5-SPT16 peaks are bound by OCT4. Interestingly, 34.6% of gene-distal OCT4 ChIP-seq peaks are bound by V5-SPT16, implying greater convergence between FACT and pluripotency factors at gene-distal sites, in line with known trends for OCT4 binding. Overall, 59.63% of V5-SPT16 peaks are co-bound by at least one of OCT4, SOX2, or NANOG.

Can you show 1 heatmap split into 3 groups, a. SPT16-V5 unique, common between SPT16-V5 and Oct4 ChIP-seq, Oct4 ChIP-seq unique, with indication of numbers each group has? Also make the same figures for Sox2 and Nanog. (E is less important. If the authors want, they can use the published FACT ChIP-seq data in the same loci.)

We appreciate the suggestion and have plotted V5-SPT16 CUT&RUN data and pluripotency factor ChIP-seq over unique and shared regions for OCT4 (top) SOX2 (middle) and NANOG (bottom). Interestingly, although some peaks in the non-overlapping cluster were not called as peaks by the algorithms’ threshold, one can observe that a subset do seem to have overlapping binding. We again appreciate the suggestion and think that this was an excellent way to display the data and have included these data as a new panel (Fig. 3E) but also show below in Response Figure 9.

Fig. 5. Basic information what % (how many) of SPT16-V5 CUT&RUN peaks belong to this 'enhancer' category is missing.

We apologize for the oversight and have added numbers to the figure and legend.

I am not sure the meaning of separating enhancers and TSS of coding genes in the analyses, though. If majority of SPT16-V5 CUT&RUN peaks overlap with Oct4 binding sites, it is not surprising that SPT16-V5 CUT&RUN peaks overlaps with ATAC-seq signal and enhancer marks.

We agree that it is unsurprising that V5-SPT16 overlaps with accessible chromatin and enhancers, given the extensive overlap with OCT4 ChIP-seq peaks. We wanted to emphasize our novel finding of gene-distal FACT binding, given the more established trend of binding at promoters.

1. Fig 6A. I could not figure out what % of DHSs overlaps with FACT binding sites.

We have added this percentage to Fig 5C and included an analysis of altered chromatin accessibility in a new Table 3 (page 20). Briefly, 11,234 replicated V5-SPT16 peaks (out of 16,043; 70%) directly overlap a gene distal DHS. Orthogonally, 11,234 DHSs (out of 132,555; 8.5%) directly overlap a V5-SPT16 peak.

I do not see the point of showing DHSs which do not overlap with FACT binding sites.

In agreement with Reviewer 1, we believe that it is important to include FACT-unbound DHSs for a clearer understanding of the direct vs indirect effects of FACT depletion. We have condensed some of these data into a single heatmap, clustered between FACT-bound DHSs, non-FACT-bound DHSs, and FACT-bound non-DHS sites to streamline the information (now shown in Fig 3E).

Response Figure 9. Heatmaps of clustered SPT16 and OSN binding data. Shown are clustered heatmaps depicting V5-SPT16 CUT&RUN binding overlapping ChIP-seq peaks for OCT4 (top) SOX2 (middle) and NANOG (bottom). In each set of heatmaps the top cluster is pluripotency factor-unique, the middle cluster is shared, and the V5-unique cluster is on the bottom. Each cluster is sorted by descending strength of V5-SPT16 binding (CUT&RUN). Clusters were assigned by directly overlapping peaks.

How ATAC-seq signal changes upon depletion of FACT at FACT binding sites (Fig 6B) is important. Can you explain why ATAC-seq signals increase at the FACT binding site flanking regions (across +/- 2kb) where FACT binding is strong (without changing the chromatin accessibility at the FACT binding sites)? Perhaps authors need to show actual ATAC-seq track with EtOH or 3-IAA treatment over ~10kb regions flanking FACT binding sites. It is difficult to understand what is happening seeing only the changes (ratio) of ATAC-seq read counts, how big the differences are.

We agree that the local window and ratio of ATAC-seq signal somewhat muddles the true biological trends. We have plotted non-differential ATAC-seq signal for each SPT16-AID clone over V5 binding sites, ±10 kb, to more accurately depict the local chromatin status (shown below in Response Figure 10). There is an apparent trend at V5-SPT16 CUT&RUN peaks of accessible chromatin, and this high local accessibility very likely contributes to the high ATAC-seq signal immediately flanking V5 binding sites; over the binding sites themselves, however, FACT depletion consistently triggers decreased accessibility (see Fig. 6).

Can you identify differentially open loci based on 3-IAA- and Et-OH treated ATAC-seq data at each time point, and then how many of them overlap with FACT binding sites? There are a few tools to identify differential open regions with ATAC-seq data. That could help to understand the direct roles of FACT binding.

We appreciate the suggestion and have performed this analysis using a combination of PEPATAC and HOMER (see Response Tables 4-6 below). FACT depletion leads to the following accessibility changes:

3-hour

6-hour

12-hour

24-hour

Decreased accessibility

220 (0.35%)

3,713 (5.99%)

6,885 (11.11%)

8,441 (13.62%)

Increased accessibility

2 (0.00%)

12 (0.02%)

276 (0.45%)

6,031 (9.73%)

Response Table 4. Accessibility changes over consensus ATAC-seq peaks. Consensus ATAC-seq peaks were defined per PEPATAC standards (peaks called by MACS2 in (n/2)+1 samples, irrespective of condition.

3-hour

6-hour

12-hour

24-hour

Decreased accessibility

848 (1.64%)

1870 (3.51%)

2525 (4.83%)

4,092 (7.90%)

Increased accessibility

107 (0.21%)

283 (0.55%)

534 (1.03%)

2,449 (4.73%)

Response Table 5. Accessibility changes over regions bound by V5-SPT16.

Response Figure 10. ATAC-seq data shown over a 20kb window. Heatmaps depicting non-differential ATAC-seq data over FACT binding sites for SPT16-AID clones 1 (top) and 2 (bottom). Data are sorted by V5-SPT16 binding strength.

