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Referee #1

Evidence, reproducibility and clarity

In this study, Müller, Matthews, Vali, and Baulcombe have used data-driven machine learning approaches to annotated and classified sRNA loci of Chlamydomonas reinhardtii. I have found the manuscript very interesting and a handy handbook for the appropriate way to annotate sRNA loci in different organisms. I believe this is not only a great resource paper on its own, but it also contains essential information to start understanding how Chalmydomonas silence TEs without a RdDM pathway. I have a few comments that may help to improve the manuscript.

-L50-52: Can you predict where the unmapped read came from? Could viral infections be the source as in land plants? -L67-68, which is the explanation?

• Fig 1D the reference to the A,C,G,U 5' should be re-positioned within Figure 1D panel space. -Figure 3: it could be a supplementary figure based on the relevance given in the manuscript to this point. -P5, line 107: while commenting on strand bias there seems to be a mistake in strong bias definition, it should be x < 0.2 and x > 0.8, not "strong bias (0.2 < x < 0.8)", as in the text. -P5, line 110: marked changes regarding locus size are not as striking in my opinion, in particular log size 6 and following, which is not marked in the graph (the cut off between 6 and 8). Maybe this curve should be split into two distribution graphs based on some important features (as repetitiveness?) that might allow a better definition of cut-offs.
• Fig 5: the legend has the C subfigure twice, the second should be D.
• Table 1: I believe the data would be better presented in a plot, potentially something similar to the plot in Figure 1 A and B. The numbers are already presented in the supplementary spreadsheet.
• Fig 6A: The boxplots regarding Stability of the clusters should be better described. What exactly does the y-axis in each "small plot" represent?
• P6, line 142: analyses of stability and variance shows 7 as the optimal k, while gap statistics and NMI suggested 6 as the optimal. It is not clear why 6 was preferred. The MCA section in Methods is unclear regarding this point too.
• Fig S2-S5: please check legends, they are identical, although they should cover examples of loci in LC2 through LC5. These figures are not cited in the text, only S1 and S2. -Fig 9: I suggest using different colors in density plots to ease interpretation. LC tracks could share a color and Gene, TEs, DNA meth, and All loci should have a different color each. -Supplementary Files S1: The full-annotated locus map should be provided as a spreadsheet file or as a text (.csv) file, not as a pdf file. -I may be misunderstanding Fig. 6E, but it looks strange that the observed sum-of-squares is smooth, but the expected is not. Is it possible that the in-figure reference is inverted?

Significance

This is a very interesting aticle. It may looks a little bit technical but is provide useful information for people studying Chlamydomonas. In addition, the way the authors approached the annotation of sRNA is very meticulous and elegant. I would suggest people exploring small RNAs in non-model organisms to use this article as a handbook of how to annotate sRNAs. In this particular way the artivle will be of interest beyong the Chlamydomonas, and event plant, research field.

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Reply to the reviewers

Reviewer1

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

The manuscript is clearly written and the figures appropriate and informative. Some descriptions of data analyses are a little dense but reflect what would appear long hard efforts on the part of the authors to identify and control for possible sources of misinterpretation due to sensitivities of parameters in their fitness model. The authors efforts to retest interactions under non-competition conditions allay fears of most concerns that I would have. One problem though that I could not see explicitly addressed was that of potential effects of interactions between methotrexate and the other conditions and how this is controlled for. Specifically, I could be argued that the fact that a particular PPI is observed under a specific condition could have more to do with a synthetic effect of treatment of cells with a drug plus methotrexate. Is this controlled for and how? I raise this because in a chemical genetic screen for fitness it was shown that methotrexate is particularly promiscuous for drug-drug interactions (Hillenmeyer ME ,et al. Science 2008). I tried to think of how this works but couldn't come up with anything immediately. I'd appreciate if the authors would take a crack at resolving this issue. Otherwise I have no further concerns about the manuscript.

We thank the reviewer for the kind comments. We agree with the reviewer’s point that methotrexate could be interacting with drugs or other perturbagens, similar to how the chosen nitrogen source, carbon source, or other growth conditions may interact with a drug. However, the methotrexate concentration is held constant across all conditions, as is the rest of the media components such as the nitrogen and carbon source (with the exception of the raffinose perturbation). Any interactions with methotrexate, or other media components, is undetectable without systematically varying all components for all stressors. Therefore, we use the typical experimental design of measuring molecular variation from a reference, holding invariant media components (such as methotrexate, glucose, or vitamins) fixed between conditions. This is a general practice, and we describe that every condition contains methotrexate on page 3, line 10.

The library was grown under mild methotrexate selection in 9 environments for 12-18 generations in serial batch culture, diluting 1:8 every ~3 generations, with a bottleneck population size greater than 2 x 109 cells (Table S1).

We also list the full details of each environment in Table S1.

Reviewer #1 (Significance (Required)):

Lui et al expand on previous work from the Levy group to explore a massive in vivo protein interactome in the yeast S. cerevisiae. They achieve this by performing screens cross 9 growth conditions, which, with replication, results in a total of 44 million measurements. Interpreting their results based on a fitness model for pooled growth under methotrexate selection, they make the key observation that there is a vastly expanded pool of protein-protein interactions (PPI) that are found under only one or two condition compared to a more limited set of PPI that are found under a broad set of conditions (mutable versus immutable interactors). The authors show that this dichotomy suggests some important features of proteins and their PPIs that raise important questions about functionality and evolution of PPIs. Among these are that mutable PPIs are enriched for cross-compartmental, high disorder and higher rates of evolution and subcellular localization of proteins to chromatin, suggesting roles in gene regulation that are associated with cellular responses to new conditions. At the same time these interactions are not enriched for changes in abundance. These results are in contrast to those of immutable PPIs, which seem to form a core background noise, more determined by changes in abundance than what the authors interpret must be post-translational processes that may drive, for instance, changes in subcellular localization resulting in appearance of PPIs under specific conditions. The authors are also able to address a couple of key issues about protein interactomes, including the controversial Party-date Hub hypothesis of Vidal, in which they could now affirm support for this hypothesis based on their results and notably negative correlation of PPIs to protein abundance for mutable PPIs. Finally, they also addressed the problem of predicting the upper limit of PPIs in yeast, showing the remarkable results that it may be no more than about 2 times the number of proteins expressed by yeast. Such an upper limit is profoundly important to modelling cellular network complexity and, if it holds up, could define a general upper limit on organismal complexity.

This manuscript is a very important contribution to understanding dynamics of molecular networks in living cells and should be published with high priority.

Reviewer 2

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

Report on Liu et al. "A large accessory protein interactome is rewired across environments"

Liu et al. use a mDHFR-based, pooled barcode sequencing / competitive growth / mild methotrexate selection method to investigate changes of PPI abundance of 1.6 million protein pairs across different 9 growth conditions. Because most PPI screens aim to identify novel PPIs in standard growth conditions, the currently known yeast PPI network may be incomplete. The key concept is to define immutable" PPIs that are found in all conditions and "mutable" PPIs that are present in only some conditions.

The assay identified 13764 PPIs across the 9 conditions, using optimized fitness cut offs. Steady PPI i.e. across all environments, were identified in membrane compartments and cell division. Processes associated with the chromosome, transcription, protein translation, RNA processing and ribosome regulation were found to change between conditions. Mutable PPIs are form modules as topological analyses reveals.

Interestingly, a correlation on intrinsic disorder and PPI mutability was found and postulated as more flexible in the conformational context, while at the same time they are formed by less abundant proteins.

I appreciate the trick to use homodimerization as an abundance proxy to predict interaction between heterodimers (of proteins that homodimerize). This "mass-action kinetics model" explains the strength of 230 out of 1212 tested heterodimers.

A validation experiment of the glucose transporter network was performed and 90 "randomly chosen" PPIs that were present in the SD environment were tested in NaCl (osmotic stress) and Raffinose (low glucose) conditions through recording optical density growth trajectories. Hxt5 PPIs stayed similar in the tested conditions, supported by the current knowledge that Hxt5 is highly expressed in stationary phase and under salt stress. In Raffinose, Hxt7, previously reported to increase the mRNA expression, lost most PPIs indicating that other factors might influence Hxt7 PPIs.

**Points for consideration:**

*) A clear definition of mutable and immutable is missing, or could not be found e.g. at page 4 second paragraph.

We thank the reviewer for pointing this out. We have now added better definition of mutable and immutable on line 19 page 4:

We partitioned PPIs by the number of environments in which they were identified and defined PPIs at opposite ends of this spectrum as “mutable” PPIs (identified in only 1-3 environments) and “immutable” (identified in 8-9 environments).

*) Approximately half of the PPIs have been identified in one environment. Many of those mutable PPIs were detected in the 16{degree sign}C condition. Is there an explanation for the predominance of this specific environment? What are these PPIs about?

The reviewer is correct that ~40% of the PPIs identified in only one environment were found in the 16 ℃ environment. One reason for this could be technical: the positive predictive value (PPV) is the lowest amongst the conditions (16 ℃: 31.6%, mean: 57%, Table SM6). It must be noted, however, that PPVs are calculated using reference data that has generally been collected in standard growth conditions. So, it might be expected that the most divergent environment from standard growth conditions (resulting in the most differences in PPIs) would result in a lower PPV in our study even if the true frequency of false positives was equivalent across environments. We have attempted to be transparent about the quality of the data in each environment by reporting PPVs and other metrics in Table SM6. However, we suspect that the large number of PPIs unique to 16 ℃ is due in part to the fact that it causes the largest changes in the protein interactome, and believe that it should be included, even at the risk of lowering the overall quality of the data. The main reason for this is that this data is likely to contain valuable information about how the cell copes with this stress. For example, we find, but do not highlight in the manuscript, that 16 ℃-specific PPIs contain two major hubs (DID4: 285 PPIs involved in endocytosis and vacuolar trafficking, and DED1: 102 PPIs involved in translation), both of which are reported to be associated with cold adaptation in yeast (Hilliker et al., 2011; Isasa et al., 2015).

To assess whether the potentially higher false-positive rate in 16 ℃ could be impacting our conclusions related to PPI network organization and features of immutable and mutable PPIs, we repeated these analyses leaving out the 16 ℃ data and found that our main conclusions did not change. This new analysis is now presented in Figure S8 and described on page 5, line 10.

Finally, we used a pair of more conservative PPI calling procedures that either identified PPIs with a low rate of false positives across all environments (FPR

We have also added references to other panels in Figure S8 throughout the manuscript, where appropriate.

*) 50 % overall retest validation rate is fair and reflects a value comparable to other large-scale approaches. However what is the actual variation, e.g. between mutable PPIs and immutable or between condition. e.g. at 16{degree sign}C.

We validated 502 PPIs present in the SD environment and an additional 36 PPIs in the NaCl environment. As the reviewer suggests, we do indeed observe differences in the validation rate across mutability bins. This data is reported in Figures 3B and S6B, and we use this information to provide a confidence score for each PPI on page 5, line 4.

To better estimate how the number of PPIs changes with PPI mutability, we used these optical density assays to model the validation rate as a function of the mean PPiSeq fitness and the number of environments in which a PPI is detected. This accurate model (Spearman's r =0.98 between predicted and observed, see Methods) provided confidence scores (predicted validation rates) for each PPI (Table S5) and allowed us to adjust the true positive PPI estimate in each mutability bin. Using this more conservative estimate, we still found a preponderance of mutable PPIs (Figure S6E).

The validation rate in NaCl is similar to SD (39%, 14/36), suggesting that validation rates do not vary excessively across environments. Because validation experiments are time consuming (we performed 6 growth experiments per PPI), performing a similar scale of validations in all environments as in SD would be resource intensive. Insead, we report a number of metrics (true positive rate, false positive rate, positive predictive value) in Table SM6 using large positive and random reference sets. We believe these metrics are sufficient for readers to compare the quality of data across environments.

*) What is the R correlation cutoff for PPIs explained in the mass equilibrium model vs. not explained?

We do not use an R correlation cutoff to assess if a PPI is explained by the mass-action equilibrium model. We instead rely on ordinary least-squares regression as detailed in the methods on page 68, line 13.

...we used ordinary least-squares linear regression in R to fit a model of the geometric mean of the homodimer signals multiplied by a free constant and plus a free intercept. Significantly explained heterodimer PPIs were judged by a significant coefficient (FDR 0.05, single-test). This criteria was used to identify PPIs for which protein expression does or does not appear to play as significant of a role as other post-translational mechanisms.

The first criterion identifies a quantitative fit to the model of variation being related. The second criterion is used to filter out PPIs for which the relationship appears to be explained by more than just the homodimer signals. This approach is more stringent, but we believe this is the most appropriate statistical test to assess fit to this linear model.

*) 90 "randomly chosen" PPIs for validation. It needs to be demonstrated that these interaction are a random subset otherwise is could also mean cherry picked interactions.

We selected 90 of the 284 glucose transport-related PPIs for validation using the “sample” function in R (replace = FALSE). We have now included text that describes this on page 63, line 3 in the supplementary methods:

Diploids (PPIs) on each plate were randomly picked using the “sample” function in R (replace = FALSE) from PPIs that meet specific requirements.

*) Figure 4 provides interesting correlations with the goal to reveal properties of mutable and less mutable PPIs. PPIs detected in the PPIseq screen can partially be correlated to co-expression (4A) as well as co-localization. Does it make sense to correlate the co-expression across number of conditions? Are the expression correlation condition specific. In this graph it could be that expression correlation stems from condition 1 and 2 and the interaction takes place in 4 and 5 still leading to the same conclusion ... Is the picture of the co-expression correlation similar when you simply look at individual environments like in S4A?

We use co-expression mutual rank scores from the COXPRESdb v7.3 database (Obayashi et al., 2019). These mutual rank scores are derived from a broad set of 3593 environmental perturbations that are not limited to the environments we tested here. By using this data, we are asking if co-expression in general is correlated with mutability and report that it is in Figure 4A. We thank the reviewer for pointing out that this was not clear and have now added text to clarify that the co-expression analysis is derived from external data on page 6, line 7.

We first asked whether co-expression is indeed a predictor of PPI mutability and found that it is: co-expression mutual rank (which is inversely proportional to co-expression across thousands of microarray experiments) declined with PPI mutability (Figures 4A and S11) (Obayashi and Kinoshita, 2009; Obayashi et al., 2019).

The new figure S11 examines how the co-expression mutual rank changes with PPI mutability for PPIs identified in each environment, as the reviewer suggested. For each environment, we find the same general pattern as in Figure 4A (which considers PPIs from all environments).

*) Figure 4C: Interesting, how dependent are the various categories?

It is well known that many of these categories are correlated (e.g. mRNA expression level and protein abundance, and deletion fitness effect and genetic interaction degree). However, we believe it is most valuable to report the correlation of each category with PPI mutability independently in Figures 4C and S12, since similar correlations with related categories provide more confidence in our conclusions.

*) Figure 4 F: When binned in the number of environments in which the PPI was found, the distribution peaks at 6 environments and decreases with higher and lower number of environments. The description /explanation in the text clearly says something else.

We reported on page 7, line 15:

We next used logistic regression to determine what features may underlie a good or poor fit to the model (Figure S14C) and found that PPI mutability was the best predictor, with more mutable PPIs being less frequently explained (Figure 4F). Unexpectedly, mean protein abundance was the second best predictor, with high abundance predicting a poor fit to the model, particularly for less mutable PPIs (Figure S14D and S14E).

As the reviewer notes, Figure 4F shows that the percent of heterodimers explained by the model does appear to decrease for PPIs observed in the most environments. We suspect that the reviewer is correct that something more complicated is going on. One possibility is that extraordinarily stable PPIs (stable in all conditions) would have less quantitative variation in protein or PPI abundance across environments. If this is true, it would be statistically difficult to fit the mass action kinetics model for these PPIs (lower signal relative to noise), thereby resulting in the observed dip.

A second possibility is that multiple correlated factors are associated with contributing positively or negatively to a good fit, and the simplicity of Figure 4F or a Pearson correlation does not capture this interplay. This second possibility is why we used multivariate logistic regression (Figure S14C) to dissect the major contributing factors. In the text quote above, we report that high abundance is anti-correlated with a good fit to the model (S14D, S14E). Figure 4C shows that immutable PPIs tend to be formed from highly abundant proteins. One possible explanation is that highly abundant proteins saturate the binding sites of their binding partners, breaking from the assumptions of mass action kinetics model. We have now changed the word “limit” to “saturate” on page 7, line 22 to make this concept more explicit.

Taken together, these data suggest that mutable PPIs are subject to more post-translational regulation across environments and that high basal protein abundance may saturate the binding sites of their partners, limiting the ability of gene expression changes to regulate PPIs.

A third possibility is that the dip is simply due to noise. Given the complexity of the possible explanations and our uncertainty about which is more likely, we chose to leave this description out of the main text and focus on the major finding: that PPIs detected in more environments are generally associated with a better fit to the mass action kinetics model.

*) Figure 6: I apologize, but for my taste this is not a final figure 6 for this study. Investigation of different environments increases the PPI network in yeast, yes, yet it is very well known that a saturation is reached after testing of several conditions, different methods and even screening repetition (sampling). It does not represent an important outcome. Move to suppl or remove.

We included Figure 6 to summarize and illustrate the path forward from this study. This is an explicit reference to impactful computational analyses done using earlier generations of data to assess the completeness of single-condition interaction networks (Hart et al., 2006; Sambourg and Thierry-Mieg, 2010). Here, we are extending PPI measurement of millions-scale networks across multiple environments, and are using this figure to extend these concepts to multi-condition screens. We agree that the property of saturation in sampling is well known, but it is surprising that we can quantitatively estimate convergence of this expanded condition-specific PPI set using only 9 conditions. Thus, we agree with Reviewer 1 that these are “remarkable results” and that the “upper limit is profoundly important to modelling cellular network complexity and, if it holds up, could define a general upper limit on organismal complexity.” We think this is an important advance of the paper, and this figure is useful to stimulate discussion and guide future work.

Reviewer #2 (Significance (Required)):

Liu et al. increase the current PPI network in yeast and offer a substantial dataset of novel PPIs seen in specific environments only. This resource can be used to further investigate the biological meaning of the PPI changes. The data set is compared to previous DHFR providing some sort of quality benchmarking. Mutable interactions are characterized well. Clearly a next step could be to start some "orthogonal" validation, i.e. beyond yeast growth under methotrexate treatment.

The reviewer makes a great point that we also discuss on page 9, line 33:

While we used reconstruction of C-terminal-attached mDHFR fragments as a reporter for PPI abundance, similar massively parallel assays could be constructed with different PCA reporters or tagging configurations to validate our observations and overcome false negatives that are specific to our reporter. Indeed, the recent development of “swap tag” libraries, where new markers can be inserted C- or N-terminal to most genes (Weill et al., 2018; Yofe et al., 2016), in combination with our iSeq double barcoder collection (Liu et al., 2019), makes extension of our approach eminently feasible.

Reviewer 3

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

**Summary**

The manuscript "A large accessory protein interactome is rewired across environments" by Liu et al. scales up a previously-described method (PPiSeq) to test a matrix of ~1.6 million protein pairs of direct protein-protein interactions in each of 9 different growth environments.

While the study found a small fraction of immutable PPIs that are relatively stable across environments, the vast majority were 'mutable' across environments. Surprisingly, PPIs detected only in one environment made up more than 60% of the map. In addition to a false positive fraction that can yield apparently-mutable interactions, retest experiments demonstrate (not surprisingly) that environment-specificity can sometimes be attributed to false-negatives. The study authors predict that the whole subnetwork within the space tested will contain 11K true interactions.

Much of environment-specific rewiring seemed to take place in an 'accessory module', which surrounds the core module made of mostly immutable PPIs. A number of interesting network clustering and functional enrichment analyses are performed to characterize the network overall and 'mutable' interactions in particular. The study report other global properties such as expression level, protein abundance and genetic interaction degree that differ between mutable and immutable PPIs. One of the interesting findings was evidence that many environmentally mutable PPI changes are regulated post-translationally. Finally, authors provide a case study about network rewiring related to glucose transport.

**Major issues**

-The results section should more prominently describe the dimensions of the matrix screen, both in terms of the set of protein pairs attempted and the set actually screened (I think this was 1741 x 1113 after filtering?). More importantly, the study should acknowledge in the introduction that this was NOT a random sample of protein pairs, but rather focused on pairs for which interaction had been previously observed in the baseline condition. This major bias has a potentially substantial impact on many of the downstream analyses. For example, any gene which was not expressed under the conditions of the original Tarrasov et al. study on which the screening space was based will not have been tested here. Thus, the study has systematically excluded interactions involving proteins with environment-dependent expression, except where they happened to be expressed in the single Tarrasov et al. environment. Heightened connectivity within the 'core module' may result from this bias, and if Tarrasov et al had screened in hydrogen peroxide (H2O2) instead of SD media, perhaps the network would have exhibited a code module in H2O2 decorated by less-densely connected accessory modules observed in other environments. The paper should clearly indicate which downstream analyses have special caveats in light of this design bias.

We have now added text the matrix dimensions of our study on page 3, line 3:

To generate a large PPiSeq library, all strains from the protein interactome (mDHFR-PCA) collection that were found to contain a protein likely to participate in at least one PPI (1742 X 1130 protein pairs), (Tarassov et al., 2008) were barcoded in duplicate using the double barcoder iSeq collection (Liu et al., 2019), and mated together in a single pool (Figure 1A). Double barcode sequencing revealed that the PPiSeq library contained 1.79 million protein pairs and 6.05 million double barcodes (92.3% and 78.1% of theoretical, respectively, 1741 X 1113 protein pairs), with each protein pair represented by an average of 3.4 unique double barcodes (Figure S1).

We agree with the reviewer that our selection of proteins from a previously identified set can introduce bias in our conclusions. Our research question was focused on how PPIs change across environments, and thus we chose to maximize our power to detect PPI changes by selecting a set of protein pairs that are enriched for PPIs. We have now added a discussion of the potential caveats of this choice to the discussion on page 9, line 4:

Results presented here and elsewhere (Huttlin et al., 2020) suggest that PPIs discovered under a single condition or cell type are a small subset of the full protein interactome emergent from a genome. We sampled nine diverse environments and found approximately 3-fold more interactions than in a single environment. However, the discovery of new PPIs began to saturate, indicating that most condition-specific PPIs can be captured in a limited number of conditions. Testing in many more conditions and with PPI assays orthogonal to PPiSeq will undoubtedly identify new PPIs, however a more important outcome could be the identification of coordinated network changes across conditions. Using a test set of ~1.6 million (of ~18 million) protein pairs across nine environments, we find that specific parts of the protein interactome are relatively stable (core modules) while others frequently change across environments (accessory modules). However, two important caveats of our study must be recognized before extrapolating these results to the entire protein interactome across all environment space. First, we tested for interactions between a biased set of proteins that have previously been found to participate in at least one PPI as measured by mDHFR-PCA under standard growth conditions (Tarassov et al., 2008). Thus, proteins that are not expressed under standard growth conditions are excluded from our study, as are PPIs that are not detectable by mDHFR-PCA or PPiSeq. It is possible that a comprehensive screen using multiple orthogonal PPI assays would alter our observations related to the relative dynamics of different regions of the protein interactome and the features of mutable and immutable PPIs. Second, we tested a limited number of environmental perturbations under similar growth conditions (batch liquid growth). It is possible that more extreme environmental shifts (e.g. growth as a colony, anaerobic growth, pseudohyphal growth) would introduce new accessory modules or alter the mutability of the PPIs we detect. Nevertheless, results presented here provide a new mechanistic view of how the cell changes in response to environmental challenges, building on the previous work that describes coordinated responses in the transcriptome (Brauer et al., 2007; Gasch et al., 2000) and proteome (Breker et al., 2013; Chong et al., 2015).

-Related to the previous issue, a quick look at the proteins tested (if I understood them correctly) showed that they were enriched for genes encoding the elongator holoenzyme complex, DNA-directed RNA polymerase I complex, membrane docking and actin binding proteins, among other functional enrichments. Genes related to DNA damage (endonuclease activity and transposition), were depleted. It was unclear whether the functional enrichment analyses described in the paper reported enrichments relative to what would be expected given the bias inherent to the tested space?

We did two functional enrichment analyses in this study: network density within Gene Ontology terms (related to Figure 2) and gene ontology enrichment of network communities (related to Figure 3). For both analyses, we performed comparisons to proteins included in PPiSeq library. This is described in the Supplementary Materials on page 63, line 35:

To estimate GO term enrichment in our PPI network, we constructed 1000 random networks by replacing each bait or prey protein that was involved in a PPI with a randomly chosen protein from all proteins in our screen. This randomization preserves the degree distribution of the network.

And on page 66, line 38:

The set of proteins used for enrichment comparison are proteins that are involved in at least one PPI as determined by PPiSeq.

-Re: data quality. To the study's great credit, they incorporated positive and random reference sets (PRS and RRS) into the screen. However, the results from this were concerning: Table SM6 shows that assay stringency was set such that between 1 and 3 out of 67 RRS pairs were detected. This specificity would be fine for an assay intended for retest or validate previous hits, where the prior probability of a true interaction is high, but in large-scale screening the prior probability of true interactions that are detectable by PCA is much lower, and a higher specificity is needed to avoid being overwhelmed by false positives. Consider this back of the envelope calculation: Let's say that the prior probability of true interaction is 1% as the authors' suggest (pg 49, section 6.5), and if PCA can optimistically detect 30% of these pairs, then the number of true interactions we might expect to see in an RRS of size 67 is 1% * 30% * 67 = 0.2 . This back of the envelope calculation suggests that a stringency allowing 1 hit in RRS will yield 80% [ (1 - 0.2) / 1 ] false positives, and a stringency allowing 3 hits in RRS will yield 93% [ (3 - 0.2) / 3] false positives. How do the authors reconcile these back of the envelope calculations from their PRS and RRS results with their estimates of precision?

We thank the reviewer for bringing up with this issue. We included positive and random reference sets (PRS:70 protein pairs, RRS:67 protein pairs) to benchmark our PPI calling (Yu et al., 2008). The PRS reference lists PPIs that have been validated by multiple independent studies and is therefore likely to represent true PPIs that are present in some subset of the environments we tested. For the PRS set, we found a rate of detection that is comparable to other studies (PPiSeq in SD: 28%, Y2H and yellow fluorescent protein-PCA: ~20%) (Yu et al., 2008). The RRS reference, developed ten years ago, is randomly chosen protein pairs for which there was no evidence of a PPI in the literature at the time (mostly in standard growth conditions). Given the relatively high rate of false negatives in PPI assays, this set may in fact contain some true PPIs that have yet to be discovered. We could detect PPIs for four RRS protein pairs in our study, when looking across all 9 environments. Three of these (Grs1_Pet10, Rck2_Csh1, and YDR492W_Rpd3) could be detected in multiple environments (9, 7, and 3, respectively), suggesting that their detection was not a statistical or experimental artifact of our bar-seq assay (see table below derived from Table S4). The remaining PPI detected in the RRS, was only detected in SD (standard growth conditions) but with a relatively high fitness (0.35), again suggesting its detection was not a statistical or experimental artifact. While we do acknowledge it is possible that these are indeed false positives due to erroneous interactions of chimeric DHFR-tagged versions of these proteins, the small size of the RRS combined with the fact that some of the protein pairs could be true PPIs, did not give us confidence that this rate (4 of 70) is representative of our true false positive rate. To determine a false positive rate that is less subject to biases stemming from sampling of small numbers, we instead generated 50 new, larger random reference sets, by sampling for each set ~ 60,000 protein pairs without a reported PPI in BioGRID. Using these new reference sets, we found that the putative false positive rate of our assay is generally lower than 0.3% across conditions for each of the 50 reference sets. We therefore used this more statistically robust measure of the false positive rate to estimate positive predictive values (PPV = 62%, TPR = 41% in SD). We detail these statistical methods in Section 6 of the supplementary methods and report all statistical metrics in Table SM6.

PPI

Environment_number

SD

H2O2

Hydroxyurea

Doxorubicin

Forskolin

Raffinose

NaCl

16℃

FK506

Rck2_Csh1

7

0.35

0.35

0

0.20

0.54

0.74

0

0.17

0.59

Grs1_Pet10

9

0.44

0.39

0.34

0.25

0.65

1.19

0.2

0.16

0.95

YDR492W_Rpd3

3

0

0.18

0

0

0

0

0

0.17

0.61

Mrps35_Bub3

1

0.35

0

0

0

0

0

0

0

0

Positive_control

9

1

0.8

0.73

0.62

1.4

2.44

0.4

0.28

1.8

Table. Mean fitness in each environment

-Methods for estimating precision and recall were not sufficiently well described to assess. Precision vs recall plots would be helpful to better understand this tradeoff as score thresholds were evaluated.

We describe in detail our approach to calling PPIs in section 6.6 of the supplementary methods, including Table SM6, and Figures SM3, SM4, SM6, and now Figure SM5. We identified positive PPIs using a dynamic threshold that considers the mean fitness and p-value in each environment. For each dynamic threshold, we estimated the precision and recall based on the reference sets (described supplementary methods in section 6.5). We then chose the threshold with the maximal Matthews correlation coefficient (MCC) to obtain the best balance between precision and recall. We have now added an additional plot (Figure SM5) that shows the precision and recall for the chosen dynamic threshold in each environment.

-Within the tested space, the Tarassov et al map and the current map could each be compared against a common 'bronze standard' (e.g. literature curated interactions), at least for the SD map, to have an idea about how the quality of the current map compares to that of the previous PCA map. Each could also be compared with the most recent large-scale Y2H study (Yu et al).

We thank the reviewer for this suggestion. We have now added a figure panel (Figure S4) that compares PPiSeq in SD (2 replicates) to mDHFR PCA (Tarassov et al., 2008), Y2H (Yu et al., 2008), and our newly constructed ‘bronze standard’ high-confidence positive reference set (PRS, supplementary method section 6.4).

• Experimental validation of the network was done by conventional PCA. However, it should be noted that this is a form of technical replication of the DHFR-based PCA assay, and not a truly independent validation. Other large-scale yeast interaction studies (e.g., Yu et al, Science 2008) have assessed a random subset of observed PPIs using an orthogonal approach, calibrated using PRS and RRS sets examined via the same orthogonal method, from which overall performance of the dataset could be determined.

We appreciate the reviewer’s perspective, since orthogonal validation experiments have been a critical tool to establish assay performance following early Y2H work. We know from careful work done previously that modern orthogonal assays have a low cross validation rate ((Yu et al., 2008) and that they tend to be enriched for PPIs in different cellular compartments (Jensen and Bork, 2008), indicating that high false negative rates are the likely explanation. High false negative rates have been confirmed here and elsewhere using positive reference sets (e.g. Y2H 80%, PCA 80%, PPiSeq 74% using the PRS in (Yu et al., 2008)). Therefore, the expectation is that PPiSeq, as with other assays, will have a low rate of validation using an orthogonal assay -- although we would not know if this rate is 10%, 30% or somewhere in between without performing the work. However, the exact number -- whether it be 10% or 30% -- has no practical impact on the main conclusions of this study (focused on network dynamics rather than network enumeration). Neither does that number speak to the confidence in our PPI calls, since a lower number may simply be due to less overlap in the sets of PPIs that are callable by PPiSeq and another assay. Our method uses bar-seq to extend an established mDHFR-PCA assay (Tarassov et al., 2008). The validations we performed were aimed at confirming that our sequencing, barcode counting, fitness estimation, and PPI calling protocols were not introducing excessive noise relative to mDHFR-PCA that resulted in a high number of PPI miscalls. Confirming this, we do indeed find a high rate of validation by lower throughput PCA (50-90%, Figure 3B). Finally, we do include independent tests of the quality of our data by comparing it to positive and random reference sets from literature curated data. We find that our assay performs extremely well (PPV > 61%, TPR > 41%) relative to other high-throughput assays.

-The Venn diagram in Figure 1G was not very informative in terms of assessing the quality of data. It looks like there is a relatively little overlap between PPIs identified in standard conditions (SD media) in the current study and those of the previous study using a very similar method. Is there any way to know how much of this disagreement can be attributed to each screen being sub-saturation (e.g. by comparing replica screens) and what fraction to systematic assay or environment differences?

We have now added a figure panel (Figure S4) that compares PPiSeq in SD (2 replicates) to mDHFR-PCA (Tarassov et al., 2008), Y2H (Yu et al., 2008), and our newly constructed ‘bronze standard’ high-confidence positive reference sets (PRS, supplementary methods section 6.4). We find that SD replicates have an overlap coefficient of 79% with each other, ~45% with mDHFR-PCA, ~45% the ‘bronze standard’ PRS, and ~13% with Y2H. Overlap coefficients between the SD replicates and mDHFR-PCA are much higher than those found between orthologous methods ((Yu et al., 2008), indicating that these two assays are identifying a similar set of PPIs. We do note that PPiSeq and mDHFR-PCA do screen for PPIs under different growth conditions (batch liquid growth vs. colonies on agar), so some fraction of the disagreement is due to environmental differences. PPIs that overlap between the two PPiSeq SD replicates are more likely to be found in mDHFR-PCA, PRS, and Y2H, indicating that PPIs identified in a single SD replicate are more likely to be false positives. However, we do find (a lower rate of) overlaps between PPIs identified in only one SD replicate and other methods, suggesting that a single PPiSeq replicate is not finding all discoverable PPIs.

-In Figure S5C, the environment-specificity rate of PPIs might be inflated due to the fact that authors only test for the absence of SD hits in other conditions, and the SD condition is the only condition that has been sampled twice during the screening. What would be the environment-specific verification rate if sample hits from each environment were tested in all environments? This seems important, as robustly detecting environment-specific PPIs is one of the key points of the study.

We use PPIs found in the SD environment to determine the environment-specificity because this provides the most conservative (highest) estimate of the number of PPIs found in other environments that were not detectable by our bar-seq assay. To identify PPIs in the SD environment, we pooled fitness estimates across the two replicates (~ 4 fitness estimates per replicate, ~ 8 total). The higher number of replicates results in a reduced rate of false positives (an erroneous fitness estimate has less impact on a PPI call), meaning that we are more confident that PPIs identified in SD are true positives. Because false positives in one environment (but not other environments) are likely to erroneously contribute to the environment-specificity rate, choosing the environment with the lowest rate of false positives (SD) should result in the lowest environment-specificity rate (highest estimate of PPIs found in other environments that were not detectable by our bar-seq assay).

**Minor issues**

-Re: "An interaction between the proteins reconstitutes mDHFR, providing resistance to the drug methotrexate and a growth advantage that is proportional to the PPI abundance" (pg 2). It may be more accurate to say "monotonically related" than "proportional" here. Fig 2 from the cited Freschi et al ref does suggests linearity with colony size over a wide range of inferred complex abundances, but non-linear at low complex abundance. Also note that Freschi measured colony area which is not linear with exponential growth rate nor with cell count.

We agree with the reviewer and have changed “proportional” to “monotonically related” on page 2, line 41.

-Re: "Using putatively positive and negative reference sets, we empirically determined a statistical threshold for each environment with the best balance of precision and recall (positive predictive value (PPV) > 61% in SD media, Methods, section 6)." (pg 3). Should state the recall at this PPV.

We agree with the reviewer and have added the recall (41%) in the main text (line 26, page3).

Using putatively positive and negative reference sets, we empirically determined a statistical threshold for each environment with the best balance of precision and recall (positive predictive value (PPV) > 61% and true positive rate > 41% in SD media, Methods, section 6).

-Authors could discuss the extent to which related methods (e.g. PMID: 28650476, PMID: 27107012, PMID: 29165646, PMID: 30217970) would be potentially suitable for screening in different environments.

We have now added a reference to a barcode-based Y2H study that examined interactions between yeast proteins to the introduction on page 2, line 2:

Yet, little is known about how PPI networks reorganize on a global scale or what drives these changes. One challenge is that commonly-used high-throughput PPI screening technologies are geared toward PPI identification (Gavin et al., 2002; Ito et al., 2001; Tarassov et al., 2008; Uetz et al., 2000; Yu et al., 2008, Yachie et al., 2016), not a quantitative analysis of relative PPI abundance that is necessary to determine if changes in the PPI network are occurring. The murine dihydrofolate reductase (mDHFR)‐based protein-fragment complementation assay (PCA) provides a viable path to characterize PPI abundance changes because it is a sensitive test for PPIs in the native cellular context and at native protein expression levels (Freschi et al., 2013; Remy and Michnick, 1999; Tarassov et al., 2008).

We have excluded the references to other barcode-based Y2H studies that reviewer mentions because they test heterologous proteins within yeast, and the effect of perturbations to yeast on these proteins would be difficult to interpret in the context of our questions. The yeast protein Y2H study, although a wonderful approach and paper, would also not be an appropriate method to examine how PPI networks change across environments because protein fusions are not expressed under their endogenous promoters and must be transported to, in many cases, a non-native compartment (cell nucleus) to be detected. Rather than explicitly discuss the caveats of this particular approach, we have instead chosen to discuss why we use PCA.

• the term "mutable" is certainly appropriate according to the dictionary definition of changeable. The authors may wish to consider though, that in a molecular biology context the term evokes changeability by mutation (a very interesting but distinct topic). Maybe another term (environment-dependent interactions or ePPIs?) would be clearer. Of course this is the authors' call.

We thank the reviewer for this suggestion, and have admittedly struggled with the terminology. For clarity of presentation, we strived to have a single word that describes the property of a PPI that is at the core of this manuscript -- how frequently a PPI is found across environments. However, the most descriptive words come with preloaded meanings in PPI research (e.g. transient, stable, dynamic), as does “mutable” with another research field. We are, quite frankly, open to suggestions from the reviewers or editors for a more appropriate word that does not raise similar objections.

-Some discussion is warranted about the phenomenon that a PPI that is unchanged in abundance could appear to change because of statistical significance thresholds that differ between screens. This would be a difficult question for any such study, and I don't think the authors need to solve it, but just to discuss.

We agree with the reviewer that significance thresholds could be impacting our interpretations and discuss this idea at length on page 4, line 23 of the Results. This section has been modified to include an additional analysis (excluding 16 ℃ data) in response to another reviewer’s comment:

Immutable PPIs were likely to have been previously reported by colony-based mDHFR-PCA or other methods, while the PPIs found in the fewest environments were not. One possible explanation for this observation is that previous PPI assays, which largely tested in standard laboratory growth conditions, and variations thereof, are biased toward identification of the least mutable PPIs. That is, since immutable PPIs are found in nearly all environments, they are more readily observed in just one. However, another possible explanation is that, in our assay, mutable PPIs are more likely to be false positives in environment(s) in which they are identified or false negatives in environments in which they are not identified. To investigate this second possibility, we first asked whether PPIs present in very few environments have lower fitnesses, as this might indicate that they are closer to our limit of detection. We found no such pattern: mean fitnesses were roughly consistent across PPIs found in 1 to 6 conditions, although they were elevated in PPIs found in 7-9 conditions (Figure S6A). To directly test the false-positive rate stemming from pooled growth and barcode sequencing, we validated randomly selected PPIs within each mutability bin by comparing their optical density growth trajectories against controls (Figures 3B). We found that mutable PPIs did indeed have lower validation rates in the environment in which they were identified, yet putative false positives were limited to ~50%, and, within a bin, do not differ between PPIs that have been previously identified and those that have been newly discovered by our assay (Figure S65B). We also note mutable PPIs might be more sensitive to environmental differences between our large pooled PPiSeq assays and clonal 96-well validation assays, indicating that differences in validation rates might be overstated. To test the false-negative rate, we assayed PPIs identified in only SD by PPiSeq across all other environments by optical density growth and found that PPIs can be assigned to additional environments (Figure S6C). However, the number of additional environments in which a PPI was detected was generally low (2.5 on average), and the interaction signal in other environments was generally weaker than in SD (Figure S6D). To better estimate how the number of PPIs changes with PPI mutability, we used these optical density assays to model the validation rate as a function of the mean PPiSeq fitness and the number of environments in which a PPI is detected. This accurate model (Spearman's r =0.98 between predicted and observed, see Methods) provided confidence scores (predicted validation rates) for each PPI (Table S5) and allowed us to adjust the true positive PPI estimate in each mutability bin. Using this more conservative estimate, we still found a preponderance of mutable PPIs (Figure S6E). Finally, we used a pair of more conservative PPI calling procedures that either identified PPIs with a low rate of false positives across all environments (FPR

We later examine major conclusions of our study using more conservative calling procedures, and find that they are consistent. On page 6, line 14:

Both the co-expression and co-localization patterns were also apparent in our higher confidence PPI sets (Figures S7B, and S7C, S8B, S8C ), indicating that they are not caused by different false positive rates between the mutability bins.

And on page 6, line 19:

We binned proteins by their PPI degree, and, within each bin, determined the correlation between the mutability score and another gene feature (Figure 4C and S12A, Table S8) (Costanzo et al., 2016; Finn et al., 2014; Gavin et al., 2006; Holstege et al., 1998; Krogan et al., 2006; Levy and Siegal, 2008; Myers et al., 2006; Newman et al., 2006; Östlund et al., 2010; Rice et al., 2000; Stark et al., 2011; Wapinski et al., 2007; Ward et al., 2004; Yang, 2007; Yu et al., 2008). These correlations were also calculated using our higher confidence PPI sets, confirming results from the full data set (Figures S7D and, S7E, S8D, S8E). We found that mutable hubs (> 15 PPIs) have more genetic interactions, in agreement with predictions from co-expression data (Bertin et al., 2007; Han et al., 2004), and that their deletion tends to cause larger fitness defects.

-More discussion would be helpful about the idea that immutability may to some extent favor interactions that PCA is better able to detect (possibly including membrane proteins?)

We agree with the reviewer and now added a discussion of this potential caveats to the discussion on page 9, line 4:

Results presented here and elsewhere (Huttlin et al., 2020) suggest that PPIs discovered under a single condition or cell type are a small subset of the full protein interactome emergent from a genome. We sampled nine diverse environments and found approximately 3-fold more interactions than in a single environment. However, the discovery of new PPIs began to saturate, indicating that most condition-specific PPIs can be captured in a limited number of conditions. Testing in many more conditions and with PPI assays orthogonal to PPiSeq will undoubtedly identify new PPIs, however a more important outcome could be the identification of coordinated network changes across conditions. Using a test set of ~1.6 million (of ~18 million) protein pairs across nine environments, we find that specific parts of the protein interactome are relatively stable (core modules) while others frequently change across environments (accessory modules). However, two important caveats of our study must be recognized before extrapolating these results to the entire protein interactome across all environment space. First, we tested for interactions between a biased set of proteins that have previously been found to participate in at least one PPI as measured by mDHFR-PCA under standard growth conditions (Tarassov et al., 2008). Thus, proteins that are not expressed under standard growth conditions are excluded from our study, as are PPIs that are not detectable by mDHFR-PCA or PPiSeq. It is possible that a comprehensive screen using multiple orthogonal PPI assays would alter our observations related to the relative dynamics of different regions of the protein interactome and the features of mutable and immutable PPIs. Second, we tested a limited number of environmental perturbations under similar growth conditions (batch liquid growth). It is possible that more extreme environmental shifts (e.g. growth as a colony, anaerobic growth, pseudohyphal growth) would introduce new accessory modules or alter the mutability of the PPIs we detect. Nevertheless, results presented here provide a new mechanistic view of how the cell changes in response to environmental challenges, building on the previous work that describes coordinated responses in the transcriptome (Brauer et al., 2007; Gasch et al., 2000) and proteome (Breker et al., 2013; Chong et al., 2015).

-Re: "As might be expected, we also found that mutable hubs, but not non-hubs, are more likely to participate in multiple protein complexes than less mutable proteins." (pg 6) This is a cool result. To what extent was this result driven by members of one or two complexes? If so, it would worth noting them.