All

3-hour

6-hour

12-hour

24-hour

Decreased accessibility

3,294 (2.46%)

3,175 (2.37%)

3,636 (2.71%)

7,018 (5.23%)

Increased accessibility

102 (0.08%)

313 (0.23%)

1,797 (1.34%)

5,975 (4.45%)

V5-bound DHSs (11,234 total)

3-hour

6-hour

12-hour

24-hour

Decreased accessibility

1 (0.01%)

9 (0.08%)

96 (0.85%)

2006 (17.86%)

Increased accessibility

5 (0.04%)

28 (0.25%)

71 (0.63%)

87 (0.77%)

Response Table 6. Accessibility changes over gene-distal DHSs and over only FACT-bound gene-distal DHSs.

Together with Fig 1A and Fig 6C, do they mean the more FACT binding, the more transcription (Fig 1A). Also the higher transcription rate, the more increased chromatin accessibility upon depletion of FACT (Fig 6C)?

While we do see that FACT binding correlates with transcription and with FACT-dependent chromatin accessibility, we do not wish to make the argument that FACT binding alone is indicative of high transcription, nor that transcription is necessarily the deciding factor in FACT-depleted chromatin accessibility changes. We do want to note that transcriptional disruption is a likely contributor to increased chromatin accessibility in the absence of FACT as it pertains to paused RNAPII, as speculated in our discussion, but that experiments to truly test this hypothesis are beyond the scope of this work. That being said, in response to Reviewer 1, we did assess the potential correlation of FACT binding to locations with greater paused RNAPII (Response Figure 3) and see a connection. We are excited to explore this more in future work.

Perhaps plotting nascent transcripts at 12hr, 24 hr of FACT depletion next to these heatmaps might show if it colleates with transcription changes as well?

We appreciate the suggestion, and have included this plot in Response Figure 8, sorted by FACT binding to gene promoters; however, we find it difficult to visualize differences in transcription with non-differential heatmaps.

Sites losing chromatin accessibility (bottom half of Fig 6C) seem not to have FACT binding (bottom half of Fig 1A), thus it is likely to be indirect effects. It is better to make figures focussing on 'direct effects'.

We agree that there are sites with reduced chromatin accessibility upon FACT depletion that are not bound by FACT; however, given the extensive binding of FACT at gene-distal regulatory regions (F2D, F4A, F5, F6A/D), we would suggest that these “indirect” effects are possibly the result of FACT-dependent gene-distal regulation.

Fig 1A and Fig 6C indicated that FACT binding sites (i.e. TSS) decrease chromatin accessibility. I thought it does not fit with the idea of increasing nucleosome occupancy. But actually the data (Fig 7F) shows that TSS does not show increased nucleosome occupancy unlike Fig 7A-E. In fact, Fig 6B showed that about bottom 50% of weaker V5 binding sites decreased chromatin accessibility at 24 hr, which fits with increased nucleosome occupancy in Fig 7A. But then if you looked at only top 50% of stronger V5 binding sites, which did not decrease chromatin accessibility, nucleosome occupancy did not change as well? Why don't you make heatmap of MNase-seq next to Fig 6B?

We have added heatmaps of non-differential MNase-seq data to Fig. 7A to address both concerns. Regarding Figure 6B, we note that the V5-SPT16 peaks themselves invariantly show decreased chromatin accessibility, and that it is the surrounding chromatin, not the V5-SPT16 peak itself, that shifts from increased to decreased chromatin accessibility at 12-24 hours of depletion. We would also like to clarify that the original heatmaps in Fig 6B were sorted by change in chromatin accessibility at 24h, rather than V5 binding.

We disagree that the TSSs do not show increased nucleosome occupancy in Fig. 7F, as there is an increase in signal above background directly over the TSS in both replicates, per the differential metaplot shown in Fig. 7B, that is specific to the AID-tagged lines. However, the two clones did show variable results. To address this, we have plotted the non-differential MNase-seq plots (Fig. 7A), which show more consistent trends; it appears that the transformation of the data into differential at this location was the cause of the slightly variable plots over TSSs.

1. I could not follow based on which data the model in Fig 8 is made. Again it is better to focus in the direct effects.

Thank you for the suggestion; we have updated our model to focus more on the direct effects.

Minor points. 10. Line 1 page 5, Kolundzic paper did not have MEF reprograming data. They reported human fibroblast reprogramming was enhanced by FACT KD.

We appreciate the correction and have clarified the language to specify that the work of Kolundzic et al. included human fibroblast reprogramming and Shen et al. performed MEF reprogramming.

1. Line 3, I disagree with "these data establish FACT as essential in pluripotent cells". One paper said FACT KD increased proliferation of mESCs, the other paper said chemical inhibition of FACT was necessary for passaging ESCs, but not proliferation. Importance of FACT in pluripotent cells was very unclear to me.

We have clarified our language to specify that pluripotent cells have a FACT dependency that differentiated cells do not. We note that we were unable to recapitulate a relationship between FACT and trypsinization/passaging of ES cells, suggesting a more nuanced role for FACT in pluripotent cells, in line with work from the Tessarz and Gurova labs.

Line 7 Page 7, reference the paper with the ChIP-seq data.

We apologize for the oversight and have added the reference.

Line 16, Page 7. It doesn't seem the the Cut&run and previously published ChIP-seq data agree well.. >50% look different. It is nothing the authors can do, but can you show venn diagram of peak overlap?

In response to Reviewer 1, we have generated Response Figure 1 where we display a pie chart of the overlap. In addition to displaying this again to the right in Response Figure 11 this, we have included another analysis below in Response Figure 11, to address this comment. Specifically, we have plotted peak overlaps as a Venn diagram to compare peaks identified in at least two experimental replicates from either the CUT&RUN or ChIP-seq data (left). We have also overlapped replicated peaks between the individual targets and displayed them as a pie chart (right; same as Response Figure 1). While the CUT&RUN data do display a greater signal:noise ratio and call far more peaks, we note that more peak conservation between experiments is relatively consistent (1-6%) between all datasets, including the ChIP-seq experiments profiling opposite factors.

Overall, we see strongly reproducible trends (albeit with less sharp definition in the ChIP-seq), complemented by highly similar biological feature assignment in Fig. 2D and Pearson correlation values of between 0.76 and 0.78 between SPT16 ChIP-seq and V5-SPT16 CUT&RUN (Supp Fig. S2A).