We thank the reviewer for this question. We have now included Figue S13, which shows the number and size of protein complexes that underlie the finding that mutable hubs are more likely to participate in multiple protein complexes. We find that proteins in our screen that participate in multiple complexes are distributed over a wide range of complexes, indicating that this observation is not driven by one or two complexes. On page 6, line 34:

As might be expected, we also found that mutable hubs, but not non-hubs, are more likely to participate in multiple protein complexes than less mutable proteins (Figures S13A-C) (Costanzo et al., 2016).

-Re: "Borrowing a species richness estimator from ecology (Jari Oksanen et al., 2019), we estimate that there are ~10,840 true interactions within our search space across all environments, ~3-fold more than are detected in SD (note difference to Figure 3, which counts observed PPIs)." (pg 8) Should note that this only allows estimation of the number of interactions that are detectable by PCA methods. Previous work (Braun et al, 2019) showed that every known protein interaction assay (including PCA approaches) can only detect a fraction of bona fide interactions.

We agree with the reviewer and have modified the discussion to make this point explicit on page 9, line 4:

Results presented here and elsewhere (Huttlin et al., 2020) suggest that PPIs discovered under a single condition or cell type are a small subset of the full protein interactome emergent from a genome. We sampled nine diverse environments and found approximately 3-fold more interactions than in a single environment. However, the discovery of new PPIs began to saturate, indicating that most condition-specific PPIs can be captured in a limited number of conditions. Testing in many more conditions and with PPI assays orthogonal to PPiSeq will undoubtedly identify new PPIs, however a more important outcome could be the identification of coordinated network changes across conditions.

We continue in this paragraph to discuss the implications:

Using a test set of ~1.6 million (of ~18 million) protein pairs across nine environments, we find that specific parts of the protein interactome are relatively stable (core modules) while others frequently change across environments (accessory modules). However, two important caveats of our study must be recognized before extrapolating these results to the entire protein interactome across all environment space. First, we tested for interactions between a biased set of proteins that have previously been found to participate in at least one PPI as measured by mDHFR-PCA under standard growth conditions (Tarassov et al., 2008). Thus, proteins that are not expressed under standard growth conditions are excluded from our study, as are PPIs that are not detectable by mDHFR-PCA or PPiSeq. It is possible that a comprehensive screen using multiple orthogonal PPI assays would alter our observations related to the relative dynamics of different regions of the protein interactome and the features of mutable and immutable PPIs.

-Re: "This analysis shows that the number of PPIs present across all environments is much larger than the number observed in a single condition, but that it is feasible to discover most of these new PPIs by sampling a limited number of conditions." (pg 8). The main point is surely correct, but it is worth noting that extrapolation to the number of true interactions depends on the nine chosen environments being representative of all environments. The situation could change under more extreme, e.g., anaerobic, conditions.

We agree with the reviewer and make this point explicit, continuing from the paragraph quoted above on page 9, line 22:

Second, we tested a limited number of environmental perturbations under similar growth conditions (batch liquid growth). It is possible that more extreme environmental shifts (e.g. growth as a colony, anaerobic growth, pseudohyphal growth) would introduce new accessory modules or alter the mutability of the PPIs we detect. Nevertheless, results presented here provide a new mechanistic view of how the cell changes in response to environmental challenges, building on the previous work that describes coordinated responses in the transcriptome (Brauer et al., 2007; Gasch et al., 2000) and proteome (Breker et al., 2013; Chong et al., 2015).

-It stands to reason that proteins expressed in all conditions will yield less mutable interactions, if 'mutability' is primarily due to expression change at the transcriptional level. They should at least discuss that measuring mRNA levels could resolve questions about this. Could use Waern et al G3 2013 data (H202, SD, HU, NaCl) to predict the dynamic interactome purely by node removal, and see how conclusions would change

We agree with the reviewer that mRNA abundance could potentially be used as a proxy for protein abundance and have added this point on page 10, line 28:

Here we use homodimer abundance as a proxy for protein abundance. However, genome-wide mRNA abundance measures could be used as a proxy for protein abundance or protein abundance could be measured directly in the same pool (Levy et al., 2014) by, for example, attaching a full length mDHFR to each gene using “swap tag” libraries mentioned above (Weill et al., 2018; Yofe et al., 2016).

However, using mRNA abundance as a proxy for protein abundance in this study has several important caveats that would make interpretation difficult. First, mRNA and protein abundance correlate, but not perfectly (R2 = 0.45) (Lahtvee et al., 2017), and our findings suggest that post-translational regulation may be important to driving PPI changes. Second, mRNA abundance measures are for a single time point, while our PPI measures coarse grain over a growth cycle (lag, exponential growth, diauxic shift, saturation). Although we may be able to take multiple mRNA measures across the cycle, time delays between changes in mRNA and protein levels, combined with the fact that we do not know when a PPI is occurring or most prominent over the cycle, would pose a significant challenge to making any claims that PPI changes are driven by changes in protein abundance. We instead chose to focus on a subset of proteins (homodimers) where abundance measures can be coarse grained in the same way as PPI measures. In the above quote, we point to a potential method by which this can be done for all proteins. We also point to how a continuous culturing design could be used to better determine how protein (or mRNA proxy) abundance impacts PPI abundance on page 10, line 6:

Finally, our assays were performed across cycles of batch growth meaning that changes in PPI abundance across a growth cycle (e.g. lag, exponential growth, saturation) are coarse grained into one measurement. While this method potentially increases our chance of discovering a diverse set of PPIs, it might have an unpredictable impact on the relationship between fitness and PPI abundance (Li et al., 2018). To overcome these issues, strains containing natural or synthetic PPIs with known abundances and intracellular localizations could be spiked into cell pools to calibrate the relationship between fitness and PPI abundance in each environment. In addition, continuous culturing systems may be useful for refining precision of growth-based assays such as ours.

-The analysis showing that many interactions are likely due to post-translational modifications is very interesting, but caveats should be discussed. Where heterodimers do not fit the expression-level dependence model, some cases of non-fitting may simply be due to measurement error or non-linearity in the relationship between abundance and fitness.

We show the measurement error in Figures 1, S2, S3. While we agree with the reviewer that measurement error is a general caveat for all results reported, we do not feel that it is necessary to point to that fact in this particular case, which uses a logistic regression to report that PPI mutability was the best predictor of fit to the expression-level dependence model. We discuss the non-linearity caveat on page 9, line 41:

Our assay detected subtle fitness differences across environments (Fig S5B and S5C), which we used as a rough estimate for changes in relative PPI abundance. While it would be tempting to use fitness as a direct readout of absolute PPI abundance within a cell, non-linearities between fitness and PPI abundance may be common and PPI dependent. For example, the relative contribution of a reconstructed mDHFR molecule to fitness might diminish at high PPI abundances (saturation effects) and fitness differences between PPIs may be caused, in part, by differences in how accessible a reconstructed mDHFR molecule is to substrate. In addition, environmental shifts might impact cell growth rate, initiate a stress response, or result in other unpredictable cell effects that impact the selective pressure of methotrexate and thereby fitness (Figure S2 and S3).

-Line numbers would have been helpful to note more specific minor comments

We are sorry for this inconvenience. We have added line numbers in our revised manuscript.

-Sequence data should be shared via the Short-Read Archive.

The raw sequencing data have been uploaded to the Short-Read Archive. We mentioned it in the Data and Software Availability section on page 68, line 41.

Raw barcode sequencing data are available from the NIH Sequence Read Archive as accession PRJNA630095 (https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRP259652).

Reviewer #3 (Significance (Required)):

Knowledge of protein-protein interactions (PPIs) provides a key window on biological mechanism, and unbiased screens have informed global principles underlying cellular organization. Several genome-scale screens for direct (binary) interactions between yeast proteins have been carried out, and while each has provided a wealth of new hypotheses, each has been sub-saturation. Therefore, even given multiple genome-scale screens our knowledge of yeast interactions remains incomplete. Different assays are better suited to find different interactions, and it is now clear that every assay evaluated thus far is only capable (even in a saturated screen) of detecting a minority of true interactions. More relevant to the current study, no binary interaction screen has been carried out at the scale of millions of protein pairs outside of a single 'baseline' condition.

The study by Liu et al is notable from a technology perspective in that it is one of several recombinant-barcode approaches have been developed to multiplex pairwise combinations of two barcoded libraries. Although other methods have been demonstrated at the scale of 1M protein pairs, this is the first study using such a technology at the scale of >1M pairs across multiple environments.

A limitation is that this study is not genome-scale, and the search space is biased towards proteins for which interactions were previously observed in a particular environment. This is perhaps understandable, as it made the study more tractable, but this does add caveats to many of the conclusions drawn. These would be acceptable if clearly described and discussed. There were also questions about data quality and assessment that would need to be addressed.

Assuming issues can be addressed, this is a timely study on an important topic, and will be of broad interest given the importance of protein interactions and the status of S. cerevisiae as a key testbed for systems biology.

*Reviewers' expertise:* Interaction assays, next-generation sequencing, computational genomics. Less able to assess evolutionary biology aspects.

References

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Referee #3

Evidence, reproducibility and clarity

Summary

The manuscript "A large accessory protein interactome is rewired across environments" by Liu et al. scales up a previously-described method (PPiSeq) to test a matrix of ~1.6 million protein pairs of direct protein-protein interactions in each of 9 different growth environments.

While the study found a small fraction of immutable PPIs that are relatively stable across environments, the vast majority were 'mutable' across environments. Surprisingly, PPIs detected only in one environment made up more than 60% of the map. In addition to a false positive fraction that can yield apparently-mutable interactions, retest experiments demonstrate (not surprisingly) that environment-specificity can sometimes be attributed to false-negatives. The study authors predict that the whole subnetwork within the space tested will contain 11K true interactions.

Much of environment-specific rewiring seemed to take place in an 'accessory module', which surrounds the core module made of mostly immutable PPIs. A number of interesting network clustering and functional enrichment analyses are performed to characterize the network overall and 'mutable' interactions in particular. The study report other global properties such as expression level, protein abundance and genetic interaction degree that differ between mutable and immutable PPIs. One of the interesting findings was evidence that many environmentally mutable PPI changes are regulated post-translationally. Finally, authors provide a case study about network rewiring related to glucose transport.

Major issues

-The results section should more prominently describe the dimensions of the matrix screen, both in terms of the set of protein pairs attempted and the set actually screened (I think this was 1741 x 1113 after filtering?). More importantly, the study should acknowledge in the introduction that this was NOT a random sample of protein pairs, but rather focused on pairs for which interaction had been previously observed in the baseline condition. This major bias has a potentially substantial impact on many of the downstream analyses. For example, any gene which was not expressed under the conditions of the original Tarrasov et al. study on which the screening space was based will not have been tested here. Thus, the study has systematically excluded interactions involving proteins with environment-dependent expression, except where they happened to be expressed in the single Tarrasov et al. environment. Heightened connectivity within the 'core module' may result from this bias, and if Tarrasov et al had screened in hydrogen peroxide (H2O2) instead of SD media, perhaps the network would have exhibited a code module in H2O2 decorated by less-densely connected accessory modules observed in other environments. The paper should clearly indicate which downstream analyses have special caveats in light of this design bias.

-Related to the previous issue, a quick look at the proteins tested (if I understood them correctly) showed that they were enriched for genes encoding the elongator holoenzyme complex, DNA-directed RNA polymerase I complex, membrane docking and actin binding proteins, among other functional enrichments. Genes related to DNA damage (endonuclease activity and transposition), were depleted. It was unclear whether the functional enrichment analyses described in the paper reported enrichments relative to what would be expected given the bias inherent to the tested space?

-Re: data quality. To the study's great credit, they incorporated positive and random reference sets (PRS and RRS) into the screen. However, the results from this were concerning: Table SM6 shows that assay stringency was set such that between 1 and 3 out of 67 RRS pairs were detected. This specificity would be fine for an assay intended for retest or validate previous hits, where the prior probability of a true interaction is high, but in large-scale screening the prior probability of true interactions that are detectable by PCA is much lower, and a higher specificity is needed to avoid being overwhelmed by false positives. Consider this back of the envelope calculation: Let's say that the prior probability of true interaction is 1% as the authors' suggest (pg 49, section 6.5), and if PCA can optimistically detect 30% of these pairs, then the number of true interactions we might expect to see in an RRS of size 67 is 1% 30% 67 = 0.2 . This back of the envelope calculation suggests that a stringency allowing 1 hit in RRS will yield 80% [ (1 - 0.2) / 1 ] false positives, and a stringency allowing 3 hits in RRS will yield 93% [ (3 - 0.2) / 3] false positives. How do the authors reconcile these back of the envelope calculations from their PRS and RRS results with their estimates of precision?

-Methods for estimating precision and recall were not sufficiently well described to assess. Precision vs recall plots would be helpful to better understand this tradeoff as score thresholds were evaluated.

-Within the tested space, the Tarassov et al map and the current map could each be compared against a common 'bronze standard' (e.g. literature curated interactions), at least for the SD map, to have an idea about how the quality of the current map compares to that of the previous PCA map. Each could also be compared with the most recent large-scale Y2H study (Yu et al).

• Experimental validation of the network was done by conventional PCA. However, it should be noted that this is a form of technical replication of the DHFR-based PCA assay, and not a truly independent validation. Other large-scale yeast interaction studies (e.g., Yu et al, Science 2008) have assessed a random subset of observed PPIs using an orthogonal approach, calibrated using PRS and RRS sets examined via the same orthogonal method, from which overall performance of the dataset could be determined.

-The Venn diagram in Figure 1G was not very informative in terms of assessing the quality of data. It looks like there is a relatively little overlap between PPIs identified in standard conditions (SD media) in the current study and those of the previous study using a very similar method. Is there any way to know how much of this disagreement can be attributed to each screen being sub-saturation (e.g. by comparing replica screens) and what fraction to systematic assay or environment differences?

-In Figure S5C, the environment-specificity rate of PPIs might be inflated due to the fact that authors only test for the absence of SD hits in other conditions, and the SD condition is the only condition that has been sampled twice during the screening. What would be the environment-specific verification rate if sample hits from each environment were tested in all environments? This seems important, as robustly detecting environment-specific PPIs is one of the key points of the study.

Minor issues

-Re: "An interaction between the proteins reconstitutes mDHFR, providing resistance to the drug methotrexate and a growth advantage that is proportional to the PPI abundance" (pg 2). It may be more accurate to say "monotonically related" than "proportional" here. Fig 2 from the cited Freschi et al ref does suggests linearity with colony size over a wide range of inferred complex abundances, but non-linear at low complex abundance. Also note that Freschi measured colony area which is not linear with exponential growth rate nor with cell count. -Re: "Using putatively positive and negative reference sets, we empirically determined astatistical threshold for each environment with the best balance of precision and recall (positive predictive value (PPV) > 61% in SD media, Methods, section 6)." (pg 3). Should state the recall at this PPV.

-Authors could discuss the extent to which related methods (e.g. PMID: 28650476, PMID: 27107012, PMID: 29165646, PMID: 30217970) would be potentially suitable for screening in different environments.

• the term "mutable" is certainly appropriate according to the dictionary definition of changeable. The authors may wish to consider though, that in a molecular biology context the term evokes changeability by mutation (a very interesting but distinct topic). Maybe another term (environment-dependent interactions or ePPIs?) would be clearer. Of course this is the authors' call.

-Some discussion is warranted about the phenomenon that a PPI that is unchanged in abundance could appear to change because of statistical significance thresholds that differ between screens. This would be a difficult question for any such study, and I don't think the authors need to solve it, but just to discuss.

-More discussion would be helpful about the idea that immutability may to some extent favor interactions that PCA is better able to detect (possibly including membrane proteins?)

-Re: "As might be expected, we also found that mutable hubs, but not non-hubs, are more likely to participate in multiple protein complexes than less mutable proteins." (pg 6) This is a cool result. To what extent was this result driven by members of one or two complexes? If so, it would worth noting them.

-Re: "Borrowing a species richness estimator from ecology (Jari Oksanen et al., 2019), we estimate that there are ~10,840 true interactions within our search space across all environments, ~3-fold more than are detected in SD (note difference to Figure 3, which counts observed PPIs)." (pg 8) Should note that this only allows estimation of the number of interactions that are detectable by PCA methods. Previous work (Braun et al, 2019) showed that every known protein interaction assay (including PCA approaches) can only detect a fraction of bona fide interactions.

-Re: "This analysis shows that the number of PPIs present across all environments is much larger than the number observed in a single condition, but that it is feasible to discover most of these new PPIs by sampling a limited number of conditions." (pg 8). The main point is surely correct, but it is worth noting that extrapolation to the number of true interactions depends on the nine chosen environments being representative of all environments. The situation could change under more extreme, e.g., anaerobic, conditions.

-It stands to reason that proteins expressed in all conditions will yield less mutable interactions, if 'mutability' is primarily due to expression change at the transcriptional level. They should at least discuss that measuring mRNA levels could resolve questions about this. Could use Waern et al G3 2013 data (H202, SD, HU, NaCl) to predict the dynamic interactome purely by node removal, and see how conclusions would change

-The analysis showing that many interactions are likely due to post-translational modifications is very interesting, but caveats should be discussed. Where heterodimers do not fit the expression-level dependence model, some cases of non-fitting may simply be due to measurement error or non-linearity in the relationship between abundance and fitness.

-Line numbers would have been helpful to note more specific minor comments

-Sequence data should be shared via the Short-Read Archive.

Significance

Knowledge of protein-protein interactions (PPIs) provides a key window on biological mechanism, and unbiased screens have informed global principles underlying cellular organization. Several genome-scale screens for direct (binary) interactions between yeast proteins have been carried out, and while each has provided a wealth of new hypotheses, each has been sub-saturation. Therefore, even given multiple genome-scale screens our knowledge of yeast interactions remains incomplete. Different assays are better suited to find different interactions, and it is now clear that every assay evaluated thus far is only capable (even in a saturated screen) of detecting a minority of true interactions. More relevant to the current study, no binary interaction screen has been carried out at the scale of millions of protein pairs outside of a single 'baseline' condition.

The study by Liu et al is notable from a technology perspective in that it is one of several recombinant-barcode approaches have been developed to multiplex pairwise combinations of two barcoded libraries. Although other methods have been demonstrated at the scale of 1M protein pairs, this is the first study using such a technology at the scale of >1M pairs across multiple environments.

A limitation is that this study is not genome-scale, and the search space is biased towards proteins for which interactions were previously observed in a particular environment. This is perhaps understandable, as it made the study more tractable, but this does add caveats to many of the conclusions drawn. These would be acceptable if clearly described and discussed. There were also questions about data quality and assessment that would need to be addressed.

Assuming issues can be addressed, this is a timely study on an important topic, and will be of broad interest given the importance of protein interactions and the status of S. cerevisiae as a key testbed for systems biology.

Reviewers' expertise: Interaction assays, next-generation sequencing, computational genomics. Less able to assess evolutionary biology aspects.

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Referee #2

Evidence, reproducibility and clarity

Report on Liu et al. "A large accessory protein interactome is rewired across environments" Liu et al. use a mDHFR-based, pooled barcode sequencing / competitive growth / mild methotrexate selection method to investigate changes of PPI abundance of 1.6 million protein pairs across different 9 growth conditions. Because most PPI screens aim to identify novel PPIs in standard growth conditions, the currently known yeast PPI network may be incomplete. The key concept is to define immutable" PPIs that are found in all conditions and "mutable" PPIs that are present in only some conditions. The assay identified 13764 PPIs across the 9 conditions, using optimized fitness cut offs. Steady PPI i.e. across all environments, were identified in membrane compartments and cell division. Processes associated with the chromosome, transcription, protein translation, RNA processing and ribosome regulation were found to change between conditions. Mutable PPIs are form modules as topological analyses reveals.

Interestingly, a correlation on intrinsic disorder and PPI mutability was found and postulated as more flexible in the conformational context, while at the same time they are formed by less abundant proteins.

I appreciate the trick to use homodimerization as an abundance proxy to predict interaction between heterodimers (of proteins that homodimerize). This "mass-action kinetics model" explains the strength of 230 out of 1212 tested heterodimers.

A validation experiment of the glucose transporter network was performed and 90 "randomly chosen" PPIs that were present in the SD environment were tested in NaCl (osmotic stress) and Raffinose (low glucose) conditions through recording optical density growth trajectories. Hxt5 PPIs stayed similar in the tested conditions, supported by the current knowledge that Hxt5 is highly expressed in stationary phase and under salt stress. In Raffinose, Hxt7, previously reported to increase the mRNA expression, lost most PPIs indicating that other factors might influence Hxt7 PPIs.

Points for consideration:

*) A clear definition of mutable and immutable is missing, or could not be found e.g. at page 4 second paragraph.

*) Approximately half of the PPIs have been identified in one environment. Many of those mutable PPIs were detected in the 16{degree sign}C condition. Is there an explanation for the predominance of this specific environment? What are these PPIs about?

*) 50 % overall retest validation rate is fair and reflects a value comparable to other large-scale approaches. However what is the actual variation, e.g. between mutable PPIs and immutable or between condition. e.g. at 16{degree sign}C.

*) What is the R correlation cutoff for PPIs explained in the mass equilibrium model vs. not explained?

*) 90 "randomly chosen" PPIs for validation. It needs to be demonstrated that these interaction are a random subset otherwise is could also mean cherry picked interactions ...

*) Figure 4 provides interesting correlations with the goal to reveal properties of mutable and less mutable PPIs. PPIs detected in the PPIseq screen can partially be correlated to co-expression (4A) as well as co-localization. Does it make sense to correlate the co-expression across number of conditions? Are the expression correlation condition specific. In this graph it could be that expression correlation stems from condition 1 and 2 and the interaction takes place in 4 and 5 still leading to the same conclusion ... Is the picture of the co-expression correlation similar when you simply look at individual environments like in S4A?

*) Figure 4C: Interesting, how dependent are the various categories?

*) Figure 4 F: When binned in the number of environments in which the PPI was found, the distribution peaks at 6 environments and decreases with higher and lower number of environments. The description /explanation in the text clearly says something else.

*) Figure 6: I apologize, but for my taste this is not a final figure 6 for this study. Investigation of different environments increases the PPI network in yeast, yes, yet it is very well known that a saturation is reached after testing of several conditions, different methods and even screening repetition (sampling). It does not represent an important outcome. Move to suppl or remove.

Significance

Liu et al. increase the current PPI network in yeast and offer a substantial dataset of novel PPIs seen in specific environments only. This resource can be used to further investigate the biological meaning of the PPI changes. The data set is compared to previous DHFR providing some sort of quality benchmarking. Mutable interactions are characterized well. Clearly a next step could be to start some "orthogonal" validation, i.e. beyond yeast growth under methotrexate treatment.

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Referee #1

Evidence, reproducibility and clarity

The manuscript is clearly written and the figures appropriate and informative. Some descriptions of data analyses are a little dense but reflect what would appear long hard efforts on the part of the authors to identify and control for possible sources of misinterpretation due to sensitivities of parameters in their fitness model. The authors efforts to retest interactions under non-competition conditions allay fears of most concerns that I would have. One problem though that I could not see explicitly addressed was that of potential effects of interactions between methotrexate and the other conditions and how this is controlled for. Specifically, I could be argued that the fact that a particular PPI is observed under a specific condition could have more to do with a synthetic effect of treatment of cells with a drug plus methotrexate. Is this controlled for and how? I raise this because in a chemical genetic screen for fitness it was shown that methotrexate is particularly promiscuous for drug-drug interactions (Hillenmeyer ME ,et al. Science 2008). I tried to think of how this works but couldn't come up with anything immediately. I'd appreciate if the authors would take a crack at resolving this issue. Otherwise I have no further concerns about the manuscript.

Significance

Lui et al expand on previous work from the Levy group to explore a massive in vivo protein interactome in the yeast S. cerevisiae. They achieve this by performing screens cross 9 growth conditions, which, with replication, results in a total of 44 million measurements. Interpreting their results based on a fitness model for pooled growth under methotrexate selection, they make the key observation that there is a vastly expanded pool of protein-protein interactions (PPI) that are found under only one or two condition compared to a more limited set of PPI that are found under a broad set of conditions (mutable versus immutable interactors). The authors show that this dichotomy suggests some important features of proteins and their PPIs that raise important questions about functionality and evolution of PPIs. Among these are that mutable PPIs are enriched for cross-compartmental, high disorder and higher rates of evolution and subcellular localization of proteins to chromatin, suggesting roles in gene regulation that are associated with cellular responses to new conditions. At the same time these interactions are not enriched for changes in abundance. These results are in contrast to those of immutable PPIs, which seem to form a core background noise, more determined by changes in abundance than what the authors interpret must be post-translational processes that may drive, for instance, changes in subcellular localization resulting in appearance of PPIs under specific conditions. The authors are also able to address a couple of key issues about protein interactomes, including the controversial Party-date Hub hypothesis of Vidal, in which they could now affirm support for this hypothesis based on their results and notably negative correlation of PPIs to protein abundance for mutable PPIs. Finally, they also addressed the problem of predicting the upper limit of PPIs in yeast, showing the remarkable results that it may be no more than about 2 times the number of proteins expressed by yeast. Such an upper limit is profoundly important to modelling cellular network complexity and, if it holds up, could define a general upper limit on organismal complexity.

This manuscript is a very important contribution to understanding dynamics of molecular networks in living cells and should be published with high priority.

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Reply to the reviewers

We thank the reviewers for their close reading and constructive comments on our manuscript. We believe that their insight has substantially strengthened our manuscript. Please find our response/revision plan for each comment below (in blue). Note, because of the substantial changes to the figures and the additional experiments that are we are undertaking, we have not initially revised the text. The proposed textual revisions will be included in the full revision.

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

The Katz lab has contributed greatly to the field of epigenetic reprogramming over the years, and this is

another excellent paper on the subject. I enjoyed reviewing this manuscript and don't have any major

comments/suggestions for improving it. The findings presented are novel and important, the results are clear

cut, and the writing is clear.

It's important to stress the novelty of the findings, which build upon previous studies from the same lab (upon

a shallow look one might think that some of the conclusions were described before, but this is not the case).

Despite the fact that this system has been studied in depth before, it remained unclear why and how

germline genes are bookmarked by H3K36 in the embryo, and it wasn't known why germline genes are not

expressed in the soma.

To study these questions Carpenter et al. examine multiple phenotypes (developmental aberrations,

sterility), that they combine with analysis of multiple genetic backgrounds, RNA-seq, CHIP-seq, single

molecule FISH, and fluorescent transgenes.

Previous observations from the Katz lab suggested that progeny derived from spr-5;met-2 double mutants

can develop abnormally. They show here that the progeny of these double mutants (unlike spr-5 and met-2

single mutants) develop severe and highly penetrate developmental delays, a Pvl phenotype, and sterility.

They show also that spr-5; met-2 maternal reprogramming prevents developmental delay by restricting

ectopic MES-4 bookmarking, and that developmental delay of spr-5;met-2 progeny is the result of ectopic

expression of MES-4 germline genes. The bottom line is that they shed light on how SPR-5, MET-2 and

MES-4 balance inter-generational inheritance of H3K4, H3K9, and H3K36 methylation, to allow correct

specification of germline and somatic cells. This is all very important and relevant also to other organisms.

**(very) Minor comments:**

-Since the word "heritable" is used in different contexts, it could be helpful to elaborate, perhaps in the

introduction, on the distinction between cellular memory and transgenerational inheritance.

We are happy to elaborate on this in the revised manuscript.

-It might be interesting in the Discussion to expand further about the links between heritable chromatin

marks and heritable small RNAs. The do hint that the result regarding the silencing of the somatic transgene

are especially intriguing.

We are happy to expand this in the revised manuscript.

Reviewer #1 (Significance (Required)):

This is an exciting paper which build upon years of important work in the Katz lab. The novelty of the paper

is in pinpointing the mechanisms that bookmark germline genes by H3K36 in the embryo, and explaining

why and how germline genes are prevented from being expressed in the soma.

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

Katz and colleagues examine the interaction between the methyltransferase MES-4 and spr-5; met-2 double

mutants. Their prior analysis (PNAS, 2014) showed the dramatic enhancement in sterility and development

for spr-5; met-2; this paper extends that finding by showing these effects depend on MES-4. The results are

interesting and the genetic interactions dramatic. The examination by RNAseq and ChIP helps move the

phenotypes into a more molecular analysis. The authors hypothesize that SPR-5 and MET-2 modify

chromatin of germline genes (MES-4 targets) in somatic cells, and this is required to silence germline genes

in the soma. A few issues need to be resolved to test these ideas and rule out others.

**Main comments:**

The authors' hypothesis is that SPR-5 and MET-2 act directly, to modify chromatin of germline genes (MES-

4 targets), but alternate hypothesis is that the key regulated genes are i) MES-4 itself and/or ii) known

regulators of germline gene expression e.g. the piwi pathway. Mis regulation of these factors in the soma

could be responsible for the phenotypes. Therefore, the authors should analyze expression (smFISH and

where possible protein stains) for MES-4 and PIWI components in the embryo and larvae of wildtype, double

and triple mutant strains. These experiments are essential and not difficult to perform.

In our RNA-seq analysis we see a small elevation of MES-4 itself (average 1.18 log2 fold change across 5 replicates). This does not seem likely to be solely driving such a dramatic phenotype. Nevertheless, it is possible that the small increase in expression of MES-4 itself could be contributing. To determine if MES-4 is being ectopically expressed in spr-5; met-2 double mutants, we have obtained a tag version of MES-4 from Dr. Susan Strome and will use this to examine the localization of MES-4 protein in spr-5; met-2 double mutants. We are definitely interested in the potential interaction between PIWI components and the histone modifying enzymes that we have explored in this study. However, since RNAi of MES-4 is sufficient to rescue the developmental delay of spr-5; met-2 mutants, we have chosen to focus on that interaction in this paper. In the future, we hope to examine the role of PIWI components in this system.

A second aspect of the hypothesis is that spr-5 and met-2 act before mes-4 and that while these genes are

maternally expressed, they act in the embryo. There really aren't data to support these ideas - the timing and

location of the factors' activities have not been pinned down. One way to begin to address this question

would be to perform smFISH on the target genes and on mes-4 in embryos and determine when and where

changes first appear. smFISH in embryos is critical - relying on L1 data is too late. If timing data cannot be

obtained, then I suggest that the authors back off of the timing ideas or at least explain the caveats.

Certainly, figure 8 should be simplified and timing removed. (note: Typical maternal effect tests probably

won't work because if the genes' RNAs are germline deposited, then a maternal effect test will reflect when

the RNA is expressed but not when the protein is active. A TS allele would be needed, and that may not be

available.)

To determine the timing of the ectopic expression of MES-4 targets, we have performed smFISH on two MES-4 targets in embryos. Thus far, these experiments show that MES-4 targets are ectopically expressed in the embryo, but only after the maternal to zygotic transition. This is consistent with our proposed model. A figure containing this data will be added to the revised manuscript. In addition, our model is predicated on the known embryonic protein localization of SPR-5 and MES-4. Maternal SPR-5 protein is present in the early embryo up to around the 8-cell stage, but absent in later embryos (Katz et al., 2009). In addition, in mice, the SPR-5 ortholog LSD1 is required maternally prior to the 2-cell stage (Wasson et al., 2016 and Ancelin et al., 2016). In contrast, MES-4 continues to be expressed in the embryo until later embryonic stages where it is concentrated into the germline precursors Z2 and Z3 (Fong et al., 2002). This is consistent with SPR-5 establishing a chromatin state that continues to be antagonized by MES-4. There is evidence that MET-2 is expressed both in early embryos and later embryos. However, since the phenotype of MET-2 so closely resembles the phenotype of SPR-5 (Kerr et al., 2014), we have included it in our model as working with SPR-5. Further experimentation will be required to substantiate the model, but we believe the model is consistent with all of the current data.

Writing/clarity:

-It would be helpful to include a table that lists the specific genes studied in the paper and how they behaved

in the different assays e.g. RNAseq 1, RNAseq 2, MES-4 target, ChIP. That way, readers will understand

each of the genes better.

We are happy to include a table in the revised manuscript.

-At the end of each experiment, it would be helpful to explain the conclusion and not wait until the

Discussion. For readers not in the field, the logic of the Results section is hard to follow.

This seems like a stylistic choice. Traditionally, papers did not include any conclusions in the results section, and it is our preference to keep our paper organized this way. However, if the reviewer would still like us to change this, we are happy to do so.

-The model is explained over three pages in the Discussion. It would be great to begin with a single

paragraph that summarizes the model/point of the paper simply and clearly.

The discussion in the revised manuscript will altered to include this.

**Specific comments:**

-Figure 1 has been published previously and should be moved to the supplement.

In our original paper (Kerr et al.) we reported in the text that spr-5; met-2 mutants have a developmental delay. However, we did not characterize this developmental delay. Nor did we include any images of the double mutants, except for one image of the adult germline phenotype. As a result, we believe that the inclusion of the developmental delay in the main body of this manuscript is warranted.

-Cite their prior paper for the vulval defects e.g. page 6 or show in supplement.

We are happy to include a citation of our previous paper for the vulval defects in the revised manuscript.

-The second RNAseq data should be shown in the Results since it is much stronger. The first RNAseq,

which is less robust, should be moved to supplement.

The revised manuscript will include this alteration.

-Figure 3 is very nice. Please explain why the RNAs were picked (+ the table, see comment above), and

please add here or in a new figure mes-4 and piwi pathway expression data in wildtype vs double/triple

mutants.

We performed RT-PCR on 9 MES-4 targets. These 9 targets were picked because they had the highest ectopic expression in spr-5; met-2 mutants and largest change in H3K36me3 in spr-5; met-2 mutants versus Wild Type. Amongst these 9 genes, we performed smFISH on htp-1 and cpb-1 because they are relatively well characterized as germline genes.

The revised manuscript will include added panels to supplemental figure 2 showing the expression of PIWI pathway components.

-Figure 3 here or later, please show if mes-4 RNAi removes somatic expression of target genes.

We are currently carrying out this experiment. Once it is completed, the data will hopefully be added to the paper.

-Is embryogenesis delayed?

Embryogenesis seems to be sped up in spr-5; met-2 mutants. A supplemental figure will be added to the revised manuscript showing this. It is unclear why embryogenesis is sped up. However, this confirms that the developmental delay is unique to the L1/L2 stages.

-Figure 4 since htp-1 smFISH is so dramatic, it would be helpful to include htp-1 in the lower panels.

htp-1 will be added to the lower panels in the revised manuscript.

-Figure 4, please add an extra 2 upper panels showing all the genes in N2 vs spr-5;met-2, for comparison to

the mes-4 cohort.

As a control, we will add panels showing a comparison to all germline genes, excluding MES-4 targets. This new data shows that germline genes that are not MES-4 targets do not have ectopic H3K36me3. This data, which further suggests that the phenomenon is confined to MES-4 targets, is consistent with our results showing that MES-4 RNAi is sufficient to suppress the developmental delay.

-Figure 6. Please show a control that met-1 RNAi is working.

We performed RT-PCR to try and confirm that met-1 RNAi was working. Despite controls repeating the MES-4 suppression and verifying that RNAi was working, we were unable to demonstrate that met-1 was knocked down. As a result, we will remove this result from the paper. Importantly, this does not affect the conclusion of the paper.

-To quantify histone marks more clearly, it would be wonderful to have a graph of the mean log across the

gene. showing the mean numbers would help clarify the degree of the effect. we had an image as an

example but it does not paste into the reviewer box. Instead, see figure 2 or figure 4

here: https://www.nature.com/articles/ng.322

We will attempt to include this analysis in the revised manuscript.

Reviewer #2 (Significance (Required)):

Katz and colleagues examine the interaction between the methyltransferase MES-4 and spr-5; met-2 double

mutants. Their prior analysis (PNAS, 2014) showed the dramatic enhancement in sterility and development

for spr-5; met-2; this paper extends that finding by showing these effects depend on MES-4. The results are

interesting and the genetic interactions dramatic. The examination by RNAseq and ChIP helps move the

phenotypes into a more molecular analysis.

This work will be of interest to people following transgenerational inheritance, generally in the C. elegans

field. People using other organisms may read it also, although some of the worm genetics may be

complicated. Some of the writing suggestions could make a difference.

I study C. elegans embryogenesis, chromatin and inheritance.

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

In the paper entitled "C. elegans establishes germline versus soma by balancing inherited histone

methylation" Carpenter BS et al examined a double mutant worm strain they had previously produced of the

H3K4me1/2 demethylase spr-5 and the predicted H3K9me1/me2 methylase met-2. These mutant worms

have a developmental delay that arises by the L2 larval stage. They performed an analysis of what genes

get misexpressed in these double mutants by performing RNAseq and compare this to datasets generated

from other labs on an H3K36me2/me3 methylase MES-4 where they see a high degree of overlap. They

validate the misexpression of some germline specific genes in the soma by in situ and validate that there is a

dysregulation of H3K36me3 in their double mutant worms. They further find that knocking down mes-4

reverts the developmental delay.

I think that the authors need to make more of an effort to be a bit more scholarly in terms of placing their

work in the context of the field as a whole and also need to add a few additional experiments as well as

reorganize a bit before this is ready for publication. Remember that the average reader is not necessarily an

expert in C. elegans or this particular field and you really want to try and make the manuscript as accessible

to everyone as possible.

**Major Points**

1)It would be good to see western blots or quantitative mass spec examining H3K36me3 in the WT and spr-

5;met-2 double mutant worms. I believe this was also previously reported by Greer EL et al Cell Rep 2014 in

the single spr-5 mutant worm so that work should be cited here in addition to the identification of JMJD-2 as

an enzyme involved in the inheritance of H3K4me2 phenotype.

The ectopic H3K36me3 is confined to a small set of MES-4 targets. We don’t even see ectopic H3K36me3 at non-MES-4 germline genes (see above). Therefore, we don’t expect to see any global differences in bulk H3K36me3. Greer et al reported that there are elevated H3K36me3 levels in spr-5 mutants. This discrepancy may be due to different stages (embryos, germline) present in their bulk preparation. Alternatively, the met-2 mutant may counteract the effect of the spr-5 mutation on H3K36me3. Regardless, we believe that the genome-wide ChIP-seq is more informative than bulk H3K36me3 levels.

We will add a citation for the Greer paper in the revised manuscript.

2)Missing from Fig.5 is mes-4 KD by itself. This is needed to determine whether these effects are specific to

the spr-5;met-2 double mutants or more general effects that KD of mes-4 would decrease the expression of

all these genes to a similar extent. Then statistics should be done to see if the decrease in the WT context is

the same or greater than the decrease in the double mutants.

The MES-4 targets are generally expressed only in the germline and defined by having mes-4 dependent H3K36me3. Knocking down mes-4 would be expected to prevent the expression of these genes in the germline, but this is difficult to test because mes-4 mutants basically don’t make a germline. Regardless, knocking down mes-4 by itself would only assess the role of MES-4 in germline transcription, not the ectopic expression that is being assayed in spr-5; met-2 mutants in Fig 5. Importantly, it remains possible that spr-5; met-2 mutants might also result in an increase in the expression of MES-4 targets in the germline. However, the experiments performed in this manuscript were conducted on L1 larvae, which do not have any germline expression, to eliminate this potential confounding contribution.

**Minor Points**

1)A greater attempt needs to be made to be more scholarly for citing previously published literature. This

includes work on the inheritance of H3K27 and H3K36 methylation in C. elegans and other species as well.

A few papers which seem germane to this story which should be cited in the intro are (Nottke AC et al PNAS

2011, Gaydos LJ et al Science 2014, Ost A et al Cell 2014, Greer EL et al Cell Rep 2014, Siklenka K et al

Science 2015, Tabuchi TM et al Nat Comm 2018, Kaneshiro KR et al Nat Comm 2019). This problem is not

restricted to the intro.

Although many of these excellent papers are broadly relevant to this current work, they are not necessarily directly relevant to this paper. For this reason, they were not originally cited. Nevertheless, we will attempt to cite these papers in the revised version when possible.

2)I think that the authors need to be a little less definitive with your language. Theories should be introduced

as possibilities rather than conclusions. Should remove "comprehensive" from intro as there are many other

methods which could be done to test this.

Throughout the manuscript, we have tried to be clear what the data suggests versus what is model based on the data. Nevertheless, to further clarify this, we are happy to remove “comprehensive” from the intro.

3)The authors should describe what PIE-1 is. Is this a transcription factor?

PIE-1 is a transcriptional inhibitor that is thought to block RNA polII elongation by mimicking the CTD of RNA polII and competing for phosphorylation. We are happy to add a reference to this function in the revised manuscript.

4)The language needs clarification about MES-4 germline genes and bookmark genes. Are these bound by

MES-4 or marked with K36me2/3?

The revised manuscript will be modified to make this definition more clear.

5)I think Fig S1 E+F should be in the main figure 1 so readers can see the extent of the phenotype.

The original single image of the spr-5; met-2 adult germline phenotype (including the protruding vulva) was included in our previous publication. In this manuscript, we have now quantified this phenotype, which is why it is included in the supplement here. However, because the original picture was included in our original publication, we prefer to leave it as supplemental.

6)For Fig S2 it would be good to do the same statistics that is done in Fig 2 and mention them in the text so

the readers can see that the overlap is statistically significant.

We are happy to include these statistics in the revised manuscript.

7)Fig S2.2 should be yellow blue rather than red green for the colorblind out there.

Thanks for pointing this out. We are happy to change the colors in the revised manuscript.

8)When saying "Many of these genes involved in these processes..." the authors need to include numbers

and statistics.

We will amend the revised text to make the definition of the MES-4 genes more clear.

9)Should use WT instead of N2 and specify what wildtype is in methods.

We will use WT instead of N2 in the revised manuscript.

10)Fig. 2A + B could be displayed in a single figure. And Fig 2D seems superfluous and could be combined

with 2C or alternatively it could be put in supplementary.

Figure 2A and 2B were purposely separated to make it clear how many of the overlapped changes are up versus down. In the revised manuscript, Figure

2D will be moved to the supplement.

11)Non-C. elegans experts won't understand what balancers are. An effort should be made to make this

accessible to all. Explaining when genes are heterozygous or homozygous mutants seems relevant

here.

The text of the revised manuscript will be amended to make it more accessible for non-C. elegans readers.

12)The GO categories (Fig. S2) should be in the main figure and need to be made to look more scientific

rather than copied and pasted from a program.

The GO categories were included to be comprehensive and do not contribute substantially to the main conclusion of the paper. This is why they are supplemental. In the revised manuscript, we will edit the GO results so that they look more scientific.

13)Fig. 7 seems a bit out of place. If the authors were to KD mes-4 and similarly show that the phenotype

reverts that would help justify its inclusion in this paper. Without it seems like a bit of an add on that belongs

elsewhere.

We believe that the somatic expression of a transgene in spr-5; met-2 mutants adds to our potential understanding of how this double mutant may lead to developmental delay. This is true, regardless of whether of whether the somatic transgene expression is mes-4 dependent or not.

Reviewer #3 (Significance (Required)):

I think this is an interesting and timely piece of work. A little more effort needs to be put in to make sure it is

accessible to the average reader and has sufficient inclusion of more of the large body of work on

inheritance of histone modifications. I think C. elegans researchers as well as people interested in

inheritance and the setup of the germline will be interested in this work.

REFEREES CROSS COMMENTING

I agree with Reviewer #2's comments on experiments to include or exclude alternative models. I also agree

about their statement about rewriting to make it more accessible to others who aren't experts in this

specialized portion of C. elegans research. All in all it seems like the experiments which are required by

reviewer #2 and myself as well as the rewriting should be quite feasible.

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Referee #3

Evidence, reproducibility and clarity

In the paper entitled "C. elegans establishes germline versus soma by balancing inherited histone methylation" Carpenter BS et al examined a double mutant worm strain they had previously produced of the H3K4me1/2 demethylase spr-5 and the predicted H3K9me1/me2 methylase met-2. These mutant worms have a developmental delay that arises by the L2 larval stage. They performed an analysis of what genes get misexpressed in these double mutants by performing RNAseq and compare this to datasets generated from other labs on an H3K36me2/me3 methylase MES-4 where they see a high degree of overlap. They validate the misexpression of some germline specific genes in the soma by in situ and validate that there is a dysregulation of H3K36me3 in their double mutant worms. They further find that knocking down mes-4 reverts the developmental delay.

I think that the authors need to make more of an effort to be a bit more scholarly in terms of placing their work in the context of the field as a whole and also need to add a few additional experiments as well as reorganize a bit before this is ready for publication. Remember that the average reader is not necessarily an expert in C. elegans or this particular field and you really want to try and make the manuscript as accessible to everyone as possible.

Major Points

1)It would be good to see western blots or quantitative mass spec examining H3K36me3 in the WT and spr-5;met-2 double mutant worms. I believe this was also previously reported by Greer EL et al Cell Rep 2014 in the single spr-5 mutant worm so that work should be cited here in addition to the identification of JMJD-2 as an enzyme involved in the inheritance of H3K4me2 phenotype.

2)Missing from Fig.5 is mes-4 KD by itself. This is needed to determine whether these effects are specific to the spr-5;met-2 double mutants or more general effects that KD of mes-4 would decrease the expression of all these genes to a similar extent. Then statistics should be done to see if the decrease in the WT context is the same or greater than the decrease in the double mutants.

Minor Points

1)A greater attempt needs to be made to be more scholarly for citing previously published literature. This includes work on the inheritance of H3K27 and H3K36 methylation in C. elegans and other species as well. A few papers which seem germane to this story which should be cited in the intro are (Nottke AC et al PNAS 2011, Gaydos LJ et al Science 2014, Ost A et al Cell 2014, Greer EL et al Cell Rep 2014, Siklenka K et al Science 2015, Tabuchi TM et al Nat Comm 2018, Kaneshiro KR et al Nat Comm 2019). This problem is not restricted to the intro.

2)I think that the authors need to be a little less definitive with your language. Theories should be introduced as possibilities rather than conclusions. Should remove "comprehensive" from intro as there are many other methods which could be done to test this.

3)The authors should describe what PIE-1 is. Is this a transcription factor?

4)The language needs clarification about MES-4 germline genes and bookmark genes. Are these bound by MES-4 or marked with K36me2/3?

5)I think Fig S1 E+F should be in the main figure 1 so readers can see the extent of the phenotype.

6)For Fig S2 it would be good to do the same statistics that is done in Fig 2 and mention them in the text so the readers can see that the overlap is statistically significant.

7)Fig S2.2 should be yellow blue rather than red green for the colorblind out there.

8)When saying "Many of these genes involved in these processes..." the authors need to include numbers and statistics.

9)Should use WT instead of N2 and specify what wildtype is in methods.

10)Fig. 2A + B could be displayed in a single figure. And Fig 2D seems superfluous and could be combined with 2C or alternatively it could be put in supplementary.

11)Non-C. elegans experts won't understand what balancers are. An effort should be made to make this accessible to all. Explaining when genes are heterozygous or homozygous mutants seems relevant here.

12)The GO categories (Fig. S2) should be in the main figure and need to be made to look more scientific rather than copied and pasted from a program.

13)Fig. 7 seems a bit out of place. If the authors were to KD mes-4 and similarly show that the phenotype reverts that would help justify its inclusion in this paper. Without it seems like a bit of an add on that belongs elsewhere.

Significance

I think this is an interesting and timely piece of work. A little more effort needs to be put in to make sure it is accessible to the average reader and has sufficient inclusion of more of the large body of work on inheritance of histone modifications. I think C. elegans researchers as well as people interested in inheritance and the setup of the germline will be interested in this work.

REFEREES CROSS COMMENTING

I agree with Reviewer #2's comments on experiments to include or exclude alternative models. I also agree about their statement about rewriting to make it more accessible to others who aren't experts in this specialized portion of C. elegans research. All in all it seems like the experiments which are required by reviewer #2 and myself as well as the rewriting should be quite feasible.

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Referee #2

Evidence, reproducibility and clarity

Katz and colleagues examine the interaction between the methyltransferase MES-4 and spr-5; met-2 double mutants. Their prior analysis (PNAS, 2014) showed the dramatic enhancement in sterility and development for spr-5; met-2; this paper extends that finding by showing these effects depend on MES-4. The results are interesting and the genetic interactions dramatic. The examination by RNAseq and ChIP helps move the phenotypes into a more molecular analysis. The authors hypothesize that SPR-5 and MET-2 modify chromatin of germline genes (MES-4 targets) in somatic cells, and this is required to silence germline genes in the soma. A few issues need to be resolved to test these ideas and rule out others.

Main comments:

The authors' hypothesis is that SPR-5 and MET-2 act directly, to modify chromatin of germline genes (MES-4 targets), but alternate hypothesis is that the key regulated genes are i) MES-4 itself and/or ii) known regulators of germline gene expression e.g. the piwi pathway. Mis regulation of these factors in the soma could be responsible for the phenotypes. Therefore, the authors should analyze expression (smFISH and where possible protein stains) for MES-4 and PIWI components in the embryo and larvae of wildtype, double and triple mutant strains. These experiments are essential and not difficult to perform.

A second aspect of the hypothesis is that spr-5 and met-2 act before mes-4 and that while these genes are maternally expressed, they act in the embryo. There really aren't data to support these ideas - the timing and location of the factors' activities have not been pinned down. One way to begin to address this question would be to perform smFISH on the target genes and on mes-4 in embryos and determine when and where changes first appear. smFISH in embryos is critical - relying on L1 data is too late. If timing data cannot be obtained, then I suggest that the authors back off of the timing ideas or at least explain the caveats. Certainly, figure 8 should be simplified and timing removed. (note: Typical maternal effect tests probably won't work because if the genes' RNAs are germline deposited, then a maternal effect test will reflect when the RNA is expressed but not when the protein is active. A TS allele would be needed, and that may not be available.)

Writing/clarity:

-It would be helpful to include a table that lists the specific genes studied in the paper and how they behaved in the different assays e.g. RNAseq 1, RNAseq 2, MES-4 target, ChIP. That way, readers will understand each of the genes better.

-At the end of each experiment, it would be helpful to explain the conclusion and not wait until the Discussion. For readers not in the field, the logic of the Results section is hard to follow.

-The model is explained over three pages in the Discussion. It would be great to begin with a single paragraph that summarizes the model/point of the paper simply and clearly.

Specific comments:

-Figure 1 has been published previously and should be moved to the supplement.

-Cite their prior paper for the vulval defects e.g. page 6 or show in supplement.

-The second RNAseq data should be shown in the Results since it is much stronger. The first RNAseq, which is less robust, should be moved to supplement.

-Figure 3 is very nice. Please explain why the RNAs were picked (+ the table, see comment above), and please add here or in a new figure mes-4 and piwi pathway expression data in wildtype vs double/triple mutants.

-Figure 3 here or later, please show if mes-4 RNAi removes somatic expression of target genes.

-Is embryogenesis delayed?

-Figure 4 since htp-1 smFISH is so dramatic, it would be helpful to include htp-1 in the lower panels.

-Figure 4, please add an extra 2 upper panels showing all the genes in N2 vs spr-5;met-2, for comparison to the mes-4 cohort.

-Figure 6. Please show a control that met-1 RNAi is working.

-To quantify histone marks more clearly, it would be wonderful to have a graph of the mean log across the gene. showing the mean numbers would help clarify the degree of the effect. we had an image as an example but it does not paste into the reviewer box. Instead, see figure 2 or figure 4 here: https://www.nature.com/articles/ng.322

Significance

Katz and colleagues examine the interaction between the methyltransferase MES-4 and spr-5; met-2 double mutants. Their prior analysis (PNAS, 2014) showed the dramatic enhancement in sterility and development for spr-5; met-2; this paper extends that finding by showing these effects depend on MES-4. The results are interesting and the genetic interactions dramatic. The examination by RNAseq and ChIP helps move the phenotypes into a more molecular analysis.

This work will be of interest to people following transgenerational inheritance, generally in the C. elegans field. People using other organisms may read it also, although some of the worm genetics may be complicated. Some of the writing suggestions could make a difference.

I study C. elegans embryogenesis, chromatin and inheritance.

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Referee #1

Evidence, reproducibility and clarity

The Katz lab has contributed greatly to the field of epigenetic reprogramming over the years, and this is another excellent paper on the subject. I enjoyed reviewing this manuscript and don't have any major comments/suggestions for improving it. The findings presented are novel and important, the results are clear cut, and the writing is clear.

It's important to stress the novelty of the findings, which build upon previous studies from the same lab (upon a shallow look one might think that some of the conclusions were described before, but this is not the case). Despite the fact that this system has been studied in depth before, it remained unclear why and how germline genes are bookmarked by H3K36 in the embryo, and it wasn't known why germline genes are not expressed in the soma.

To study these questions Carpenter et al. examine multiple phenotypes (developmental aberrations, sterility), that they combine with analysis of multiple genetic backgrounds, RNA-seq, CHIP-seq, single molecule FISH, and fluorescent transgenes.

Previous observations from the Katz lab suggested that progeny derived from spr-5;met-2 double mutants can develop abnormally. They show here that the progeny of these double mutants (unlike spr-5 and met-2 single mutants) develop severe and highly penetrate developmental delays, a Pvl phenotype, and sterility. They show also that spr-5; met-2 maternal reprogramming prevents developmental delay by restricting ectopic MES-4 bookmarking, and that developmental delay of spr-5;met-2 progeny is the result of ectopic expression of MES-4 germline genes. The bottom line is that they shed light on how SPR-5, MET-2 and MES-4 balance inter-generational inheritance of H3K4, H3K9, and H3K36 methylation, to allow correct specification of germline and somatic cells. This is all very important and relevant also to other organisms.

(very) Minor comments:

-Since the word "heritable" is used in different contexts, it could be helpful to elaborate, perhaps in the introduction, on the distinction between cellular memory and transgenerational inheritance.

-It might be interesting in the Discussion to expand further about the links between heritable chromatin marks and heritable small RNAs. The do hint that the result regarding the silencing of the somatic transgene are especially intriguing.

Significance

This is an exciting paper which build upon years of important work in the Katz lab. The novelty of the paper is in pinpointing the mechanisms that bookmark germline genes by H3K36 in the embryo, and explaining why and how germline genes are prevented from being expressed in the soma.

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Reply to the reviewers

We thank the three reviewers for providing insightful critiques on our manuscript.

Changes to document and comments made are marked e.g. “Reply 1.1” (referring the Reviewer #1 item #1, etc.) as described below.

Reviewer #1

I found this study to be very convincing. Prior studies are referenced appropriately, the text is well written and clear, the figures are clear also. In my opinion the paper does not need further experiment.

[1.1] The conclusions are well supported by the data. However, the concatenation model seems very speculative at this point. Also, it does not take into account the dynamics of these molecules.

Reply 1.1: The concatenation model combines the structural data from our manuscript with prior biochemical insights into tetraspanin homodimerization and with scanning-EM data on immunogold-labeled CD81 and CD9 on cells. It is not completely clear to us what reviewer #1 refers to with “the dynamics of these molecules”. The cryo-EM data revealed that CD9 - EWI-F is a dynamic complex with straight and bent conformations, which could account for both circular and linear arrangements of tetraspanin-microdomains in cell membranes through the higher-order oligomerization of stable CD9 - EWI-F tetramers. Moreover, transient CD9 - CD9 interactions likely yield a variable number of complexes present in these concatenated and flexible strings of complexes. Such a concatenation model indeed requires further validation. However, it is consistent with experimental data and, importantly, provides a long-awaited molecular basis for TEM assembly. Although it was not within the scope of the current study, it will be of great interest to further investigate the concatenation model through detailed cell-biology based approaches.

**Minor comment:**

[1.2] There seems to be a mix up between the two structures in the following sentence p4: "In CD9EC2 - 4C8, the D loop adopts a partially helical conformation and central residue F176 is sandwiched by 4E8 residues W59 of CDR2 and W102 and R105 of CDR3 (Fig. 1D). In the 4C8-bound CD9EC2 structure the tip of the D loop points more outward and the Cα atom of F176"

Reply 1.2: The first sentence indeed mixed up the two structures and wrongfully mentioned CD9EC2 - 4C8 instead of CD9EC2 - 4E8. This has now been updated: “In CD9EC2 - 4E8, the D loop adopts …”

Reviewer #2

The paper is well written and the conclusions made are supported by the data presented.

[2.1] The ternary structure is in agreement with that of CD9 in complex with the related EWI-2 published earlier this year by Umeda et al (ref #25). The present work thus adds little structural insights but may be useful in showing that the interaction pattern seen extends to another EWI protein family member.

Reply 2.1: We agree with reviewer #2 that that the CD9 - EWI-F structure presented in our work is similar to the CD9 - EWI-2 structure published recently by Umeda et al. (ref #25). However, as also pointed out by reviewer #1, we believe that the CD9 - EWI-F structure adds new important information to understand the molecular mechanism underlying the assembly of tetraspanin-enriched microdomains. Notably, the different conformations of the CD9 - EWI-F complex observed in the cryo-EM data provide structural biology evidence for the dynamic nature of the interaction between a tetraspanin and a partner protein, which is consistent with a wealth of prior biochemical data. Guided by the distinct shape of the CD9EC2 - 4C8 densities, we were able to distinguish a range of straight to bent conformations of the complex. CD9 regions that represent known tetraspanin homo-dimerization sites, orient away from EWI-F and are available for interactions. Thus, combining our structural data with previous biochemical interaction data allowed for the generation of a long-awaited model for the assembly of tetraspanin-microdomains at the molecular level. We believe that these implications for TEM assembly will stimulate new, innovative research into the molecular principles that govern the function of tetraspanins.

[2.2] As such it may be acceptable for publication. In this case, the authors should improve the quality of Figs. 3D and 4D.

Reply 2.2: Figures 3D and 4D depict raw cryo-electron microscopy images (micrographs). The protein complexes imaged in this study only contain light atoms (H, N, C, O, S). Therefore, the collected micrographs only reveal low-contrast images of protein particles, and, for a typical cryo-EM experiment, it is required to average particles from thousands of micrographs to obtain a 3-dimensional reconstruction. We would like to keep the raw micrographs in figures 3 and 4, as it will aid cryo-EM scientists in judging the quality of the data.

Reviewer #3

The work is technically well performed and clearly presented including methodological details. I just have a few minor comments:

[3.1] Page 4 and Figure S1: it is hard to see how a reliable affinity for 4E8 can be obtained from the cell binding data in S1A, as there is no indication of saturation. It would be good to at acknowledge that this is at best a rough estimate. Fortunately the data for this nanobody in purified situation seems solid.

Reply 3.1: The obtained affinities are indeed an ±estimation based on a non-linear regression curve fitting on the measured data, performed in triplicate. The text has been updated and now reads as “4C8 and 4E8 bind to purified, full-length CD9 as well as to endogenous CD9 expressed on HeLa cells with apparent binding affinities in the nanomolar range (Fig. S1A, B, C)”. Next to that, a table stating the calculated KDs has been included as Fig. S1C.

[3.2] Page 6: Does the absence of micellar density for the EWI-F complex indicate flexibility of the extracellular domain relative to the TM? Does this happen because the classification focuses on the highly elongated Ig region?

Reply 3.2: These are indeed plausible assumptions. We observed highly heterogeneous, elongated particles in the micrograph shown in Fig. 3D, indicating inter-domain flexibility. If the alignment software focusses on certain Ig-like domains, other regions of the protein complex will be averaged out. An additional complexity with these elongated particles was to select an appropriate box size for particle picking and particle extraction, because the particles differ greatly in size based on their orientation (fully elongated side-views vs. much smaller top-views). When taken together, the complex of CD9 with full-length EWI-F was unsuitable for high-resolution structure determination; the subsequent strategy using EWI-FΔIg1-5 resulted in globular particles with less flexibility (Fig. 4D), which allowed for a more detailed structural characterization of the complex.

[3.3] Page 8: "Recently, a cryo-EM density map has been reported..." - please reference here.

Reply 3.3: We added the appropriate reference to the sentence: “Recently, a cryo-EM density map has been reported of CD9 in complex with an EWI-F homolog, EWI-2 (25).”

[3.4] Relatively little is known about how tetraspanins help to organize partner receptors into defined membrane domains, evidence for which has emerged from super-resolution light microscopy. Based on their structural analysis of the CD9-EWI-F complex, including the heterogeneity apparent in the cryo-EM structure, they propose a feasible concatenation model for higher order oligomerization of these complexes in the membrane. Obviously the model will need to be tested rigorously by mutational analysis, particularly the EWI Ig6 interface, but as it stands the paper is a significant contribution to the field of tetraspanins.

Reply 3.4: From the 8.6 Å cryo-EM data, the amino-acid residues that form the EWI-F Ig6 dimer interface can indeed not be distinguished. However, our data on CD9 in complex with full-length EWI-F (Fig. 3E) and previous cross-linking data (André et al. In situ chemical cross-linking on living cells reveals CD9P-1 cis-oligomer at cell surface - PMID: 19703604) support that EWI-F forms dimeric assemblies. Regarding the concatenation model, we therefore think that it will be of great interest to establish the putative CD9 - CD9 interactions (identified through biochemical approaches), that would link CD9 - EWI-F tetramers into higher assemblies, in the context of native membranes. However, investigating these transient interactions would require various non-trivial experiments and was therefore not within the scope of the current study.

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Referee #3

Evidence, reproducibility and clarity

This paper describes the structure of the tetraspanin CD9 and its interaction with the single pass protein EWI-F. The variability in the D loop of EC2 and the domain swapping is a useful addition to the limited structural database of these proteins and correlates with the relatively poor sequence conservation of this region. The key message is that dimerization of the single pass protein extracellular region, and interaction of its transmembrane helix with the tetraspanin, produces a heterodimeric structure that may further oligomerize. The authors propose a feasible concatenation model for higher order oligomerization of these complexes in the membrane.

The work is technically well performed and clearly presented including methodological details. I just have a few minor comments:

Page 4 and Figure S1: it is hard to see how a reliable affinity for 4E8 can be obtained from the cell binding data in S1A, as there is no indication of saturation. It would be good to at acknowledge that this is at best a rough estimate. Fortunately the data for this nanobody in purified situation seems solid.

Page 6: Does the absence of micellar density for the EWI-F complex indicate flexibility of the extracellular domain relative to the TM? Does this happen because the classification focuses on the highly elongated Ig region?

Page 8: "Recently, a cryo-EM density map has been reported..." - please reference here.

Significance

Relatively little is known about how tetraspanins help to organize partner receptors into defined membrane domains, evidence for which has emerged from super-resolution light microscopy. Based on their structural analysis of the CD9-EWI-F complex, including the heterogeneity apparent in the cryo-EM structure, they propose a feasible concatenation model for higher order oligomerization of these complexes in the membrane. Obviously the model will need to be tested rigorously by mutational analysis, particularly the EWI Ig6 interface, but as it stands the paper is a significant contribution to the field of tetraspanins.

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Referee #2

Evidence, reproducibility and clarity

In this paper, Dr. Oosterheert and colleagues report the crystal structures of CD9EC2 bound to nanobodies 4C8 and 4E8. The CD9EC2/4C8 structure was useful in determining a low resolution cryo-EM structure of EWI-F in complex with CD9/4C8. The observed sample heterogeneity of this ternary complex was reduced by deleting the n-terminal five Ig domains of EWI-F, yielding a modest maximum global resolution of ~ 8.6 Å. The structural approaches used are standard. The crystallographic and structure refinement statistics are sound as are the cryo-EM image processing. The overall cryo-EM structure of the ternary complex shows a central EWI-F protein dimer flanked by one CD9 molecule on each side. The paper is well written and the conclusions made are supported by the data presented.

Significance

The ternary structure is in agreement with that of CD9 in complex with the related EWI-2 published earlier this year by Umeda et al (ref #25). The present work thus adds little structural insights but may be useful in showing that the interaction pattern seen extends to another EWI protein family member. As such it may be acceptable for publication. In this case, the authors should improve the quality of Figs. 3D and 4D.

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Referee #1

Evidence, reproducibility and clarity

In this article, the authors provide new insights into the structure of the tetraspanin CD9. On the one hand, they provide crystal structures of the large extracellular domain of CD9, alone or bound to two nanobodies. The 3 structures are similar and similar to that of CD81, a related tetraspanin, except for a portion of the molecule, the so-called D-domain, showing flexibility of this domain. On the other hand, they obtained the cryo-EM structure of CD9 in association with a known-partner (EWI-F) with a resolution of 8.6A. More precisely, the complex of CD9 and the full-length EWI-F showed heterogeneity which they interpret as a consequence of the flexibility between the six Ig-like domains of EWI-F, precluding high-resolution structure determination. However, they showed that CD9 still interacted with a molecule lacking the 5 most membrane-distal Ig domains of EWI-F, and obtained the structure using this construct and an anti-CD9 nanobody. This structure reveals a hetero-tetrameric arrangement of CD9-EWIF, with a central EWI-F dimer flanked by a CD9 molecule on each side. CD9 and EWI-F interact through their transmembrane domains and the two truncated EWI-F molecules through the remaining Ig domains. Importantly, CD9 and EWI-F do not make contacts in the extracellular region, and CD9 shows a semi-open conformation. The structure also shows different configurations of the complex.

I found this study to be very convincing. Prior studies are referenced appropriately, the text is well written and clear, the figures are clear also.

In my opinion the paper does not need further experiment.

The conclusions are well supported by the data. However, the concatenation model seems very speculative at this point. Also, it does not take into account the dynamics of these molecules.

Minor comment:

There seems to be a mix up between the two structures in the following sentence p4: "In CD9EC2 - 4C8, the D loop adopts a partially helical conformation and central residue F176 is sandwiched by 4E8 residues W59 of CDR2 and W102 and R105 of CDR3 (Fig. 1D). In the 4C8-bound CD9EC2 structure the tip of the D loop points more outward and the Cα atom of F176"

Significance

Tetraspanins have been shown over the years to play an essential role in various biological functions. Among them, CD9 which is strongly expressed on the oocyte plasma membrane is essential for sperm-egg fusion. However, the mechanisms by which CD9 regulates this fusion process as well as other cell-cell fusion events remain unknown. The elucidation of its structure and of how it interacts with well characterized partner proteins is clearly a major advance in our understanding of the function of this molecule.

The absence of a structure for tetraspanins has been for a long time a knowledge gap. Following a breakthrough in 2001 with the publication of the crystal structure of the large extracellular domain of CD81 (Kitadokoro et al., EMBO J 2001), it was only recently that the structure of a full length tetraspanin, again that of CD81, was published (Zimmermann et al., Cell 2016). Earlier this year was published the crystal structure of a truncated version of CD9 as well as the cryo-EM structure of CD9 in association with another molecular partner EWI-2 (Umeda et al.,Nature com 2020).

The present structure adds new important information such as the existence of different conformation in the large extracellular domain of CD9 or the structure of CD9 with another molecular partner. It also highlights the different configurations of the complex. It will be of interest to researchers interested in tetraspanins, in membrane organization as well as researchers interested in the biological processes regulated by CD9, notably sperm-egg fusion.

My field of expertise concerns tetraspanins. I cannot comment on the technical aspects of the structures.

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Reply to the reviewers

Reviewer #1

1. The first hypothesis of the manuscript is that, rather than a change in a single immune pathway being responsible for the lack of response to the virus, the response will be systemic involving multiple inter-related pathways. The data show that this was the case after presenting convincing transcriptome analysis.

We thank the reviewer agreeing that we have convincingly shown that the response to the virus is systemic involving the induction of interrelated pathways

The second hypothesis is that the differences in responses between bats and humans are due to evolutionarily divergent genes. The authors provide evidence for this in the transcriptome differences in the C-reactive protein, aspects of the complement system, iron regulation and M1/M2 macrophage polarization. The second hypothesis is broad, but there are clearly differences in the genes involved in humans and bats. Without mechanistic information on the function of the proteins/cells investigated, it is hard to determine that the changes the authors are observing are the cause of the different responses, rather than an effect of some upstream response, and so difficult to pin-point specific divergent genes.

We agree that mechanistic studies will be required to test causal links between the genes we identified and specific anti-viral responses, an effort that is likely to require multiple laboratories and some time. The aim of this study was to enable this effort by identifying a list of candidate genes affected by EBOV and MARV infection in bats, not merely in cultured bat cells.

The authors wish to compare the response to the virus in bats to the better characterized human tissue responses, but because this relies on previously published work in humans, it is sometimes unclear whether "more bat-like" responses are definitely associated with positive outcomes in humans. As the benefit of certain responses in human infections can depend on the timing of the response, it might be helpful to include summarized human data in manuscript to aid comparison with the bat responses.

We agree and have added the following data and discussion (inserted into Discussion, page 9, and added two new tables, Tables 2 and 3).

Comparing our observations to human responses to filoviruses is limited by the scarcity of studies in humans. Nevertheless, this comparison suggests potential directions to explore. In one study, individuals who succumbed to the disease showed stronger upregulation of interferon signaling and acute phase responses compared to survivors during the acute phase of infection[1], consistent with the anti-inflammatory response gene expression signature identified in this study in bats. However, most of the genes used in the study by Liu et al. to classify survivors are either barely expressed in bats or do not respond to filoviral infection (Table 2), the differences that provide potential clues to find why bats can tolerate the infection.

A study of patients infected with Sudan Ebola virus (SUDV) analyzed protein levels for a panel of genes using a Luminex multiplex assay (using antibodies)[2]. The panel was based on results from other studies and pathways involved in the response to infections. The patients were classified into 3 possible dichotomies (fatal/non-fatal, hemorrhaging/non-hemorrhaging, or high/low viremia) correlated with genes that characterized these states. Most of these genes either are barely expressed, if at all, or are unaffected by infection in bats, except for ferritin (FTL, FTH1) whose expression is lowered by MARV infection, consistent with the observation that ferritin is higher is fatal human cases (Table 3).

For instance, the T-cell response section concludes "Bats mount a T cell response against the infection" but there is no discussion of the impaired but complex lymphocyte response in humans, so comparison is not possible.

We have expanded the discussion on T cells (Results, page 7) as follows.

Previous studies on the adaptive immune response to Ebola and Marburg viruses in humans, non-human primates, and non-primate mammals, shows that long-term immunity is conferred by both T cell and antibody responses. Mostly CD8+ T cells were elicited and helpful against Ebola in mice[3],[4], while SUDV infection in humans[5]) and MARV infection in cynomolgus monkeys[6] and humans[7] ) elicited mostly CD4+ T cells . In most human EBOV infections, CD8+ T cells against the EBOV NP protein dominated the responses, while a minority of individuals harbored memory CD8+ T cells against the EBOV-GP [8].

Consistent with this, in MARV-infected bats, CD4 expression (specific to CD4+ T cells) was higher, while in EBOV-infected bats, CD8 expression (specific to CD8+ T cells) was higher, the overall levels are low, because the tissue samples are heterogenous and expression of these markers is not high in the T cells to begin with. T cell markers (such as CCL3, ANAX1, TIMD4 and MAGT1) are also upregulated in liver, suggesting a T cell response is mounted.

Mock infected IHC should be included in Figure 1F to demonstrate the antibodies are not background.

We have added IHC data of two mock-infected animals (Fig. S1 panels A and B).

See comment in hypotheses- a summarized table of findings from previous studies of early responses to the virus would be helpful for comparisons to the bat response and for determining the second hypothesis.

We have expanded our comparisons to previous studies by adding the following text to Introduction (page 3)

A potential source of the difficulty to understand how bats tolerate or eliminate the viruses that are deadly to humans is the lack of studies that analyze the response to infection in bats rather than in cultured bat cells. The results obtained using cell lines have been contradictory. Some studies claim both EBOV and MARV replicate to similar levels in ERB and human derived cell lines[9], with a robust innate immune response mounted by ERB and to a lesser degree, human cells, while others claim MARV inhibited the antiviral program in ERB cells, like in primate cells, and did not induce almost any IFN gene [10], or little anti-viral gene induction[11]. An experiment with the pig (PK15A) and bat (EhKiT) cells suggested they responded to EBOV through the upregulation of immune, inflammatory, and coagulation pathway, in contrast to a limited response in the human (HEK293T) cells[12]. To comprehensively understand the pathways involved in the bat filoviral response, we infected bats, rather than their isolated cells, and analyzed tissue-specific RNA expression through mRNA-seq in the organs of the infected animals.

Reviewer #2

1. The authors provide this contribution to the extremely interesting topic of the immunobiology that facilitates filovirus infections of bats without overt pathology. They focused entirely on gene transcription signatures from different tissue sites following experimental infection, and sometimes compare those signatures with those generated in humans following natural exposures to filoviruses. The strengths of the paper is the shear breadth of data generated that is available openly to the scientific community and the development of novel mRNA datasets from bats, in the absence and presence of infection. One of the major limitations of this systems-based approach is that there is no mechanistic data that links gene function to the immune response to filovirus infection. Rather, associations are made and functional links are inferred. This limitation makes the title of the manuscript "...is controlled by a systemic response" an overstatement.

We thank the reviewer and agree that mechanistic studies were out of scope of this study and have reflected this fact in the title by replacing “is controlled” with “induces”:

Ebola and Marburg filovirus infection in bats induces a systemic response

The authors indicate that one of their main objectives is to understand differences in the responses to infection between bats and humans. But this submission says little about the transcriptome-level responses to filovirus infection in humans. It does, on at least one occasion, state that some of the bat genes with altered expression levels were also altered in a study of human filovirus infections (reference #67). I think it would be helpful if the authors devoted a figure or table to the direct comparison between their analysis of MARV- and EBOV-infected bats and the findings of filovirus-infected humans, highlighting genes that are differentially up- or downregulated between the two species.

This discussion, which was also requested by Reviewer 1, is now included in the manuscript (Discussion page 9 and Tables 2 and 3).

Figure 2 is not described nor presented usefully. Instead of providing a figure title ""Upset plot..." the authors should clearly describe the type of transcriptomic data being presented. Moreover, it way the data is plotted does not reveal any direct information about the genes that are up- or downregulated in each condition, thus reducing its utility to the reader. I suggest that this Figure be placed in the Supplemental information. In fact, Figures 3 could also be moved to the Supplemental information

Figure 2 makes that point that the response is a broad one while Figure 3 presents evidence from expression data that there is tissue-specific responses to the viruses. Both together provide convincing evidence of a systemic, wide-ranging response to both MARV and EBOV infections. We have edited the caption to Figure 2 by changing it to the following:

Figure 2: Broad response of bat liver genes to filoviral infection. Many genes in the liver respond to filoviral infections, with MARV having a bigger impact compared to EBOV (840 genes that are responsive to MARV alone, compared to the 43 specific to EBOV alone). The EBOV-specific (EBOV/MARV) and MARV-specific (MARV/EBOV)genes are likely host responses specific to the viral VP40, VP35 and VP24 genes. In the plot, mock refers to mock-infected bats, EBOV to EBOV-infected bats, and MARV to MARV-infected bat livers. Each row in the lower panel represents a set, there are six sets of genes based on various comparisons, e.g., EBOV/mock is the set of genes at least 2-fold up regulated in EBOV infection, compared to the mock samples. The gray bars at the lower left representing membership in the sets. The vertical blue lines with bulbs represent set intersections, e.g., the last bar is the set of genes common to EBOV/MARV, EBOV/mock and MARV/mock, so the genes in this set are up 2-fold in EBOV compared to the mock and MARV samples, and at least 2-fold up in MARV compared to mock. The main bar plot (top) is number of genes unique to that intersection, so the total belonging to a set, say mock/EBOV, is a sum of the numbers in all sets that have mock/EBOV as a member (41+203+6+31=281).

The authors do not specify in the main text, figure captions, or methods sections how they objectively assigned bat homologs as being "similar to " or "divergent from" their human counterparts. What is the cut-off in terms of sequence similarity?

We apologize for this omission. In addition to a description in Methods, we have added the following statement to the Results section (Page 4).

To identify divergent genes, we relied on BLASTn[13]. Genes detected as homologues (16004, 87% out of 18443 genes in our databse) using BLASTn default settings were labelled “similar”. The remaining 2439 genes (13%) were considered “divergent”. Of these genes, 1,548 transcripts (8% of the total), could be identified as homologous by reducing the word-size in BLASTn from 11, the default, to 9. This approach is equivalent to matching at the protein level, but we find that using nucleotide level matches provides a cleaner separation of the two classes than using translated proteins (Fig. 4, Methods).

In the Discussion, it is surprising that the authors state that "the majority of interferon response genes are not divergent from human homologs" since genes involved in innate immunity are some of the most rapidly evolving genes known to exist. Again, clarification over what dictates "divergence" over "similarity" is warranted. Many previous studies have shown how a single residue change in an innate immune effector can drastically alter its specificity and/or potency.

We have clarified this point by adding the following statement in the Discussion (pages 8,9)

There are hundreds of genes involved in the interferon response, some key components can mutate to change specificity of their interactions, but most, especially those in the core ISG category[14], evolve slowly and have conserved function and sequence[15]. Our analysis of gene divergence shows that the majority of interferon response genes are not divergent from their human homologs, consistent with prior observations that the innate responses are quite similar between human and bat cell lines[9]. This implies that other systems are involved in generating the difference in response between bats and humans.

The authors state in the introduction, and point to citation #21, that ERBs are "refractory to infection." In Figure 1, the authors indicate that experimental of ERBs with EBOV led to detectable infection in some animals, particularly in the liver. At this point in the manuscript, the authors should state if and how this result differs from what is published in #21, and they should comment on whether this is scientifically significant, or not. This is eventually discussed briefly in the Discussion but adding a sentence to Results section would be helpful for readers.

To emphasize that our results contradict prior reports of ERB being refractory to EBOV infection, we have modified the statement in the Results (page 3) as follows.

Two of the three EBOV-inoculated animals presented with histopathological lesions in the liver, consisting of pigmented and unpigmented infiltrates of aggregated mononuclear cells compressing adjacent tissue structures, and eosinophilic nuclear and cytoplasmic inclusions, changes consistent with previous reports[16], [17]. In EBOV-infected animals, focal immunostaining with both pan-filovirus and EBOV-VP40 antibodies was observed in the liver of one animal, but very few foci were found, suggesting limited viral replication.

The research question at hand, concerning how bats serve as reservoirs for multiple viruses which are pathogenic to humans without succumbing to disease, is one of the hottest topics in immunology and virology. However, the authors do not provide a clear enough explanation of how their approach to study the transcriptome response following filovirus infection goes beyond what has been published in previous studies. This manuscript would greatly benefit from a discussion of its novelty in the Introduction and Discussion sections.

We have reviewed prior human and bat studies (Introduction -page 3 and Discussion- page 9 shown above) to highlight the novelty of our findings. We have also added the following sentence at the end of the Introduction highlighting the novelty of the study.

This is the first in vivo study that focuses on the coordinated transcriptional response to filoviruses at the level of individual organs in bats.

References

[1] X. Liu et al., “Transcriptomic signatures differentiate survival from fatal outcomes in humans infected with Ebola virus,” Genome Biology, vol. 18, no. 1, p. 4, Jan. 2017, doi: 10.1186/s13059-016-1137-3.

[2] A. K. McElroy et al., “Ebola hemorrhagic Fever: novel biomarker correlates of clinical outcome,” J. Infect. Dis., vol. 210, no. 4, pp. 558–566, Aug. 2014, doi: 10.1093/infdis/jiu088.

[3] S. B. Bradfute, K. L. Warfield, and S. Bavari, “Functional CD8+ T cell responses in lethal Ebola virus infection,” J. Immunol., vol. 180, no. 6, pp. 4058–4066, Mar. 2008, doi: 10.4049/jimmunol.180.6.4058.

[4] M. N. Rahim et al., “Complete protection of the BALB/c and C57BL/6J mice against Ebola and Marburg virus lethal challenges by pan-filovirus T-cell epigraph vaccine,” PLOS Pathogens, vol. 15, no. 2, p. e1007564, Feb. 2019, doi: 10.1371/journal.ppat.1007564.

[5] A. Sobarzo et al., “Multiple viral proteins and immune response pathways act to generate robust long-term immunity in Sudan virus survivors,” EBioMedicine, vol. 46, pp. 215–226, Aug. 2019, doi: 10.1016/j.ebiom.2019.07.021.

[6] L. Fernando et al., “Immune Response to Marburg Virus Angola Infection in Nonhuman Primates,” J Infect Dis, vol. 212, no. suppl_2, pp. S234–S241, Oct. 2015, doi: 10.1093/infdis/jiv095.

[7] S. W. Stonier et al., “Marburg virus survivor immune responses are Th1 skewed with limited neutralizing antibody responses,” J. Exp. Med., vol. 214, no. 9, pp. 2563–2572, Sep. 2017, doi: 10.1084/jem.20170161.

[8] S. Sakabe et al., “Analysis of CD8+ T cell response during the 2013–2016 Ebola epidemic in West Africa,” PNAS, vol. 115, no. 32, pp. E7578–E7586, Aug. 2018, doi: 10.1073/pnas.1806200115.

[9] I. V. Kuzmin et al., “Innate Immune Responses of Bat and Human Cells to Filoviruses: Commonalities and Distinctions,” J. Virol., vol. 91, no. 8, Apr. 2017, doi: 10.1128/JVI.02471-16.

[10] C. E. Arnold et al., “Transcriptomics Reveal Antiviral Gene Induction in the Egyptian Rousette Bat Is Antagonized In Vitro by Marburg Virus Infection,” Viruses, vol. 10, no. 11, 02 2018, doi: 10.3390/v10110607.

[11] M. Hölzer et al., “Differential transcriptional responses to Ebola and Marburg virus infection in bat and human cells,” Scientific Reports, vol. 6, p. 34589, Oct. 2016, doi: 10.1038/srep34589.

[12] J. W. Wynne et al., “Comparative Transcriptomics Highlights the Role of the Activator Protein 1 Transcription Factor in the Host Response to Ebolavirus,” Journal of Virology, vol. 91, no. 23, Dec. 2017, doi: 10.1128/JVI.01174-17.

[13] S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman, “Basic local alignment search tool,” J. Mol. Biol., vol. 215, no. 3, pp. 403–410, Oct. 1990, doi: 10.1016/S0022-2836(05)80360-2.

[14] A. E. Shaw et al., “Fundamental properties of the mammalian innate immune system revealed by multispecies comparison of type I interferon responses,” PLOS Biology, vol. 15, no. 12, p. e2004086, Dec. 2017, doi: 10.1371/journal.pbio.2004086.

[15] T. B. Sackton, B. P. Lazzaro, T. A. Schlenke, J. D. Evans, D. Hultmark, and A. G. Clark, “Dynamic evolution of the innate immune system in Drosophila,” Nat. Genet., vol. 39, no. 12, pp. 1461–1468, Dec. 2007, doi: 10.1038/ng.2007.60.

[16] M. E. B. Jones et al., “Experimental Inoculation of Egyptian Rousette Bats (Rousettus aegyptiacus) with Viruses of the Ebolavirus and Marburgvirus Genera,” Viruses, vol. 7, no. 7, pp. 3420–3442, Jun. 2015, doi: 10.3390/v7072779.

[17] J. T. Paweska, N. Storm, A. A. Grobbelaar, W. Markotter, A. Kemp, and P. Jansen van Vuren, “Experimental Inoculation of Egyptian Fruit Bats (Rousettus aegyptiacus) with Ebola Virus,” Viruses, vol. 8, no. 2, Jan. 2016, doi: 10.3390/v8020029.

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Referee #2

Evidence, reproducibility and clarity

The authors provide this contribution to the extremely interesting topic of the immunobiology that facilitates filovirus infections of bats without overt pathology. They focused entirely on gene transcription signatures from different tissue sites following experimental infection, and sometimes compare those signatures with those generated in humans following natural exposures to filoviruses. The strengths of the paper is the shear breadth of data generated that is available openly to the scientific community and the development of novel mRNA datasets from bats, in the absence and presence of infection. One of the major limitations of this systems-based approach is that there is no mechanistic data that links gene function to the immune response to filovirus infection. Rather, associations are made and functional links are inferred. This limitation makes the title of the manuscript "...is controlled by a systemic response" an overstatement.

Major points:

The authors indicate that one of their main objectives is to understand differences in the responses to infection between bats and humans. But this submission says little about the transcriptome-level responses to filovirus infection in humans. It does, on at least one occasion, state that some of the bat genes with altered expression levels were also altered in a study of human filovirus infections (reference #67). I think it would be helpful if the authors devoted a figure or table to the direct comparison between their analysis of MARV- and EBOV-infected bats and the findings of filovirus-infected humans, highlighting genes that are differentially up- or downregulated between the two species.

Figure 2 is not described nor presented usefully. Instead of providing a figure title ""Upset plot..." the authors should clearly describe the type of transcriptomic data being presented. Moreover, it way the data is plotted does not reveal any direct information about the genes that are up- or downregulated in each condition, thus reducing its utility to the reader. I suggest that this Figure be placed in the Supplemental information. In fact, Figures 3 could also be moved to the Supplemental information.

The authors do not specify in the main text, figure captions, or methods sections how they objectively assigned bat homologs as being "similar to " or "divergent from" their human counterparts. What is the cut-off in terms of sequence similarity?

In the Discussion, it is surprising that the authors state that "the majority of interferon response genes are not divergent from human homologs" since genes involved in innate immunity are some of the most rapidly evolving genes known to exist. Again, clarification over what dictates "divergence" over "similarity" is warranted. Many previous studies have shown how a single residue change in an innate immune effector can drastically alter its specificity and/or potency.

Minor points:

The authors state in the introduction, and point to citation #21, that ERBs are "refractory to infection." In Figure 1, the authors indicate that experimental of ERBs with EBOV led to detectable infection in some animals, particularly in the liver. At this point in the manuscript, the authors should state if and how this result differs from what is published in #21, and they should comment on whether this is scientifically significant, or not. This is eventually discussed briefly in the Discussion but adding a sentence to Results section would be helpful for readers.

Significance

The research question at hand, concerning how bats serve as reservoirs for multiple viruses which are pathogenic to humans without succumbing to disease, is one of the hottest topics in immunology and virology. However, the authors do not provide a clear enough explanation of how their approach to study the transcriptome response following filovirus infection goes beyond what has been published in previous studies. This manuscript would greatly benefit from a discussion of its novelty in the Introduction and Discussion sections.

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

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Referee #1

Evidence, reproducibility and clarity

Summary:

The manuscript by Jayaprakash et al investigates the response to the filoviruses Marburg and Ebola virus in Rousettus aegyptiacus bats, the natural reservoir of Marburg virus. The response to infection is investigated by comparing transcriptomes of different bat tissues in infected and uninfected bats. The manuscript groups the observed transcriptome changes into pathways that are impacted, and discusses how those pathways may cause subclinical infection in bats, compared to severe disease in humans. The data included also sheds light on bat immunology and reservoir characteristics more generally, which is particularly timely during the SARS-CoV-2 pandemic.

Major comments:

Are the key conclusions convincing?

The first hypothesis of the manuscript is that, rather than a change in a single immune pathway being responsible for the lack of response to the virus, the response will be systemic involving multiple inter-related pathways. The data show that this was the case after presenting convincing transcriptome analysis. The second hypothesis is that the differences in responses between bats and humans are due to evolutionarily divergent genes. The authors provide evidence for this in the transcriptome differences in the C-reactive protein, aspects of the complement system, iron regulation and M1/M2 macrophage polarization. The second hypothesis is broad, but there are clearly differences in the genes involved in humans and bats. Without mechanistic information on the function of the proteins/cells investigated, it is hard to determine that the changes the authors are observing are the cause of the different responses, rather than an effect of some upstream response, and so difficult to pin-point specific divergent genes. The authors wish to compare the response to the virus in bats to the better characterized human tissue responses, but because this relies on previously published work in humans, it is sometimes unclear whether "more bat-like" responses are definitely associated with positive outcomes in humans. As the benefit of certain responses in human infections can depend on the timing of the response, it might be helpful to include summarized human data in manuscript to aid comparison with the bat responses. For instance, the T-cell response section concludes "Bats mount a T cell response against the infection" but there is no discussion of the impaired but complex lymphocyte response in humans, so comparison is not possible.

Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

No, speculative discussion of potential drugs is already qualified as speculative, and adds to the understanding of the significance of the data.

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.

No

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.

N/A

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.

Mock infected IHC should be included in Figure 1F to demonstrate the antibodies are not background.

Are prior studies referenced appropriately?

Mostly yes. The discussion of the T-cell responses in infection could be expanded to include more information on human responses

Are the text and figures clear and accurate?

Yes

Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

See comment in hypotheses- a summarized table of findings from previous studies of early responses to the virus would be helpful for comparisons to the bat response and for determining the second hypothesis.

Significance

Nature and Significance of the advance.

Bat immune responses to filoviruses are poorly characterized, and this paper contains much information that can aid future investigation of reservoir responses. This data also has broad application to other bat-borne pathogens.

Compare to existing published knowledge.

There is little about in vivo bat immune response to filoviral infections. Significantly, this report has a non-refractory response to Ebola virus infection in Rousettus aegyptiacus.

Audience

This paper would be of interest to filovirologists and those interested in zoonotics and bat immunology.

Your expertise.

I am a viral immunologist with >15 years' experience with filoviruses. Ms. Clarke is a senior graduate student whose thesis focuses on immune responses to filovirus glycoproteins.

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Reply to the reviewers

INITIAL RESPONSE TO REVIEWERS / REVISION PLAN

We are grateful to the three reviewers for reviewing our manuscript and providing their comments which helped to improve further the quality of the current study. We attach an initial revised version of the manuscript with changes corresponding to reviewers’ comments being highlighted. We now provide:

• 18 new main figure panels (Fig.1E, Figs.2D-F, Figs.3E-F, Figs.4B,C,E, Figs.6B-F, Figs.7B,D,E,F),
• 9 new supplementary figures, and
• 13 new supplementary tables, that correspond to the points raised by the reviewers. In this initial response to reviewers and revision plan we have already performed the bioinformatics analysis and the majority of new wet lab experiments requested by the reviewers, while we are still awaiting only for the results of three sets of wet lab experiments (RIP-seq, additional protein/RT-qPCR confirmations and B2 incubations with other proteins), which, due to their nature, take longer. We have also revised the main text accordingly with only a number of updates (regarding some methods of experiments currently in progress and the respective discussion) still missing.

In detail:

REVIEWER 1

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

B2 RNAs, encoded from SINE B2 elements has been directly implicated in stress response by its inherent ability to bind RNA Pol II and suppress stress response genes (SRG) in homeostatic conditions. However, upon stimuli, B2 RNAs are cleaved and degraded, resulting in the release of RNA pol II and upregulation of SRGs. Previous work from the senior author identified PRC2 component EZH2 to be the B2 RNA processing factor, cleaving B2, and releasing POL2. SRGs are upregulated upon stress, for example in age-associated neuropathologies like Alzheimer's disease (AD). Considering that the hippocampus is a primary target of amyloid pathologies as well as since SRGs are suggested to be key for the function of a healthy hippocampus, the authors set to understand the role of B2 RNAs that are linked to SRG regulation in the mouse hippocampus with amyloid pathology. They use disease-relevant in vivo and in vitro models combined with unbiased RNA seq data analysis for this endeavor, which indicates the potential relevance of B2 RNAs in APP mediated neuronal pathologies in mice as well as identifies Hsf1 as the factor cleaving B2 RNAs in the hippocampus.

This reviewer generally remarks that “The work is interesting and identification of Hsf1 as the processing factor for B2 RNAs in the hippocampus is significant. I would like to credit the authors for their elegant in vivo experimental design in Figure 2.”

We appreciate the encouraging comments made by this reviewer.

General comment: The reviewer finds “some of the conclusions to be overstated” and has brought a number of concerns to our attention. Indeed, we agree that provision of additional data and details is needed to avoid any confusion about the gene pathways to which our findings apply. In the initial manuscript, (Figures 2 D, F and 6 D, F), we presented the gene expression levels of all B2 RNA regulated SRGs identified in our previous study (Zovoilis et al, Cell 2016), referred as B2 RNA regulated SRGs or B2-SRGs throughout the manuscript. To this end, we performed the respective statistical tests between the different conditions considering these genes, in order to show the transcription dynamics of these genes in either amyloid beta pathology (APP mice /Figs. 2D, F) or amyloid beta toxicity (HT22 cells / Figs. 6D, F). Since we were not looking for new candidate genes upregulated in APP mice or in our HT22 cell culture system, we did not narrow our analysis only to genes delivered by a general-purpose differential gene expression approach such as DESeq but tested all B2-SRGs. However, based on the reviewer’s comments below, we realize that the paper would benefit by presenting in the main figures only those B2 RNA regulated SRGs that overlap with differentially expressed genes identified by DEseq in each experimental system. This will help to avoid confusion and any misunderstanding that all B2 RNA regulated genes are equally affected in our system, which is not the case and would be an overstatement. We are now presenting in new Figure 2 (2E, 2F) only those B2-SRGs that overlap with upregulated genes identified by DESeq in 6m old APP mice (listed in new Suppl. Table 5) and in new Figure 7 (7D, F) we are now presenting only those B2-SRGs that overlap with upregulated genes identified by DESeq in HT22 cells treated with amyloid beta (listed in new Suppl. Table 11). The conclusions drawn by the new figures remain the same as with the old ones and we believe that this new way of presentation of this data will prevent confusion and potential over-statements. We thank the reviewer for bringing this to our attention. Based also on this reviewer’s minor point 3, we recommend that the old figures that included all B2-SRGs (and not only the differentially expressed ones identified by DESeq) are moved to the Supplement as new Supplementary Figures 1 and 7, respectively, so that readers can still get a view of all the data and the transcription dynamics of all B2-SRGs, while we provide both in text and the supplement an explanation about the value as well as limitations of these figures.

**Major comments:**

Major point 1. The reviewer asks: “In figure 1, the authors indicate a strong connection between B2 RNA regulated SRGs and learning and memory. In figure 2, they identify the SRGs in the hippocampus, please provide a direct comparison of learning and memory associated SRGs and the SRGs they identify in figure 2 that are significantly upregulated in APP mice in 6 months.”

In the revised version of the manuscript we now provide: i) As a new figure panel (lower panel in new Fig.1E), the number of B2 RNA regulated SRGs that are associated with learning based on our Peleg et al, Science 2010 paper and as a new Supplementary Table 3, the exact list of these genes. ii) As a new Supplementary Table 4, the list of all genes that are significantly upregulated in APP mice (6 months). iii) As a new Supplementary Table 5, the list of those genes upregulated in amyloid pathology (APP 6 months) that are B2-SRGs (expression levels of these genes are presented in new Figure 2E,F). Per reviewer’s question, we now provide as a new Supplementary Table 6, the list of B2 RNA regulated SRGs that are both learning associated genes and upregulated in 6 month old APP mice. In the text (first two sections of the results), we provide direct comparisons of the number of genes in each category and their overlap.

Major point 2. The reviewer asks: “To better understand the data in the context of hippocampal function, please include functional annotation of SRGs they identified in Figure 2F as they do it in Figure 1 (desirably for each time point, at least for 6M). How many of the SRGs they identify in Figure 1 are part of Figure 2F? Please include functional annotation of significantly upregulated B2 regulated SRGs in Fig2 and compare them with that of Figure 1.”

The number of B2 RNA regulated SRGs in Figure 1 that are part of Figure 2 (in particular Figs.2E,F) is now presented in the new Supplementary Table 5 and also in the text. We now provide as a new Supplementary Table 7 the functional annotation of these genes (see also general comment for this reviewer) and discuss the findings in the text.

We recommend to include only the 6M old mice as this is the time point in which B2 RNA processing was found to differ between WT and APP mice. However, if the reviewer thinks that this is necessary we will add also differential expression lists of other ages as additional supplementary tables.

Major point 3. The reviewer asks: “In figure 3, the authors report that the B2 processing rates are high at the 6M time point at in hippocampi of the APP mice. Please include the levels of unprocessed and processed B2 RNAs in these samples along with this figure, without which it is difficult to gauge the significance of its correlation with SRGs in Figure 2.”

We now provide as new figure panels 3E and 3F the levels of processed B2 RNA fragments and unprocessed (full length) B2 RNAs in these samples, respectively, along with the processing ratio which is now labeled as subfigure 3G.

Major point 4. The reviewer asks: “What is the % of B2 regulated SRGs that are hsf1 bound in Figure 4C? What is there dynamics in the wild type and APP hippocampi?”.

Old Figure 4C is now Figure 4A. The exact number of B2 RNA regulated SRGs that are close to Hsf1 binding sites is now presented as a new figure (Figure 4C) and discussed in the text. A list of these genes is provided as new Supplementary Table 8. For genes that are upregulated in APP mice compared to wild type, the difference in Hsf1 binding dynamics between B2 RNA regulated and not regulated genes is now presented as Suppl. Figure 4D.

Major point 5. The reviewer asks: “What is the distribution of Hsf1 binding sites on (a) non-B2 regulated SRGs and (b) non-SRG genes in hippocampi?”.

This point is related with point 4. We now present a new panel (Fig. 4B) for non B2 RNA regulated genes (listed in Suppl. Table 13) along with the distribution we have in the initial manuscript for all B2 RNA regulated SRGs (now presented as Fig. 4A). The direct comparison of these genes is presented in the new Suppl Figure 4C together with a similar comparison only for genes upregulated in APP mice (Suppl. Fig.4D)

Major point 6. The reviewer notes: “In Figure 4D, the 3months old Wt HSF1 levels are high, yet B2 processing (Figure 3E) is low. Please comment.”

The reviewer’s comment made us realize that we should include a plot that describes the correlation between Hsf1 levels and B2 RNA processing ration across all sequenced samples. This should reveal whether differences such as those observed by the reviewer affect our conclusion regarding the relationship between these two parameters. We now provide this in the new Supplementary Figure 6D, where we found a strong positive correlation between Hsf1 levels and B2 RNA processing ratio. We thank the reviewer for this comment which helped us to substantiate further this relationship.

Major point 7. The reviewer notes: While the authors show in vitro cleavage of B2 RNA by Hsf1, the experiment lacks controls to be conclusive. At least, please include a similar size protein as HSF1 with no-known RNA binding activity and a similar size protein with RNA binding activity as controls in 5A. Please justify the use of PNK as the control protein. Please include the use domain-based deletions of Hsf1 to map the region of HSF1 that is binding and potentially cleaving the B2 RNA. Please include an RNA of similar size and Antisense-B2 RNA to show the specificity of the Hsf1 based cleavage of B2 RNA. Without these controls, the conclusions in Figure 5 cannot be substantiated.

The endogenous ribozyme activity of B2 RNA compared to other control RNAs has already been shown in two previous works but we will also include the relative controls here by providing control incubations with other RNAs. We will also include the incubations with additional control proteins as suggested by the reviewer. We are currently performing these experiments and will include them in the revised version. PNK is used as a control protein because it is an RNA binding protein that is used in the construction of our short RNA libraries and we wanted show that short RNA seq data are free of such confounding factors that could potentially generate artificial fragments. We now include this information in the text.

We feel that the application of domain based deletions for Hsf1, while it would add additional information on the exact biochemistry underlying B2 RNA processing though Hsf1, is beyond the scope of this manuscript. In the current manuscript we are just focusing on the fact that Hsf1 can accelerate B2 RNA processing in vitro and not on the mechanism how this happens. This should be addressed in our opinion on a separate manuscript.

Major point 8. The reviewer asks: “The authors should show that the incubated APP peptides are taken up by the cells (experiments in Figure 5F and Figure 6).” These figures are now labelled as Fig.6C and Figure 7, respectively. That’s a very interesting point and we thank the reviewer for this comment. Multiple studies have shown that toxicity after incubation by amyloid beta is mediated mainly by cell surface receptors, which through cell signalling leads to the response to cellular toxicity that induces stress genes such as Hsf1. Nevertheless, APP peptides may enter the cell, and the reviewer’s questions raised the possibility that oligomers entering the cell could have a direct impact on the stability of the B2 RNA. In that case, providing evidence that the amyloid enters the cell would be important if we had indications that amyloid beta interacts directly with B2 RNA. We did test this and we found no direct effect of amyloid beta on B2 RNA, so the processing in our case is not induced by oligomers that may have entered the cell. We were planning to present this information in a different manuscript, but if the reviewer or editor thinks that it would be beneficial for the paper, we could present this as supplement figure that shows that amyloid beta incubations with B2 RNA do not induce further processing beyond what Hsf1 causes. For the moment we just present this below:

Major point 9. The reviewer asks: “Please provide the list, functional annotation, and % of the SRGs upregulated upon incubation with APP in HT22 cells in comparison to 6month old APP mice. Comment on learning-related Genes.”

In the revised version, we now provide and mention in the text the following data: i) a list of genes upregulated in HT22 cells during amyloid toxicity upon incubation with amyloid beta (new Suppl. Table 9), ii) a list of genes according to point (i) that are common with genes upregulated in APP mice (new Suppl. Table 10), iii) the list and number of B2-SRGs that are upregulated in HT22 cells during amyloid toxicity (the reviewer’s question) (new Suppl. Table 10). We mention in the text the gene numbers and also the genes that are common in all three lists. iv) Functional annotation of genes of point (iii) (new Suppl. Table 12),

We also mention in the text the limitations of our comparisons between the in vivo model of amyloid pathology (APP mice) and the in vitro cell culture model of amyloid toxicity (HT 22 cells) and we clarify that the cell culture model is used just as a simulation of the effect of amyloid beta in gene pathways associated with response to cellular stress and the role of Hsf1 on B2 RNA processing.

Major point 10. The reviewer asks: “The authors should show the efficient downregulation of Hsf1 (protein) upon anti-Hsf1 LNA transfection.”

In the revised version, in addition to the RNA-seq data we provide a second confirmation at the mRNA level with an independent method (RT-qPCR) in new figures 4E and 7B (lower panel). We are currently performing the protein extractions and will provide a WB or an Elisa in the revised version.

Major point 11. The reviewer asks: “Please present the total B2 RNA levels for conditions in Figure 6C.”

We now provide as new supplementary figure (Suppl. Fig. 6B and C) the levels of processed B2 RNA fragments and the total levels of unprocessed full length B2 RNAs of these samples that relate to old Figure 6C (now labeled as Fig.7C)

Major point 12. The reviewer notes: “Hsf1 levels are not significantly downregulated in Control cells which were inoculated with the reverse APP peptide. Please comment.”

We assume that the reviewer here refers to the lack of reduction in Hsf1 levels in the cells inoculated with the reverse peptide and the anti-Hsf1 LNA. Indeed, this lack of reduction is confirmed also by the new qPCR we performed (new Figure 7B, lower panel, R-ctrl vs R-anti-Hsf1). This should likely be attributed to compensation during non-stress conditions. In contrast, under stress conditions, Hsf1 is heavily used in stress response, which could explain the differences we see as cellular needs surpass the available Hsf1 transcripts due to degradation by the LNA. This is also supported by the new RT-qPCR experiments we have performed for B2-SRGs (new Figure 7E). In agreement with what is known for stress response genes such as immediately early genes (for example FosB), levels of these genes are minimal in both R-ctrl and R-anti-Hsf1 conditions and only become activated during stress response. We now discuss this in the text of the revised manuscript.

Major point 13. The reviewer asks: “Please compare and contrast the % of genes, the overlap, and the functional distinctions in 6F to that of 5G and Figure1. What are the genes that are common between Figure1, and that are specifically upregulated upon Anti-Hsf1 LNA transfection along with 1-42 APP. What is % of the occurrence of B2 binding sites in those genes? What are their functional annotations and what is their connection to learning, memory, and cell survival?”

Old Figure 6F is now Figure 7F, while old Figure 5G is now Figure 6C. This point is discussed in the response to points 1 and 9 of this reviewer. In summary, genes upregulated in our amyloid toxicity model included 25 B2-SRGs (new Suppl. Table 11). When testing for enriched terms in these 25 genes, biological processes related with apoptosis, such as regulation of apoptotic process and programmed cell death were at the top of the list (new Suppl. Table 12) and included, among others, genes such as FosB and Mitf that have been connected with Alzheimer’s disease. Out of the 25 genes that are up-regulated in both mice and our cell culture system, six are B2-SRGs (4932438A13Rik, Fosb, Pag1, Ptprs, Sema5a, and Sgms1) and include a well-known immediate early gene (Fosb), genes associated with sensitivity to amyloid toxicity (Pag1, Sema5a, Sgms1, Fosb), as well as genes associated with p53 (Ptprs, Fosb). All these genes get upregulated in amyloid toxicity (42-Ctrl vs R-Ctrl) but are not upregulated when Hsf1 LNA is applied (42-anti-Hsf1 vs R-anti-Hsf1, no significant difference). This information is now included in the text.

**Minor.**

1 . Please include TPM/ FPKM values for hippocampal markers as control in Figure 2 to do justice to the hippocampus specific RNA seq conducted by the Authors.

To our understanding, the reviewer here suggests the testing of well-known hippocampal markers in our mouse data as controls to confirm that they are indeed hippocampus specific. We have selected as reference markers, the genes employed by the Allen Brain Atlas RNA-sequencing project and we provide a comparison of their data in hippocampal cells with our data from mouse hippocampus. This is now presented as new Supplementary Figure 2.

2 . In figure 2D the authors show that B2 RNA regulated SRGs in the 3 months' wild type mice are significantly high. P53 has been reported to be high in young wild types hippocampus, but not SRGs in my opinion. The authors should comment on this.

Old Figure 2D is now Figure 2E. We now mention the reviewer’s comment particularly in the discussion and cite a landmark review article in Neuron journal by Michael Greenberg regarding the role of stress response genes, such as FosB, early during development. As to prevent any confusion, we have also replaced SRGs with B2-SRGs since we tested only B2-SRGS in our study.

3 . In figure 2F, under the 6m APP condition, the replicate 3 looks substantially different from the other replicate. This can significantly impact the analysis and conclusions made. Either remove that replicate and present the analysis without it or please provide a valid explanation. To make the data more valid, please provide hierarchical clustering of the entire data, the non-B2 regulated genes and the B2 regulated SRGs.

We now provide in the new Supplementary Figure 9C a PCA plot, which includes 6m APP mice vs. their WT counterparts and HT22 cells, and shows that this variability is within the biological replicate variability we can expect in these models. To substantiate this further, we have constructed the correlation matrix of the RNA-seq data of both WT and APP 6 month old mice in the new Supplementary Figure 9D. As shown in this matrix, all APP mice clearly correlate with each other and not with their WT counterparts.

In the initial manuscript the heatmaps of former Figure 2 were indeed provided with hierarchical clustering of the entire data and also included non-B2 RNA regulated genes. This data is included now as Supplementary figure 2.

In Figure 2C RNA seq data is represented in TPM while its FPKM in Figure 2D.

Figure 2D is now Figure 2E, while Figure 2C remains labelled with the same number. Given that TPM already includes scaling of the data, it is unsuitable for the averaging of the gene expression levels of multiple genes (B2-SRGs) used in the boxplots of Figure 2. This does not apply in the case of single genes as in Fig 2C (p53) or in the heatmap where each gene is presented in a separate row. This explanation is now included in the methods section.

Figure 2: the number of replicates in the case of 3-month-old wild types only 2. Please specifically denote it and comment why only 2 replicates are provided.

During the hippocampal RNA extractions, the RNA of one of the three 3m old mice had very low RIN scores, which could be a confounding factor for the short-RNA-seq. As this happened some months after the hippocampal extractions, we did not have any other 3 month mice of the same cohort used for the behavioral and IHC studies. Thus, we decided to include only two replicates in this condition. Since the results presented in the current study focus mainly on 6 month old mice, we expect the impact to be minimal. We include this note in the methods section.

4 . Considering that p53 and SRGs are significantly upregulated in 6months in the APP model, it would be great if (allowing that these samples are still available) the authors can include a staining for apoptotic markers, for example, Active Casp3 or similar. This will allow us to better gauge the gene expression changes presented by the authors especially regarding SRGs.

Unfortunately, we do not have these slides but in the revised version we will provide qPCR data for some of these markers.

5 . Under subheading: Hsf1 accelerates B2 RNA processing, 3rd paragraph when the authors comment on known hsf1 binding sites on SRG genes, please correct from: Increased Hsf1-binding was found.... "To the increased number of hsf1 binding sites were found", unless the authors would like to show increased Hsf1 binding by performing CHIP-seq for Hsf1 in the hippocampus at least at the 6-month time point between Wt and APP mice.

We have changed the text accordingly.

Reviewer #1 (Significance (Required)):

B2 RNAs, encoded from SINE B2 elements has been directly implicated in stress response by its inherent ability to bind RNA Pol II and suppress stress response genes (SRG) in homeostatic conditions. However, upon stimuli, B2 RNAs are cleaved and degraded, resulting in the release of RNA pol II and upregulation of SRGs. Previous work from the senior author identified PRC2 component EZH2 to be the B2 RNA processing factor, cleaving B2, and releasing POL2. SRGs are upregulated upon stress, for example in age-associated neuropathologies like Alzheimer's disease (AD). Considering that the hippocampus is a primary target of amyloid pathologies as well as since SRGs are suggested to be key for the function of a healthy hippocampus, the authors set to understand the role of B2 RNAs that are linked to SRG regulation in the mouse hippocampus with amyloid pathology. They use disease-relevant in vivo and in vitro models combined with unbiased RNA seq data analysis for this endeavor, which indicates the potential relevance of B2 RNAs in APP mediated neuronal pathologies in mice as well as identifies Hsf1 as the factor cleaving B2 RNAs in the hippocampus.

The work is interesting and identification of Hsf1 as the processing factor for B2 RNAs in the hippocampus is significant. I would like to credit the authors for their elegant in vivo experimental design in Figure 2.

REVIEWER 2

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

**Summary:**

This manuscript follows from previous work by the corresponding author showing that SINE-encoded B2 RNAs function as regulators of the expression of stress response genes (SRGs). Specifically, stimulus triggers the processing of repressive B2 RNAs that are bound at the SRGs, thereby activating SRG transcription. In this work, the authors investigate whether a similar mechanism might be controlling the expression of genes in models of amyloid beta neuropathology (i.e. mouse hippocampi from an amyloid precursor protein knock-in mouse model, and a cell culture model of amyloid beta toxicity). They performed RNA-seq in these models. Their data show a correlation between the progression of amyloid pathology, expression of genes thought to be regulated by B2 RNA, and the processing of B2 RNA. In addition, they show biochemical data supporting a role for Hsf1 in enhancing the processing of B2 RNA. Knockdown of Hsf1 also reduced B2 RNA processing and the expression of SRGs.

**Major comments:**

Major point 1. The reviewer asks: “In the RNA-seq data one cannot distinguish between Pol III transcribed B2 RNA and Pol II transcribed B2 RNA (typically embedded within introns and UTRs of mRNAs). The models they present, and the structures they show, clearly imply regulation by Pol III transcribed B2 RNA. However, there is no way to know that the short B2 RNAs they sequence aren't coming from degraded mRNAs. This needs to addressed. Minimally, in writing as a caveat of their model. Ideally, it would be addressed experimentally.”

That’s a very interesting point, as it implies that the regulatory role of B2 RNAs may extend from PolIII transcribed B2 RNAs into B2 RNAs embedded into mRNAs (likely nascent ones) that may be also under the same endogenous ribozyme activity of this sequence, suppress PolII and are processed in response to stimuli. The RNA RIN values of our samples were pretty high except one 3m old mouse sample which was for this reason excluded from further analysis. Moreover, during the library construction shorter and longer RNAs have been separated. Thus, any generation of B2 RNA fragment that may have originated from mRNA should be biologically but not technically related and must have happened in the cell before our RNA extraction. To address this point, we now provide a new supplementary figure (Suppl. Figure 8), where we have separated the B2 elements against which we map the RNA fragments into two categories, those that fall within exonic/genic regions and those outside of these regions. Although B2 RNAs are produced by multiple copies in the genome, each copy does harbor multiple SNPs, insertions and deletions, which means that each B2 RNA fragment is mapped to a specific set of B2 elements and not to all of them. In other words, despite multiple mapping a level of spatial specificity is maintained. If the B2 RNAs we map were coming exclusively from either only Pol III B2 elements or mRNA embedded B2 elements, we would expect at least some difference in the distribution of fragments between B2 elements of these two categories, as the second one overlaps with mRNAs. As shown in the new supplementary figure 8, the fact that distribution models are very similar between the two categories indeed supports the hypothesis that both types of B2 elements may contribute to B2 RNA processing. Most importantly, the profile of B2 RNAs in genic regions shows that B2 RNA processing is not random but follows the same processing rules as B2 RNAs from Pol III promoters. Given the limitations posed by the repetitive nature of B2 RNAs, it remains difficult though to provide an exact number regarding the portion of B2 RNA fragments produced by each category and this is clearly noted in our revised discussion part. However, even the indication that B2 RNAs embedded in mRNAs may also play an important role in our model provides a new perspective that should be investigated further in future studies.

Major point 2. The reviewer asks: “The direct regulation of SRGs by B2 RNA was not shown in their model systems for amyloid beta neuropathology. Rather, the authors' used the genes identified in their prior studies as B2 RNA-regulated, which I believe were in the NIH3T3 cell line. Given that transcription is highly cell-type specific, these genes might not be regulated by B2 RNA in mouse hippocampi or their cell culture model, despite the correlations shown. This needs to be addressed. Ideally, a targeted approach to show that transcription of even a couple genes in their system is indeed regulated by B2 RNA would provide stronger support for their conclusions.”

We agree with the reviewer and we now provide a new figure (Fig.6D-F) with the targeted approach that this reviewer proposed. In particular, we have tested whether fragmentation of full length B2 RNAs is in connection with activation of target genes also in our biological system (HT22 cells) as it did in NIH/3T3 cells in our Cell paper. We now show in new Figure 6 that this is indeed the case.

Major point 3. The reviewer proposes a number of additional information that needs to be provided: “The following bioinformatics analyses would strengthen their conclusions. This should be straightforward to do because it involves data they already have, and perhaps analyses they have already have performed.”

a. Regarding the plot in Figure 3A (lower panel). The same plot should be shown for the 3m old and the 12m old APP mice (i.e. not just the 6m data). This would show the specificity of processing B2 RNA and that it indeed correlates with disease progression.

We now provide this plot as new supplementary figure (Suppl. Figure 3). It shows that increased B2 RNA processing coincides only with the active neurodegeneration phase at 6 months and not the terminal stage.

b. Regarding the plots of B2 RNA processing rate. This value could increase either due to more short RNAs or less full length RNA. Which is it for the 3m, 6m, and 12m APP mice? Showing the short and long B2 RNAs as boxplots (as opposed to only the processing rate) would address this and also provide additional insight into the regulation involved. The same applies to the data in Figure 6. (As an aside... do the authors mean processing ratio as opposed to rate? I'm not clear where the time component is coming into play to call this a rate.)

Old Figure 6 is now Figure 7. We now provide all these figures that show that increase in processing ratio at 6 months is mainly due to increase in the processed fragments and not a decrease in full length B2 RNAs. For APP mice these are new Figures 3E and F, and for HT22 cells , these are new Supp. Figures 6B and C.

c. The random genes in Figures 2E and 6E are plotted as heat maps, but statistical significance is hard to see. What do boxplots of the random genes look like, and is the significant difference between 6m old APP and 6m old WT then lost?

Old Figure 2E is now new Suppl. Figure 1C, while old Figure 6E is now new Suppl. Figure 7C. We now provide these boxplots in new supplementary figures 1B and 7B.

Major point 4. The reviewer comments: “ It is interesting that B2 RNA self-processing is enhanced by both Ezh2 and also Hsf1. It would strengthen the data to perform a control with a protein prepared more similarly to the Hsf1 (rather than PNK) to confirm that the enhanced B2 RNA breakdown is indeed attributable to Hsf1 and not a contaminant in the protein prep. Similarly, the authors should provide information on which RNA was added as the negative control for Hsf1-stimulated breakdown (i.e. the ~80 nt RNA).”

This point is also discussed in Reviewer 1 point 7. The ribozyme endogenous activity of B2 RNA has been shown already in two previous studies that performed incubations with control RNAs and proteins. We are currently preparing and will provide these additional incubations as anew supplementary figure in the revised manuscript.

**Minor comments:**

1 . Regarding the GO analyses in Figure 1 (panels B, C, and D). I wasn't clear whether the authors are showing all statistically enriched terms, or only those relevant to neuronal processes and learning. I recommend showing a supplemental table with all terms that have an adjusted p value below a specified cut-off (e.g. 0.05).

The statistical threshold used was an EASE score of 0.05 and all presented terms were above this threshold. In the initial manuscript we filtered only the top 5 terms in tissue enrichment and the top 10 terms for GO Biol process and Cell Compartment that had passed the threshold. We now provide all the terms that passed the threshold as a new Supplementary Table 2, including gene counts, exact gene numbers and related statistics.

2 . The authors show several figures that are not new data (2B, 4A, 4B, Suppl. Fig 1 and 2). I think it would be more clear if these data were summarized and referenced in the results, rather than shown.

Old Suppl. Fig1 and 2 that were results of previous studies or web resources directly available (such as Human Protein Atlas) have been now removed and they are now just referenced in the text. Old Figures 4A and 4B have been removed from the main figures but may be helpful to the readers if they are still available in the Supplement (currently as Suppl. Figure 4A and B), as not all users are familiar with the RNA-seq browsing tools of Allen Brain Atlas resources. Regarding figure 2B that contains data from our previous study on this exact cohort of mice: If the reviewer and the editor agree we recommend that it remains in the main figure (with the appropriate image credit citations), as it provides in an efficient way the clear connection between amyloid load and our results at the molecular level, and, most importantly, it clearly draws a line in amyloid pathology progression between 3m old and 6m old, that agrees with our findings in the RNA-seq data of these mice.

3 . In Figure 3A the schematic shows that B2 is 155 nt, the plots in Figures 3A,B,C show B2 RNA is 120 nt, and Figure 5 shows the RNA is 188 nt. Can the authors please clarify these differences?

The full length of B2 consensus sequence is 188nt and this is the one we use for the in vitro experiments. However, the structure of the B2 RNA has been resolved only for the first 155nt by the Kugel lab, and this is the only publicly available structure that we can reference in our figures. For the mapping of 5’ends of short fragments in Fig.3A we have used the same range tested in our Cell paper to maintain consistency of the results. The reason why this 120nt threshold was selected in the Cell paper was to exclude artifacts from short RNAs mapping partially in our metagene as well as downstream of those B2 elements that are shorter from the consensus sequence. We now explain in methods section these differences.

4 . In the Methods section, the sequence of the g block template didn't contain the T7 promoter sequence that was used as the forward primer for PCR amplification?

We have now included this sequence in lower case.

5 . In Figure 6B, why were Hsf1 levels not decreased in the R treated cells after treatment with the LNA?

Old Figure 6B is now new Figure 7B. Please see response to Reviewer 1, major point 12.

Reviewer #2 (Significance (Required)):

Finally, this reviewer generally remarks that “The models presented for the regulation of stress response genes (SRGs) in amyloid beta neuropathologies are compelling. As are the correlations they found between the progression of amyloid pathology, expression of genes thought to be regulated by B2 RNA, and the processing of B2 RNA. This is a unique direction of research for brain disease and represents an interesting conceptual advance. Most prior studies in this area use common model cell lines, and this lab seems well-positioned to unravel the proposed molecular mechanisms in neuronal systems.”

We appreciate the encouraging comments made by this reviewer.

REVIEWER 3

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

This manuscript describes a regulatory mechanism involving Hsf1 and B2 RNAs in the control of stress response genes (SRGs) during amyloid induced toxicity. In particular Hsf1, upregulated in 6m old APP mice and in HT22 cells treated with beta amyloid peptides, is shown to stimulate the B2 RNA destabilization leading to SRGs activation. While in healthy cells this upregulation can be reverted once the stimulus is removed, the pathological condition fuels the circuitry leading to p53 upregulation and neuronal cell death. The authors previously described the same mechanism acting during cellular heath shock response but in this case the protein identified as trigger of B2 RNA destabilization and SRGs activation was EZH2 (Zovoilis et al, 2016).

This reviewer generally remarks that “Indeed, the first part of the manuscript describes additional analyses of the previous data that prompts further investigation on the potential role of B2 RNA in AD condition. Nevertheless, it is not clear how the prior findings obtained in not biologically related cellular models might be used to obtain helpful indication of B2 RNA neuronal activity.”

We thank the reviewer for this comment. Indeed, the current study’s main aim was to expand the findings of our previous work on the role of B2 RNA in cellular response to thermal stress in NIH/3T3 cells to other types of cellular response to stress, in our case to amyloid toxicity and the resulting amyloid pathology in neural cells. Response to thermal stress (Heat Shock) has been used for years as a basic study model for cellular response to stress. Proteins and gene pathways initially identified in heat shock have been subsequently shown to play identical pro-survival roles in other biological systems and there are studies showing the role of Hsf1, heat shock related proteins and cell stress response pathways in neural cells and the mammalian brain (we will provide these references in the revised version). For example, pathways such as the MAPK pathway and early response genes, that constitute the basis of response to heat shock, have been shown in studies by us and others to be activated and play a critical role in hippocampal function. Thus, examining the role of B2 RNA in the context of neural response to stress constituted a natural continuation of our previous study in NIH/3T3 cells. The fact that the list of B2 RNA regulated SRGs was found to be highly enriched in neuronal tissue terms and cellular compartments related to neuronal functions plainly confirms the close relationship among cellular response pathways in the two biological systems. Due to these facts we were compelled to investigate in more detail our previous findings also in a neural cell model. However, as discussed in point 2 of Reviewer 2, the initial manuscript did not confirm the direct control of B2 RNA on expression of target genes also in our cellular model. This information is now part of the new figure 6 and we thank both reviewers for bringing this to our attention.

The reviewer also remarks that “The research fields of non coding RNAs and neurodegeneration are attractive and challenging and, in my opinion, the molecular circuitry involving B2 RNAs might add important insights for understanding beta amyloid toxicity and neuronal death; however, the data provided are not in the shape making the manuscript suitable for publication: some controls are missing, the way the experiments are presented is not easy to follow and more importantly the authors does not provide any data (tables or lists) of the NGS experiments and the study lacks validation of them. Therefore, in my opinion the manuscript needs a profound revision before to be considered for publication in Review Commons.”

Based on this reviewer’s and the other reviewers’ suggestions we now provide additional controls, detailed tables and gene lists, and qPCR validation of these results. We have also substantially revised the text in the first section of the results and beginning of the discussion, to make our rational for testing B2-SRGs more clear and easier to follow.

**major concerns:**

Major point 1. The reviewer asks: “The first paragraph of the Results is entirely dedicated to re-analyze the data previously published by the same group (Zovoilis et al., 2016). However, this is not adequately explained. In line with this, the table 1 is not required since the data are already provided by Zovoilis et al., 2016, unless the authors handled the data using additional new criteria that have to be explained.”

We now explain our rational for using this data in more detail in the text. Please see also response to the general comment of this reviewer and response to the next point.

In the Zovoilis et al (2016) study, the data presented did not include the list of regulated genes in a direct way but as part of the annotation of the B2 CHART peaks. This may pose difficulty to non-experts to extract the gene list from that data and we thought to include them as separate gene list here so that readers can directly use it for their analysis. Nevertheless, if the reviewer or the editor think that the list is redundant, we can surely omit it.

In addition, the reviewer comments: “Moreover, Zovoilis and colleagues (2016) focused on SRGs regulated upon heat shock and using NIH/3T3 and HeLa cell lines, therefore, it is difficult to me understand how, searching for "cellular function connected with B2 RNA regulated SRGs", the list resulted enriched of neuronal tissue terms or cellular compartments related to neuronal functions. Please clarify this point since the following analyses are based on these findings.”

Neural pathologies, such as amyloid pathology in brain, are often connected with cellular stress due to proteotoxicity. The ability of neural cells to respond to proteotoxicity challenges is connected with various molecular mechanisms, including stress related proteins that were firstly described in the context of heat shock. Thus, both contexts (heat shock and amyloid toxicity) refer to cellular response to stress, which explains why genes identified to be regulated during stress response in NIH/3T3 cells constitute part of the basic stress response toolbox that neural cells have also been described to possess. We have now modified the text accordingly to make our rational more clear.

Major point 2. The reviewer comments: “In Figure 1F there is no arrow indicating that some of the SRGs regulate directly miR-34 as stated in the main text. Moreover, it is more appropriate to replace SRGs with learning‐associated genes both in the figure and in text (2nd paragraph of the results) since Zovoilis and colleagues focused on them. Finally, they did not show in their manuscript the rescue of p53 expression mediated by mir-34; indeed, for miR-34-p53 regulatory axis Zovoilis and colleagues referred to Peleg et al, 2010 and Yamakuchi & Lowenstein, 2009. Please fix all these concerns.”

We have restructured the figure as suggested by the reviewer and made clear the distinction between learning genes and B2 RNA regulated SRGs (B2-SRGs) from the two different studies. In connection with point 1 of Reviewer 1, we believe that new Figure 1E, that includes the exact number of B2-SRGs that are learning associated, will represent more efficiently and accurately the data. We have also corrected in the text the citation regarding miR-34c and p53 in both the introduction and first section of the results (last paragraph).

-The Fig.1A and Fig.1F are wrongly indicated at the end of the sentence "....levels of these genes are normally downregulated in 6m and 12m old mice compared to 3m old mice (p=0.02 and p=0.04, respectively)"; please correct this point.

The error has been corrected.

Major point 3. The reviewer comments regarding Figure 2:

a) Since three mice for each condition have been used for the RNA seq analyses, please provide a blot with the Principal Component Analysis (PCA).

Please see also response to minor point 3 of Reviewer 1. We provide the PCA plots for WT and APP mice in the new Supplementary Figure 9 and we also provide a comparison of the six month old mice with the HT cell samples as well as a correlation matrix for 6 month old mice in the same figure.

b) Fig 2F comes first of Fig 2E in the text, however, I suggest to move this latter to supplementary material.

Old figure 2E has now been moved to supplementary material as new Supplementary Figure 2C and we also provide in a boxplot the exact gene expression levels as new Supplementary Figure 2B.

c) In general, this study lacks validation of the RNA-seq results. Western blot and/or qRTR-PCR to verify the variation of p53 and of some selected SRGs have to be provided.

In the current revised version we already provide qPCRs for p53 and Hsf1 in APP mice and we will include additional genes in the final version.

d) It is also not clear how the authors defined SRGs in the hippocampus: do they correspond to learning‐associated genes described by in Zovoilis et al, 2011 or to B2 RNA H/S regulated genes by Zovoilis et al, 2016?

The way we presented B2 RNA SRGs in the results with regard to learning associated genes was indeed unclear. We now present the distinction between the two gene categories and their relationship as a new Fig.1E panel and we also provide detailed gene lists of common genes and the exact numbers (please see also response to Review 1, major point 1).

-APP 12 month old mice show the sever phenotype of the terminal AD-like pathology, however this does not correlate with significant SRGs and B2 processing increase. Can the author make a comment on this?

That’s a very important point and we thank the reviewer for raising this point. We now comment on this in the discussion part explaining how our findings are characteristic of the initial active neurodegeneration phase of amyloid pathology rather than more terminal stages.

Major point 4: The reviewer comments regarding Figure 5:

a) a gel with no-protein control for the time course of panel B was cited in the text but missing among the panels. Moreover, the time course shown in the graph in 5C does not correspond to the one in 5B.

Indeed, the no-protein control time line should refer only to panel C and not to B, we have now corrected the text. Nevertheless, we now present in the new Supplementary Fig. 5 the gels, based on which the graph in panel C was calculated, including also the gel with no protein timeline. The time course shown in the initial 5C had been mislabeled. It has now been corrected. We apologize for this and we thank the reviewer for bringing this to our attention.

b) 5G indicates that four samples for each condition have been analysed by RNA-seq, since they do not seem to be homogeneous please provide a PCA analysis together with the validation by qRT-PCR of a selected group of deregulated genes.

Old Figure 5G is new Figure 6C. PCA analysis for these samples is now provided in Supplementary Figure 9 and qPCR validation of a number of these genes is provided in new Fig. 7E.

Moreover, it is not clear whether all the genes shown in the heatmap or a number of them, as stated in the text, were found upregulated in 6m old APP mice. Please clarify this point and modify the figure and the text accordingly. A Venn diagram showing the overlap between genes upregulated in 42vsR treatment and those upregulated in 6m old APP mice might help the comprehension of the experiment.

Please see response to Reviewer 1, point 9. We now provide as new supplementary tables the exact overlapping lists and mention these numbers in the text.

Major point 5: The reviewer comments regarding Figure 6 (now labeled as Fig.7):

a) The evaluation of the levels of Hsf1 mRNA and protein upon LNA transfection is missing for both R and 42 treated HT22 cells. From TPM in panel B, Hsf1 downregulation seems to have been more effective in 42 than in R condition. This would mess up the interpretation of the data.

We now provide qPCR data for Hsf1 gene expression levels which confirm the ones from the RNAseq. The reason why Hsf1 downregulation seems not to affect the R condition is discussed in our response to Reviewer 1, major point 12, and the respective explanation is provided in the revised text.

b) Again, in this case any validation of the RNA seq data is provided (any B2 regulated SRGs).

Now, we provide qPCR data for these genes in Fig.7B and new Fig.7E

c) Panels E and F should be swapped or panel E moved to supplementary material.

Panel E is now moved to supplementary material as new Suppl. Figure 7C.

Major point 6. The reviewer comments: “In a previous paper the authors discovered B2 RNAs as a class of transcripts bound to EZH2 and this interaction leads to B2 RNA destabilization in heath shock (H/S) condition. The authors also conclude that the genes controlled by B2 RNAs may not overlap with the ones controlled by Hsf1 during H/S. The author should make a comment on this explaining why during H/S B2 RNAs work independently from Hsf1 and on different target SRGs while, during beta amyloid stress ,the two act together on the same SRGs. Moreover, as shown for EZH2, Hsf1-RIP experiment should be performed in order to confirm the direct involvement of Hsf1 in the SRGs-B2 destabilization.”

In the last two paragraphs of our discussion we indicate that B2 RNA regulation is a new process implicated in the response to stress in amyloid pathology but certainly not the only one. We have revised the text in this part accordingly in the revised version to prevent any confusion. We are currently performing a series of RIP-seq experiments with various antibodies. As, to our knowledge, there is no prior published study performing RIP-seq or CLIP-seq for any tissue using Hsf1 antibodies, the success of this experiment is not guaranteed and depends on the existence of appropriate antibodies.

Major point 7. The reviewer comments: “There is any table listing the results of the RNA seq experiments performed in this paper: control vs APP 3-6-12 m old mice and in R vs 42 treated HT22 cells in presence or absence of LNA against Hsf1. Please provide these data.”

We now provide these lists as new supplementary tables. Please see response to major points 1 and 9 of reviewer 1.

Major point 8. The reviewer comments: “In the discussion the authors claim that healthy cells are able to restore the expression of Hsf1, SRGs and B2 RNA upon removal of the stress. Since there are evidence for the rescue of SRGs and B2 RNA expression post H/S, no data are available for Hsf1, SRGs and B2 RNA upon the removal of 1-42 beta amyloid peptide. This might be a nice information to add to the manuscript.”

This would indeed substantiate further our results in our HT22 cell model. We have now performed this experiment, in which HT-22 cells were removed from the amyloid 42 (and the respective R peptide control) and left to recover for 12 hours before estimating through RT-qPCR the Hsf1 levels ( see graph below, REC corresponds to recovered HT-22 cells). Hsf1 levels in 42-REC have returned to the same levels as in R, p We currently perform the RT-qPCRs of these samples also for B2-SRGs and will include them in the final version as a supplementary figure.