__Response Figure 11. Overlaps between SPT16-V5 CUT&RUN, SPT16 ChIP-seq, and SSRP1 ChIP-seq. __Called peaks were compared between V5-SPT16 CUT&RUN, SPT16 ChIP-seq, and SSRP1 ChIP-seq, using both our own analysis pipeline (left) and the peaks published with the original manuscript by Tessarz et al. (2018; right). While our ChIP-seq peak-calling appears to have applied more stringent thresholds, trends are generally agreeable.

Line 12, 22 page 10. Fig.3AB is 24 hrs. Do not match with the text.

We apologize for the error and have changed the references in the text to the new panel 3C.

1. Line 23, 24, page 10, Highlight Klf4 and Myc in the volcano plot.

We have added KLF4 and MYC annotation to the volcano plot in Fig. 3A, as well as plotted their log2FC over time in the new panel 3B.

1. Line 18, 19, page 16. This is not accurate statement. Sample 2 increased the accessibility at 6 hours. Sample 1 decreased, but even the control did so.

We apologize for the unclear wording; we intended to suggest that all timepoints after 6 hours (i.e., 12 and 24 hours) display decreased accessibility directly over the DHS. We have corrected the text.

1. Line 48-50, page 16. Two replicates show very different patterns. Difficult to agree with the statement based on the figure.

We agree that the differential replicate patterns are not ideal; however, both replicates display an increase in nucleosome-sized reads over the promoter region, consistent with our ATAC-seq results presented in Fig 6C. Size distribution plots did not suggest differences in MNase digestion between samples, and neither quartile/RPGC nor TMM-based normalization fully solved this issue. Because our ATAC-seq datasets agree with the general trends identified by MNase-seq (which are consistent, despite technical differences between clones), we do not believe that the differences constitute biological difference, but rather experimental noise. We have included a heatmap of non-differential MNase-seq signal around TSSs in Fig 7A to highlight the experimental reproducibility between replicates. Based on this analysis it appears that the transformation of the data into differential at this location was the cause of the slightly variable plots over TSSs.

1. Line 15, page 19. Where does "1.5 times" come from? which is 1.5 times more, and is that different from the proportion of those?

We apologize for the unclear reference to the altered transcripts in Table 1 and have changed our wording to be more precise.

1. Line 32, page 19. Is Fig S2B correct figure?

We appreciate the correction; the text should have referred to Fig. 4 and has been fixed.

Line 35-39, page 21. I understand FACT does not bind to silenced loci. If FACT does not bind, it is not surprising that expression from those loci does not change upon FACT deletion. I do not understand what the authors said.

We agree that a lack of binding and unchanged expression after FACT depletion at putative silencers are unsurprising; given FACT’s extensive genic and gene-distal binding, we wished to show a class of transcribed regions unbound by FACT as a control, to show that non-FACT-regulated transcription was not affected by FACT transcription. We have clarified our wording in the text to emphasize that a lack of change was expected at silencers.

Reviewer #3 (Significance (Required)):

Previously it has been shown that Oct4 physically interacts with the FAcilitates Chromatin Transactions (FACT) complex. Seemingly contradicting phenotypes have been reporting upon suppression of FACT function in the maintenance and induction of pluripotent cells. Mylonas has reported that knockdown of SSRP1, a component of FACT complex, increased ESC proliferation (2018). Shen has described that chemical inhibition of FACT complex affected passaging of ESCs, but proliferation was not affected without passaging. Kolundzic has found that both SSRP1 and SUPT16H, another component of FACT complex, enhance human fibroblast reprogramming into iPSCs (2018), while Shen has reported that chemical inhibition of FACT blocks mouse iPSC generation form MEFs.

My expertise lies on pluripotent stem cells and transcriptional regulations. I did like the Auxin-mediated FACT degradation system these authors used and acute depletion of FACT is an excellent way of evaluating FACT function in ESC, compared to previously published shRNA based knockdown or use of a chemical inhibitor. However, as I described above, it was not very clear what could the direct effects and I feel looking at 24 hours after depletion might be to late to address this question.

We appreciate the review and agree that acute depletion of FACT has great potential to understand the complex’s function in ES cells. We understand that the nature of gene-distal regulation does make it difficult to cleanly elucidate direct regulation, and hope that our revisions have clarified that our goal was to examine direct, gene-distal regulation, rather than indirect effects. We would like to note that we examined transcription and chromatin accessibility after 3, 6, 12, and 24 hours of 3-IAA treatment, with all these data included in the original manuscript, and saw minimal change (likely because FACT was not fully depleted until later timepoints); to capture the true biological effects of FACT depletion, we explored most thoroughly the 24 hour 3-IAA treatment to understand the downstream effects between FACT loss and cellular differentiation. However, we have expanded discussion and analyses of the earlier timepoints in this revised manuscript.

Author Response

Reviewer #1 (Public Review):

Overall, the science is sound and interesting, and the results are clearly presented. However, the paper falls in-between describing a novel method and studying biology. As a consequence, it is a bit difficult to grasp the general flow, central story and focus point. The study does uncover several interesting phenomena, but none are really studied in much detail and the novel biological insight is therefore a bit limited and lost in the abundance of observations. Several interesting novel interactions are uncovered, in particular for the SPS sensor and GAPDH paralogs, but these are not followed up on in much detail. The same can be said for the more general observations, eg the fact that different types of mutations (missense vs nonsense) in different types of genes (essential vs non-essential, housekeeping vs. stress-regulated...) cause different effects.

This is not to say that the paper has no merit - far from it even. But, in its current form, it is a bit chaotic. Maybe there is simply too much in the paper? To me, it would already help if the authors would explicitly state that the paper is a "methods" paper that describes a novel technique for studying the effects of mutations on protein abundance, and then goes on to demonstrate the possibilities of the technology by giving a few examples of the phenomena that can be studied. The discussion section ends in this way, but it may be helpful if this was moved to the end of the introduction.

We modified the manuscript as suggested.