**Minor criticisms:**

-In the introduction the reference Yamakuchi M and Lowenstein CJ, (2009) MiR‐34, SIRT1 and p53: the feedback loop. Cell Cycle, should be added in the sentence: "In contrast, hippocampi of mouse models of amyloid pathology and post- mortem brains of human patients of AD.....and neural death (Zovoilis et al., 2011)."

We have now changed the text at that point accordingly and also updated the legend of Figure 1F that also refers to this same study.

-Authors refer to Hernandez et al., 2020 to state that B2 self cleavage is stimulated by some proteins however, Hernandez and colleagues studied only the effect of EZH2 protein. Please rephrase the sentence accordingly.

Text has been modified accordingly.

-Indicate a reference for the sentence: "......Ezh2, was reported as being responsible for the B2 RNA accelerated destabilization and processing during response to stress."

The respective citation was added.

-The format of many references is not consistent and has to be revised.

We have switched to the Vancouver style. Some references in the legend and methods sections are referred independently from EndNote in case these text sections have to be moved to supplement in the final version in order to not create inconsistencies with endnote.

Reviewer #3 (Significance (Required)):

Finally, this reviewer generally remarks that “The research fields of non coding RNAs and neurodegeneration are attractive and challenging and, in my opinion, the molecular circuitry involving B2 RNAs might add important insights for understanding beta amyloid toxicity and neuronal death.

However, this manuscript does not really add technical advances since the authors employed experimental approaches and bioinformatic analyses previously published by Zovoilis and colleagues in 2011 and 2016.”

Our aim in the current manuscript was not to introduce a new method or experimental approach but rather to study the mechanisms behind B2 RNA regulation of gene expression in neural cells and particularly in amyloid pathology. Nevertheless, the current study constitutes the first reported short-RNA seq in this tissue and offers for the first time the ability to study B2 RNA processing in this tissue which is not possible with standard small and long RNA-seq.

The reported findings might of interest of an audience of experts in non coding RNAs and neurodegeneration. The area of my expertise almost regards the biology of non coding RNAs from biogenesis to function manly focusing on neuronal and muscular systems both in physiological and pathological conditions.

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Referee #3

Evidence, reproducibility and clarity

This manuscript describes a regulatory mechanism involving Hsf1 and B2 RNAs in the control of stress response genes (SRGs) during amyloid induced toxicity. In particular Hsf1, upregulated in 6m old APP mice and in HT22 cells treated with beta amyloid peptides, is shown to stimulate the B2 RNA destabilization leading to SRGs activation. While in healthy cells this upregulation can be reverted once the stimulus is removed, the pathological condition fuels the circuitry leading to p53 upregulation and neuronal cell death. The authors previously described the same mechanism acting during cellular heath shock response but in this case the protein identified as trigger of B2 RNA destabilization and SRGs activation was EZH2 (Zovoilis et al, 2016). Indeed, the first part of the manuscript describes additional analyses of the previous data that prompts further investigation on the potential role of B2 RNA in AD condition. Nevertheless, it is not clear how the prior findings obtained in not biologically related cellular models might be used to obtain helpful indication of B2 RNA neuronal activity. The research fields of non coding RNAs and neurodegeneration are attractive and challenging and, in my opinion, the molecular circuitry involving B2 RNAs might add important insights for understanding beta amyloid toxicity and neuronal death; however, the data provided are not in the shape making the manuscript suitable for publication: some controls are missing, the way the experiments are presented is not easy to follow and more importantly the authors does not provide any data (tables or lists) of the NGS experiments and the study lacks validation of them. Therefore, in my opinion the manuscript needs a profound revision before to be considered for publication in Review Commons.

major concerns:

-The first paragraph of the Results is entirely dedicated to re-analyze the data previously published by the same group (Zovoilis et al., 2016). However, this is not adequately explained. In line with this, the table 1 is not required since the data are already provided by Zovoilis et al., 2016, unless the authors handled the data using additional new criteria that have to be explained. Moreover, Zovoilis and colleagues (2016) focused on SRGs regulated upon heat shock and using NIH/3T3 and HeLa cell lines, therefore, it is difficult to me understand how, searching for "cellular function connected with B2 RNA regulated SRGs", the list resulted enriched of neuronal tissue terms or cellular compartments related to neuronal functions. Please clarify this point since the following analyses are based on these findings.

-In Figure 1F there is no arrow indicating that some of the SRGs regulate directly miR-34 as stated in the main text. Moreover, it is more appropriate to replace SRGs with learning‐associated genes both in the figure and in text (2nd paragraph of the results) since Zovoilis and colleagues focused on them. Finally, they did not show in their manuscript the rescue of p53 expression mediated by mir-34; indeed, for miR-34-p53 regulatory axis Zovoilis and colleagues referred to Peleg et al, 2010 and Yamakuchi & Lowenstein, 2009. Please fix all these concerns.

-The Fig.1A and Fig.1F are wrongly indicated at the end of the sentence "....levels of these genes are normally downregulated in 6m and 12m old mice compared to 3m old mice (p=0.02 and p=0.04, respectively)"; please correct this point.

-Figure 2:

a) Since three mice for each condition have been used for the RNA seq analyses, please provide a blot with the Principal Component Analysis (PCA).

b) Fig 2F comes first of Fig 2E in the text, however, I suggest to move this latter to supplementary material.

c) In general, this study lacks validation of the RNA-seq results. Western blot and/or qRTR-PCR to verify the variation of p53 and of some selected SRGs have to be provided.

d) It is also not clear how the authors defined SRGs in the hippocampus: do they correspond to learning‐associated genes described by in Zovoilis et al, 2011 or to B2 RNA H/S regulated genes by Zovoilis et al, 2016?

-APP 12 month old mice show the sever phenotype of the terminal AD-like pathology, however this does not correlate with significant SRGs and B2 processing increase. Can the author make a comment on this?

-Figure 5:

a) a gel with no-protein control for the time course of panel B was cited in the text but missing among the panels. Moreover, the time course shown in the graph in 5C does not correspond to the one in 5B.

b) 5G indicates that four samples for each condition have been analysed by RNA-seq, since they do not seem to be homogeneous please provide a PCA analysis together with the validation by qRT-PCR of a selected group of deregulated genes. Moreover, it is not clear whether all the genes shown in the heatmap or a number of them, as stated in the text, were found upregulated in 6m old APP mice. Please clarify this point and modify the figure and the text accordingly. A Venn diagram showing the overlap between genes upregulated in 42vsR treatment and those upregulated in 6m old APP mice might help the comprehension of the experiment.

-Figure 6:

a) The evaluation of the levels of Hsf1 mRNA and protein upon LNA transfection is missing for both R and 42 treated HT22 cells. From TPM in panel B, Hsf1 downregulation seems to have been more effective in 42 than in R condition. This would mess up the interpretation of the data.

b) Again, in this case any validation of the RNA seq data is provided (any B2 regulated SRGs).

c) Panels E and F should be swapped or panel E moved to supplementary material.

-In a previous paper the authors discovered B2 RNAs as a class of transcripts bound to EZH2 and this interaction leads to B2 RNA destabilization in heath shock (H/S) condition. The authors also conclude that the genes controlled by B2 RNAs may not overlap with the ones controlled by Hsf1 during H/S. The author should make a comment on this explaining why during H/S B2 RNAs work independently from Hsf1 and on different target SRGs while, during beta amyloid stress ,the two act together on the same SRGs. Moreover, as shown for EZH2, Hsf1-RIP experiment should be performed in order to confirm the direct involvement of Hsf1 in the SRGs-B2 destabilization.

-There is any table listing the results of the RNA seq experiments performed in this paper: control vs APP 3-6-12 m old mice and in R vs 42 treated HT22 cells in presence or absence of LNA against Hsf1. Please provide these data.

-In the discussion the authors claim that healthy cells are able to restore the expression of Hsf1, SRGs and B2 RNA upon removal of the stress. Since there are evidence for the rescue of SRGs and B2 RNA expression post H/S, no data are available for Hsf1, SRGs and B2 RNA upon the removal of 1-42 beta amyloid peptide. This might be a nice information to add to the manuscript.

Minor criticisms:

-In the introduction the reference Yamakuchi M and Lowenstein CJ, (2009) MiR‐34, SIRT1 and p53: the feedback loop. Cell Cycle, should be added in the sentence: "In contrast, hippocampi of mouse models of amyloid pathology and post- mortem brains of human patients of AD.....and neural death (Zovoilis et al., 2011)."

-Authors refer to Hernandez et al., 2020 to state that B2 self cleavage is stimulated by some proteins however, Hernandez and colleagues studied only the effect of EZH2 protein. Please rephrase the sentence accordingly.

-Indicate a reference for the sentence: "......Ezh2, was reported as being responsible for the B2 RNA accelerated destabilization and processing during response to stress."

-The format of many references is not consistent and has to be revised.

Significance

The research fields of non coding RNAs and neurodegeneration are attractive and challenging and, in my opinion, the molecular circuitry involving B2 RNAs might add important insights for understanding beta amyloid toxicity and neuronal death. However, this manuscript does not really add technical advances since the authors employed experimental approaches and bioinformatic analyses previously published by Zovoilis and colleagues in 2011 and 2016.

The reported findings might of interest of an audience of experts in non coding RNAs and neurodegeneration.

The area of my expertise almost regards the biology of non coding RNAs from biogenesis to function manly focusing on neuronal and muscular systems both in physiological and pathological conditions.

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

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Referee #2

Evidence, reproducibility and clarity

Summary:

This manuscript follows from previous work by the corresponding author showing that SINE-encoded B2 RNAs function as regulators of the expression of stress response genes (SRGs). Specifically, stimulus triggers the processing of repressive B2 RNAs that are bound at the SRGs, thereby activating SRG transcription. In this work, the authors investigate whether a similar mechanism might be controlling the expression of genes in models of amyloid beta neuropathology (i.e. mouse hippocampi from an amyloid precursor protein knock-in mouse model, and a cell culture model of amyloid beta toxicity). They performed RNA-seq in these models. Their data show a correlation between the progression of amyloid pathology, expression of genes thought to be regulated by B2 RNA, and the processing of B2 RNA. In addition, they show biochemical data supporting a role for Hsf1 in enhancing the processing of B2 RNA. Knockdown of Hsf1 also reduced B2 RNA processing and the expression of SRGs.

Major comments:

1 . In the RNA-seq data one cannot distinguish between Pol III transcribed B2 RNA and Pol II transcribed B2 RNA (typically embedded within introns and UTRs of mRNAs). The models they present, and the structures they show, clearly imply regulation by Pol III transcribed B2 RNA. However, there is no way to know that the short B2 RNAs they sequence aren't coming from degraded mRNAs. This needs to addressed. Minimally, in writing as a caveat of their model. Ideally, it would be addressed experimentally.

2 . The direct regulation of SRGs by B2 RNA was not shown in their model systems for amyloid beta neuropathology. Rather, the authors' used the genes identified in their prior studies as B2 RNA-regulated, which I believe were in the NIH3T3 cell line. Given that transcription is highly cell-type specific, these genes might not be regulated by B2 RNA in mouse hippocampi or their cell culture model, despite the correlations shown. This needs to be addressed. Ideally, a targeted approach to show that transcription of even a couple genes in their system is indeed regulated by B2 RNA would provide stronger support for their conclusions.

3 . The following bioinformatics analyses would strengthen their conclusions. This should be straightforward to do because it involves data they already have, and perhaps analyses they have already have performed.

a. Regarding the plot in Figure 3A (lower panel). The same plot should be shown for the 3m old and the 12m old APP mice (i.e. not just the 6m data). This would show the specificity of processing B2 RNA and that it indeed correlates with disease progression.

b. Regarding the plots of B2 RNA processing rate. This value could increase either due to more short RNAs or less full length RNA. Which is it for the 3m, 6m, and 12m APP mice? Showing the short and long B2 RNAs as boxplots (as opposed to only the processing rate) would address this and also provide additional insight into the regulation involved. The same applies to the data in Figure 6. (As an aside... do the authors mean processing ratio as opposed to rate? I'm not clear where the time component is coming into play to call this a rate.)

c. The random genes in Figures 2E and 6E are plotted as heat maps, but statistical significance is hard to see. What do boxplots of the random genes look like, and is the significant difference between 6m old APP and 6m old WT then lost?

4 . It is interesting that B2 RNA self-processing is enhanced by both Ezh2 and also Hsf1. It would strengthen the data to perform a control with a protein prepared more similarly to the Hsf1 (rather than PNK) to confirm that the enhanced B2 RNA breakdown is indeed attributable to Hsf1 and not a contaminant in the protein prep. Similarly, the authors should provide information on which RNA was added as the negative control for Hsf1-stimulated breakdown (i.e. the ~80 nt RNA).

Minor comments:

1 . Regarding the GO analyses in Figure 1 (panels B, C, and D). I wasn't clear whether the authors are showing all statistically enriched terms, or only those relevant to neuronal processes and learning. I recommend showing a supplemental table with all terms that have an adjusted p value below a specified cut-off (e.g. 0.05).

2 . The authors show several figures that are not new data (2B, 4A, 4B, Suppl. Fig 1 and 2). I think it would be more clear if these data were summarized and referenced in the results, rather than shown.

3 . In Figure 3A the schematic shows that B2 is 155 nt, the plots in Figures 3A,B,C show B2 RNA is 120 nt, and Figure 5 shows the RNA is 188 nt. Can the authors please clarify these differences?

4 . In the Methods section, the sequence of the g block template didn't contain the T7 promoter sequence that was used as the forward primer for PCR amplification?

5 . In Figure 6B, why were Hsf1 levels not decreased in the R treated cells after treatment with the LNA?

Significance

The models presented for the regulation of stress response genes (SRGs) in amyloid beta neuropathologies are compelling. As are the correlations they found between the progression of amyloid pathology, expression of genes thought to be regulated by B2 RNA, and the processing of B2 RNA. This is a unique direction of research for brain disease and represents an interesting conceptual advance. Most prior studies in this area use common model cell lines, and this lab seems well-positioned to unravel the proposed molecular mechanisms in neuronal systems.

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Referee #1

Evidence, reproducibility and clarity

B2 RNAs, encoded from SINE B2 elements has been directly implicated in stress response by its inherent ability to bind RNA Pol II and suppress stress response genes (SRG) in homeostatic conditions. However, upon stimuli, B2 RNAs are cleaved and degraded, resulting in the release of RNA pol II and upregulation of SRGs. Previous work from the senior author identified PRC2 component EZH2 to be the B2 RNA processing factor, cleaving B2, and releasing POL2. SRGs are upregulated upon stress, for example in age-associated neuropathologies like Alzheimer's disease (AD). Considering that the hippocampus is a primary target of amyloid pathologies as well as since SRGs are suggested to be key for the function of a healthy hippocampus, the authors set to understand the role of B2 RNAs that are linked to SRG regulation in the mouse hippocampus with amyloid pathology. They use disease-relevant in vivo and in vitro models combined with unbiased RNA seq data analysis for this endeavor, which indicates the potential relevance of B2 RNAs in APP mediated neuronal pathologies in mice as well as identifies Hsf1 as the factor cleaving B2 RNAs in the hippocampus. The work is interesting and identification of Hsf1 as the processing factor for B2 RNAs in the hippocampus is significant. I would like to credit the authors for their elegant in vivo experimental design in Figure 2. However, I find some of the conclusions to be overstated and I would like to bring the following concerns I have to your attention:

Major comments:

1 . In figure 1, the authors indicate a strong connection between B2 RNA regulated SRGs and learning and memory. In figure 2, they identify the SRGs in the hippocampus, please provide a direct comparison of learning and memory associated SRGs and the SRGs they identify in figure 2 that are significantly upregulated in APP mice in 6 months.

2 . To better understand the data in the context of hippocampal function, please include functional annotation of SRGs they identified in Figure 2F as they do it in Figure 1 (desirably for each time point, at least for 6M). How many of the SRGs they identify in Figure 1 are part of Figure 2F? Please include functional annotation of significantly upregulated B2 regulated SRGs in Fig2 and compare them with that of Figure 1.

3 . In figure 3, the authors report that the B2 processing rates are high at the 6M time point at in hippocampi of the APP mice. Please include the levels of unprocessed and processed B2 RNAs in these samples along with this figure, without which it is difficult to gauge the significance of its correlation with SRGs in Figure 2.

4 . What is the % of B2 regulated SRGs that are hsf1 bound in Figure 4C? What is there dynamics in the wild type and APP hippocampi?

5 . What is the distribution of Hsf1 binding sites on (a) non-B2 regulated SRGs and (b) non-SRG genes in hippocampi?

6 . In Figure 4D, the 3months old Wt HSF1 levels are high, yet B2 processing (Figure 3E) is low. Please comment.

7 . While the authors show in vitro cleavage of B2 RNA by Hsf1, the experiment lacks controls to be conclusive. At least, please include a similar size protein as HSF1 with no-known RNA binding activity and a similar size protein with RNA binding activity as controls in 5A. Please justify the use of PNK as the control protein. Please include the use domain-based deletions of Hsf1 to map the region of HSF1 that is binding and potentially cleaving the B2 RNA. Please include an RNA of similar size and Antisense-B2 RNA to show the specificity of the Hsf1 based cleavage of B2 RNA. Without these controls, the conclusions in Figure 5 cannot be substantiated.

8 . The authors should show that the incubated APP peptides are taken up by the cells (experiments in Figure 5F and Figure 6).

9 . Please provide the list, functional annotation, and % of the SRGs upregulated upon incubation with APP in HT22 cells in comparison to 6month old APP mice. Comment on learning-related Genes.

10 . The authors should show the efficient downregulation of Hsf1 (protein) upon anti-Hsf1 LNA transfection.

11 . Please present the total B2 RNA levels for conditions in Figure 6C.

12 . Hsf1 levels are not significantly downregulated in Control cells which were inoculated with the reverse APP peptide. Please comment.

13 . Please compare and contrast the % of genes, the overlap, and the functional distinctions in 6F to that of 5G and Figure1. What are the genes that are common between Figure1, and that are specifically upregulated upon Anti-Hsf1 LNA transfection along with 1-42 APP. What is % of the occurrence of B2 binding sites in those genes? What are their functional annotations and what is their connection to learning, memory, and cell survival?

Minor.

1 . Please include TPM/ FPKM values for hippocampal markers as control in Figure 2 to do justice to the hippocampus specific RNA seq conducted by the Authors.

2 . In figure 2D the authors show that B2 RNA regulated SRGs in the 3 months' wild type mice are significantly high. P53 has been reported to be high in young wild types hippocampus, but not SRGs in my opinion. The authors should comment on this.

3 . In figure 2F, under the 6m APP condition, the replicate 3 looks substantially different from the other replicate. This can significantly impact the analysis and conclusions made. Either remove that replicate and present the analysis without it or please provide a valid explanation. To make the data more valid, please provide hierarchical clustering of the entire data, the non-B2 regulated genes and the B2 regulated SRGs. In Figure 2C RNA seq data is represented in TPM while its FPKM in Figure 2D. Figure 2: the number of replicates in the case of 3-month-old wild types only 2. Please specifically denote it and comment why only 2 replicates are provided

4 . Considering that p53 and SRGs are significantly upregulated in 6months in the APP model, it would be great if (allowing that these samples are still available) the authors can include a staining for apoptotic markers, for example, Active Casp3 or similar. This will allow us to better gauge the gene expression changes presented by the authors especially regarding SRGs.

5 . Under subheading: Hsf1 accelerates B2 RNA processing, 3rd paragraph when the authors comment on known hsf1 binding sites on SRG genes, please correct from: Increased Hsf1-binding was found.... "To the increased number of hsf1 binding sites were found", unless the authors would like to show increased Hsf1 binding by performing CHIP-seq for Hsf1 in the hippocampus at least at the 6-month time point between Wt and APP mice.

Significance

B2 RNAs, encoded from SINE B2 elements has been directly implicated in stress response by its inherent ability to bind RNA Pol II and suppress stress response genes (SRG) in homeostatic conditions. However, upon stimuli, B2 RNAs are cleaved and degraded, resulting in the release of RNA pol II and upregulation of SRGs. Previous work from the senior author identified PRC2 component EZH2 to be the B2 RNA processing factor, cleaving B2, and releasing POL2. SRGs are upregulated upon stress, for example in age-associated neuropathologies like Alzheimer's disease (AD). Considering that the hippocampus is a primary target of amyloid pathologies as well as since SRGs are suggested to be key for the function of a healthy hippocampus, the authors set to understand the role of B2 RNAs that are linked to SRG regulation in the mouse hippocampus with amyloid pathology. They use disease-relevant in vivo and in vitro models combined with unbiased RNA seq data analysis for this endeavor, which indicates the potential relevance of B2 RNAs in APP mediated neuronal pathologies in mice as well as identifies Hsf1 as the factor cleaving B2 RNAs in the hippocampus.

The work is interesting and identification of Hsf1 as the processing factor for B2 RNAs in the hippocampus is significant. I would like to credit the authors for their elegant in vivo experimental design in Figure 2.

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Reply to the reviewers

We thank the reviewers for their useful suggestions to improve the manuscript and their support for publication. We have addressed all the comments that have been raised and carried out the suggested additional analyses, resulting in a significantly improved revised version of the manuscript. We provide hereafter a detailed point-by-point response to all questions and comments of the three reviewers.

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

Centriole structure has been an attractive but challenging research topic for years. Pierre Gonczy's group has been working on its structure using cryo-electron tomography (cryo-ET). While the axoneme, which has longitudinal periodicity, was analyzed by several groups by cryo-ET for more than a decade, cryo-ET study on the centriole suffers from poor signal to noise ratio due to its limited length and thus fewer periodicity. They chose the centriole of flagellate Trichonympha, which have exceptionally long centrioles and thus offer opportunity of relatively straightforward sub-tomogram averaging. Their approach has been successful, and they revealed intermediate resolution structure of the cartwheel, key of 9-fold symmetry formation, and it's joint to triplet microtubules (Guichard et al. 2012, 2013, 2018).

In this work, they employed modern state-of-art cryo-ET technique, such as direct electron detection and 3D image classification to upgrade our knowledge of centriole structure. In their past works, the central hub of the cartwheel, made of SAS-6 protein forming 9-fold complex, was described as an 8nm periodic object. With improved spatial resolution, they provided further detail with clear polarity, which will deepen our thought about the initial stage of ciliogenesis. They also compared two Trichonympha species (spp and agilis) as well as another flagellate, Teranympha mirabilis, and extended their intriguing evolutional and mechanical hypotheses based on structural differences.

Despite improved spatial resolution, it is still not possible to identify proteins in the cryo-ET map (cellular cryo-ET will not reach such high resolution in the near future). Therefore, this work is rather geometrically descriptive, which will inspire molecular biologists to identify molecules by other methods. Nevertheless, this work demonstrated capability of cellular cryo-ET, especially analysis of structural heterogeneity. Thus, while biological topics handled are rather specialized for cilia from flagellate, this work will attract attention of any biologist interested in molecular structure in vivo. It is worth for publication in a high Journal after addressing the points below. This reviewer believes that the authors can address these points easily with additional analysis.

We are grateful to the reviewer for the favorable evaluation and the many valuable suggestions, in particular concerning the processing pipeline, which we addressed by additional analyses, as detailed below.

Major points:

1. Entire scheme A graphic diagram of the entire cartwheel area, summarizing this work, is necessary for the readers' understanding (similar to Fig.6 of the other manuscript, Klena et al.).

We thank the reviewer for this interesting suggestion, which we fully adhere to. As a result, we have generated a graphical summary of the work, which is shown in the new Figure panels 6B-F. Moreover, Figure 6A provides an evolutionary perspective regarding the presence of the CID and of what is now referred to as the fCID (filamentous CID, previously: FLS, see response to reviewer 3). This also helps to link our findings with the companion manuscript by Klena et al. This new Figure 6 is referred to extensively in the discussion of the revised manuscript (pages 13-16).

Then average scheme should be shown in more detail, especially assumption of periodicity, Materials and Methods. The cartwheel hub was averaged with 25nm periodicity (as discussed below). Was the pinhead averaged with 16nm (as detected by FFT in Fig.S2L)? How about the triplet?

This reviewer is not completely sure if the longitudinal averaging strategy is justifiable. Since periodicity of each domain is not trivial, logically the initial average must be done with the size of least common multiple (or larger). It is likely 96nm, assuming 25nm of the central hub is 3 times of microtubule periodicity and 16nm of the pinhead is twice of MT. 96nm average should be possible with a long cartwheel in this work. Alternative, in case periodicity is independent of MT and thus there is no least common multiple, is random picking and classification mentioned in "4. Periodicity". This should also be possible, since they can pick enough number of particles from long cartwheels.

We apologize that the initial version of the manuscript was not sufficiently clear regarding the averaging pipeline that was pursued. To rectify this, we now provide a new Figure S1B to graphically explain the approach followed for STA. As depicted in this figure panel, the step size for sub-volume extraction was 25 nm both centrally and peripherally. This step size was selected because it corresponds to ~3x the major periodicity of ~8.5 nm observed in the power spectra of the sub-volumes. The 25 nm step size is larger than that previously used (i.e. 17 nm in Guichard et al. 2013), in order to identify potential features with larger periodicities. The fact that the step size was of 25 nm in all cases is now mentioned explicitly in the Materials and Methods section of the revised manuscript (line 649).

We agree with the reviewer that 96 nm averaging is possible given the long cartwheel analyzed here, and such a piece of data was in fact included in the original submission, although with a different purpose. Indeed, we carried out STA using ~(100 nm)3 sub-volumes (with binning 3 to reduce computational time), the results of which are reported in Figure S7 (previously Fig. S6). For the purpose of this analysis, we focused on the lateral organization of the cartwheel, but did not use this dataset to explore other periodicities because of the limitations inherent to a binning 3 data set.

• Classification*

The authors analyzed structural heterogeneity inside the cartwheel hub, employing reference-free classification by Relion software. The program reveals multiple coexisting structures - two from Trichonympha agilis and three from Teranympha, respectively. Whereas this is an exciting finding and shows future research direction of this field, interpretation of this classification must be done carefully. ** It is puzzling that major (55%) population of T. agilis shows more ambiguous features than the minor population (45%), while spatial resolutions by FSC are not so different - for example, Fig.2H vs Fig.S5C. In case of Teranympha, it is even more drastic - Fig.4D (major class) seems blurred along the centriolar axis, compared to Fig. 4E (minor class). This reviewer is afraid that these "major" classes might contain more than one structure and after subaveraging be blurred in detailed features. The apparent good spatial resolution could be explained, when two structures coexist and subtomograms are aligned within each subclass. Probably lower resolution at the spoke region of the major class (Fig.S2A) than that of the minor class (Fig.S2D) is a sign of heterogeneity within this class. Another risk could be subtomograms with poorer S/N being categorized to one class (due to lack of feature to be properly classified). Fig.S5F (black dots localized in one tomogram) raised this concern.

The following investigation will help to solve this issue. 1. Extract and re-classify subtomograms belonging to the major population. 2. Direct observation of tomograms. The authors could plot two classes of Teranympha (as they did for T. agilis in Fig.S5) and find features of the cylindrical cartwheel hub in two conformations (as shown Fig.4DE). Since such a feature was directly observed in tomograms from the other manuscript (left panels of Fig.S6AC in Klena et al.), it should be possible in this work as well.

We agree with the reviewer that the interpretation of the classification must be done with care, and share her/his interest in better understanding the structural variability between cartwheels classes in T. agilis and T. mirabilis. Although poor S/N may in theory result in erroneous joint classifications, we note that all maps in the original submission stemmed from extensive focused 3D classification, which removed defective and spurious sub-volumes, nevertheless defining distinct classes in the cases reported. Obviously, however, we cannot exclude that much larger data sets and future software advances may lead to the identification of additional features that would allow further sub-classes to be identified.

Regardless, we followed the two suggestions the reviewer offered to us and have (1) extracted and re-classified sub-tomograms belonging to the major populations and (2) undertaken a direct observation of tomograms. These two points are developed in turn below.

(1) We have performed a further round of classification of the major populations in T. agilis (55 % class) and T. mirabilis (64 % class), to assess whether additional sub-classes might be identified and thus help further improve the quality of the central cartwheel map. However, this additional round did not yield new sub-classes nor notable improvement in the map quality as judged by visual inspections. We show in Rebuttal Figure 1 a comparison in each case of the original STA and the corresponding STA upon such re-classification. Importantly, all conclusions spelled out in the original submission hold upon further re-classification, indicating that the initial classification converged to the best map quality based on the current data set and available computational resources.

(2) We have followed the suggestion of the reviewer and now show raw tomograms to confirm that the classes correspond to bona fide structures and not to processing artefacts (new Figures S1C-F). The resulting new Figure S1D for instance shows that the striking variations observed between classes in the T. agilis STA are also visible in the raw tomogram. The more subtle variations among T. mirabilis classes are more difficult to observe in the raw tomogram, but inherent variations that reflect the presence of two classes are nevertheless observed.

Furthermore, following the reviewer’s suggestion, we now mapped the distribution of the two T. mirabilis cartwheel classes onto tomograms, revealing that both classes can occur next to each other within the same centriole (new Figure S8E).

• Periodicity mismatch*

In Fig. 2CD, periodicity of CID has discrepancy from that of the stacked SAS-6 ring (8.5nm and 8.0nm). Do the authors think this is a significant difference or within an error? The same question can occur to other subtomogram averages. It would be nice to show errors as shown in their other manuscript (Fig.3C of Klena et al.) and clarify their idea. If it is systematic difference of periodicity between the stacked ring and CID, this shift will be accumulated through the entire cartwheel region - after 100nm, 8.5nm/8.0nm difference can be accumulated to ~6nm, which should change the entire view of the subtomogram - and the main factor to be classified (periodicity mismatch). This artifact (or influence) should be removed (or separately evaluated) by masking CID (out and in) and run classification separately. By clarifying this, the quality of the major subaverages (mentioned in the previous paragraph) could be improved.

The reviewer wonders whether there might be a periodicity discrepancy within one map, for instance between CID and spokes in the T. spp. cartwheel map (Fig. 2C and Fig. 2D). Here, the periodicity determined from the STA maps is 8.5 ± 0.2 nm (SD, N=4) for the CID and 8.0 ± 1.5 nm (SD, N=2) for the spokes. Based on these standard deviations, there is indeed no significant difference between the two, and thus no periodicity discrepancy. The same applies for measurements in T. agilis and T. mirabilis. The SDs were reported already in the figure legends of the original submission, and we would prefer to leave them there if possible and not mention them in the figures, which are pretty busy as is. We apologize if this was not clear enough in the initial manuscript. Likewise, one may wonder whether there might be periodicity discrepancies between structures from distinct maps, for instance between CID and A-links from T. spp. (Fig. 2C and Fig. 3D). Again, the measurements are within error, since the distance between adjacent CIDs is 8.5 ± 0.2 nm (N=4) and between adjacent A-links 8.4 ± 0.4 nm (N=6); a similar conclusion applies for the corresponding measurement comparisons in T. agilis and T. mirabilis. The figure legends have been altered in the revised manuscript to spell out that there are no significant differences between periodicities (lines 856-858).

Furthermore, we would like to stress that, by definition, STA value are average distances. For instance, in the case of T. spp., the central cartwheel STA was obtained from 511 sub-volumes, and thus the reported N=2 represents the average distance from 511 sub-volumes. Since this is an average, errors can therefore not accumulate over longer distances. This point has also been clarified in the figure legends (line 856-858).

• Periodicity*

They averaged subtomograms extracted with spacing of 252A with initial average as the first template (p.18 Line22). This means they assumed 25nm periodicity from the beginning and excluded different or larger unit size (if they take search range wide, they could detect difference periodicity, but will still be biased by initially assumed 25nm). 25nm average allowed them to see more detail than before (when they assumed 8nm periodicity), but there is still a risk of bias from references. To avoid this risk, this reviewer would propose classification of randomly extracted (but of course along the cylindrical hub or along the triplet microtubules, so one-dimensionally random picking) subtomograms. This experiment will end up with multiple sub-averages, which are 25nm (or multiple times of that) shifted from each other. Then it will prove their assumption.

We agree with the reviewer that in theory the choice of periodicity could introduce a bias. This is why we have chosen a larger step size than in our initial work, corresponding to ~3x the major periodicity of ~8.5 nm observed in the power spectrum of the sub-volumes, as mentioned above. Regardless, following the reviewer’s suggestion, we have now explored other types of periodicities by re-analyzing the dataset through extraction of non-overlapping sub-volumes along the proximal-distal centriole axis. In doing so, we randomized the starting position of the first box between tomograms, reaching the same goal as with random picking but maximizing the number of sub-volumes. We carried out this analysis for all T. spp., T. agilis and T. mirabilis cartwheel classes, and found no notable differences that would affect the conclusions of the manuscript compared to the initial overlapping sub-volume classification, albeit generally with a noisier STA due to the lower number of sub-volumes. A comparison of the two approaches is provided in Rebuttal Figure 2. Moreover, all the points regarding the choice of periodicity have been further clarified in the expanded Materials and Methods section (pages 19-21).

Minor points:

They discussed difference of stacked SAS-6 rings in the cartwheel from various species. How much is the sequence difference of SAS-6 among these species?

Unfortunately, no genomic or transcriptomic data has been published for the species investigated here, although the sparse molecular data available from small subunit rRNA sequences allows one to establish an overall molecular phylogeny. We previously identified a SAS-6 homologue in T. agilis (Guichard et al. 2013), which shares 20 % identity and 45 % similarity with C. reinhardtii SAS-6. Despite low sequence conservation, the structural conservation of SAS-6 is predicted to be high between the two organisms (Guichard et al. 2013). We apologize if these points were not expressed sufficiently clearly in the initial rendition and have adapted the wording in the revised manuscript (lines 325-332).

Are the authors sure that CID is nine-fold symmetric? It is not trivial.

We thank the reviewer for bringing up this interesting point. We have applied 9-fold symmetrization to the entire central cartwheel comprising spokes, hub and CID/ fCID, a choice guided by the apparent 9-fold symmetry of the spokes and peripheral element. We investigated the impact of symmetrization on the CID by relaxing symmetry from C9 to C1 during refinement, but did not observe a difference, and thus continued with C9 symmetry, which improves map resolution by S/N ratio enhancement and additional missing wedge compensation. In addition, we have also analyzed the CID without symmetrization, as reported in Figure S7 (previously: Fig. S6). Note that these maps were generated with larger sub-volumes centered on the spokes to comprise hub, spokes and microtubule triplets, explaining the resulting lower resolution, as the missing wedge is not compensated. Despite these limitations, however, the unsymmetrized CID shown in Figure S7A and S7E resembles the one in the symmetrized maps of Figure 2, indicating that the CID indeed exhibits 9-fold radial symmetry. That this is the case is spelled out explicitly in the revised manuscript (lines 1145-1147).

Fig.1C: Another cross-section from the distal region will be helpful. A longer scale bar is better for readers' understanding.

We understand that the reviewer is curious about the distal region, and cross-section views of resin-embedded sections from T. agilis are available and could be provided if necessary. However, given that the focus of the manuscript is strictly on the cartwheel-bearing proximal region, we felt that featuring the distal region in detail would break the narrative. Therefore, we suggest to keep Figure 1 as in the original manuscript. Following the reviewer’s suggestion, we increased the size of the scale bars from 10 nm to 20 nm in Figure 1C as well as in the corresponding Figure S8C.

Fig.S6F: It would be informative if the subclasses (25% and 20%) are distinguished in this mapping.

As per the reviewer’s request, we provide in Rebuttal Figure 3 a side-by-side comparison of the T. agilis 25 % and 20 % classes centered on the spokes, which are noisier than the composite 45 % class due to the lower number of sub-volumes in each sub-class. Given that there are no notable differences between the two maps that would affect any of the conclusions of the manuscript, we feel it is best to keep what is now Figure S7F (previously: Fig. S6F) unchanged in the revised manuscript.

A figure to explain the classification scheme will help readers understand. How many subtomograms did classification started? Were the 45% class classified into two (25% and 20%) groups by two-step classification or at once (the entire subtomograms were classified into three groups directly?

We thank the reviewer for this useful suggestion. As a result, we have generated a new Supplemental Figure S1G-J that provides a graphical overview of the classification scheme, together with sub-volume numbers for all deposited maps, thus nicely complementing Table S1.

Reviewer #1 (Significance (Required)):

Nevertheless, this work demonstrated capability of cellular cryo-ET, especially analysis of structural heterogeneity. Thus, while biological topics handled are rather specialized for cilia from flagellate, this work will attract attention of any biologist interested in molecular structure in vivo. It is worth for publication in a high journal after addressing the points above. This reviewer believes that the authors can address these points easily with additional analysis.

We reiterate our thanks to this reviewer for her/his favorable evaluation and detailed suggestions, which enabled us to generate a strengthened manuscript.

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

Here, Nazarov and colleagues report sub-tomogram average (STA) maps of centrioles with 16 to 40 Å resolution from Trichonympha spp., Trichonympha agilis, and Teranympha mirabilis. Even though the authors have previously described the centriole architecture of T. spp, these STA maps of higher resolution revealed new features of centrioles, like polarized Cartwheel Inner Density (CID) and the pinhead. They also observed Filament-like structure (FLS) from T. mirabilis which seems to correspond to the CID from other species. Interestingly, they suggest that one and two SASS6 rings are stacked in an alternative fashion to make the central hub in T. mirabilis (Figure 5). The following issue should be addressed:

Major points

• Figure 4E. Authors mentioned in the manuscript that "We observed that every other double hub units in the 36% T. mirabilis class appears to exhibit a slight tilt angle relative to the vertical axis". When I see the other side, it does not seem to be tilted. Could the authors explain this?*

We apologize that this aspect was not explained in sufficient detail. The left and right sides of the hub indeed appeared different in transverse views across the cartwheel center (previous Fig. 4E). This was because the area we selected in the original submission was centered on one emanating spoke. Due to the 9-fold symmetry one spoke density was selected on the right side, while the region between two spokes was displayed on the left side (as was illustrated by the slice across the center in previous Figure 4A; dashed rectangles in 4.0 nm panel). We have now selected a larger area to include spokes from both sides of the hub and thus better visualize this offset as shown in the modified Figure 4D-E.

Reviewer #2 (Significance (Required)):

I believe these results are of interest for all centrosome researchers and would like to recommend this manuscript be published in the EMBO journal which is affiliated with the Review Commons.

We thank the reviewer for the recommendation to submit the revised manuscript to EMBO Journal, which we have followed.

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

In this manuscript Nazrov et al., use cryo-electron tomography (CET) to analyse the structure of the centriole cartwheel. The Gonczy lab have previously generated a ground-breaking structure of the cartwheel from Trichonympha spp (T. spp.) (Guichard et al., Science, 2012; Guichard et al., Curr. Biol., 2013). This work is a direct continuation of those studies but using modern technology to get higher resolution images of the T. spp. cartwheel and comparing this to the cartwheel from Trichonympha agilis and from another distantly related flagellate Teranympha mirabilis.

The data is generally well presented and of high quality. I am not an expert in CET, so it would be advisable to get the opinion from a reviewer who is, but the Gonczy lab are experienced in these techniques so I would not anticipate any problems. I have to admit that the title of the paper did not excite me, and I expected this to be a very worthy, but incremental study. It was a pleasure to find out that the extra detail provided by the increased resolution has revealed several new and unexpected features that have important implications for our understanding of cartwheel assembly and function. Most important are the potential asymmetry of the cartwheel hub, apparent variations in the packing mechanism of the stacked rings (even within the same cartwheel), and the potential offsetting of ring stacking. These findings will be of great interest to the field, and so I am strongly supportive of publication in The EMBO Journal. I have only a few points that I think the authors should consider.

We thank the reviewer for this positive feedback and the recommendation to submit to EMBO Journal, which we hereby follow.

Prompted by the comment of the reviewer, we revised the title to make it more informative and appealing to readers: “Novel features of centriole polarity and cartwheel stacking revealed by cryo-tomography”.

• Nazarov et al., conclude that the cartwheel structure is intrinsically asymmetric. This is most convincingly based on the displacement of the CID within the hub, but they state that the Discussion that the potential offset between the Sas-6 double rings generates an inherently polar structure. I didn't understand why this is the case. Looking at Fig.S9A,B I can see that the offset in B could tilt to the left (as shown here) or to the right (if the structure was flipped by 180o). But I couldn't see how this makes this structure polar in the sense that a molecule coming into dock with the structure could only bind to one side of the offset structure shown in B, but to both sides of the aligned structure shown in A. I think this needs to be explained better, as it is crucial to understand where any potential polarity in the cartwheel structure comes from.*

We apologize for not having been sufficiently clear about how two SAS-6 rings with an offset could impart organelle polarity. The reviewer is correct that an offset between superimposed rings alone is not sufficient to generate polarity at a larger scale. The important point we would like to stress, however, is that we discovered concerted polarity in multiple locations, from the central hub to the peripheral elements as illustrated in Fig. S7C-D, S7G-H, S7K-L and S7O-P (previously: Fig. S6). Prompted by the reviewer’s comment, we now better emphasize the asymmetric tilt angles of merging spokes, as highlighted also in the improved Figure S7. This asymmetric spoke tilt angle allows one to discriminate the proximal and distal side of a double SAS-6 ring, which is now explained better in the text (lines 259-263 & 502-510).

• Related to this last point, in a co-submitted paper Klena et al. do not report such an asymmetry in the hub structures they have solved from several different species (neither in the tilting of the hub, or the displacement of the CID). I think it would be worth both sets of authors commenting on this point.*

We agree that comparing and contrasting the results of the two companion manuscripts is important and we have updated the text as a consequence in several places (lines 444, 467, 507, 536, 985, 1000). We know from our previous work (Guichard et al. 2013) that the asymmetry of the hub and spoke is not visible at lower resolution. In the accompanying manuscript by Klena et al., no offset in the hub or asymmetric CID localization is reported, probably due to lower resolution and differences between species.

• The authors data strongly suggests that the T. ag. and Te. mir. hubs are composed of a mixture of single and double Sas-6 rings. In contrast, the T. spp. cartwheel only has a single class of rings, but it wasn't absolutely clear if the authors think this comprises a single or double ring. In the text it is presented as though the elongation of the hub densities in the vertical direction is a new feature of the T. ag cartwheel (Fig.2H,I), but to me it looks as though this is also apparent in the T. spp. cartwheel (Fig.2C,D). The authors should address this directly and, if they believe that T. spp. has a double ring, they should comment on whether this more regular structure seems to have offset rings. If not, then the offset rings are unlikely to be the source of asymmetry that leads to the asymmetric displacement of the CID. Finally, if the authors think these are double rings, they should also be clear that they would now slightly re-interpret their original T. spp. cartwheel model (Figure 2, Guichard et al., Curr. Biol.). There is no embarrassment in this-a higher resolution structure has simply revealed more detail.*

We apologize if the conclusions drawn about T. spp. cartwheel hubs were not sufficiently clearly expressed. Like the reviewer, we think that elongated hub elements are also discernible in T. spp., something that is also illustrated by the intensity plot profile in Figure 2C (double peaks on light blue line). These points are spelled out more explicitly in the revised manuscript (lines 177-179). In addition, to emphasize the conservation of the double hub units in both Trichonympha species, we have likewise adapted the text for T. agilis (lines 198-201).

As for the offset observed within T. spp. spoke densities in Figure S10H, we interpret this as evidence for an offset of the double ring at the level of the hub, although we have not observed such offset in T. spp. for reasons that are unclear. The fact that this revises our previous interpretation based on a lower resolution map of T. spp. was already mentioned in the initial submission but is now better emphasized (lines 171-172 & 179-181).

• The authors conclude that T. mirabilis cartwheels lack a CID and instead have a filament-like structure (FLS). I wonder whether it is more likely that the FLS is really a highly derived CID that appears to be structurally distinct when analysed in this way, but that will ultimately have a similar molecular composition. This situation might be analogous to the central tube in C. elegans, which by EM appears to be distinct from the central cartwheel seen in most other species, but is of course still composed of Sas-6. This historical tube/cartwheel nomenclature is now cumbersome to deal with, so perhaps it would be better to be cautious and not give the T. mirabilis structure a completely new name-how about "unusual CID" (uCID).*

We share the view that the CID and the “FLS” –the term used in the initial submission- may have a related molecular composition and function, as we had also speculated in the discussion of the original submission. Following the reviewer’s suggestion, and in an effort to have a more uniform nomenclature, we propose to dub the T. mirabilis structure “filamentous CID” (fCID). This highlights better the similar location of these two entities and their potential shared function, while stressing the filamentous nature of the fCID. We further emphasize this point by providing the new Figure 6A to compare the presence of the two entities in select species. The discussion has also been adapted accordingly (pages 13-14).

Rebuttal Figure Legends

Rebuttal Figure 1: Re-classification of major classes

(A-D) Transverse (top) and longitudinal (bottom) views of T. agilis (A, B) and T. mirabilis (C, D) central cartwheel 3D maps. The final major classes reported in the manuscript (A: 55 % class, C: 64 % class) were subjected to re-classification, which again yielded one major class in each case, with no notable improvement (B, D).

Rebuttal Figure 2: Reclassification with non-overlapping sub-volumes

(A-F) Transverse (top) and longitudinal (bottom) views of T. spp. (A, B) T. agilis (C, D) and T. mirabilis (E, F) central cartwheel 3D maps. The final maps reported in the manuscript (A, C, E) were generated with a 25 nm step size, yielding overlapping sub-volumes, whereas the maps in (B, D, F) were generated from non-overlapping sub-volumes, with no notable differences between the two that would affect the conclusions of the manuscript.

Rebuttal Figure 3: Polar centriolar cartwheel upon sub-classification

(A-C) 3D transverse views of non-symmetrized STA centered on the spokes to jointly show the central cartwheel and peripheral elements in the T. agilis 45 % class (A), as well as separately in the 25 % class (B) and 20% class (C). No notable differences are apparent following such re-classification, apart from the output being noisier due to the lower number of sub-volumes in each sub-class.

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Referee #3

Evidence, reproducibility and clarity

In this manuscript Nazrov et al., use cryo-electron tomography (CET) to analyse the structure of the centriole cartwheel. The Gonczy lab have previously generated a ground-breaking structure of the cartwheel from Trichonympha spp (T. spp.) (Guichard et al., Science, 2012; Guichard et al., Curr. Biol., 2013). This work is a direct continuation of those studies but using modern technology to get higher resolution images of the T. spp. cartwheel, and comparing this to the cartwheel from Triconympha agilis and from another distantly related flagellate Tetranympha mirabilis.

The data is generally well presented and of high quality. I am not an expert in CET, so it would be advisable to get the opinion from a reviewer who is, but the Gonczy lab are experienced in these techniques so I would not anticipate any problems. I have to admit that the title of the paper did not excite me, and I expected this to be a very worthy, but incremental study. It was a pleasure to find out that the extra detail provided by the increased resolution has revealed several new and unexpected features that have important implications for our understanding of cartwheel assembly and function. Most important are the potential asymmetry of the cartwheel hub, apparent variations in the packing mechanism of the stacked rings (even within the same cartwheel), and the potential offsetting of ring stacking. These findings will be of great interest to the field, and so I am strongly supportive of publication in The EMBO Journal. I have only a few points that I think the authors should consider.

1. Nazarov et al., conclude that the cartwheel structure is intrinsically asymmetric. This is most convincingly based on the displacement of the CID within the hub, but they state that the Discussion that the potential offset between the Sas-6 double rings generates an inherently polar structure. I didn't understand why this is the case. Looking at Fig.S9A,B I can see that the offset in B could tilt to the left (as shown here) or to the right (if the structure was flipped by 180o). But I couldn't see how this makes this structure polar in the sense that a molecule coming into dock with the structure could only bind to one side of the offset structure shown in B, but to both sides of the aligned structure shown in A. I think this needs to be explained better, as it is crucial to understand where any potential polarity in the cartwheel structure comes from.

2. Related to this last point, in a co-submitted paper Klena et al. do not report such an asymmetry in the hub structures they have solved from several different species (neither in the tilting of the hub, or the displacement of the CID). I think it would be worth both sets of authors commenting on this point.

3. The authors data strongly suggests that the T. agg. and Te. mir. hubs are composed of a mixture of single and double Sas-6 rings. In contrast, the T. spp. cartwheel only has a single class of rings, but it wasn't absolutely clear if the authors think this comprises a single or double ring. In the text it is presented as though the elongation of the hub densities in the vertical direction is a new feature of the T. agg cartwheel (Fig.2H,I), but to me it looks as though this is also apparent in the T. spp. cartwheel (Fig.2C,D). The authors should address this directly and, if they believe that T. spp. has a double ring, they should comment on whether this more regular structure seems to have offset rings. If not, then the offset rings are unlikely to be the source of asymmetry that leads to the asymmetric displacement of the CID. Finally, if the authors think these are double rings, they should also be clear that they would now slightly re-interpret their original T. spp. cartwheel model (Figure 2, Guichard et al., Curr. Biol.). There is no embarrassment in this-a higher resolution structure has simply revealed more detail.

4. The authors conclude that T. mirabilis cartwheels lack a CID and instead have a filament-like structure (FLS). I wonder whether it is more likely that the FLS is really a highly derived CID that appears to be structurally distinct when analysed in this way, but that will ultimately have a similar molecular composition. This situation might be analogous to the central tube in C. elegans, which by EM appears to be distinct from the central cartwheel seen in most other species, but is of course still composed of Sas-6. This historical tube/cartwheel nomenclature is now cumbersome to deal with, so perhaps it would be better to be cautious and not give the T. mirabilis structure a completely new name-how about "unusual CID" (uCID).

Significance

see above

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Referee #2

Evidence, reproducibility and clarity

Here, Nazarov and colleagues report sub-tomogram average (STA) maps of centrioles with 16 to 40 Å resolution from Trichonympha spp., Trichonympha agilis, and Teranympha mirabilis. Even though the authors have previously described the centriole architecture of T. spp, these STA maps of higher resolution revealed new features of centrioles, like polarized Cartwheel Inner Density (CID) and the pinhead. They also observed Filament-like structure (FLS) from T. mirabilis which seems to correspond to the CID from other species. Interestingly, they suggest that one and two SASS6 rings are stacked in an alternative fashion to make the central hub in T. mmirabilis (Figure 5). The following issue should be addressed:

Major points

1. Figure 4E. Authors mentioned in the manuscript that "We observed that every other double hub units in the 36% T. mirabilis class appears to exhibit a slight tilt angle relative to the vertical axis". When I see the other side, it does not seem to be tilted. Could the authors explain this?

Minor Points

1. Page 11, I think Fig. 9G indicates Fig. S9G.

Significance

I believe these results are of interest for all centrosome researchers, and would like to recommend this manuscript be published in the EMBO journal which is affiliated with the Review Commons.

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Referee #1

Evidence, reproducibility and clarity

Centriole structure has been an attractive but challenging research topic for years. Pierre Gonczy's group has been working on its structure using cryo-electron tomography (cryo-ET). While the axoneme, which has longitudinal periodicity, was analyzed by several groups by cryo-ET for more than a decade, cryo-ET study on the centriole suffers from poor signal to noise ratio due to its limited length and thus fewer periodicity. They chose the centriole of flagellate Trichonympha, which have exceptionally long centrioles and thus offer opportunity of relatively straightforward subtomogram averaging. Their approach has been successful and they revealed intermediate resolution structure of the cartwheel, key of 9-fold symmetry formation, and it's joint to triplet microtubules (Guichard et al. 2012, 2013, 2018). In this work, they employed modern state-of-art cryo-ET technique, such as direct electron detection and 3D image classification to upgrade our knowledge of centriole structure. In their past works, the central hub of the cartwheel, made of SAS-6 protein forming 9-fold complex, was described as an 8nm periodic object. With improved spatial resolution, they provided further detail with clear polarity, which will deepen our thought about the initial stage of ciliogenesis. They also compared two Trichonympha species (spp and agilis) as well as another flagellate, Teranympha micabilis, and extended their intriguing evolutional and mechanical hypotheses based on structural differences. Despite improved spatial resolution, it is still not possible to identify proteins in the cryo-ET map (cellular cryo-ET will not reach such high resolution in the near future). Therefore this work is rather geometrically descriptive, which will inspire molecular biologists to identify molecules by other methods. Nevertheless this work demonstrated capability of cellular cryo-ET, especially analysis of structural heterogeneity. Thus, while biological topics handled are rather specialized for cilia from flagellate, this work will attract attention of any biologist interested in molecular structure in vivo. It is worth for publication in a high Journal after addressing the points below. This reviewer believes that the authors can address these points easily with additional analysis.

Major points:

1. Entire scheme A graphic diagram of the entire cartwheel area, summarizing this work, is necessary for the readers' understanding (similar to Fig.6 of the other manuscript, Klena et al.). Then average scheme should be shown in more detail, especially assumption of periodicity, Materials and Methods. The cartwheel hub was averaged with 25nm periodicity (as discussed below). Was the pinhead averaged with 16nm (as detected by FFT in Fig.S2L)? How about the triplet? This reviewer is not completely sure if the longitudinal averaging strategy is justifiable. Since periodicity of each domain is not trivial, logically the initial average must be done with the size of least common multiple (or larger). It is likely 96nm, assuming 25nm of the central hub is 3 times of microtubule periodicity and 16nm of the pinhead is twice of MT. 96nm average should be possible with a long cartwheel in this work. Alternative, in case periodicity is independent of MT and thus there is no least common multiple, is random picking and classification mentioned in "4. Periodicity". This should also be possible, since they can pick enough number of particles from long cartwheels.

2. Classification The authors analyzed structural heterogeneity inside the cartwheel hub, employing reference-free classification by Relion software. The program reveals multiple coexisting structures - two from Trichonympha agilis and three from Teranympha, respectively. Whereas this is an exciting finding and shows future research direction of this field, interpretation of this classification must be done carefully. It is puzzling that major (55%) population of T. agilis shows more ambiguous features than the minor population (45%), while spatial resolutions by FSC are not so different - for example, Fig.2H vs Fig.S5C. In case of Teranympha, it is even more drastic - Fig.4D (major class) seems blurred along the centriolar axis, compared to Fig. 4E (minor class). This reviewer is afraid that these "major" classes might contain more than one structure and after subaveraging be blurred in detailed features. The apparent good spatial resolution could be explained, when two structures coexist and subtomograms are aligned within each subclass. Probably lower resolution at the spoke region of the major class (Fig.S2A) than that of the minor class (Fig.S2D) is a sign of heterogeneity within this class. Another risk could be subtomograms with poorer S/N being categorized to one class (due to lack of feature to be properly classified). Fig.S5F (black dots localized in one tomogram) raised this concern. The following investigation will help to solve this issue. 1. Extract and re-classify subtomograms belonging to the major population. 2. Direct observation of tomograms. The authors could plot two classes of Teranympha (as they did for T. agilis in Fig.S5) and find features of the cylindrical cartwheel hub in two conformations (as shown Fig.4DE). Since such a feature was directly observed in tomograms from the other manuscript (left panels of Fig.S6AC in Klena et al.), it should be possible in this work as well.

3. Periodicity mismatch In Fig. 2CD, periodicity of CID has discrepancy from that of the stacked SAS-6 ring (8.5nm and 8.0nm). Do the authors think this is a significant difference or within an error? The same question can occur to other subtomogram averages. It would be nice to show errors as shown in their other manuscript (Fig.3C of Klena et al.) and clarify their idea. If it is systematic difference of periodicity between the stacked ring and CID, this shift will be accumulated through the entire cartwheel region - after 100nm, 8.5nm/8.0nm difference can be accumulated to ~6nm, which should change the entire view of the subtomogram - and the main factor to be classified (periodicity mismatch). This artifact (or influence) should be removed (or separately evaluated) by masking CID (out and in) and run classification separately. By clarifying this, the quality of the major subaverages (mentioned in the previous paragraph) could be improved.

4. Periodicity They averaged subtomograms extracted with spacing of 252A with initial average as the first template (p.18 Line22). This means they assumed 25nm periodicity from the beginning and excluded different or larger unit size (if they take search range wide, they could detect difference periodicity, but will still be biased by initially assumed 25nm). 25nm average allowed them to see more detail than before (when they assumed 8nm periodicity), but there is still a risk of bias from references. To avoid this risk, this reviewer would propose classification of randomly extracted (but of course along the cylindrical hub or along the triplet microtubules, so one-dimensionally random picking) subtomograms. This experiment will end up with multiple subaverages, which are 25nm (or multiple times of that) shifted from each other. Then it will prove their assumption.

Minor points: They discussed difference of stacked SAS-6 rings in the cartwheel from various species. How much is the sequence difference of SAS-6 among these species? Are the authors sure that CID is nine-fold symmetric? It is not trivial. p.7 Line21 "Fig.S1D-O": D-L p.8 Line1: It would be nice if more detailed description about MIPs, correlating to recent high resolution works from Bui and Brown labs. p.9 Line6 "Focused 3D classification...": This sentence is unclear. p.18 5 lines from bottom "S6C, S6F": How can these panels be power spectra to measure spacing? Typo? Fig.1C: Another cross-section from the distal region will be helpful. A longer scale bar is better for readers' understanding. p.29 Line6: pin -> pink Fig.S6F: It would be informative if the subclasses (25% and 20%) are distinguished in this mapping. A figure to explain the classification scheme will help readers understand. How many subtomograms did classification started? Were the 45% class classified into two (25% and 20%) groups by two-step classification or at once (the entire subtomograms were classified into three groups directly?

Significance

Nevertheless this work demonstrated capability of cellular cryo-ET, especially analysis of structural heterogeneity. Thus, while biological topics handled are rather specialized for cilia from flagellate, this work will attract attention of any biologist interested in molecular structure in vivo. It is worth for publication in a high journal after addressing the points above. This reviewer believes that the authors can address these points easily with additional analysis.

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Reply to the reviewers

Note from the authors (AU): This manuscript has been reviewed by subject experts for Review Commons. The authors would like to thank the reviewers for their comments to the manuscript, and the editor for patience with our response. Our reponse was delayed due to the COVID-19 lock-down situation in our institution. Now we are pleased to provide the following point-by-point response, as detailed below.

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

The manuscript by Suomalainen et al. describes a fluorescence-based approach combined with high-resolution confocal microscopy to study the heterogeneity of adenovirus infection in a population of human cells. The main focus of the authors is the detection of viral transcripts in infected cells, how this correlates with viral genomes, the cell state, and how it varies between different cells in a single population. The paper is generally well written and easy to read, with a few typos, although I found parts of it to be somewhat length and repetitive. Particularly the results section could be pruned somewhat for readability and clarity. The major limitation of the study as it stands is it's overall impact and novelty, which limits journal selection somewhat. A very similar study was recently published, which the authors cite (Krzywkowski et al, 2017). Nevertheless, I think the study design is rigorous and well executed, but I do have some specific comments which may enhance it's overall impact and novelty.

**Major:**

Results "Visualization of AdV-C5..." section:

Why not also look at normal cells that can be synchronized? Cancer cells, such as A549 will by definition be highly heterogenous and at all phases of the cell cycle. Primary non-transformed cells can easily be synchronized by contact inhibition and are much more physiologically relevant.

AU: In the current manuscript, we concentrated on the early phases of the AdV-C5 infection, on the question how virus gene expression is initiated and whether the cell cycle phase of the host cell impacts the initiation of virus gene expression. Answering these questions requires use of cells that express good amount of virus receptors so that viruses efficiently bind to the cells and infections can be synchronized so that extended time does not elapse between virus addition and accumulation of E1A transcripts; extended time between these two steps would make interpretation of the results more complex since cells could have progressed from one cell cycle stage to another during the experiment. Furthermore, having cells at all phases of the cell cycle is actually a benefit since then the experiment can be carried out under an “unperturbed” condition; all cell cycle synchronization methods have pleiotropic effects on the cells.

It is true that primary non-transformed cells are physiologically more relevant than cancer cells, but primary cells have issues with donor-to-donor variability and many primary cells express rather low amounts of AdV-C5 receptors, so synchronized infections in these cells are not possible. Furthermore, the extended cell morphology of many normal fibroblast cell lines and the tendency of cell extensions from neighboring cells to overlap makes fluorescent images of these cells incompatible for automated cell segmentation.

Here, we provide data also from HDF-TERT cells (nontransformed human diploid fibroblasts immortalized by human telomerase expression) to show that two of our key findings from A549 cells are not artefacts of cancer cells. This is, that akin to A549 cells, the infected HDF-TERT cells accumulate high number of E1A transcripts (Fig.1C), and also in these cells nuclear vDNA numbers do not predict the cytoplasmic E1A transcript counts during early phases of infection (S2C Fig). However, since HDF-TERT cells are rather inefficiently infected by AdV-C5, correlation of early E1A transcript accumulation to the cell cycle phase of the host cell could not been done in these cells. We have been unable to identify primary or normal immortalized cells that would be easily available and efficiently infected by AdV-C5 (synchronized infection with short time elapsed between virus addition and accumulation of E1A transcripts).

"The virus particles bound..." - Can the spatial resolution of a confocal microscope truly differentiate individual particles that are sub-wavelength in size? What about the sensitivity for single particles? Some sort of experiment to show that single particles can be detected should be performed and shown to assure the readers that this is in fact possible. Furthermore, even when based on the particle to pfu ratio, the MOI would still be nearly 2000pfu/cell, so the actual number of observed particles is an order of magnitude lower than what was applied to the cells.

AU: The fluorescence signal from individual fluorophore-tagged AdV or anti-hexon antibody-decorated particle is bright enough to be picked up by PMT or HyD detectors of the current confocal laser scanning microscopes. In fact, tracking fluorophore-tagged particles of the size of AdV has been a standard microscopy procedure since late 1990’s.

Because the Reviewers were questioning the apparently high multiplicity of infection used in the experiments, we clarify the difference between “standard” MOI estimations and our infection set-up. First of all, as described in Material and Methods, we estimated the number of physical virus particles in our virus preparations using A260 measurements (J.A. Sweeney et al., Virol. 2002, doi: 10.1006/viro.2002.1406). This method, like all other methods used to estimate virus particle numbers, is likely not 100% reliable.

Second, we incubated the virus inoculum with cells only for 60 min, after which the unbound viruses were washed away. During this short incubation time only a small fraction of input virus particles bind to cells, and indeed as shown in Fig.1A, a theoretical MOI of 54400 physical virus particles/cell or 13600 physical virus particles/cell yielded Median of 75 and 26 bound virus particles per cell, respectively. Interpretation of the results from the cell cycle assays required that there was a relatively short time between infection and analysis so that cells in a large scale did not change their cell cycle status during the experiment. This required use of a rather high MOI. Furthermore, for collection of a large data set, it is convenient that every cell is infected.

Third, what exactly does one pfu mean in terms of physical adenovirus particles? There is no clear answer to this, since several parameters affect the pfu. In which cells was the titration carried out? How long was the input virus inoculum incubated with the cells? How many of the virus particles entering the cell actually established an infection? And, as described in A. Yakimovich et al. (J. Virol. 2012, DOI: 10.1128/JVI.01102-12), only a fraction of infected cells produce a plaque. The majority of papers stating that x pfu/cell was used for infection, usually incubate the cells with the virus inoculum for several hours at 37°C, and never make any attempts to estimate exactly how many virus particles entered into the cells.

Fig. 4 - I am not certain that the observed difference is significant, at least looking at it, beyond the width difference of the peaks, highest expression for both is largely in G1. It would be nice to see this using a western blot of cell cycle sorted cells, which can easily be accomplished using FACS.

AU: In the highest GFP expression bin, CMV-eGFP expressing cells have 43% cells in G1 and 50% in S/G2/M. In comparison, E1A-GFP expressing cells have 58% cells in G1 and 35% in S/G2/M. The difference in G1 cells in the highest eGFP bin is statistically significant (p Page 15, 2nd paragraph. It would be valuable and informative to determine whether there is heterogeneity in histone association with these different vDNAs and whether these histones exhibit divergent modifications (enabling or restricting transcription). Same as above. I am rather surprised that the DBP signal did not correlate well with vDNA signal, particularly for the larger replication centers. How can this be reconciled? Was there an increase in overall vDNA signal later in infection? It is important to know this as it determines whether the observed vDNA signal is real or could be caused by viral RNA or other background causes (non-infected controls notwithstanding). Can the signal be detected with inactivated viruses (via UV for example?)

AU: Whether histone modifications impact the transcriptional output of adenovirus genomes early in infection is indeed an intriguing question, but unfortunately this is very challenging, if not impossible, to study at single-cell / single vDNA level with the existing technology. Techniques for single-cell measurements of chromatin states are still in infancy, although some notable advancements in this field were reported in 2019 (e.g. K. Grosselin et al. Nature Genetics, DOI: https://doi.org/10.1038/s41588-019-0424-9 and S. Ai et al. Nature Cell Biology, DOI: https://doi.org/10.1038/s41556-019-0383-5).

Furthermore, current literature offers a confused picture as to when exactly protein VII on incoming virus genomes is replaced by histones (reviewed in the reference 39, Giberson et al.). Of note, the vast majority of incoming nuclear vDNA molecules scored protein VII-positive with anti-VII staining under the experimental conditions used for the Fig. 2C data. However, we did not include these results into the manuscript because VII-positive signal on vDNAs does not exclude these vDNAs having histones on certain parts of the genome.

The Reviewer wonders why the DBP signal in Fig.6C does not correlate with vDNA signal. There is no discrepancy here because DBP signal in the figure is a proxy for replicating vDNA whereas the click vDNA signal reports incoming vDNA. The one DBP spot without an associated click vDNA signal could be due to a replication center originated from a replicated viral genome, not from incoming viral genome. The figure shows that incoming vDNAs within the same nucleus initiate replication asynchronously.

Page 18, 1st paragraph. It would be interesting to determine whether there was association between pol II and those genomes that showed no E1A, similarly to the histone suggestion. What about things like viral chromatin organization? Soriano et al. 2019 showed how E1A and E4orf3 work in tandem to alter viral chromatin organization by varying histone loading on the viral genome.

AU: This again would be technically very challenging to show. We actually tried to visualize active transcription using an antibody against RNA polymerase II CTD repeat YSPTSPS (phosphor S5), azide-alexa fluor488 and anti-alexa fluor488 antibody to mark EdC-labeled incoming vDNAs and proximity ligation assay for signal amplification. However, this method was not sensitive enough to detect RNA polymerase II association with individual viral genomes. We only detected the proximity ligation signal in replication centers when replicated viral genomes were tagged with EdC.

Fig. 2. Can you really say that a single dot correlates with a single transcript? Has that been validated in any way?

AU: Signal amplification with branched DNA technology leads to binding of a large number of fluorescent probes to a mRNA and thus enables detection of single nucleic acid molecules. This has been validated e.g. in A.N. Player et al. 2001. J. Histochem. Cytochem (https://doi.org/10.1177/002215540104900507) and N. Battich et al. 2013. Nature Methods (https://doi.org/10.1038/nmeth.2657).

**Minor:**

Page 5, last paragraph. "Transcirpts from the viral late transcription unit,..." This is not correct as recently shown by Crisostomo et al, 2019.

AU: The data in Crisostomo et al. paper suggest that some late gene expression can occur before vDNA replication, but an abundant accumulation of late transcripts coincides with onset of vDNA replication. However, the Crisostomo et al. study did not test what the levels of late gene transcripts are if the vDNA replication was inhibited. But to acknowledge the possibility that there might be some level of late gene transcription prior to replication of the viral genomes, the sentence is modified as follows: “Transcripts from the viral late transcription unit, amongst them mRNAs for the viral structural proteins, vastly increase in abundance concomitant with the onset of vDNA replication”. Furthermore, we have added the Crisostomo et al. reference here as well.

Page 10, "... because AdvV-infected cells are less well adherent..." This is not strictly true as loss of attachment only occurs later on in infection. It would be helpful to have statistical significance indicated directly in the figures.

AU: Although clearly visible cell rounding indeed occurs only late in infection, also during early stages of infection the HAdV-C5-infected cells are less adherent than non-infected cells. In many assays this is not obvious, but the RNA FISH staining procedure includes several incubation and washing steps in rather harsh buffers, and we observed random, sometimes considerable, cell loss with infected cultures but not with non-infected cultures.

In the revised manuscript we have included the statistical significance P values both into the main text and the figure legends, but not to the figures directly, because the P values were generated with different statistical tests and P values should not be shown/mentioned without stating which statistical test was used. However, we noticed that we had in some cases omitted to mention what was the number of pairs analyzed in some of the Spearman’s correlation tests. This has now been corrected in the revised manuscript.

The very high MOIs used are concerning, could these have negative effects on the cell viability or overall state?

AU: We refer to our explanation above about the theoretical MOI and the actual MOI. Furthermore, in the experiment described in Fig.2C (correlation of E1A transcripts per cell vs. viral genomes per cell), 42% of analyzed cells had ≤ 5 viral genomes/cell and 27.5% of analyzed cells had between 6-10 viral genomes per cell; these are not high numbers. We also provide controls that the EdC-labeled genomes are detected with good efficiency. Hence the EdC-labeled genomes per cell are a good estimate of the numbers of virus particles that indeed entered into the cells.

There are a few typos and such that should be corrected. AU: We have tried to find and correct the typos.

Reviewer #1 (Significance (Required)):

As I stated above, the work is interesting and significant, to a degree. The major limitation is that the novelty is low as a paper published in 2017 (cited by the authors) used a very similar approach to investigate a similar problem. In addition, there are multiple other recent papers looking at cell populations in the context of adenovirus infection, and whether a single cell or population based approach is better is unclear. This is something the authors might want to strengthen prior to submission.

AU: In the current study, we focused on the early phase of HAdV-C5 infection, on how viral gene expression is initiated and how individual nuclear viral genomes proceed to a replicative phase. The Krzywkowski et al. 2017 J. Virol. Paper that the reviewer refers to used padlock probe-based rolling circle amplification technique to simultaneously detect HAdV-C5 genomes and viral mRNAs in individual infected cells.

The shortcoming of this method is inferior sensitivity compared to the branched DNA technology-based method used by us in the current study. Krzywkowski et al. were able to pick up signals from virus mRNAs and virus genome only relatively late in the infection, i.e. at the time when incoming genomes were expected to have multiplied by replication. Thus the study by Krzywkowski et al. was unable to provide information for the questions addressed in our study, i.e. do the levels of E1A transcripts early in infection correlate with viral vDNA counts in the nucleus and is there variability in the transcription output from individual vDNAs within the same nucleus, or variability in how individual vDNAs within the same nucleus proceed into the replication phase. We hence do provide novel information, and do not consider this as a limitation of our paper.

We emphasize that population assays are done to attempt to understand molecular basis of a phenomenon by correlations. Instead, deep molecular insights require to-the-point-assays, in the case of transcription, single-molecule live cell assays at the level of single genes. Technically, we (and also the field) are not quite there yet.

Regardless, our study is a first step towards understanding transcription output of nuclear HAdV-genome at single-cell, single-genome levels. It has revealed insight that was not apparent from population assays. It is clear that the next step will be time-resolved live cell assays with simultaneous detection of transcription output, genome detection and transcription factor clustering on the genomic loci. With current technology the simultaneous detection of all these events is challenging, and requires the development of further technology.

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

The authors show heterogeneity of AdV-C5 mRNA transcript quantity and dynamics in different cell types, which is regulated by the cell cycle phase and does not correlate to incoming viral DNA, using single molecule RNA FISH technologies and detection of incoming viral DNA by EdC labeling.

**Major Comments:**

The authors change the MOI used in their experiments (7 different MOIs are used throughout the paper) in a manner that appears randomly and without explanation. (54400 for Figure 1A, 1B, 3B, S3B; 37500 for Figure 1C; 23440 for Figure 2A, 2C, S5A; 13600 for Figure 1A, 1D; 36250 for Figure 3C, S3D; 11200 for Figure 4B; 23400 for Figure 6B). The authors should provide explanation, why these changes in MOIs are necessary.

AU: The MOIs given are theoretical MOIs, and essentially all figures indicate what was the actual MOI, that is, the real number of virus particles entering into the cells. This is beyond what is commonly provided in virology. It is essential, however, since MOI differs between different cell types. Therefore, we prefer to use the actual MOI as shown in Fig.1A, or we indicate the number of vDNAs that were delivered to the cells of interest.

Variable MOIs had to be used to ensure that different cell lines received comparable numbers of virions, in particular virus particle binding to and entering into the cells. Infection kinetics are different in different types of cells, but can be tuned by MOIs used. Furthermore, different virus preparations were used in the experiments and we performed analyses at different stages of the infection cycle. Due to all these different facettes provided by our experiments, it was impossible to choose one standard (theoretical) MOI for all the experiments.

The authors use mean fluorescence intensity of E1A probes per cell as estimate for viral transcript abundance for some of their experiments (Figure 1D, E, 3B), and count E1A punctae as measure for E1A transcripts in other experiments (Figure 2C, 3C, 5), without showing data, that these measures correlate. Problematic is hereby, that not all E1A punctae have the same signal intensity, as can be seen in Figure S1, which makes the estimation of the correlation of E1A punctae (= number of transcripts) and fluorescence intensity difficult. The authors should provide both (E1A punctae counts and estimation via fluorescence intensity) for at least one experiment, to prove, that the estimation of E1A transcript levels via fluorescence intensity is feasible.

AU: The quantification method had to be adjusted to the number of virus transcripts in the cell at the time of analysis. The best quantification method is segmentation and counting the individual fluorescent puncta per cell, but, as stated in the manuscript, this method does not accurately quantify the mRNA puncta from maximum projections of confocal or widefield image stacks when the number of puncta per cell exceeds ~ 200.

On the other hand, as shown in the quantification below, mean fluorescence intensity measurements per cell do not of course distinguish between cells having one vs. two mRNA puncta. Yet, as shown in the figure below, a relatively good correlation between puncta counting and fluorescence intensity measurements is achieved when cells have ≥ 10 transcripts per cell. Subsets of randomly picked images of the Fig.2C/Fig.5 dataset were included into the analysis (rs is Spearman’s correlation rank coefficient, approximate P p.15: "The nuclear E1A signals in AraC-treated cells were resistant to RNase A, but they were dampened by treatment with S1 nuclease (S6B Fig)." The authors make this statement based on (i) two completely different timepoints (12 h.p.i. for RNaseA treatment, 24.5 h.p.i. for S1 nuclease treatment) and (ii) in different clones of the A549 cells as stated in the methods section on p.21 (Two different clones of human lung epithelial carcinoma A549 cells were used in the study: our laboratory's old A549 clone (experiments shown in Fig. 1, Fig. 3B and S1 Fig., S3B and S3C Fig., S6A and S6B Fig., RNase A treatment) and A549 from American Type Culture Collection (ATCC, experiments shown in Fig. 2 and Fig. 5, Fig. 6, S2B Fig., S4 Fig., S5 Fig., and S6B Fig. S1 nuclease-treatment)). This makes it difficult to interpret, if the data is due to differences in the timepoints or cell types, or if it is due to binding of the E1A probe to single stranded vDNA.

AU: This is a fair criticism, thank you. We have replaced the RNase A figure S6B in the revised manuscript. A new RNase A experiment was repeated in ATCC A549 cells using the same infections conditions as with the S1 nuclease-treated cells.

**Minor Comments:**

p.4: "AdV are non-enveloped, double-stranded DNA viruses that cause mild respiratory infections in immuno-competent hosts, and establish persistent infections, which can develop into life-threatening infections if the host becomes immuno-compromised [reviewed in 6]." Not all AdV cause respiratory diseases, the disease outcome of human AdV depends on the site of primary infection, which differs between the different AdV types.

AU: We have modified the text as follows: AdV are non-enveloped, double-stranded DNA viruses that cause mild respiratory, gastrointestinal or ocular infections…

p.7: The authors state, that "At the 17 h time point, about half of the cells had high numbers of protein VI transcripts, and most of them very high numbers of E1A transcripts.", however, the picture shown in Figure 1F shows a different phenotype, with low transcript levels of VI in E1A high cells and high transcript levels of VI in E1A low cells.

AU: This was perhaps a bit difficult to see in the overlay images since one has to distinguish between green and yellowish green. We have provided the individual channels along the overlay picture in Fig. S1D, and now it is clear that at 17h pi cells with high numbers of VI transcripts have also high numbers of E1A transcripts.

p.8: "This nuclear E1A signal is due to binding of the E1A probe to single-stranded vDNA in the replication centers (see below)." The authors should state here, that due to the binding of the probes to the single stranded vDNA in the replication centers, the nucleus was excluded from the analysis for Figure 1F in late timepoints.

AU: We have modified the text according to the Reviewer’s suggestion. The text is now as follows: ‘Due to further studies (see below), we assume that this nuclear E1A signal represents binding of the E1A probe to single-stranded vDNA in the replication centers. Accordingly, the nuclear area was excluded when quantifying the viral transcripts per cell in late timepoints (Fig. 1F).’

Due to this time point the author cannot state that the E1A staining seen (Fig. 1F; indicated with white arrows) are replication centers; this is just an assumption, since there is no evidence in Fig 1 the author cannot be sure; the author should change the text: "taking the following experiments into account...", "due to further studies (see below)..... we assume that..."

AU: We have modified the text according to the Reviewer’s suggestion; see also the previous comment above.

p.8: The authors should mention the figure they refer to, since there is no E1B-55K staining in Fig. 1F

AU: The text has been modified as follows: Whereas other time points showed relatively few E1A, E1B-55K or VI puncta over the nuclear area (Fig. 1B, 1F, S1A Fig.), clustered nuclear E1A signals were apparent at 23 h.

p.9: Which test was used to calculate the additional p-values?

AU: As stated in the Material and Methods section or the figure legends, the p-values were calculated either by a permutation test using custom-programmed R-script (the code has been deposited on Mendeley Data along with other data associated with this manuscript), or by Kolmogorov-Smirnov test using GraphPad Prism. GraphPad Prism was also used to calculate Spearman’s correlation coefficients and the associated approximate p values. In the revised manuscript, we have added the following sentense into the Material and Methods section / Statistical analyses: Spearman’s correlation tests were done using GraphPad Prism.

p.10: For the experiment for the correlation of viral genomes per cell and E1A transcripts in HDF-TERT cells (Figure S2C), the MOI is missing in the description of the results, as well as in the corresponding figure legends.

AU: We have indicated the theoretical MOI (~ 4800 virus particles per cell) in the figure legend and in the Material and Methods section. The actual MOI, i.e. the actual number of virus particles entering into the cells, could not be determined due to the long (15 h) incubation time of virus inoculum with the cells, which in turn was required because these cells bind AdV-C5 rather inefficiently. However, between 1 and 32 EdC-labeled virus genomes were detected per cell nucleus at 22 h pi.

11: calculation of correlation? rs? Why does the author combine S and G2/M phase? Fig. S3A show different values for the phases

AU: rs is the abbreviation for Spearman’s correlation coefficient, and, as indicated in the Material and Methods, we used GraphPad Prism to calculate the Spearman’s correlation coefficients.

Different methods to estimate cell cycle stages. DNA content method cannot separate S and G2/M with great confidence, whereas Kusabira Orange-hCdt1 and Azami-Green-hGeminin expressions in HeLa-Fucci cells allow more fine-tuned assessment of the cell cycle phases.

p.11: "Thus, the total intensity of nuclear DAPI signal can be used to accurately assign G1 vs S/G2/M stage to cells." The authors should also here refer to other papers, which showed that this correlation is feasible, as they did in the methods section (67. Roukos V, Pegoraro G, Voss TC, Misteli T. Cell cycle staging of individual cells by fluorescence microscopy. Nature protocols. 2015;10(2):334-48. Epub 2015/01/31. doi: 10.1038/nprot.2015.016. PubMed PMID: 25633629; PubMed Central PMCID:PMCPMC6318798.), and maybe also refer to a newer paper which deals with this technique: Ferro, A., Mestre, T., Carneiro, P. et al. Blue intensity matters for cell cycle profiling in fluorescence DAPI-stained images. Lab Invest 97, 615-625 (2017). https://doi.org/10.1038/labinvest.2017.13

AU: The integrated nuclear DAPI signal intensity is indeed a widely used method to assign cell-cycle stage to individual cells. We have added the second reference suggested by the Reviewer to the reference list for this method.

p.11: "Furthermore, when focusing on the highest E1A expressing cells, i.e. the cells with mean cytoplasmic E1A intensities larger than 1.5 × interquartile range from the 75th percentile, 71.9% of these cells were found to be in the G1 phase of cell cycle, whereas only 55.8% of cells in the total sampled cell population were G1 cells." The authors do not provide any reference to a figure within the manuscript or the supplements, which contains these data. Are these data not shown in the manuscript?

AU: These values are calculated from the data shown in Fig.3B. The source data supporting findings of this study (maximum projection images, excel files of the CellProfiler and Knime workflows) have now been deposited to Mendeley Data as stated in the Material and Methods / Data availability section of the revised manuscript and listed in Supplementary tables.

p.12: punctuation mistake; . instead of , To enrich G1 cells. AdV-C-5 (moi ~ 36250) was added. Why does the author switch between signal intensities and counting E1A puncta per cell (limited to 200) in the different experiments to illustrate accumulation of E1A transcripts?

AU: The same answer as above: the quantification method had to be adjusted to the number of virus transcripts in the cell at the time of analysis. The best quantification method is segmentation and counting the individual fluorescent puncta per cell, but, as stated in the manuscript, this method does not accurately quantify the mRNA puncta from maximum projections of confocal or widefield image stacks when the number of puncta per cell exceeds ~ 200. On the other hand, as shown in the quantification in the new S1C Fig., mean fluorescence intensity measurements per cell do not of course distinquish between cells having one vs. two mRNA puncta, but a relatively good correlation between puncta counting and fluorescence intensity measurements is achieved when cells have ≥ 10 transcripts per cell.

p.14: "For E1A (or E1B-55K), we did not detect transcriptional bursts with bDNA-FISH probes on nuclear vDNAs, either prior to or after accumulation of viral transcripts in the cell cytoplasm." The authors do not provide any reference to a figure within the manuscript or the supplements, which contains these data. Are these data not shown in the manuscript?

AU: This statement is based on hundreds of images we have analyzed during the course of the study. It is impossible to show all of these images, so in principle, this is “data not shown”. We have modified the text as follows: With hundreds of images analyzed, we never unambiguously detected transcriptional bursts with E1A (or E1B-55K) bDNA-FISH probes on nuclear vDNAs, either prior to or after accumulation of viral transcripts in the cell cytoplasm.

p.14: space between number and %

AU: Thank you for pointing this out. It has been corrected.

p.15: "This is was also seen in AdV-C5-EdC-infected cells" should be changed to "This was also seen in AdV-C5-EdC-infected cells"

AU: Thank you for pointing this out. It has been corrected.

Fig. 1B:

−figure legend does not indicate how cells were staine −also no description in the continuous text −which E1A transcripts are stained? all? 12S? 13S?

AU: The first sentence in Results section states that “We used fluorescent in situ hybridization (FISH) with probes targeting E1A, E1B-55K and protein VI transcripts followed by branched DNA (bDNA) signal amplification to visualize the appearance and abundance of viral transcripts in AdV-C5-infected A549 lung carcinoma cells.” Furthermore, the legend to Figure 1 starts with the title “Visualization of AdV-C5 E1A, E1B-55K and protein VI transcripts in infected cells by bDNA-FISH technique”, and the legend to Fig.1B mentions that “cells were stained with probes against E1A and E1B-55K mRNAs or E1A and protein VI mRNAs”. We are of the opinion that this is enough information to understand the figures.

The main text to Fig.1 also states that “The E1A probes covered the entire E1A primary transcript region and thus all E1A splice variants. The temporal control of E1A primary transcript splicing and E1A mRNA stability give rise predominantly to 13S and 12S E1A mRNAs at 5 h pi (references)”.

Fig. 1D: −difference in accumulation of viral transcripts is not that visible as in IF staining (Fig. 1B; Fig. 1S);

Fig. 1 or S1 Fig. do not show IF staining but signals from FISH.

−graph does not show any difference between E1A and E1B-55K

AU: The y-axes values in Fig.1D graph are arbitrary units and thus E1A and E1B-55K graphs are not directly comparable to each other. We have included into the revised manuscript S1B Fig., which shows quantification of E1A and E1B-55K fluorescent puncta per cell at the 5 h pi; the difference between E1A and E1B-55K was statistically significant.

Fig. 1F: −figure legend does not fit with labelling of IF images and continuous text −description says 22 h, while IF labeling and text (p. 7, last lane) mentions 23 h pi

AU: The figure annotations state the time of analyses as total time after virus addition to cells, whereas text stated the time of analyses as x h post virus removal since we wanted to stress that the input virus was incubated only for 1 h with the cells. However, Reviewers found this confusing, so we have changed the text in the revised manuscript so that time of analysis is stated as total time after virus addition to cells (as in the figure annotations). Only in the Material and Methods section we maintain the original 1 h + x h statement for the time of analysis.

Fig. 2A: −figure legend: lane 5 Punctuation wrong: azide-Alexa Fluor488. Alexa Fluor647

AU: Thank you for pointing this out. It has been corrected.

Fig. 4A: −difficulties to understand −author stated that promoter-driven EGFP expression is clearly dominated by G1 cells for E1A and by S/G2/M cells for CMV, however this is not clearly visible in the graph −no severe differences visible between CMV-eGFP and E1A-eGFP −author should include numbers for quantification and statistical calculations to illustrate the differences

AU: In the highest GFP expression bin, CMV-eGFP expressing cells have 43% cells in G1 and 50% in S/G2/M (n=2149). In comparison, E1A-GFP expressing cells have 58% cells in G1 and 35% in S/G2/M (n=2258). The difference in G1 cells in the highest eGFP bin is statistically significant (p

Fig. 4B: −amount of E1A protein levels calculated via IF (signal intensities) −immunofluorescence is not a suitable tool for protein quantification

AU: It is true that not all antibodies are suitable for IF (or for Western blot), and we cannot be certain that the monoclonal anti-E1A antibody used by us detects all E1A forms with different post-translational modifications with equal efficiency. However, IF is a widely accepted method to estimate protein levels in the cell, especially if the proteins like E1A accumulate in the nucleus (makes segmentation of the signal easy) and give a rather uniform nuclear staining pattern.

Fig. 5: −in A. it is stated, that E1A bDNA -FISH is not suitable, since it is too short to be detectable. However, in B E1A bDNA-FISH is used. is there a difference? −according to the method part just one E1A mRNA was used for the assays, why is it then not possible to use that one in Fig. 5A? −explanation of the procedure and the experiment is very confusing

AU: The Reviewer probably refers to Fig.6 here, not to Fig.5. The E1A introns are short (about 100 bases) and cannot be picked up with bDNA FISH probes. In Fig. 6B we were using the E1A bDNA-FISH probes, which were made against the AdV-C5 genome map positions 551-1630 to detect vDNA single strands of the E1A region and these single strands were long enough to be picked out by our E1A probes.

Fig. S6B: −authors want to show that it is RNase-insensitive, but S1 nuclease-sensitive

−two different A549 cell clones and two different time points are used for the treatments → not compareable to each other

AU: This is a fair criticism. We have replaced the RNase A figure in S6B Fig. in the revised manuscript. The new RNase A experiment was carried out in ATCC A549 cells using the same infections conditions as with the S1 nuclease-treated cells.

Material and Methods: −headings do not indicate which methods are explained −no clear structure AU: We have made minor changes to the headings of Material and Methods section. We have first explained in detail the bDNA-FISH method, but otherwise the order is according to the order of the figures.