Reviewer #2 (Public Review):

Schubert et al. describe a new pooled screening strategy that combines protein abundance measurements of 11 proteins determined via FACS with genome-wide mutagenesis of stop codons and missense mutations (achieved via a base editor) in yeast. The method allows to identify genetic perturbations that affect steady state protein levels (vs transcript abundance), and in this way define regulators of protein abundance. The authors find that perturbation of essential genes more often alters protein abundance than of nonessential genes and proteins with core cellular functions more often decrease in abundance in response to genetic perturbations than stress proteins. Genes whose knockouts affected the level of several of the 11 proteins were enriched in protein biosynthetic processes while genes whose knockouts affected specific proteins were enriched for functions in transcriptional regulation. The authors also leverage the dataset to confirm known and identify new regulatory relationships, such as a link between the SDS amino acid sensor and the stress response gene Yhb1 or between Ras/PKA signalling and GAPDH isoenzymes Tdh1, 2, and 3. In addition, the paper contains a section on benchmarking of the base editor in yeast, where it has not been used before.

Strengths and weaknesses of the paper

The authors establish the BE3 base editor as a screening tool in S. cerevisiae and very thoroughly benchmark its functionality for single edits and in different screening formats (fitness and FACS screening). This will be very beneficial for the yeast community.

The strategy established here allows measuring the effect of genetic perturbations on protein abundances in highly complex libraries. This complements capabilities for measuring effects of genetic perturbations on transcript levels, which is important as for some proteins mRNA and protein levels do not correlate well. The ability to measure proteins directly therefore promises to close an important gap in determining all their regulatory inputs. The strategy is furthermore broadly applicable beyond the current study. All experimental procedures are very well described and plasmids and scripts are openly shared, maximizing utility for the community.

There is a good balance between global analyses aimed at characterizing properties of the regulatory network and more detailed analyses of interesting new regulatory relationships. Some of the key conclusions are further supported by additional experimental evidence, which includes re-making specific mutations and confirming their effects on protein levels by mass spectrometry.

The conclusions of the paper are mostly well supported, but I am missing some analyses on reproducibility and potential confounders and some of the data analysis steps should be clarified.

The paper starts on the premise that measuring protein levels will identify regulators and regulatory principles that would not be found by measuring transcripts, but since the findings are not discussed in light of studies looking at mRNA levels it is unclear how the current study extends knowledge regarding the regulatory inputs of each protein.

See response to Comment #10.

Specific comments regarding data analysis, reproducibility, confounders

1) The authors use the number of unique barcodes per guide RNA rather than barcode counts to determine fold-changes. For reliable fold changes the number of unique barcodes per gRNA should then ideally be in the 100s for each guide, is that the case? It would also be important to show the distribution of the number of barcodes per gRNA and their abundances determined from read counts. I could imagine that if the distribution of barcodes per gRNA or the abundance of these barcodes is highly skewed (particularly if there are many barcodes with only few reads) that could lead to spurious differences in unique barcode number between the high and low fluorescence pool. I imagine some skew is present as is normal in pooled library experiments. The fold-changes in the control pools could show whether spurious differences are a problem, but it is not clear to me if and how these controls are used in the protein screen.

Because of the large number of screens performed in this study (11 proteins, with 8 replicates for each) we had to trade off sequencing depth and power against cell sorting time and sequencing cost, resulting in lower read and barcode numbers than what might be ideally aimed for. As described further in the response to Comment #5, we added a new figure to the manuscript that shows that the correlation of fold-changes between replicates is high (Figure 3–S1A). The second figure below shows that the correlation between the number of unique barcodes and the number of reads per gRNA is highly significant (p < 2.2e-16).

2) I like the idea of using an additional barcode (plasmid barcode) to distinguish between different cells with the same gRNA - this would directly allow to assess variability and serve as a sort of replicate within replicate. However, this information is not leveraged in the analysis. It would be nice to see an analysis of how well the different plasmid barcodes tagging the same gRNA agree (for fitness and protein abundance), to show how reproducible and reliable the findings are.

We agree with the reviewer that this would be nice to do in principle, but our sequencing depth for the sorted cell populations was not high enough to compare the same barcode across the low/unsorted/high samples. See also our response to Comment #5 for the replicate analyses.

3) From Fig 1 and previous research on base editors it is clear that mutation outcomes are often heterogeneous for the same gRNA and comprise a substantial fraction of wild-type alleles, alleles where only part of the Cs in the target window or where Cs outside the target window are edited, and non C-to-T edits. How does this reflect on the variability of phenotypic measurements, given that any barcode represents a genetically heterogeneous population of cells rather than a specific genotype? This would be important information for anyone planning to use the base editor in future.

We agree with the reviewer that the heterogeneity of editing outcomes is an important point to keep in mind when working with base editors. In genetic screens, like the ones described here, often the individual edit is less important, and the overall effects of the base editor are specific/localized enough to obtain insights into the effects of mutations in the area where the gRNA targets the genome. For example, in our test screens for Canavanine resistance and fitness effects, in which we used gRNAs predicted to introduce stop codons into the CAN1 gene and into essential genes, respectively, we see the expected loss-of-function effect for a majority of the gRNAs (canavanine screen: expected effect for 67% of all gRNAs introducing stop codons into CAN1; fitness screen: expected effect for 59% of all gRNAs introducing stop codons into essential genes) (Figure 2). In the canavanine screen, we also see that gRNAs predicted to introduce missense mutations at highly conserved residues are more likely to lead to a loss-of-function effect than gRNAs predicted to introduce missense mutations at less conserved residues, further highlighting the differentiated results that can be obtained with the base editor despite the heterogeneity in editing outcomes overall. We would certainly advise anyone to confirm by sequencing the base edits in individual mutants whenever a precise mutation is desired, as we did in this study when following up on selected findings with individual mutants.

4) How common are additional mutations in the genome of these cells and could they confound the measured effects? I can think of several sources of additional mutations, such as off-target editing, edits outside the target window, or when 2 gRNA plasmids are present in the same cell (both target windows obtain edits). Could some of these events explain the discrepancy in phenotype for two gRNAs that should make the same mutation (Fig S4)? Even though BE3 has been described in mammalian cells, an off-target analysis would be desirable as there can be substantial differences in off-target behavior between cell types and organisms.