Reviewer #2 (Significance (Required)):

highly significant manuscript very important for the virology field

my research topics are human adenoviruses and their replication cycle

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

**Summary:** Soumalainen et al have studied adenovirus viral gene expression and replication at a single-cell level. They explore the extent of correlation between incoming genome copy number and early gene expression and progression into the late phase, revealing substantial variation between cells in the numbers of E1A transcripts (the first gene expressed upon infection) that is not explained by differences in the numbers of viral genome templates in the cells. They also explore the relevance of cell cycle stage to this variability and show a positive correlation between G1 cell cycle stage and higher levels of gene activity, which explains at least part of the variation. To form these conclusions they have applied new methods to visualise and quantify single molecules of nucleic acid in single cells. The experiments are all carefully and fully described with full detail of materials. Overall the manuscript is well written and easy to follow.

**Major comments:**

All of the experiments appear to be done with rigour and their results reported with due regard to statistical significance etc. My major concern though is that they have been done, perhaps out of necessity to get detectable signals, at very high multiplicities of infection. A well-accepted standard to achieve infection of all cells in a culture is an MOI of 10 infectious units per cell. Even this is acknowledged not to represent the biology of natural infection and it is striking that, where technically feasible, lower MOI studies are more revealing of how a virus actually works. Here, the authors have used counts of particles rather than infectious units to determine MOI and for Ad5, the particle/pfu ratio is typically 20-100. Their MOIs though are 13,000 - 50,000 per cell, implying an infectious MOI of at least 130 for their A549 experiments, which are known to be readily infected by Ad5 from other work.

AU: Unlike common experiments done by others, we used a synchronized infection and removed the input virus after 1h incubation at 37°C. This type of infection initiation requires high input virus amounts, as opposed to studies in which the virus inoculum is incubated with cells for several hours/days, as is typically done in studies determining the infectious or plaque forming units in virus inoculum. Hence, the MOI used by others involved incubation of inoculum with cells over extended periods of time, and they cannot be compared to our pulsed infection conditions.

Although the calculated theoretical MOIs (physical particles/cell) were high in our experiments, only 0.1% – 0.2% of input virus particles bound to cells during the 1h incubation period (Fig. 1 A; this estimation is based on the ratios between Median values for the number of cell-associated viruses vs input virus numbers).

Furthermore, in the experiment described in Fig.2C (correlation of E1A transcripts per cell vs. viral genomes per cell), 42% of analyzed cells had ≤ 5 viral genomes/cell and 27.5% of analyzed cells had between 6-10 viral genomes per cell. Please note, that these are not high numbers.

The input virus amounts used were selected this way, because we aimed at getting a broader view of how virus transcription at early phases of infection responds to a varying number of virus genomes delivered to the nucleus. Therefore, we did not limit the analyses to a situation with 1 or less than 1 virus particles/genomes per cell.

In addition, the analyses of how cell cycle phase impacts the initiation of virus gene expression requires a relatively short time between virus inoculation and time point of analysis (i.e. a rather high MOI). Otherwise, as also pointed out by the Reviewer, the cells could have experienced more than one cell cycle phase during the duration of the experiment. Furthermore, although the initial natural infection probably starts with a very low MOI, the second round of infection is a high MOI infection due to a large number of progeny virus particles released from an infected cell.

Surprisingly, the authors do not see intracellular vDNA copy numbers that are fully reflective of this high MOI, with median intracellular vDNA of 75 /cell at the highest MOI. The authors should consider how the population distribution of vDNA /cell does or does not fit the predicted Poisson distribution. Nonetheless, at these high copy numbers / cell, there must surely be a risk that the variation in gene expression activity arises stochastically, out of competition between genomes for essential transcription factors. Given that multiple cellular factors are each required for E1A transcription, high genome copy numbers could actually inhibit E1A expression relative to cells with more modest copy numbers because limited supplies of individual factors are recruited to different viral genome copies.

AU: The “discrepancy” between theoretical MOI and the actual observed number of cell-associated virus particles or cell-associated virus genomes is explained above. Furthermore, we would like to point out that we have directly estimated the number of virus particles bound to cells with the input virus amounts used, something that is usually not done in other studies.

It is indeed theoretically possible that high nuclear genome numbers could lead to inhibition of transcription due to competition for limiting essential host factors. However, if we included only cells with ≤4 vDNA molecules per nucleus into the analysis (total number of cells analyzed was 258), then Spearman’s correlation coefficient for vDNA per nucleus vs E1A mRNAs per cell was 0.186 (p=0.0027). Thus, this would not support the notion that cells with moderate nuclear vDNA copy numbers would have a better correlation between the nuclear vDNA copies vs E1A mRNA counts per cell.

The vDNA/cell in Fig.2C does not fit predicted Poisson distribution, var/mean=9.129.

It is important for the analysis of correlation of gene expression with cell cycle that the virus has not, at the time point analysed, already perturbed the cell cycle (a well-known effect of infection) which the authors document in Suppl Fig3B. To my eye, the G1 peak in infected cells is somewhat narrower than in the control while the S/G2 bump is a little greater. The % of cells in each of the two gates needs to be shown to support the conclusion.

AU: In non-infected sample G1= 54.63% and S/G2/M = 45.37%, in infected cells G1= 51.4% and S/G2/M= 48.6%. We have added this information into the S3B Fig.

Turning to the experiments documenting a correlation between E1A expression and cell cycle stage, the authors interpret their findings in terms of the stage the cells are at when the analysis was done (G1 stage cells have more E1A transcripts). The key experiment (Fig 3B) is analysed at only 4 h pi, so substantial progression from G2/M back to G1 after virus addition can probably be discounted, but the point should be discussed. The authors also use release from G1 in another cell line to support their argument that G1 supports higher levels of E1A expression (Fig 3C). Here, they elect to exclude all cells with fewer than 50 E1A transcripts from their analysis. The reason for this is completely obscure and isn't obviously justified; conceivably it could bias the outcome of the experiment. At minimum, this decision needs to be carefully explained; ideally, the full data set should be used.

AU: Fig.3B: As suggested by the Reviewer, we have added to the main text the following explanation: “We used a high MOI infection (median 75 cell-associated virus particles, Fig. 1A) in order to achieve a rapid onset of E1A expression so that the time between virus addition and analysis was short. Thus, it is not expected that a substantial number of cells would have changed their cell cycle status during the experiment.”

Fig.3C: We show the results also from the full data set of infected cells, i.e., cells with ≥ 1 E1A puncta in S3D Fig. We excluded the cells without zero E1A puncta because with these cells it is impossible to know whether they received no virus or whether E1A transcription had not yet started. Permutation test indicated that the difference between the starved+starved and starved+FCS is statistically significant even in this case. Because both samples are dominated by cells with low E1A counts, we log-transformed the E1A values for the box plot figure.

The authors note the highest level of E1A activity (as opposed to RNA) was in G1/S cells and suggest that high E1A cells advance preferentially into S. Whilst in line with the literature that E1A promotes progression into S, an alternative explanation is simply that there is a time lag between RNA accumulation and protein accumulation, during which progression through the cycle would be expected.

AU: This is a valid point, and we have modified the text as follows: “… which could reflect the advancement of high E1A expressing cells into S-phase. However, considering the time between virus addition and analysis (10.5 h), we cannot exclude the possibility that the observed G1/S preference is at least partly due to time-dependent progression of G1 cells to G1/S.”

**Minor comments:** Fig 1 and elsewhere. Given that the 1 h incubations with virus were done at 37 C, the convention would be to include this period in the time post-infection at which harvest / fix time points are quoted. There is inconsistency between text and legend with 12 h pi being sometimes represented as 11 h after virus removal; this is an unnecessary confusion.

AU: We have modified the text so that hours pi always include the 1h incubation with the input virus. Only in the Material and Methods section we kept the original 1h virus binding – fixing at xh post virus removal.

Results description prior to the ref to Fig 1B: unclear what this is supposed to mean.

AU: We have now slightly modified the first paragraph of the Results section. We mention the benefits of the bDNA signal amplification method and explain the experimental set up, i.e. that the input virus was incubated with the cells only for 1h. We also justify why we used a short incubation for the virus inoculum.

Fig 4A: provide % of cells in each gate in each histogram.

AU: In the highest GFP expression bin, CMV-eGFP expressing cells have 43% of cells in G1 and 50% in S/G2/M. In comparison, E1A-GFP expressing cells have 58% of cells in G1 and 35% in S/G2/M. This has been added to the figure, and it is also mentioned in the main text. Furthermore, we added to the text the results from Two Proportion Z-test to show that the proportion difference of G1 cells in the highest bin was statistically significant (p

Fig 5: bottom right panel x axis label is wrong

AU: Thank you for pointing out this. This has been corrected.

In the presentation of Fig 6, it would be much clearer for the reader if the detected replication foci (ss DNA detected as E1A puncta) were referred to as something other than E1A puncta. There is too much scope for confusion with the earlier experiments in which E1A RNA was detected.

AU: We agree. In the revised manuscript, we refer to these puncta in the text as E1A ssDNA-foci.

Reviewer #3 (Significance (Required)):

The study represents the application of state of the art single-molecule visualization techniques to an as yet not understood aspect of virus infection. That said, there is prior experimentation in this area, which the authors fully acknowledge and build upon. The new work is largely descriptive, in that it reveals very clearly the discrepancy between genome copy number and amounts of mRNA without seeking to explain these, beyond the cell cycle analysis. Whilst there is a better correlation between vDNA number and transcript once the data are stratified by cell cycle stage, it is still not strong (Fig 5), indicating that other substantial contributing factors remain to be described.

The work will be of interest certainly to adenovirologists, but also to others who study virus infections - particularly nuclear-replicating DNA viruses such as herpesviruses - where similar considerations are likely to apply.

Expertise: adenovirus; gene expression; virus-host interactions; molecular biology

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

Learn more at Review Commons

---

Referee #3

Evidence, reproducibility and clarity

Summary: Soumalainen et al have studied adenovirus viral gene expression and replication at a single-cell level. They explore the extent of correlation between incoming genome copy number and early gene expression and progression into the late phase, revealing substantial variation between cells in the numbers of E1A transcripts (the first gene expressed upon infection) that is not explained by differences in the numbers of viral genome templates in the cells. They also explore the relevance of cell cycle stage to this variability and show a positive correlation between G1 cell cycle stage and higher levels of gene activity, which explains at least part of the variation. To form these conclusions they have applied new methods to visualise and quantify single molecules of nucleic acid in single cells. The experiments are all carefully and fully described with full detail of materials. Overall the manuscript is well written and easy to follow.

Major comments:

All of the experiments appear to be done with rigour and their results reported with due regard to statistical significance etc. My major concern though is that they have been done, perhaps out of necessity to get detectable signals, at very high multiplicities of infection. A well-accepted standard to achieve infection of all cells in a culture is an MOI of 10 infectious units per cell. Even this is acknowledged not to represent the biology of natural infection and it is striking that, where technically feasible, lower MOI studies are more revealing of how a virus actually works. Here, the authors have used counts of particles rather than infectious units to determine MOI and for Ad5, the particle/pfu ratio is typically 20-100. Their MOIs though are 13,000 - 50,000 per cell, implying an infectious MOI of at least 130 for their A549 experiments, which are known to be readily infected by Ad5 from other work.

Surprisingly, the authors do not see intracellular vDNA copy numbers that are fully reflective of this high MOI, with median intracellular vDNA of 75 /cell at the highest MOI. The authors should consider how the population distribution of vDNA /cell does or does not fit the predicted Poisson distribution. Nonetheless, at these high copy numbers / cell, there must surely be a risk that the variation in gene expression activity arises stochastically, out of competition between genomes for essential transcription factors. Given that multiple cellular factors are each required for E1A transcription, high genome copy numbers could actually inhibit E1A expression relative to cells with more modest copy numbers because limited supplies of individual factors are recruited to different viral genome copies. It is important for the analysis of correlation of gene expression with cell cycle that the virus has not, at the time point analysed, already perturbed the cell cycle (a well-known effect of infection) which the authors document in Suppl Fig3B. To my eye, the G1 peak in infected cells is somewhat narrower than in the control while the S/G2 bump is a little greater. The % of cells in each of the two gates needs to be shown to support the conclusion.

Turning to the experiments documenting a correlation between E1A expression and cell cycle stage, the authors interpret their findings in terms of the stage the cells are at when the analysis was done (G1 stage cells have more E1A transcripts). The key experiment (Fig 3B) is analysed at only 4 h pi, so substantial progression from G2/M back to G1 after virus addition can probably be discounted, but the point should be discussed. The authors also use release from G1 in another cell line to support their argument that G1 supports higher levels of E1A expression (Fig 3C). Here, they elect to exclude all cells with fewer than 50 E1A transcripts from their analysis. The reason for this is completely obscure and isn't obviously justified; conceivably it could bias the outcome of the experiment. At minimum, this decision needs to be carefully explained; ideally, the full data set should be used.

The authors note the highest level of E1A activity (as opposed to RNA) was in G1/S cells and suggest that high E1A cells advance preferentially into S. Whilst in line with the literature that E1A promotes progression into S, an alternative explanation is simply that there is a time lag between RNA accumulation and protein accumulation, during which progression through the cycle would be expected.

Minor comments:

Fig 1 and elsewhere. Given that the 1 h incubations with virus were done at 37 C, the convention would be to include this period in the time post-infection at which harvest / fix time points are quoted. There is inconsistency between text and legend with 12 h pi being sometimes represented as 11 h after virus removal; this is an unnecessary confusion.

Results description prior to the ref to Fig 1B: unclear what this is supposed to mean.

Fig 4A: provide % of cells in each gate in each histogram.

Fig 5: bottom right panel x axis label is wrong

In the presentation of Fig 6, it would be much clearer for the reader if the detected replication foci (ss DNA detected as E1A puncta) were referred to as something other than E1A puncta. There is too much scope for confusion with the earlier experiments in which E1A RNA was detected.

Significance

The study represents the application of state of the art single-molecule visualization techniques to an as yet not understood aspect of virus infection. That said, there is prior experimentation in this area, which the authors fully acknowledge and build upon. The new work is largely descriptive, in that it reveals very clearly the discrepancy between genome copy number and amounts of mRNA without seeking to explain these, beyond the cell cycle analysis. Whilst there is a better correlation between vDNA number and transcript once the data are stratified by cell cycle stage, it is still not strong (Fig 5), indicating that other substantial contributing factors remain to be described.

The work will be of interest certainly to adenovirologists, but also to others who study virus infections - particularly nuclear-replicating DNA viruses such as herpesviruses - where similar considerations are likely to apply.

Expertise: adenovirus; gene expression; virus-host interactions; molecular biology

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

Learn more at Review Commons

---

Referee #2

Evidence, reproducibility and clarity

The authors show heterogeneity of AdV-C5 mRNA transcript quantity and dynamics in different cell types, which is regulated by the cell cycle phase and does not correlate to incoming viral DNA, using single molecule RNA FISH technologies and detection of incoming viral DNA by EdC labeling.

Major Comments:

The authors change the MOI used in their experiments (7 different MOIs are used throughout the paper) in a manner that appears randomly and without explanation. (54400 for Figure 1A, 1B, 3B, S3B; 37500 for Figure 1C; 23440 for Figure 2A, 2C, S5A; 13600 for Figure 1A, 1D; 36250 for Figure 3C, S3D; 11200 for Figure 4B; 23400 for Figure 6B). The authors should provide explanation, why these changes in MOIs are necessary. The authors use mean fluorescence intensity of E1A probes per cell as estimate for viral transcript abundance for some of their experiments (Figure 1D, E, 3B), and count E1A punctae as measure for E1A transcripts in other experiments (Figure 2C, 3C, 5), without showing data, that these measures correlate. Problematic is hereby, that not all E1A punctae have the same signal intensity, as can be seen in Figure S1, which makes the estimation of the correlation of E1A punctae (= number of transcripts) and fluorescence intensity difficult. The authors should provide both (E1A punctae counts and estimation via fluorescence intensity) for at least one experiment, to prove, that the estimation of E1A transcript levels via fluorescence intensity is feasible. p.15: "The nuclear E1A signals in AraC-treated cells were resistant to RNase A, but they were dampened by treatment with S1 nuclease (S6B Fig)." The authors make this statement based on (i) two completely different timepoints (12 h.p.i. for RNaseA treatment, 24.5 h.p.i. for S1 nuclease treatment) and (ii) in different clones of the A549 cells as stated in the methods section on p.21 (Two different clones of human lung epithelial carcinoma A549 cells were used in the study: our laboratory's old A549 clone (experiments shown in Fig. 1, Fig. 3B and S1 Fig., S3B and S3C Fig., S6A and S6B Fig., RNase A treatment) and A549 from American Type Culture Collection (ATCC, experiments shown in Fig. 2 and Fig. 5, Fig. 6, S2B Fig., S4 Fig., S5 Fig., and S6B Fig. S1 nuclease-treatment)). This makes it difficult to interpret, if the data is due to differences in the timepoints or cell types, or if it is due to binding of the E1A probe to single stranded vDNA.

Minor Comments:

p.4: "AdV are non-enveloped, double-stranded DNA viruses that cause mild respiratory infections in immuno-competent hosts, and establish persistent infections, which can develop into life-threatening infections if the host becomes immuno-compromised [reviewed in 6]." Not all AdV cause respiratory diseases, the disease outcome of human AdV depends on the site of primary infection, which differs between the different AdV types.

p.7: The authors state, that "At the 17 h time point, about half of the cells had high numbers of protein VI transcripts, and most of them very high numbers of E1A transcripts.", however, the picture shown in Figure 1F shows a different phenotype, with low transcript levels of VI in E1A high cells and high transcript levels of VI in E1A low cells.

p.8: "This nuclear E1A signal is due to binding of the E1A probe to single-stranded vDNA in the replication centers (see below)." The authors should state here, that due to the binding of the probes to the single stranded vDNA in the replication centers, the nucleus was excluded from the analysis for Figure 1F in late timepoints. Due to this time point the author cannot state that the E1A staining seen (Fig. 1F; indicated with white arrows) are replication centers; this is just an assumption, since there is no evidence in Fig 1 the author cannot be sure; the author should change the text: "taking the following experiments into account...", "due to further studies (see below)..... we assume that..." p.8: The authors should mention the figure they refer to, since there is no E1B-55K staining in Fig. 1F

p.9: Which test was used to calculate the additional p-values?

p.10: For the experiment for the correlation of viral genomes per cell and E1A transcripts in HDF-TERT cells (Figure S2C), the MOI is missing in the description of the results, as well as in the corresponding figure legends.

p. 11: calculation of correlation? rs? Why does the author combine S and G2/M phase? Fig. S3A show different values for the phases

p.11: "Thus, the total intensity of nuclear DAPI signal can be used to accurately assign G1 vs S/G2/M stage to cells." The authors should also here refer to other papers, which showed that this correlation is feasible, as they did in the methods section (67. Roukos V, Pegoraro G, Voss TC, Misteli T. Cell cycle staging of individual cells by fluorescence microscopy. Nature protocols. 2015;10(2):334-48. Epub 2015/01/31. doi: 10.1038/nprot.2015.016. PubMed PMID: 25633629; PubMed Central PMCID:PMCPMC6318798.), and maybe also refer to a newer paper which deals with this technique: Ferro, A., Mestre, T., Carneiro, P. et al. Blue intensity matters for cell cycle profiling in fluorescence DAPI-stained images. Lab Invest 97, 615-625 (2017). https://doi.org/10.1038/labinvest.2017.13

p.11: "Furthermore, when focusing on the highest E1A expressing cells, i.e. the cells with mean cytoplasmic E1A intensities larger than 1.5 × interquartile range from the 75th percentile, 71.9% of these cells were found to be in the G1 phase of cell cycle, whereas only 55.8% of cells in the total sampled cell population were G1 cells." The authors do not provide any reference to a figure within the manuscript or the supplements, which contains these data. Are these data not shown in the manuscript?

p.12: punctuation mistake; . instead of , To enrich G1 cells. AdV-C-5 (moi ~ 36250) was added. Why does the author switch between signal intensities and counting E1A puncta per cell (limited to 200) in the different experiments to illustrate accumulation of E1A transcripts?

p.14: "For E1A (or E1B-55K), we did not detect transcriptional bursts with bDNA-FISH probes on nuclear vDNAs, either prior to or after accumulation of viral transcripts in the cell cytoplasm." The authors do not provide any reference to a figure within the manuscript or the supplements, which contains these data. Are these data not shown in the manuscript?

p.14: space between number and %

p.15: "This is was also seen in AdV-C5-EdC-infected cells" should be changed to "This was also seen in AdV-C5-EdC-infected cells"

Fig. 1B:

−figure legend does not indicate how cells were staine

−also no description in the continuous text

−which E1A transcripts are stained? all? 12S? 13S?

Fig. 1D:

−difference in accumulation of viral transcripts is not that visible as in IF staining (Fig. 1B; Fig. 1S);

−graph does not show any difference between E1A and E1B-55K

Fig. 1F:

−figure legend does not fit with labelling of IF images and continuous text

−description says 22 h, while IF labeling and text (p. 7, last lane) mentions 23 h pi

Fig. 2A:

−figure legend: lane 5 Punctuation wrong: azide-Alexa Fluor488. Alexa Fluor647

Fig. 4A:

−difficulties to understand

−author stated that promoter-driven EGFP expression is clearly dominated by G1 cells for E1A and by S/G2/M cells for CMV, however this is not clearly visible in the graph

−no severe differences visible between CMV-eGFP and E1A-eGFP

−author should include numbers for quantification and statistical calculations to illustrate the differences

Fig. 4B:

−amount of E1A protein levels calculated via IF (signal intensities)

−immunofluorescence is not a suitable tool for protein quantification

Fig. 5:

−in A. it is stated, that E1A bDNA -FISH is not suitable, since it is too short to be detectable. However, in B E1A bDNA-FISH is used. is there a difference?

−according to the method part just one E1A mRNA was used for the assays, why is it then not possible to use that one in Fig. 5A?

−explanation of the procedure and the experiment is very confusing

Fig. S6B:

−authors want to show that it is RNase-insensitive, but S1 nuclease-sensitive

−two different A549 cell clones and two different time points are used for the treatments → not compareable to each other

Material and Methods:

−headings do not indicate which methods are explained

−no clear structure

Significance

highly significant manuscript very important for the virology field

my research topics are human adenoviruses and their replication cycle

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Referee #1

Evidence, reproducibility and clarity

The manuscript by Suomalainen et al. describes a fluorescence-based approach combined with high-resolution confocal microscopy to study the heterogeneity of adenovirus infection in a population of human cells. The main focus of the authors is the detection of viral transcripts in infected cells, how this correlates with viral genomes, the cell state, and how it varies between different cells in a single population. The paper is generally well written and easy to read, with a few typos, although I found parts of it to be somewhat length and repetitive. Particularly the results section could be pruned somewhat for readability and clarity. The major limitation of the study as it stands is it's overall impact and novelty, which limits journal selection somewhat. A very similar study was recently published, which the authors cite (Krzywkowski et al, 2017). Nevertheless, I think the study design is rigorous and well executed, but I do have some specific comments which may enhance it's overall impact and novelty.

Major: Results "Visualization of AdV-C5..." section:

Why not also look at normal cells that can be synchronized? Cancer cells, such as A549 will by definition be highly heterogenous and at all phases of the cell cycle. Primary non-transformed cells can easily be synchronized by contact inhibition and are much more physiologically relevant. "The virus particles bound..." - Can the spatial resolution of a confocal microscope truly differentiate individual particles that are sub-wavelength in size? What about the sensitivity for single particles? Some sort of experiment to show that single particles can be detected should be performed and shown to assure the readers that this is in fact possible. Furthermore, even when based on the particle to pfu ratio, the MOI would still be nearly 2000pfu/cell, so the actual number of observed particles is an order of magnitude lower than what was applied to the cells.

Fig. 4 - I am not certain that the observed difference is significant, at least looking at it, beyond the width difference of the peaks, highest expression for both is largely in G1. It would be nice to see this using a western blot of cell cycle sorted cells, which can easily be accomplished using FACS. Page 15, 2nd paragraph. It would be valuable and informative to determine whether there is heterogeneity in histone association with these different vDNAs and whether these histones exhibit divergent modifications (enabling or restricting transcription). Same as above. I am rather surprised that the DBP signal did not correlate well with vDNA signal, particularly for the larger replication centers. How can this be reconciled? Was there an increase in overall vDNA signal later in infection? It is important to know this as it determines whether the observed vDNA signal is real or could be caused by viral RNA or other background causes (non-infected controls notwithstanding). Can the signal be detected with inactivated viruses (via UV for example?)

Page 18, 1st paragraph. It would be interesting to determine whether there was association between pol II and those genomes that showed no E1A, similarly to the histone suggestion. What about things like viral chromatin organization? Soriano et al. 2019 showed how E1A and E4orf3 work in tandem to alter viral chromatin organization by varying histone loading on the viral genome. Fig. 2. Can you really say that a single dot correlates with a single transcript? Has that been validated in any way?

Minor:

Page 5, last paragraph. "Transcirpts from the viral late transcription unit,..." This is not correct as recently shown by Crisostomo et al, 2019.

Page 10, "... because AdvV-infected cells are less well adherent..." This is not strictly true as loss of attachment only occurs later on in infection. It would be helpful to have statistical significance indicated directly in the figures.

The very high MOIs used are concerning, could these have negative effects on the cell viability or overall state?

There are a few typos and such that should be corrected.

Significance

As I stated above, the work is interesting and significant, to a degree. The major limitation is that the novelty is low as a paper published in 2017 (cited by the authors) used a very similar approach to investigate a similar problem. In addition, there are multiple other recent papers looking at cell populations in the context of adenovirus infection, and whether a single cell or population based approach is better is unclear. This is something the authors might want to strengthen prior to submission.

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Reply to the reviewers

First of all, we thank all reviewers for their constructive suggestions and comments.

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

This group has been at the forefront recently of using imaging technologies to understand how chromosome segregation is coordinated in mammalian oocytes, and why errors occur. In the current paper they examine the dynamics of microtubule organising centres (which effectively replace centrioles/centrosomes in oocytes) in MI. The imaging of oocytes in this paper is beautiful. The major findings are (1) that MTOCs that are supposed to be at the spindle pole sometimes end up at the spindle equator, and this is documented very beautifully and (2) the correct positioning of MTOCs at the spindle pole appears to require kinetochore microtubules, as indicated by experiments manipulating the kinetochore component NDC80.

We appreciate the reviewer’s comment and clear description of our study.

**Major Comments**

As such the major claims of the paper are basically well supported. However, the analyses are is almost entirely restricted to prometaphase/metaphase, and the conclusions are relatively limited. The salient omission is any analysis of MTOC/chromosome relationship during anaphase. Were the paper to be extended to determine whether the lingering of MTOCs at the spindle equator is related to chromosome segregation error, that would increase the reach and importance of the work substantially. Specifically:

Can tracking experiments be performed to determine whether the chromosome that shows movement similarities to the errant MTOC is more/less likely to missegregate? Complete tracking as these authors are expert at should achieve this, or photo-labelling the desired chromosome.

Thank you for your comment. In our experimental system, oocytes rarely exhibit chromosome segregation errors (

Can the position of MTOCs (proportion that linger at the equator) be manipulated in the absence of other defects to determine whether this increases errors (lagging at anaphase, metaphase-II chromosome counting spreads)?

We agree with the reviewer that a specific manipulation of MTOC positions is exactly what we would need to investigate the significance of central MTOCs. Unfortunately, there are currently no tools available to specifically manipulate MTOC positions without other defects. Therefore, the significance of central MTOCs is currently unclear. In the revised manuscript, we will state these points in Discussion.

The above analysis would have to be well supported by controls showing that these constructs are having no impact on normal anaphase (proportion of oocytes completing meiosis-I, likelihood of lagging chromosomes etc).

Thank you for the comment. As we answered above, control oocytes rarely exhibit chromosome segregation errors or lagging chromosomes (

Related to the above, though I appreciate a fixed metaphase image of MTOC immunofluorescence is presented, the paper is about the dynamics of MTOCs and thus nonetheless relies heavily on the live imaging of cep192. The core results should be confirmed using another (substantially different) MTOC probe. *This final comment applies to the current metaphase data, regardless of whether the study is ultimately extended*

Thank you for the suggestion. We will confirm the dynamics of MTOCs at metaphase with mEGFP-Cdk5Rap2, another established marker of MTOCs.

Reviewer #1 (Significance (Required)):

As explained above, as presented this paper is largely scientifically sound, but far more limited in scope than this groups other recent papers. As explained above, the paper would be made more impactful and the readership broadened if a relationship between MTOC position/movement and segregation problems were established. Or on the other hand if it were established why some MTOCs sometimes linger at the spindle equator. Whilst to my knowledge this is the first time that equator MTOCs have been documented so carefully, oocyte cell biologists may not find the core observation that MTOCs are occasionally at the spindle equator extremely surprising.

Thank you for your helpful suggestions. Due to lack of tools to specifically manipulate MTOC positions, we are unfortunately not able to directly address whether MTOC position/movement contributes to chromosome segregation problems. On the other hand, we are currently investigating to answer your important question ‘why some MTOCs sometimes linger at the spindle equator’. We speculate that MTOCs become central due to unstable kinetochore-microtubule attachments, which are predominantly observed at early metaphase in normal oocytes. To test this idea, we are currently investigating whether the appearance of central MTOCs are prevented by forced stabilization of kinetochore-microtubule attachments with Ndc80-9A. Our pilot analysis thus far supports this idea. In light of your suggestions, we will incorporate the results into the revised manuscript.

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

I am commenting on the work of Courtois et al. as an expert in the biochemistry of spindle formation with a focus on acentriolar assembly.

First and foremost, this a technically excellent study with a number of very interesting and well-documented observations, which are highly relevant for our understanding of the mechanisms of acentriolar spindle formation in the mouse oocyte model. In principle, the manuscript is in a very mature state. However, my major concern at this point would be that there is a break in the story. It starts describing the (very interesting) observation of "central MTOCs". After thoroughly investigating how these behave, the authors stop and look at overall MTOCs distribution after loss of stable MT-kinetochore interactions based on oocytes expressing the Ndc80_9D mutant instead of wt Ndc80. The two parts are experimentally and conceptually not well connected.

We appreciate your comments on our techniques and novel observations in this study, and thank you for your helpful suggestions.

Answering the following questions may help to further develop the paper:

If I understand the arguments correctly, central MTOCs are an "accident" on the way to complete meiosis I spindle formation, which will eventually be corrected and all MTOCs clustered at the poles. Thus, they may serve as an assay for spindle assembly fidelity and kinetics (?). At this point, the reader is left with the observation without efforts to explain the meaning of this observation, ideally experimentally, or at least in a valid discussion.

Thank you for your thoughtful comment. We agree that we should clearly explain our view on central MTOCs. We indeed speculate that central MTOCs are an “accident” due to unstable kinetochore-microtubule attachments, which are normally pronounced at early metaphase.

We will revise the manuscript as follows: (1) Following the section for the observation of central MTOCs, we will state our hypothesis that central MTOCs may appear due to unstable kinetochore–microtubule attachments. (2) We will introduce our experiment of the manipulation of kinetochore–microtubule attachment stability as a test for our hypothesis. (3) We will present new results of our analysis for the effects of kinetochore–microtubule attachment stability on the appearance of central MTOCs (please see below).

Enthusiasm for the technically excellent experiments using the Ndc80 variants are somewhat reduced as conclusions from these experiments are published in the parallel paper of the same laboratory (Yoshida et al.). Due to my opinion, it may thus be even more important to connect these observations with the first part described central MTOCs and to clarify their significance.

Thank you for the important suggestion.

First, we agree that we should connect our observations of central MTOCs to the phenotypes of Ndc80 manipulations. To do this, we will reanalyze our dataset to quantify the effects of Ndc80 manipulations on central MTOCs. Our pilot analysis thus far suggests that the forced stabilization of kinetochore–microtubule attachments by Ndc80-9A reduces the appearance of central MTOCs. This would support our idea that central MTOCs appear due to unstable kinetochore–microtubule attachments.

Second, we agree with the reviewer that experimental clarification of the significance of central MTOCs would be nice. However, as outlined above, we unfortunately have no tool to directly address the significance of MTOC positioning in the fidelity of spindle assembly and chromosome segregation. Although we assume that MTOC positioning is critical for spindle assembly fidelity, as generally thought based on previous studies (Breuer et al., 2010; Clift and Schuh, 2015; Schuh and Ellenberg, 2007), the significance of MTOC positioning in spindle assembly remains uncertain, as you (and also the reviewer 1) point out. We will discuss these points in the revised manuscript.

Shown if in Fig. 3B but not fully explained: How does the distribution of what is defined as central MTOCs behave in Ndc80_wt and Ndc80_9A mutant oocytes? Do the variants differ, i.e. are there fewer, or less persistent central MTOCs in the 9A mutant? Would they differ in kinetics of appearance and "rescue" to the poles?

Thank you for the question. As outlined above, we will reanalyze our dataset to quantify the effects of Ndc80-9A on the behavior of central MTOCs. Our pilot analysis suggests that the forced stabilization of kinetochore–microtubule attachments suppresses the appearance of central MTOCs.

Similarly: is there a correlation of central MTOC appearance, Ndc80 phosphorylation/stability of kinetochore attachment and Anaphase I onset? The authors mention that oocytes expressing the 9A mutant go faster into Anaphase.

Thank you for this comment. First, we will investigate whether the levels of Ndc80 phosphorylation at kinetochores has any correlations to the distance to central MTOCs. Second, we will address whether microtubules connect kinetochores to central MTOCs. Third, we will perform the tracking of chromosomes that showed correlated motions to closely positioned MTOCs until anaphase onset.

The observation that "central MTOCs exhibited correlated motions with closely positioned kinetochores" is poorly defined, yet an important observation. Does this mean some sort of short k-fibers remain to connect central MTOCs and kinetochores? Wouldn't one expect that the loss of stable end-on-attachment causes MTOCs to become central? How does this fit into a/the model?

We believe these concerns will be addressed by the experiments/analyses proposed above. First, we will check if central MTOCs are connected to kinetochores by microtubules. Second, we indeed speculate that loss of stable kinetochore-microtubule attachment allows MTOCs to become central. We will test this idea by quantifying the appearance of central MTOCs in Ndc80-9A-expressing oocytes.

Along the same lines: The authors hype their conclusion that kinetochores dominate meiosis I spindle formation based on the observation that loss of kinetochore functions results in less well-organized spindle poles and worse MTOC "confinement". This may mean that kinetochores, together with MTOCs, maintain stable k-fibers in meiosis, as shown here and in Yoshida et al. When one or the other end of k-fibers is destabilized (loss of end-on-attachment, loss of MTOC attachment), the fibers collapse and the remaining minus-or-plus-end associated structure loses its destination. We then see central MTOCs and/or kinetochores at poles. In this respect, the interpretation / discussion should be less "kinetochore-centered".

We agree with your thoughtful comment that the regulations of minus-ends (e.g. MTOCs) and of plus-ends (e.g. kinetochores) are equally relevant for spindle bipolarization. We will tone down our kinetochore-centered view in the Abstract and Discussion and revise them into more balanced statements.

Is there any way to determine the efficiency of Ndc80 knockdown in the gene replacement respective experiment? I share the view of the authors that their method may be more efficient and may explain apparent discrepancies to previous studies on Ndc80-9A (Guy and Homer, 2013) with more dramatic effects on spindle geometry. However, at that point, this remains speculative. For instance, one may also speculate vice versa that the ko strategy used here is less efficient in a maternally dominated system and leaves behind more wt Ndc80, which better compensates defects seen in the 9A mutant.

Our gene deletion strategy (Zp3-Cre Ndc80f/f) resulted in >90% depletion of the Ndc80 protein (estimated by Western blot; Supplementary Figure 1c in Yoshida et al, Nat Commun 2020). On the other hand, Gui and Homer report that their morpholino-based depletion strategy resulted in 60–70% depletion of the Ndc80 protein (estimated by Western blot; Figure 1B in Gui and Homer, Dev Cell 2013). Thus, the depletion was more efficient in our experimental system. We will add this information in the manuscript.

Reviewer #2 (Significance (Required)):

Courtois et al present data on mechanisms governing spindle assembly in mouse oocytes. Mouse oocytes serve as model system for spindle formation in the absence of centriole-based MTOCs. At the onset of meiosis I, numerous MTOCs form, which shape a mass ("ball") of MT nucleated around chromatin into a bipolar structure. Accumulating evidence indicates that kinetochores play an important role in acentriolar spindle formation in mouse oocytes, yet the mechanisms behind kinetochore action remains unclear.

Here, Courtois et al. analyze spindle formation in live mouse oocytes using 3D-time-lapse imaging. They use fluorescently tagged Cep192 to track MTOCs and Histone H2B or CENP-C to visualize chromatin or kinetochores. In the first part, the authors deal with the appearance of "central MTOCs", i.e. aggregates of centrosomal protein(s) that, apparently, fail to remain stably integrated into the spindle pole clusters on MTOCs during spindle formation. The authors convincingly demonstrate that these central MTOCs can be seen in the majority of spindles investigated. They demonstrate that central MTOCs generally come from positions at poles from where they "fall back" towards chromosomes. Central MTOCs may even cross the spindle and end up at opposite poles from where they originated from. Interestingly, central MTOCs are often found next to kinetochores.

In the second part, the authors focus on the role of kinetochores and their stable MT attachment for spindle formation in general and bipolarity/pole organization in particular. The same lab has published data on the role of kinetochores in meiosis I spindle very recently (Yoshida et al. Nat Comm, 2020). Here, they successfully exploit Ndc80 phospho-mutants to compare MTOC distribution in oocytes with reduced or increased end-on-attachment. The data show that stable end-on attachment determines stable MTOC clustering at spindle poles and governs the maintenance of bipolarity and spindle length.

Thank you for your clear description of our study.

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

In order to assemble a bipolar structure, acentrosomal spindles relay on multiple non-centrosomal pathways. Mouse oocytes specifically build bipolar spindles by sorting and clustering of microtubule organizing centers (MTOCs). While microtubule cross-linkers, spindle motors and microtubule nucleators are involved; the role of kinetochores and kinetochore-microtubule attachments in meiotic spindle assembly and maintenance has not been thoroughly tested. Using an impressive combination of live cell imaging and semi-automated image analysis, Courtois et al. quantified MTOC behavior in bipolar mouse oocyte spindles and found an ongoing MTOC sorting in metaphase and instances of MTOC-kinetochore associations. The authors further employed an elegant genetic system to replace NDC80 in maturing oocytes with a mutant almost completely unable to form stable microtubule-kinetochore attachments. The data show lack of MTOC confinement at the spindle poles and increased spindle elongation while maintaining spindle bipolarity. The authors concluded that stable kinetochore-microtubule attachments are required to confine MTOCs at the poles, which in turn sets an optimal spindle length. Overall, the data are of very high quality and clearly presented, the manuscript is easy to follow, and the methods are comprehensively described. One concern is the lack of mechanistic link between the natural metaphase MTOC sorting (Fig. 1-2) and massive MTOC rearrangements observed with the NDC80-9D mutant (Fig. 3). A second concern is that deficient MTOC confinements and spindle elongation observed with the 9D mutant could be due unaligned chromosomes rather than lack of stable kinetochore-microtubule attachments, which is the authors' interpretation.

**Major Points:**

1) Massive MTOC rearrangements (Supplementary Video 6) are reminiscent of spindle assembly defects or spindle collapse. Since these spindles do not reach a normal metaphase and seem to change shape (Supplementary Video 6; 11:10), it is difficult to differentiate between spindle assembly and spindle maintenance defects. Is there a difference in the timing of bipolar spindle assembly for NDC80-9D vs WT? If so, one interpretation is that stable attachments not only ensure MTOC confinement but also contribute to bipolar spindle assembly.

We apologize for the lack of explanation for the spindle dynamics seen in Supplementary Video 6, 11:10. At this time point, the spindle rotated in 3D, which appeared as if the spindle collapsed in the z-projection movie. We will add this explanation into the legend.

Our quantitative analysis of spindle shape in 3D indicated no increased collapse in Ndc80-9D, based on the signals of the spindle marker EGFP-Map4. Moreover, we observed no detectable difference in the timing of the onset of bipolar spindle assembly, as long as we define it with EGFP-Map4 signals. These results are shown in Figure 4B.

2) Fig. 1-2 vs Fig. 3 - It is not clear how the discrete MTOC sorting phenotype presented in Fig. 1-2 relates to the massive MTOC collapse shown in Fig. 3. The natural MTOC sorting and MTOC-kinetochore associations seem to be happening within the bipolar structure confined by the polar MTOCs. The MTOC rearrangements (e.g., Supplementary Video 6) are much more drastic, reminiscent of a spindle collapse. To make a mechanistic link between the phenotypes, it would be useful to use an intermediate NCD80 mutant (ex. NDC80-4D; Zaytsev et al., 2014 JCB) that may support chromosome alignment and maintenance of the canonical bipolar spindle structure, but still show effects on MTOC sorting.

Thank you for your nice suggestion. We will test Ndc80-4D. The construct is ready.

3) Fig. 4 - The authors should provide evidence that unstable kinetochore-microtubule attachments, rather than chromosome-derived signals of misaligned chromosomes (e.g., from Ran or Aurora B), limit spindle elongation. For example, the authors could measure spindle elongation in oocytes with misaligned chromosomes but stable attachments: for example, NDC80-9A oocytes released from an Eg5 inhibition block should carry a number of polar chromosomes with stable attachments. The expectation would be that such spindles form with confined MTOCs and do not elongate as much as NDC80-9D expressing oocytes.

Thank you for this important suggestion. Following your suggestion, we have conducted a pilot experiment using monastrol washout. However, unfortunately, we did not observe increased chromosome misalignment in Ndc80-9A. We will play around experimental conditions.

Moreover, we propose to perform an additional experiment. We will use cohesin depletion with Rec8 TRIM-Away, which will produce chromosome misalignment and reduce kinetochore-microtubule attachment stability. We expect that these oocytes exhibit excessive spindle elongation. Then, we ask if Ndc80-9A, which would force to stabilize kinetochore-microtubule attachment (but fail to align chromosomes due to loss of chromosome cohesion), can suppress excessive spindle elongation.

These experiments will allow us to address direct contribution of kinetochore-microtubule attachment to proper spindle elongation. However, in our opinion, regardless of the results, we cannot exclude the possibility that chromosome alignment contributes to proper spindle elongation, which is indeed an intriguing hypothesis. We will discuss these possibilities in Discussion.

4) Figure 5D - The authors' model suggests that MTOCs are confined due to their connection to stably attached k-fibers. It would be useful to speculate on the molecular mechanism behind the confinement. Does a maximal k-fiber length restrict the elongation, or is there a pulling force exerted by the kinetochores?

Thank you for your thoughtful suggestion. As the reviewer suggests, we speculate that the length of k-fibers is critical for restricting MTOC position and spindle elongation. K-fibers may prevent excessive spindle elongation by anchoring MTOCs at their minus ends. Alternatively, k-fibers may act as a platform that inactivates spindle bipolarizers. We will discuss these possibilities in our revised manuscript.

5) Discussion - Lines 203-204 - "The findings of this study, together with recent studies, suggest a model for how kinetochore-microtubule attachments contribute to acentrosomal spindle assembly (Figure 5D)". - Throughout the paper the authors underscore that biopolar spindles do assembly with the NDC80-9D mutant. The authors should clarify whether spindle assembly is affected by the NDC80-9D mutant or not?

Thank you for your comment. We agree with the reviewer that we should clearly state our conclusion based on the phenotype of the Ndc80-9D mutant. Our conclusion is that stable kinetochore-microtubule attachment fine-tunes bipolar spindle assembly. If oocytes lack stable attachments, they can form a bipolar-shaped spindle composed of microtubule arrays that are largely bipolar, but the spindle becomes too much elongated and lacks MTOCs at its poles. We will explicitly state these ideas in our revised manuscript.