Generally, we are not very concerned about random off-target activity of the base editor because we would not expect this to cause a consistent signal that would be picked up in our screen as a significant effect of a particular gRNA. Reproducible off-target editing with a specific gRNA at a site other than the intended target site would be problematic, though. We limited the chance of this happening by not using gRNAs that may target similar sequences to the intended target site in the genome. Specifically, we excluded gRNAs that have more than one target in the genome when the 12 nucleotides in the seed region (directly upstream of the PAM site) are considered (DiCarlo et al., Nucleic Acids Research, 2013).

We do observe some off-target editing right outside the target window, but generally at much lower frequency than the on-target editing in the target window (Figure 1B and Figure 1–S2). Since for most of our analyses we grouped perturbations per gene, such off-target edits should not affect our findings. In addition, we validated key findings with independent experiments. For our study, we used the Base Editor v3 (Komor et al., Nature, 2016); more recently, additional base editors have been developed that show improved accuracy and efficiency, and we would recommend these base editors when starting a new study (see, e.g., Anzalone et al., Nature Biotechnology, 2020).

We are not concerned about cases in which one cell gets two gRNAs, since the chance that the same two gRNAs end up in one cell repeatedly is low, and such events would therefore not result in a significant signal in our screens.

We don’t think that off-target mutations can explain the discrepancy between pairs of gRNAs that should introduce the same mutation (Figure 3–S1. The effect of the two gRNAs is actually well-correlated, but, often, one of the two gRNAs doesn’t pass our significance cut-off or simply doesn’t edit efficiently (i.e., most discrepancies arise from false negatives rather than false positives). We may therefore miss the effects of some mutations, but we are unlikely to draw erroneous conclusions from significant signals.

5) In the protein screen normalization uses the total unique barcode counts. Does this efficiently correct for differences from sequencing (rather than total read counts or other methods)? It would be nice to see some replicate plots for the analysis of the fitness as well as the protein screen to be able to judge that.

We made a new figure that shows a replicate comparison for the protein screen (see below; in the manuscript it is Figure 3–S1A) and commented on it in the manuscript. For this analysis, the eight replicates for each protein were split into two groups of four replicates each and analyzed the same way as the eight replicates. The correlation between the two groups of replicates is highly significant (p < 2.2e-16). The second figure shows that the total number of reads and the total number of unique barcodes are well correlated.

For the fitness screen, we used read counts rather than barcode counts for the analysis since read counts better reflect the dropout of cells due to reduced fitness. The figure below shows a replicate comparison for the fitness screen. For this analysis, the four replicates were split into two groups of two replicates each and analyzed the same way as the four replicates. The correlation between the two groups of replicates is highly significant (p < 2.2e-16).

6) In the main text the authors mention very high agreement between gRNAs introducing the same mutation but this is only based on 20 or so gRNA pairs; for many more pairs that introduce the same mutation only one reaches significance, and the correlation in their effects is lower (Fig S4). It would be better to reflect this in the text directly rather than exclusively in the supplementary information.

We clarified this in the manuscript main text: “For 78 of these gRNA pairs, at least one gRNA had a significant effect (FDR < 0.05) on at least one of the eleven proteins; their effects were highly correlated (Pearson’s R2 = 0.43, p < 2.2E-16) (Figure 3–S1B). For the 20 gRNA pairs for which both gRNAs had a significant effect, the correlation was even higher (Pearson’s R2 = 0.819, p = 8.8e-13) (Figure 3–S1C). These findings show that the significant gRNA effects that we identify have a low false positive rate, but they also suggest that many real gRNA effects are not detected in the screen due to limitations in statistical power.”

7) When the different gRNAs for a targeted gene are combined, instead of using an averaged measure of their effects the authors use the largest fold-change. This seems not ideal to me as it is sensitive to outliers (experimental error or background mutations present in that strain).

We agree that the method we used is more sensitive to outliers than averaging per gene. However, because many gRNAs have no effect either because they are not editing efficiently or because the edit doesn’t have a phenotypic consequence, an averaging method across all gRNAs targeting the same gene would be too conservative and not properly capture the effect of a perturbation of that gene.

8) Phenotyping is performed directly after editing, when the base editor is still present in the cells and could still interact with target sites. I could imagine this could lead to reduced levels of the proteins targeted for mutagenesis as it could act like a CRISPRi transcriptional roadblock. Could this enhance some of the effects or alter them in case of some missense mutations?

To reduce potential “CRISPRi-like” effects of the base editor on gene expression, we placed the base editor under a galactose-inducible promoter. For both the fitness and protein screens we grew the cultures in media without galactose for another 24 hours (fitness screen) or 8-9 hours (protein screens) before sampling. In the latter case, this recovery time corresponded to more than three cell divisions, after which we assume base editor levels to have strongly decreased, and therefore to no longer interfere with transcription. This is also supported by our ability to detect discordant effects of gRNAs targeting the same gene (e.g., the two mutations leading to loss-of-function and gain-of-function of RAS2), which would otherwise be overshadowed by a CRISPRi effect.

9) I feel that the main text does not reflect the actual editing efficiency very well (the main numbers I noticed were 95% C to T conversion and 89% of these occurring in a specific window). More informative for interpreting the results would be to know what fraction of the alleles show an edit (vs wild-type) and how many show the 'complete' edit (as the authors assume 100% of the genotypes generated by a gRNA to be conversion of all Cs to Ts in the target window). It would be important to state in the main text how variable this is for different gRNAs and what the typical purity of editing outcomes is.

We now show the editing efficiency and purity in a new figure (Figure 1B), and discuss it in the main text as follows: “We found that the target window and mutagenesis pattern are very similar to those described in human cells: 95% of edits are C-to-T transitions, and 89% of these occurred in a five-nucleotide window 13 to 17 base pairs upstream of the PAM sequence (Figure 1A; Figure 1–S2) (Komor et al., 2016). Editing efficiency was variable across the eight gRNAs and ranged from 4% to 64% if considering only cases where all Cs in the window are edited; percentages are higher if incomplete edits are considered, too (Figure 1B).”

Comments regarding findings

10) It would be nice to see a comparison of the results to the effects of ~1500 yeast gene knockouts on cellular transcriptomes (https://doi.org/10.1016/j.cell.2014.02.054). This would show where the current study extends established knowledge regarding the regulatory inputs of each protein and highlight the importance of directly measuring protein levels. This would be particularly interesting for proteins whose abundance cannot be predicted well from mRNA abundance.

We agree with the reviewer that it would be very interesting to compare the effect of perturbations on mRNA vs protein levels. We have compared our protein-level data to mRNA-level data from Kemmeren and colleagues (Kemmeren et al., Cell 2014), and we find very good agreement between the effects of gene perturbations on mRNA and protein levels when considering only genes with q < 0.05 and Log2FC > 0.5 in both studies (Pearson’s R = 0.79, p < 5.3e-15).

Gene perturbations with effects detected only on mRNA but not protein levels are enriched in genes with a role in “chromatin organization” (FDR = 0.01; as a background for the analysis, only the 1098 genes covered in both studies were considered). This suggests that perturbations of genes involved in chromatin organization tend to affect mRNA levels but are then buffered and do not lead to altered protein levels. There was no enrichment of functional annotations among gene perturbations with effects on protein levels but not mRNA levels.

We did not include these results in the manuscript because there are some limitations to the conclusions that can be drawn from these comparisons, including that our study has a relatively high number of false negatives, and that the genes perturbed in the Kemmeren et al. study were selected to play a role in gene regulation, meaning that differences in mRNA-vs-protein effects of perturbations are limited to this function, and other gene functions cannot be assessed.

11) The finding that genes that affect only one or two proteins are enriched for roles in transcriptional regulation could be a consequence of 'only' looking at 10 proteins rather than a globally valid conclusion. Particularly as the 10 proteins were selected for diverse functions that are subject to distinct regulatory cascades. ('only' because I appreciate this was a lot of work.)

We agree with this, and we think it is clear in the abstract and the main text of the manuscript that here we studied 11 proteins. We made this point also more explicit in the discussion, so that it is clear for readers that the findings are based on the 11 proteins and may not extrapolate to the entire yeast proteome.

Reviewer #3 (Public Review):

This manuscript presents two main contributions. First, the authors modified a CRISPR base editing system for use in an important model organism: budding yeast. Second, they demonstrate the utility of this system by using it to conduct an extremely high throughput study the effects of mutation on protein abundance. This study confirms known protein regulatory relationships and detects several important new ones. It also reveals trends in the type of mutations that influence protein abundances. Overall, the findings are of high significance and the method appears to be extremely useful. I found the conclusions to be justified by the data.

One potential weakness is that some of the methods are not described in main body of the paper, so the reader has to really dive into the methods section to understand particular aspects of the study, for example, how the fitness competition was conducted.

We expanded the first section for better readability.

Another potential weakness is the comparison of this study (of protein abundances) to previous studies (of transcript abundances) was a little cursory, and left some open questions. For example, is it remarkable that the mutations affecting protein abundance are predominantly in genes involved in translation rather than transcription, or is this an expected result of a study focusing on protein levels?

We thank the reviewer for pointing out that this paragraph requires more explanation. We expanded it as follows: “Of these 29 genes, 21 (72%) have roles in protein translation—more specifically, in ribosome biogenesis and tRNA metabolism (FDR < 8.0e-4, Figure 5C). In contrast, perturbations that affect the abundance of only one or two of the eleven proteins mostly occur in genes with roles in transcription (e.g., GO:0006351, FDR < 1.3e-5). Protein biosynthesis entails both transcription and translation, and these results suggest that perturbations of translational machinery alter protein abundance broadly, while perturbations of transcriptional machinery can tune the abundance of individual proteins. Thus, genes with post-transcriptional functions are more likely to appear as hubs in protein regulatory networks, whereas genes with transcriptional functions are likely to show fewer connections.”

Overall, the strengths of this study far outweigh these weaknesses. This manuscript represents a very large amount of work and demonstrates important new insights into protein regulatory networks.

For me this I really am a first timer in terms of learning about the Asian American experience. It is very interesting to learn about more cultures and think of how we all face some sort of inequality.

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Reply to the reviewers

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

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The authors proposed that the stable and opened membrane neck that connects the bud to the cytoplasm may persist for a long time in the infected cell during active RNA production. The viral ring-shaped nsPs is supposed to have an important role of maintaining this stable high-curvature membrane neck. It is suggested that the nsP1 dodecamer may pull together the membrane inner surface in the neck region via electrostatic interactions. Namely the authors observed that in the absence of negatively charged membrane lipids nsP1 did not bind appreciably to the membrane. The presented experimental data and theoretical consideration suggest that the CHIKV spherule consists of a membrane bud filled with viral RNA, and has a macromolecular complex gating the opening of this bud to the cytoplasm.

The presented results are interesting, the manuscript is well written and can be published after revision. The following comments are offered to the authors' consideration.

We thank the reviewer for this positive overall assessment.

1.Since there is no protein coating over the curved surface of the membrane bud, the authors concluded that the membrane neck must be stabilised by specific mechanism involving nsP1. It was further assumed that the viral protein nsP1 serves as a base for the assembly of of a larger protein complex at the neck of the membrane bud. In addition to suggested mechanism of the neck stabilization, thin highly curved membrane neck can be stabilised also by accumulation of the membrane components having the appropriate membrane curvature (i. E. negative intrinsic curvature or anisotropic intrinsic curvature), see Kralj-Iglic et al., Eur. Phys. J. B., 10: 5-8 (1999), https://doi.org/10.1007/s100510050822.

Please discuss this issue in the manuscript.

This is a good point, thank you for making it. In the revised manuscript we discuss both the possibility of lipid sorting into the neck region by nsP1 (lines 217-222), and the mentioned paper regarding anisotropic inclusions (lines 268-271).

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2.In Eq. (1) the Gaussian curvature term (appearing in Helfrich bending energy term) is not included. Usually this term is omitted in the case of closed membrane shapes (i.e. so-called spherical topology) due to validity of the Gauss-Bonnet theorem. In the present manuscript/work the shape equation was solved for the membrane patch. Can you therefore please explain shortly to the reader why you can omit the Gaussian curvature term from Eq.(1). For example due fixed inclination angle and foxed curvature at the boundary, .....

Thanks for finding this omission. We have now revised the manuscript to describe why we can omit the Gaussian curvature term (lines 241-245).

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3.«Sigma« and »P« can be considered also as global Lagrange multipliers for the constraint of the fixed total membrane area of the bud (including the neck membrane) and the constraint of the fixed volume of the bud. If you then take into account separately also the equation for the fixed membrane area you could predict different shapes of the bud (by solving the shape equation) at fixed area of the bud, calculated for different values of the model parameters (and different boundary conditions) - in this case Sigma is the result of variational procedure (as well P if you consider also the constraint for the fixed volume of the bud). See for example Medical & Biological Engineering & Computing, vol. 37, pp. 125-129, 1999 and J. Phys. Condens. Matter, vol. 4, pp. 1647-1657, 1992. Can you please shortly discuss in the manuscript also this issue.

This is an interesting point. We now discuss this and cite the mentioned papers at the end of the theory section in the supplementary information (lines 203-205) as well as briefly mentioning it when discussing Eq. 1 (lines 240-242).

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**Referees cross-commenting**

I agree as well.

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Reviewer #1 (Significance (Required)):

The presented experimental and theoretical results are interesting, the manuscript is well written and can be published after revision.

We thank the reviewer for this appreciative comment.

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

Summary:

In their manuscript "Architecture of the chikungunya virus replication organelle" Laurent and colleagues show:

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- the 3D structure of the "neck complex" that forms the gateway between the Chikungunya virus replication/transcription organelle (termed "spherule") and the cytoplasm of infected cells. The structure was obtained by native electron cryo-tomography and sub-tomogram averaging of BHK cells infected with a single-cycle replicon system encoding all components of the viral replication machinery. The nominal resolution of the structure is 28 Å. The viral nsP1 protein, for which two high-resolution structures have previously been published, could unambiguously be located within the density of the neck complex.

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- nsP1 interaction with membranes relies on lipids with a single negative net charge, such as POPS, POPG and PI, whereas two different PIPs with a negative net charge greater than one support nsP1 binding less efficiently. These membrane determinants for nsP1 binding were elucidated using two complementary methods: multilamellar vesicle pulldown assays and confocal imaging of fluorescently labeled giant unilamellar vesicles in the presence of fluorescently labeled nsP1. Purified nsP1 was produced in E. coli.

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- nsP1 recruits nsP2 (another component of the neck complex) to membranes with suitable lipid composition. This observation was made using the same multilamellar vesicle pulldown assay.

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- the 3D organization of the viral genome within the spherule, demonstrating that each spherule contains one copy of the genome as a double-stranded RNA molecule. This analysis was carried out by segmentation of the same tomograms that were used to visualize the neck complex.

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- the force exerted by RNA polymerization within the spherules is sufficient to drive membrane remodeling. This is a theoretical argument based on mathematical modelling.

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Major comments:

The article is written clearly and all major claims seem justified. The biochemical assays are presented in duplicates or triplicates, which is sufficient to derive the provided conclusions. The workflow for electron cryo-tomography analysis seems sound, even though the low number of individual particles (=64) for sub-tomogram averaging of the neck complex limits the resolution of its final structure. Given the strong competition in the field, and considering the high experimental workload that would be required for further improvement of the resolution, I do not recommend any additional benchwork for this paper.

We thank the reviewer for this assessment, especially for recognising the challenge in obtaining a larger number of spherule subtomograms under the complex replicon particle protocol we had to use in order to study the BSL3 CHIKV under BSL2 conditions.

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My only concern is the accuracy of the experimental genome length measurements, which has important implications for their mechanistic interpretation. The type of tomograms that have been recorded here inherently suffers from anisotropy with respect to both resolution and contrast. This makes accurate tracing of tangled filaments very challenging, and in this light, I congratulate the authors for the impressively good agreement of their average experimentally determined genome length with the theoretical genome length (Figure 4C). As to be expected, however, the second supplementary video clearly shows multiple gaps in the traced genome, implying that there must necessarily be errors in the length measurements. Unless there is a possibility to confidently estimate the magnitude of these errors, my preferred interpretation would be that the vast majority of imaged spherules - regardless of their temporary volume in the moment of sample freezing - likely contains precisely one copy of the double-stranded RNA genome, and not fractions thereof as is suggested in the text (for example, line 305: "Analysis of the cryo-electron tomograms gave a clear answer to the question of the membrane bud contents: the lumen of full-size spherules consistently contains 0.8-0.9 copies."). I feel that this subject deserves more discussion in the manuscript. If the authors prefer to keep their original interpretation that the majority of spherules contains only fractions of full genomes, I invite them to provide an explanation for why their experimental genome length measurements are sufficiently accurate to favor this rather surprising conclusion over my more trivial interpretation. If I understand correctly, my preferred interpretation has implications for the mathematical model for membrane remodeling (Equation 2).

This is a good point. In fact, we agree that our original manuscript and wording was unclear and we agree with the reviewer’s interpretation (“my preferred interpretation would be that the vast majority of imaged spherules - regardless of their temporary volume in the moment of sample freezing - likely contains precisely one copy of the double-stranded RNA genome”). We have now changed the text to reflect that we believe we have a 10-20% false negative rate in the filament tracing and that the most likely interpretation is indeed that each spherule has exactly one genome copy (lines 207-210). In addition, we looked at the possible consequences of the slight underestimation of the filament length for the mathematical model, and describe on lines 257-264 why this in fact would have no impact on the conclusions of the modeling.

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Minor comments:

Virus taxa should be capitalized and written in italics wherever applicable. I recommend adhering to the following rules:

https://talk.ictvonline.org/information/w/faq/386/how-to-write-virus-species-and-other-taxa-names

Thank you for helping us clarify this. In response to this we have now italicized and capitalized all virus taxa.

Figure 2I looks as if the pink cross-section of nsP1 has not been scaled correctly. Comparison to Figure 2H gives me the impression that the diameter of the pink nsP1 ring in Figure 2I should be scaled down relative to the greyscale neck complex.

We would like to than the reviewer for their keen eye. There was indeed a scaling problem, which we have now solved in the updated Fig. 2.

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The caption of Figure 2 calls more panels than are provided in the figure. The caption "panel E" seems to be obsolete.

Thanks for finding this mistake. We have now revised Fig. 2 and its legend.

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In the methods, centrifugation speed should be given in units of relative centrifugal force (rcf) instead of revolutions per minute (rpm), especially for the MLV pulldown assay where no rotor is indicated.

We agree and have changed this on lines 482,490,524,531,543 and 597 of the manuscript

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In the methods for the MLV assay, the lipid:protein ratio is given with 500:1. It should be specified whether this is a mass ratio or a molar ratio.

It was molar ratio which we have now specified on line 595.

In the methods, the buffer composition for the mass photometry measurement should be indicated.

Good point. We added this on lines 632-633.

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**Referees cross-commenting**

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I agree to the other reviewers' remarks.

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Reviewer #2 (Significance (Required)):

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Chikungunya virus is a very important human pathogen, and research on the architecture of its replication/transcription organelle holds great promise for the development of future therapies. Laurent and colleagues advanced this field by providing pioneering low-resolution 3D structures of the membrane-bound viral protein complex and the viral RNA content of this organelle in situ. In addition, they also assessed the lipid requirements for membrane interaction of the primary viral membrane anchor of this complex, nsP1, in vitro. Underlining the importance of these results, a competing group submitted a partially overlapping study to BioRXiv three months ahead (https://doi.org/10.1101/2022.04.08.487651). Whereas the competing group describes the structure of the neck complex at a much higher resolution, it neither analyzes the RNA content of the spherules nor does it address the lipid preferences of nsP1. The present study by Laurent and colleagues should therefore be of great interest to many virologists and cellular biologists.

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I am a structural virologist with a focus on envelope glycoproteins. Of relevance to this review, I have experience with cellular electron cryo-tomography and sub-tomogram averaging, as well as in-vitro protein/liposome interaction assays. I do not feel qualified to evaluate the details of the mathematical model for membrane remodeling that is used in the last results section of this manuscript.

We thank reviewer 2 for this positive overall assessment of our work.

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

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This is an interesting and well written paper describing the replication spherules generated by Chikungunya virus. Cryo-electron tomography was used to determine a low-resolution structure of the spherule, suggesting that nsP1 is located at the neck of the spherule. Segmentation of the tomograms combined with mathematical modeling was used to produce a structural model for RNA organization in the spherule, suggesting that each spherule contained approximately one copy of a full double-stranded RNA genome. I have a few minor comments:

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We are thankful for this positive overall assessment of our work.

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The structural studies were complemented with lipid binding assays, showing that nsP1 has an affinity for anionic lipids. While interesting, the connection of these experiments to the rest of the study seems tenuous. There is no further mention of them in the discussion or how they relate to the tomography and their replication model.

We agree that those data were not as well integrated into the paper as they could have been, and are thankful that the reviewer pointed this out. To improve the integration of these data into the manuscript, we have expanded on two ways in which the reconstitution data relate to the rest of the paper: (i) the tomography led us to hypothesise that nsP1 recruits other nsPs to the membrane, which we could confirm with the reconstitution (lines 151-152, and throughout that parapgraph), and (ii) the lipid preferences of nsP1 that we could measure using the titrating pulldown experiments inform the possible models for how the spherule memebrane is remodeled since nsP1 binds lipids that cannot on their own stabilize a neck shape (lines 217-222). We have also slightly expanded the discussion of the biochemistry and its relation to other data in the paper (lines 307-311).

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It is a nice match between the calculated length of the RNA (assumed to be ds) and the length of the vector, but the segmentation of the RNA is not completely convincing based on the provided images. It is difficult to distinguish the RNA strands from the noise and other components in the spherule and, at least by eye, the segments do not seem very connected. Please provide some more details on the tracing algorithm. Has it been validated on a known system?

We appreciate this comment and recognise that we did not sufficiently explain the tracing algorithm. This software was in fact custom written (by others, ca 10 years ago) for cryo-electron tomography and has since been used by others in several studies of cellular cryo-electron tomograms, e.g. to study actin cytoskeleton. We now mention this in the results (lines 195-196) and methods (lines 462-463).

The tomogram video is nice, but it would be good to see a raw image as well, preferably covering a wider view that includes the whole cell, as well as a tomogram that represents the entire field of the reconstruction.

This is a good suggestion. We unfortunately cannot provide images covering the entire cells since this is beyond the field of view of the electron microscope (and an image montage was not acquired at the time of data collection). However, we are now providing an additional supplementary movie that shows the entire field of view of the tomogram. In addition, we have uploaded two of the tomograms (including the uncropped tomogram from Figure 1) to EMDB where they will be downloadable by everyone after publication. We hope the reviewer appreciates that this is all that is technically possible at the moment.

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In figure 2, the panels are mislabeled relative to the legend, which refers to the color guide as its own panel.

Thanks for pointing this out, we have rectified this in the revised Fig. 2 and its legend.

Line 405: C36 symmetry? Why? Shouldn't it be C12 symmetry?

36-fold symmetry was applied to the lipid membrane part to smoothen it further. The membrane part of the structure is simply outlining the neck shape and this is better visualised in this smoothened representation as also done e.g. in the study of the coronavirus neck complex (Wolff et al, Science 2020). We changed the methods text to make this more clear (line 449).

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Line 409: "fit" should be "fitted"

Thanks, Corrected in the revised manuscript line 454.

**Referees cross-commenting**

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I think we are all in good agreement, and I believe that the concerns raised can be addressed though a better explanation of the methods and improved discussion of their results.

We also agree and believe we have addressed all of the remaining concerns in the revised manuscript.

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Reviewer #3 (Significance (Required)):

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This is a rather focused study, showing tomography data on the alphavirus replication complex. The main significance of the study is the description of the spherule's dimension and its relationship to the nature of the RNA, which provided a model for the replication process. While somewhat narrow in scope, the study should be of interest to people working in the virus replication and virus structure field. The lipid data are interesting, but does not seem well integrated with the rest of the study.

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.

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

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.