**Minor Points:**

1) Introduction - Lines 38-44 - The authors should cite the role of the Augmin complex in acentrosomal spindle assembly (Watanabe et al., 2016 Cell Reports).

Thank you for your excellent suggestion. We will cite this relevant paper.

2) Results - Lines 55-56 - "However, the precise manipulation of the stability of kinetochore-microtubule attachments has not been tested" - Gui et Homer 2013 studied the outcome of NDC80 depletion and tested the NDC80-9A mutant in the context of oocyte spindle assembly. Although, as the authors point out in the Discussion section, there might be differences in the experimental design that lead to different conclusions, it is not entirely accurate that precise manipulations of attachments stability have not been tested. A different wording (e.g., "has not been comprehensively tested") may be better.

Thank you for your suggestion. We agree that “has not been comprehensively tested” fits better.

3) Results - Lines 162-164 - "Ndc80-9D-expressing oocytes had no significant delay in the onset of spindle elongation, but had significantly faster kinetics of elongation compared to Ndc80-WT- and Ndc80-9D-expressing oocytes" - The authors probably meant "... Ndc80-9A expressing oocytes."

Thank you for pointing out this mistake. We will correct it.

4) Discussion - Lines 239-242 - "... microtubule nucleation in later stages may not be determined by MTOCs but are largely attributed to nucleation within the spindle, as observed by microtubule plus-end tracking in bipolar-shaped spindles (Supplementary Figure 4)." - Strictly speaking, EB3 comets indicate microtubule polymerization rather than nucleation. Microtubule nucleation within the spindle is, however, supported by studies of the Augmin complex (e.g., Watanabe et al., 2016 Cell Rep).

Thank you for your comment. We will correct our wording for EB3 comets and discuss that microtubule nucleation within the spindle is shown in Watanabe et al., 2016 Cell Rep.

5) Discussion - Lines 257-260 - "The lagging MTOCs can be positioned close to kinetochores on bi-oriented chromosomes, underscoring the importance of active error corrections of kinetochore-microtubule attachments during metaphase (Lane and Jones, 2014; Yoshida et al., 2015)." - The reasoning here is not clear. Does the number/persistence of lagging MTOCs correlate with chromosome mis-alignment or with the efficiency/timing of chromosome alignment in WT cells?

We apologize that our discussion was not clear. Previous studies (Lane and Jones, 2014; Yoshida et al., 2015) show that kinetochore-microtubule attachment errors are found on aligned chromosomes during metaphase and must be corrected until anaphase onset in oocytes. We speculate that lagging (or central) MTOCs may be a source of such kinetochore-microtubule attachment errors, although we cannot directly test this hypothesis due to lack of tools to specifically manipulate MTOC positions. We will discuss these points in Discussion.

To check if central MTOCs are correlated with chromosome misalignment, we will perform the tracking of chromosomes that were closely positioned to lagging MTOCs.

6) Discussion - Line 266 - "Yoshida et al., 2020" - This article is cited elsewhere in the text as "Yoshida et al., in press".

Thank you for pointing out these mistakes. We will correct them.

Reviewer #3 (Significance (Required)):

Courtois et al., have found a new mechanism contributing to acentrosomal spindle assembly in mouse oocytes. Although kinetochore-dependent spindle assembly occurs in mitotic cells (e.g., Toso et al., 2009 JCB), only the recent work from the Kitajima lab (Yoshida et al., 2020 Nat Comm; this manuscript) showed that kinetochores also impact acentrosomal spindle assembly in meiosis. The genetic model presented here brings a significant technical advance in dissecting relative contributions of spindle assembly pathways in mouse oocytes (ex. Schuh and Ellenberg 2007 Cell; Watanabe et al., 2016 Cell Rep; Drutovic et al., 2020 EMBO J) and complements current methods used to study meiotic error-correction (e.g., Chmatal et al., 2015 Curr Biol, Yoshida et al., 2015 Dev Cell; Vallot et al., 2018 Curr Biol and many others). This model expands an existing toolbox of techniques allowing complete elimination of the endogenous protein specifically in mature mouse oocytes (Clift et al., 2017 Cell; Clift et al., 2018 Nat Protocols), which is a difficult feat due to a limited capacity of ex-vivo culture (Pfender et al., 2015 Nature). Therefore, the work presented in this manuscript may encourage other researchers to establish similar systems for oocyte-specific manipulations, which will allow more precise insight into oocyte biology.

Expertise keywords: spindle dynamics, chromosome segregation, mitosis, meiosis

We appreciate your comments. Additional experiments following on your constructive comments will further improve our manuscript.

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Referee #3

Evidence, reproducibility and clarity

In order to assemble a bipolar structure, acentrosomal spindles relay on multiple non-centrosomal pathways. Mouse oocytes specifically build bipolar spindles by sorting and clustering of microtubule organizing centers (MTOCs). While microtubule cross-linkers, spindle motors and microtubule nucleators are involved; the role of kinetochores and kinetochore-microtubule attachments in meiotic spindle assembly and maintenance has not been thoroughly tested. Using an impressive combination of live cell imaging and semi-automated image analysis, Courtois et al. quantified MTOC behavior in bipolar mouse oocyte spindles and found an ongoing MTOC sorting in metaphase and instances of MTOC-kinetochore associations. The authors further employed an elegant genetic system to replace NDC80 in maturing oocytes with a mutant almost completely unable to form stable microtubule-kinetochore attachments. The data show lack of MTOC confinement at the spindle poles and increased spindle elongation while maintaining spindle bipolarity. The authors concluded that stable kinetochore-microtubule attachments are required to confine MTOCs at the poles, which in turn sets an optimal spindle length. Overall, the data are of very high quality and clearly presented, the manuscript is easy to follow, and the methods are comprehensively described. One concern is the lack of mechanistic link between the natural metaphase MTOC sorting (Fig. 1-2) and massive MTOC rearrangements observed with the NDC80-9D mutant (Fig. 3). A second concern is that deficient MTOC confinements and spindle elongation observed with the 9D mutant could be due unaligned chromosomes rather than lack of stable kinetochore-microtubule attachments, which is the authors' interpretation.

Major Points:

1) Massive MTOC rearrangements (Supplementary Video 6) are reminiscent of spindle assembly defects or spindle collapse. Since these spindles do not reach a normal metaphase and seem to change shape (Supplementary Video 6; 11:10), it is difficult to differentiate between spindle assembly and spindle maintenance defects. Is there a difference in the timing of bipolar spindle assembly for NDC80-9D vs WT? If so, one interpretation is that stable attachments not only ensure MTOC confinement but also contribute to bipolar spindle assembly.

2) Fig. 1-2 vs Fig. 3 - It is not clear how the discrete MTOC sorting phenotype presented in Fig. 1-2 relates to the massive MTOC collapse shown in Fig. 3. The natural MTOC sorting and MTOC-kinetochore associations seem to be happening within the bipolar structure confined by the polar MTOCs. The MTOC rearrangements (e.g., Supplementary Video 6) are much more drastic, reminiscent of a spindle collapse. To make a mechanistic link between the phenotypes, it would be useful to use an intermediate NCD80 mutant (ex. NDC80-4D; Zaytsev et al., 2014 JCB) that may support chromosome alignment and maintenance of the canonical bipolar spindle structure, but still show effects on MTOC sorting.

3) Fig. 4 - The authors should provide evidence that unstable kinetochore-microtubule attachments, rather than chromosome-derived signals of misaligned chromosomes (e.g., from Ran or Aurora B), limit spindle elongation. For example, the authors could measure spindle elongation in oocytes with misaligned chromosomes but stable attachments: for example, NDC80-9A oocytes released from an Eg5 inhibition block should carry a number of polar chromosomes with stable attachments. The expectation would be that such spindles form with confined MTOCs and do not elongate as much as NDC80-9D expressing oocytes.

4) Figure 5D - The authors' model suggests that MTOCs are confined due to their connection to stably attached k-fibers. It would be useful to speculate on the molecular mechanism behind the confinement. Does a maximal k-fiber length restrict the elongation, or is there a pulling force exerted by the kinetochores?

5) Discussion - Lines 203-204 - "The findings of this study, together with recent studies, suggest a model for how kinetochore-microtubule attachments contribute to acentrosomal spindle assembly (Figure 5D)". - Throughout the paper the authors underscore that biopolar spindles do assembly with the NDC80-9D mutant. The authors should clarify whether spindle assembly is affected by the NDC80-9D mutant or not?

Minor Points:

1) Introduction - Lines 38-44 - The authors should cite the role of the Augmin complex in acentrosomal spindle assembly (Watanabe et al., 2016 Cell Reports).

2) Results - Lines 55-56 - "However, the precise manipulation of the stability of kinetochore-microtubule attachments has not been tested" - Gui et Homer 2013 studied the outcome of NDC80 depletion and tested the NDC80-9A mutant in the context of oocyte spindle assembly. Although, as the authors point out in the Discussion section, there might be differences in the experimental design that lead to different conclusions, it is not entirely accurate that precise manipulations of attachments stability have not been tested. A different wording (e.g., "has not been comprehensively tested") may be better.

3) Results - Lines 162-164 - "Ndc80-9D-expressing oocytes had no significant delay in the onset of spindle elongation, but had significantly faster kinetics of elongation compared to Ndc80-WT- and Ndc80-9D-expressing oocytes" - The authors probably meant "... Ndc80-9A expressing oocytes."

4) Discussion - Lines 239-242 - "... microtubule nucleation in later stages may not be determined by MTOCs but are largely attributed to nucleation within the spindle, as observed by microtubule plus-end tracking in bipolar-shaped spindles (Supplementary Figure 4)." - Strictly speaking, EB3 comets indicate microtubule polymerization rather than nucleation. Microtubule nucleation within the spindle is, however, supported by studies of the Augmin complex (e.g., Watanabe et al., 2016 Cell Rep).

5) Discussion - Lines 257-260 - "The lagging MTOCs can be positioned close to kinetochores on bi-oriented chromosomes, underscoring the importance of active error corrections of kinetochore-microtubule attachments during metaphase (Lane and Jones, 2014; Yoshida et al., 2015)." - The reasoning here is not clear. Does the number/persistence of lagging MTOCs correlate with chromosome mis-alignment or with the efficiency/timing of chromosome alignment in WT cells?

6) Discussion - Line 266 - "Yoshida et al., 2020" - This article is cited elsewhere in the text as "Yoshida et al., in press".

Significance

Courtois et al., have found a new mechanism contributing to acentrosomal spindle assembly in mouse oocytes. Although kinetochore-dependent spindle assembly occurs in mitotic cells (e.g., Toso et al., 2009 JCB), only the recent work from the Kitajima lab (Yoshida et al., 2020 Nat Comm; this manuscript) showed that kinetochores also impact acentrosomal spindle assembly in meiosis. The genetic model presented here brings a significant technical advance in dissecting relative contributions of spindle assembly pathways in mouse oocytes (ex. Schuh and Ellenberg 2007 Cell; Watanabe et al., 2016 Cell Rep; Drutovic et al., 2020 EMBO J) and complements current methods used to study meiotic error-correction (e.g., Chmatal et al., 2015 Curr Biol, Yoshida et al., 2015 Dev Cell; Vallot et al., 2018 Curr Biol and many others). This model expands an existing toolbox of techniques allowing complete elimination of the endogenous protein specifically in mature mouse oocytes (Clift et al., 2017 Cell; Clift et al., 2018 Nat Protocols), which is a difficult feat due to a limited capacity of ex-vivo culture (Pfender et al., 2015 Nature). Therefore, the work presented in this manuscript may encourage other researchers to establish similar systems for oocyte-specific manipulations, which will allow more precise insight into oocyte biology.

Expertise keywords: spindle dynamics, chromosome segregation, mitosis, meiosis

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Referee #2

Evidence, reproducibility and clarity

I am commenting on the work of Courtois et al. as an expert in the biochemistry of spindle formation with a focus on acentriolar assembly.

First and foremost, this a technically excellent study with a number of very interesting and well-documented observations, which are highly relevant for our understanding of the mechanisms of acentriolar spindle formation in the mouse oocyte model. In principle, the manuscript is in a very mature state. However, my major concern at this point would be that there is a break in the story. It starts describing the (very interesting) observation of "central MTOCs". After thoroughly investigating how these behave, the authors stop and look at overall MTOCs distribution after loss of stable MT-kinetochore interactions based on oocytes expressing the Ndc80_9D mutant instead of wt Ndc80. The two parts are experimentally and conceptually not well connected.

Answering the following questions may help to further develop the paper:

1. If I understand the arguments correctly, central MTOCs are an "accident" on the way to complete meiosis I spindle formation, which will eventually be corrected and all MTOCs clustered at the poles. Thus, they may serve as an assay for spindle assembly fidelity and kinetics (?). At this point, the reader is left with the observation without efforts to explain the meaning of this observation, ideally experimentally, or at least in a valid discussion.
2. Enthusiasm for the technically excellent experiments using the Ndc80 variants are somewhat reduced as conclusions from these experiments are published in the parallel paper of the same laboratory (Yoshida et al.). Due to my opinion, it may thus be even more important to connect these observations with the first part described central MTOCs and to clarify their significance.
3. Shown if in Fig. 3B but not fully explained: How does the distribution of what is defined as central MTOCs behave in Ndc80_wt and Ndc80_9A mutant oocytes? Do the variants differ, i.e. are there fewer, or less persistent central MTOCs in the 9A mutant? Would they differ in kinetics of appearance and "rescue" to the poles?
4. Similarly: is there a correlation of central MTOC appearance, Ndc80 phosphorylation/stability of kinetochore attachment and Anaphase I onset? The authors mention that oocytes expressing the 9A mutant go faster into Anaphase.
5. The observation that "central MTOCs exhibited correlated motions with closely positioned kinetochores" is poorly defined, yet an important observation. Does this mean some sort of short k-fibers remain to connect central MTOCs and kinetochores? Wouldn't one expect that the loss of stable end-on-attachment causes MTOCs to become central? How does this fit into a/the model?
6. Along the same lines: The authors hype their conclusion that kinetochores dominate meiosis I spindle formation based on the observation that loss of kinetochore functions results in less well-organized spindle poles and worse MTOC "confinement". This may mean that kinetochores, together with MTOCs, maintain stable k-fibers in meiosis, as shown here and in Yoshida et al. When one or the other end of k-fibers is destabilized (loss of end-on-attachment, loss of MTOC attachment), the fibers collapse and the remaining minus-or-plus-end associated structure loses its destination. We then see central MTOCs and/or kinetochores at poles. In this respect, the interpretation / discussion should be less "kinetochore-centered".
7. Is there any way to determine the efficiency of Ndc80 knockdown in the gene replacement respective experiment? I share the view of the authors that their method may be more efficient and may explain apparent discrepancies to previous studies on Ndc80-9A (Guy and Homer, 2013) with more dramatic effects on spindle geometry. However, at that point, this remains speculative. For instance, one may also speculate vice versa that the ko strategy used here is less efficient in a maternally dominated system and leaves behind more wt Ndc80, which better compensates defects seen in the 9A mutant.

Significance

Courtois et al present data on mechanisms governing spindle assembly in mouse oocytes. Mouse oocytes serve as model system for spindle formation in the absence of centriole-based MTOCs. At the onset of meiosis I, numerous MTOCs form, which shape a mass ("ball") of MT nucleated around chromatin into a bipolar structure. Accumulating evidence indicates that kinetochores play an important role in acentriolar spindle formation in mouse oocytes, yet the mechanisms behind kinetochore action remains unclear.

Here, Courtois et al. analyze spindle formation in live mouse oocytes using 3D-time-lapse imaging. They use fluorescently tagged Cep192 to track MTOCs and Histone H2B or CENP-C to visualize chromatin or kinetochores. In the first part, the authors deal with the appearance of "central MTOCs", i.e. aggregates of centrosomal protein(s) that, apparently, fail to remain stably integrated into the spindle pole clusters on MTOCs during spindle formation. The authors convincingly demonstrate that these central MTOCs can be seen in the majority of spindles investigated. They demonstrate that central MTOCs generally come from positions at poles from where they "fall back" towards chromosomes. Central MTOCs may even cross the spindle and end up at opposite poles from where they originated from. Interestingly, central MTOCs are often found next to kinetochores.

In the second part, the authors focus on the role of kinetochores and their stable MT attachment for spindle formation in general and bipolarity/pole organization in particular. The same lab has published data on the role of kinetochores in meiosis I spindle very recently (Yoshida et al. Nat Comm, 2020). Here, they successfully exploit Ndc80 phospho-mutants to compare MTOC distribution in oocytes with reduced or increased end-on-attachment. The data show that stable end-on attachment determines stable MTOC clustering at spindle poles and governs the maintenance of bipolarity and spindle length.

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Referee #1

Evidence, reproducibility and clarity

This group has been at the forefront recently of using imaging technologies to understand how chromosome segregation is coordinated in mammalian oocytes, and why errors occur. In the current paper they examine the dynamics of microtubule organising centres (which effectively replace centrioles/centrosomes in oocytes) in MI. The imaging of oocytes in this paper is beautiful. The major findings are (1) that MTOCs that are supposed to be at the spindle pole sometimes end up at the spindle equator, and this is documented very beautifully and (2) the correct positioning of MTOCs at the spindle pole appears to require kinetochore microtubules, as indicated by experiments manipulating the kinetochore component NDC80.

Major Comments

As such the major claims of the paper are basically well supported. However, the analyses are is almost entirely restricted to prometaphase/metaphase, and the conclusions are relatively limited. The salient omission is any analysis of MTOC/chromosome relationship during anaphase. Were the paper to be extended to determine whether the lingering of MTOCs at the spindle equator is related to chromosome segregation error, that would increase the reach and importance of the work substantially. Specifically:

1. Can tracking experiments be performed to determine whether the chromosome that shows movement similarities to the errant MTOC is more/less likely to missegregate? Complete tracking as these authors are expert at should achieve this, or photo-labelling the desired chromosome.
2. Can the position of MTOCs (proportion that linger at the equator) be manipulated in the absence of other defects to determine whether this increases errors (lagging at anaphase, metaphase-II chromosome counting spreads)?
3. The above analysis would have to be well supported by controls showing that these constructs are having no impact on normal anaphase (proportion of oocytes completing meiosis-I, likelihood of lagging chromosomes etc).
4. Related to the above, though I appreciate a fixed metaphase image of MTOC immunofluorescence is presented, the paper is about the dynamics of MTOCs and thus nonetheless relies heavily on the live imaging of cep192. The core results should be confirmed using another (substantially different) MTOC probe. This final comment applies to the current metaphase data, regardless of whether the study is ultimately extended

Significance

As explained above, as presented this paper is largely scientifically sound, but far more limited in scope than this groups other recent papers. As explained above, the paper would be made more impactful and the readership broadened if a relationship between MTOC position/movement and segregation problems were established. Or on the other hand if it were established why some MTOCs sometimes linger at the spindle equator. Whilst to my knowledge this is the first time that equator MTOCs have been documented so carefully, oocyte cell biologists may not find the core observation that MTOCs are occasionally at the spindle equator extremely surprising.

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Reply to the reviewers

We would like to thank Reviewer #1 and #2 for the evaluation of our research and comments to our manuscript. Their comments are highly appreciated and addressed as described below.

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

**Summary:**

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

Here Ha et al. has further developed their Pumilio RNA tagging methodology for the isolation of UV-crosslinked proteins that are suggested to associate with Xist RNA in mouse embryonic stem cells (mESCs). Within this study the authors claim to have found the Lupus antigen RNA binding protein (La) as a novel Xist interacting partner that influences the efficacy of X-chromosome inactivation (XCI). The authors use a number of different techniques such as qPCR, fluorescent imaging, ATAC-SEQ and SHAPE to show aberration of XCI upon La shRNA knockdown. However, this study has significant flaws in the efficient isolation and validation of Xist associated proteins using their FLAG-out methodology. Furthermore, later experiments predominantly focus on cell death/survival assays, which is somewhat troubling given the essential roles La plays in processes such as cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation. I feel the authors need to robustly address the potential effects La knockdown may be having on their mESCs.

Reviewer #1 did not fully understand the basic designs of the experimental systems (FLAG-out and iXist), and completely rejected these experimental systems. Reviewer #1 also ignored the majority of the functional analysis on the candidate protein, Ssb. These issues cannot be addressed by additional experiments

**Major comments:**

*-Are the key conclusions convincing?*

My major concern is in their Xist RNA purification.

First of all, I couldn't find any data on proving the enrichment of Xist RNA itself in their Pumilio pull-down experiment. It would have been useful to show Xist RNA enrichment before benzonase step. Secondly, it is hard to imagine the protocol would successfully isolated Xist RNA-protein complexes from the cell. An earlier report by Clemson et al., (J Cell Biol., 1996) has shown that majority of Xist RNA is still stuck in the nucleus after nuclear matrix prep protocol using detergent, which is not so different from the authors' protocol. Moreover, the authors used UV crosslink, which would have made even harder to purify Xist RNA without sonication. Thirdly, as the tag is located on 5' of Xist RNA, it is rather surprising to see that Spen is not detected in their pulldown. Spen is one of the main functional interactors with Xist, robustly detected by several previous reports. Similarly, other high-affinity binders of Xist such as hnRNP-K and Ciz1 were also lacking from this screen. Finally, the peptides found associated with FLAG-out Xist are extremely low in comparison with other data using glutaraldehyde or formaldehyde crosslinking. For example, HnRNP-M found in Chu et al 2015 has 1120 peptide counts in differentiated cells. The authors here use HnRNP-M as a baseline for specific interactions and show a total of 6 peptide counts in Xist expressing cells and 5 in i-Empty cells (Supplementary excel sheet 1). Similarly, the La protein of interest in this study has 8 counts in i-FLAG-Xist and 6 counts in i-Empty. I struggle to see how this result indicate specific Xist binding. Worryingly this is the starting rationale for the rest of their experiments, it is hard to therefore accept the rest of their conclusions either.

We have detected Xist RNA after Pumilio pull-down, and added the data in the revised manuscript (Figure S1). The enrichment of Xist RNA by Pumilio pull-down is about 75-fold, comparable to the enrichment reported by Minajigi et al.

Two out of three previous studies used similar protocols to prep cell lysates for co-IP, including UV cross-linking and detergent (McHugh et al. 2015 and Minajigi et al. 2015). The major difference between their protocols and ours is the co-IP step. They used antisense oligos to pull-down Xist RNA-protein complex, while we take advantage of the specific interaction between PUF and PBS to pull-down Xist RNA-protein complex. With the data in Figure S1, we are confident that our strategy is successful in isolating Xist RNA

For systematic identification of Xist binding proteins, each method has its own strength and weakness. As we described in the introduction, only 4 proteins were commonly identified by all three studies to systematically identify Xist binding proteins. There is no doubt that our method also missed some authentic Xist binding proteins (false negative) and identified some false positive candidates. Thus, we have to be careful in balancing between the false negative and false positive calls. The reason that we applied the ranking gain to identify Xist binding protein candidates, is to minimize the false negative rate. Meanwhile, we compared our Xist binding protein candidate list with previous identified Xist-binding proteins to enhance the confidence in our candidate lists.

Regardless the strength and weakness of our method, Ssb is also an Xist-binding protein identified by another study (Chu et al. 2015). More importantly, we have provided experimental validation to confirm Ssb’s involvement in XCI and extensive functional analysis to reveal the protein’s mechanistic role in XCI.

The other key conclusion the authors make is from the use of numerous cell death/survival assays for both male and female cell lines. This is extremely troubling in the context of assessing their target protein La. La is involved in multiple RNA maturation events of rRNAs, tRNAs and other polIII transcripts. Furthermore, La has been implicated in binding to the mRNA for Cyclin D1 in both human cells and mouse fibroblasts (NIH/3T3 - male) which show a significant effect on cell proliferation upon siRNA knockdown https://www.nature.com/articles/onc2010425. This, along with the observation that La knock-out blastocysts fail to develop any mice or ES cell lines (male or female) show the effect observed in the authors results is most likely not X-linked cell death https://mcb.asm.org/content/mcb/26/4/1445.full.pdf. The authors need to show that their shRNA KD isn't affecting the proliferation and general fitness of their mESC lines.

The cell death/survival assay was specially designed for analyzing the defect of XCI. The cell death of iXist ESCs upon adding Dox is due to the induction of Xist, which consequently initiates the silencing of the only X chromosome in male cells. Knockdown of genes involved in XCI compromises XCI, thus allowing cell survival. Given the diverse functions of Ssb in cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation, one would expect slow growth and/or cell death of Ssb knockdown cells. Indeed, the result is consistent with our expectation (Figure 2C, without Dox). Nevertheless, more Ssb knockdown cells survive in the presence of Dox, compared with control cells (Figure 2C-E, with Dox), suggesting that Ssb plays an important role in XCI.

*- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?*

As discussed above, I feel the authors have not clearly demonstrated Xist specific protein enrichment and haven't proven X-linked cell death. Due to the lack of necessary control experiments as discussed below, I feel the notion that La is involved directly in XCI as an RNA chaperone is currently preliminary/speculative.

The FLAG-out experiment just provided an initial point for the study. We have demonstrated the interaction between Xist and Ssb by RIP. And, Ssb knockdown antagonizes the lethal effect of induced XCI in male cells, allowing more cell to survive. This is contradictory to the diverse house-keeping functions of Ssb, which should lead to slow proliferation or cell death. Therefore, the data here (Figure 2C-E) should suggest a role of Ssb in XCI. In addition, we showed that knockdown of Ssb compromises the silencing of X-linked genes (Figure 2F, 2G, and 3E), the compaction of X chromosome (Figure 3D), Xist cloud formation (Figure 4), epigenetic modifications on Xi (Figure 5), Xist RNA folding (Figure 6F-I), and Xist RNA stability (Figure 7C and D). All these data indicate that Ssb is involved in XCI by regulating Xist RNA folding.

*- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.*

I would suggest them to show RT-qPCR results of Xist RNA enrichment from the sample after flagIP before benzonase treatment.

We have the data, and added it to Figure S1.

Also, it would have been more convincing if their negative control construct (i-Empty) would contain 25 copies of PBSb RNA at least.

This is a good alternative design of the negative control. Using i-Empty expressing 25 copies of PBSb RNA will allow us subtract the background causing by proteins binding to PBSb RNA. Yet, as discussed above, regardless how we improve the experimental setting, we cannot completely avoid the issue of false positive and false negative. Our goal of the FLAG-out experiment is to generate a list of Xist binding protein candidates, and their binding to Xist and their functions in XCI should be validated by additional experiments. With our current experimental setting, a list of Xist binding protein candidates has been generated, and we have validated the role of Ssb in XCI with subsequent experiments.

In Fig1b, the total amount of proteins loaded on the gel is not equivalent between two lanes. The gel should show equivalent amounts of proteins on the gel. It looks like if the negative control sample had been loaded at the same amount as the one with Xist, the band pattern wouldn't be distinguishable between the two samples. Furthermore, as these samples were used in the following mass spectrometry screen it may suggest that the minimal increase in peptide counts observed in the iXist FLAG-out were due to an increased amount of sample being loaded? No controls are conducted to account for this.

IP samples of i-Empty and i-FLAG-Xist were loaded in the gel in Figure 1b. It is expected that IP sample of i-FLAG-Xist should pull down more proteins than IP samples of i-Empty. The FLAG-PUFb bands (the strongest band in each lane) are about the same amount in two samples, indicating roughly equal amount of loading. After normalization of gel loading according to the FLAG-PUFb bands, the upper part of the i-FLAG-Xist lane showed some unique bands.

For mass spectrometry analysis, the loading of two samples are independent, therefore, to compare the absolute amount of each protein between the two samples does not always provide valuable information. Yet, the relative amount of different proteins within one sample is not affected by the loading amount, thus, more informative. Therefore, we used the ranking information to estimate the relative amount of different proteins in each sample and used the ranking gain to further identify protein candidates.

The authors quantify cell death in figures 2C - E. It seems clear that shSsb 1 and 2 have an effect on cell count even in the absence of Dox. The rescue effect seen upon Dox addition is minimal when compared to Empty + Dox 2D. The authors ∆A-iXist line with and without Ssb KD/Dox would be an informative control on whether the increase in cell survival that they see is X-linked.

As the reviewer pointed out earlier, Ssb plays multiple roles in cellular processes. Inevitably, KD of Ssb leads to slow growth and/or cell death with or without Dox. Thus, it is less meaningful to compare the surviving cell counts in Figure 2D. Rather, the survival rate (Figure 2E) reflects the rescuing effect more precisely. Shown in Figure 2E, both shSsb 1 and 2 increase the survival rate significantly, compared with Empty control.

Moreover, the data in Figure 3B and C demonstrated that Ssb KD compromises the survival of female differentiating cells, but not the survival of male differentiating cells, also indicating a role of Ssb in XCI. With these experiments, it should be sufficient to conclude that Ssb KD affects X-linked cell death/survival in both iXist male ESCs and WT female differentiating cells

The qPCR results used to validate silencing defects show minor changes in expression and also don't show significant silencing of X-linked genes sufficient for cell death. Could this be because only ~ 50 - 60% of Male iXist cells seem to be expressing in the movies and that this will have an effect on the observed qPCR results? Furthermore, it seems counterintuitive that expression in the Empty male cells increases in 48h compared to 14h. Is this due to cell death and positive selection of cells less able to silence their X-chromosome? How would these data look in the female XX line? How would the data look in a ∆A-iXist line in the presence and absence of shSsb/Dox?

First, high-quality live-cell imaging can only be carried out for 2 hours with 2-min time interval. The movies are meant to show the onset of Xist RNA signals. Therefore, they were taken one hour after Dox treatment (figure legend of Figure 4B-D). After overnight Dox treatment, Xist clouds can be seen in majority of cells.

Second, in Fig. 2F-G, we did not include uninduced iXist male ESCs. Therefore, it is impossible to judge whether induction of Xist in this male ESC line results in Xist-dependent silencing at 14 and 48 hr. However, in our previous publication (Li et al., JMB, 2018, 430: 2734-2746), it has been shown that Gpc4, Hprt, Mecp2, G418, and TomatoRed are silenced (4- to 16-fold reduction) at 24 and 48 hours after Dox induction.

Third, the qRT-PCR results in 14 h and in 48 h are not normalized to the same internal control. Thus, they are not directly comparable.

Confusingly, the male line in Fig 3C shows a drop in live cell count at day 6 of differentiation? Surely given their previous results in Fig 2 the Ssb KD should increase cell viability with +Dox? Ssb KD seems to have an adverse effect on ES cells during extended differentiation protocols. In Figure S1 the authors show ~ 8 - 10% survival of male lines during differentiation. Could the recombination of the Xist sequence around the loxP sites enable the cells to outcompete the dead cells? How would iEmpty and ∆A-iXist cells compare here? Have the differentiated cells been tested for their expression of Xist? Additionally, how are there similar live cell counts for male vs female lines when ~90% of male cells die during differentiation? Were more cells plated at day 4? If so, this would bias the competition of male cell survival and therefore make the male line an inappropriate control.

Given the essential role of La during development a control is needed to prove that this death is X-linked in the female 3F1 line. For example, an XO cell line retaining the Cast allele and shSsb expression could show the amount of death caused from shSsb alone independent of X-linked cell death.

The reviewer completely misunderstood the experiment. The severe cell death specifically observed in female differentiating ESCs is a strong evidence showing Ssb is involved in XCI (Figure 3).

The male ESCs in Figure 3C is a WT ESC line without the inducible Xist transgene, in which no XCI occurs upon differentiation. It is completely different from iXist male ESCs with Dox, in which forced Xist induction leads to XCI. Thus, the diverse functions of Ssb might contribute to the slight decrease in live cell count of wild type male cells at day 6 of differentiation.

Figure S2 shows the differentiation of iXist male ESCs with or without Dox. As explained above, forced Xist induction silences the only X chromosome in male cells, resulting in cell death. In addition, XCI occurs more efficiently in differentiation condition (Figure S2) than in pluripotent status (Figure 2C)

During differentiation, female ESCs silence one X chromosome, and the other X chromosome remains active. KD of Ssb compromises XCI, and two X chromosomes in some female differentiating cells maintain active, leading to cell death. The reviewer is correct that we need a control to rule out that the essential role of Ssb during development affects cell survival and death. An XO cell line can be used as a control. Similarly, a male cell line (XY) is also a good control. We already included a male cell line as a control in Figure 3B and 3C.

If I understood correctly, the RNA FISH used dsDNA probes ("Sx9") against 40 kb of the X-inactivation centre (Xic). Surely Tsix or other Xic transcripts will also be visible? Can the authors use their RNA FISH to determine the XX or XO status of their cells? In Figure S5 a number of cells appear to show a single pinpoint of transcription. This could either be low levels of Xist transcripts or Xic transcription from an XO line in which the 129 chromosome is missing. It would be best to solely quantify cells which have two x chromosomes and if a significant amount of X chromosomes have been kicked out, this should be discussed and controlled for.

This is a valid concern, but this concern can be adequately addressed with the available data in the manuscript.

First, if the female Ssb KD cell line is an “XO” cell line, in which the X129 allele is “kicked out”, the RNA allelotyping results should show an absolute “silencing” of the X129 allele. However, in complete contrast to this notion, RNA allelotyping detected “more” RNA transcripts from X129, showing the chromosome-wide XCI defects (Figure 3D).

Second, overexpression of Ssb in Ssb KD female cells restores the Xist clouds and the polycomb marks (Figure S8), suggesting that the Ssb KD female cells are XX, but not XO.

Third, the severe cell death specifically occurred in female Ssb KD lines is also against the “XO” argument (Figure 3B&C).

In Fig6, the authors generated a number of Ssb constructs for a rescue assay. However, these results complicate the matter and raise more questions than they address. It seems odd that the ∆RRM1 does not rescue based on comparison with their putative negative control, ∆NLS. However, the ∆RRM1 + 2 and ∆LAM do rescue the phenotype better than the full length Ssb? This makes no logical sense and highlights the inherent variation in cell viability these generated cell lines seem to show.

Following on from this, figure S7 quantifies the GFP tag mRNA levels, depicting all ∆RRM mutants with expression below ~30%? How can ∆RRM1 or 2 be rescuing in this scenario? Have these lines been tested for their XX or XO status? The loss of an X chromosome would lead to a rescue of the cell death phenotype, which is a process known to occur in XX lines that have been cultured for extended periods of time. Could it also be that the cell lines derived are more or less sensitive to exogenous shRNA expression? Also, further validation is needed to assess the efficiency of KD in these lines as theoretically most of these constructs will be targeted by shRNA? What is the endogenous Ssb expression level in these lines? Where in the mRNA sequence are the shRNAs targeted to? Does this make sense on the relative expression levels of ∆RRM1/2 for example? Further testing of GFP expression could also be assessed by quantitative western blot of GFP or even visualised in their RNA FISH/IF samples (Figure S8), currently neither are shown. In addition, some kind of information of stability of each Ssb protein constructs has not been demonstrated.

Our shRNA targets the LAM domain, so the expression of ∆LAM is not affected by the shRNA. The reviewer is correct that the detected GFP expression levels of ∆RRM1 and ∆RRM2 are too low to be conclusive. We have removed the data point of ∆RRM1 and ∆RRM2. Meanwhile, it is clear that ∆RRM1&2 has a better rescuing effect than ∆NLS, when ∆RRM1&2 and ∆NLS are expressed at similar levels. Ssb is a well known RNA chaperone/RNA helicase. Identifying Ssb is an Xist-binding protein already suggests the functional role of Ssb in XCI. The data of the plasmid rescue experiments further suggests that Ssb is involved in XCI as a RNA chaperone/RNA helicase.

As for the Western blot and GFP fluorescence (IF), we have tried both. Neither of them detected GFP signal, reflecting the low expression level of these GFP fusion proteins. As the reviewers pointed out that the shSsb is not targeting the 5’ or 3’-UTR region, therefore, interfering the exogenous Ssb as well. This might be a reason for the low expression of these GFP fusion proteins.

For the data shown in Figure 7A and B the authors quantify the % of cells with Xist signal. The authors have already shown a defect in Xist visualisation in Ssb KD. Surely it is plausible to assume a faster loss of Xist signal below background in weaker expressing cells. A more appropriate quantification would be the % loss of Xist signal per cell over time.

With Figure 7C and D, the samples have been treated with actinomycin D which globally affects the transcription of cells even the PolIII associated genes Ssb is needed to mature. This treatment could have an added effect on cell mortality and function. Data confirming that actinomycin D doesn't affect the cells disproportionately is needed. The difference in half-life could be attributed to such a treatment.

We agree with the reviewer that monitoring Xist signal loss per cell would be a better way to analyze the data. However, in Xist signal loss experiment, snapshot images were taken at four time points (1h, 2h, 3h and 4h). This is not a time-lapse imaging. High-quality time-lapse imaging can only be done within a 2-hour time period with 2-min time interval. Therefore, cell-tracking cannot be done in this experiment. In addition, even though Ssb KD slows down the formation of Xist cloud within the early phase (3 hours) of Xist induction (Figure 4), prolonged (overnight) Xist induction leads to Xist cloud formation in a significant fraction of Ssb KD cells, and the Xist cloud signals are about the same in WT and Ssb KD cells (Figure 7A, 0 h). Similarly, qRT-PCR also revealed that Xist RNA are at the same level in WT and Ssb KD cells (Figure 7C, 0 h). These data argue against that a faster loss of Xist signal in Ssb KD cells is due to weaker initial Xist signal.

Actinomycin D was added at the last 11 hours of the experiment. During this period, no obvious adverse effects on cells were observed.

In summarising the authors claim that La binds Xist to facilitate folding and appropriate spreading of Xist along the X-chromosome. No direct interaction has been shown, CLIP-seq data would resolve this, however I do understand this is a challenging technique. The authors have instead opted for RIP followed by qPCR (Figure S2). However, this process has a greater potential for non-specific recovery of RNAs via indirect binding. Furthermore, qPCR may also amplify the relative abundance of the RNA detected. As multiple nucleolar proteins came down in the mass spec screen and FLAG-Ssb is being over expressed, it is plausible to assume some transient Xist interactions may arise from nucleolar association at which La will be in high abundance. Positive and negative nuclear RNA controls (e.g. 7SK and U1 snRNA respectively) could be used so to determine the amount of non-specific Protein-RNA interactions in their RIP pull downs. Cytoplasmic actin is not an appropriate control as it is cytosolic.

We have to clarify one point that the mass spec screen analyzed samples pulled down by FLAG-PUFb, but not FLAG-Ssb.

We did not intend to distinguish whether Ssb directly binds Xist or is just associated with Xist. RIP followed by qPCR is sufficient to prove the association between Ssb and Xist RNA.

We can include nuclear RNA as controls, if the reviewer regards RIP as a valid method to show protein and RNA association

Other than this the authors may want to probe (via IF) for the presence of La accumulation on the X? Many other know factors such as Ciz1, hnrnpK and PRC1/2 complexes show clear accumulation on the X. If I understand correctly, there are many La antibodies on the market and endogenous levels on the X could be assessed. These antibodies may be useful in IP's and pull downs also.

Many XCI factors play extensive roles in the cell and are not clearly enriched on Xi, including Spen (Moindrot et al. 2015). We have tried the immunostaining and did not detect Ssb’s enrichment on Xi. Ssb shows a general distribution in the nucleus without a clear enrichment on Xi (data not shown).

*-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 experiments suggested above are centrally focussed on the cell lines that are currently in the authors possession with maybe exceptions with the ∆A-iXist-shSsb line suggested. However, this should be reasonably quick to obtain given their previous work for this paper. Most experiments suggested will focus on the validation of karyotype, Xist expression, rescue construct expression, further RNA FISH classification and repeating more appropriate positive and negative controls for a number of experiments. In theory this can be obtained relatively simply and quickly from current resources. But with the sheer volume of further experiments that are required here, this may take a significant amount of time.

One vital improvement needed is the replication of mass spec data and the validation of Xist specific recovery and protein enrichment. As it stands this manuscript seems to not have any replicates of the FLAG-out methodology and mass spec data. This is troubling given the poor recovery and specificity of the protein samples obtained. Repeating these experiments would be costly in time and also financially. As it stands, I feel this is essential to conclusively validate their target of interest.

*- Are the data and the methods presented in such a way that they can be reproduced?*

The data is presented relatively well, however, it would be beneficial if deailed methods were in the main text and not in a supplementary file. Similarly, more information about the process of differentiation and how cell death/survival was quantified and validated is needed.

The reviewer rejected the basic design of the experimental system and ignored the majority of the functional analysis data. No additional experiment can address these issues

We can include more information in the main text, regarding Ssb. However, there is limited space for the main text, various depending on the journals. Meanwhile, the current citation on Ssb is adequate to emphasize that Ssb is a versatile RNA binding protein involved in a variety of fundamental RNA processing events in the cell.

*- Are the experiments adequately replicated and statistical analysis adequate?*

In the most part yes, however there seems to be no replicates of the FLAG-out mass spec screen which is worrying given the minimal specificity observed in the current data.

As we mentioned above, the FLAG-out experiment only serves as a starting point to generate a list of Xist binding protein candidates. Rather than repeating the FLAG-out experiment, we compared the result of FLAG-out to previously published lists of Xist binding protein candidates. More importantly, additional experiments are carried out to validate the Xist binding proteins identified by FLAG-out.

**Minor comments:**

*- Specific experimental issues that are easily addressable.*

Unfortunately, the majority of experimental issues need to be addressed with more robust data which are highlighted above. However, some image analysis, quantification and classification can be amended relatively easily. For example, the live-cell imaging data should be quantified as loss of signal as discussed and RNA FISH should be used to classify XX positive cells and the XO cells can be discarded from analysis.

We have addressed these issue in the previous sections of this rebuttal.

*- Are prior studies referenced appropriately?*

Most papers regarding Xist pull down and biology are discussed and referenced appropriately. However, the role in which La plays during development and its aberrant affects upon KD are seemingly downplayed. I would like to see more discussion of potential defects that could be caused due to globally altering cellular RNA folding.

We have tried to cite key references about Ssb in development and RNA folding. Due to length limitation, we cannot cite all references in the topic. If necessary, we could discuss the possibility of indirect effect of Ssb KD on XCI through globally altering cellular RNA folding.

*- Are the text and figures clear and accurate?*

For the most part, lots of the figures are clear and accurate. Apart from these exceptions.

1.The Y-axis of Figure 2D is confusing. What does 0.3 as a "sum of area" equate to? 30% of the area was ES cells? This doesn't look to be the case from Fig 2C. Also, how does the intensity of the signal compare? The area may not be a good quantification due to ES cells growing in colonies.

We have revised the Y-axis labelling of Figure 2D to “sum of area cm2”. Thus, “0.3” means that the area of ESCs is 0.3 cm2. ALPP is highly expressed on ES cell surface. ALPP stain usually produce saturated stains on ES cell colonies. Thoroughly stained ES cell colonies, big and small, show similar signal intensity levels. To analyze the “total signal intensity” will be not much different from “sum of area”.

2.In the Movies S1-7 there are boxes around certain cells and marked with "Figure 5a - c". This seems to be incorrect as figure 5 is currently the IF staining of polycomb marks. I assume this is in relation to Figure 4b-d?

We have corrected the labelling mistakes.

3.Similarly, in Movies S1-7, the intensities of Xist foci seem by eye to be similar. In the paper it is claimed that the Xist clouds that do form are lower in intensity. Are the Movies depicting the same range of pixel intensities? If not, this should be amended. Similarly, figure 7 seems to show relatively equivalent RNA signal at 0 h?

All the images were collected using a fixed standard of the microscope and camera setting, and these movies depict the same range of pixel intensities. Movies S1-S3 are WT control, and Movies S4-S7 are Ssb KD cells. The Xist cloud signals are weaker in Movie S4-S7 (also quantified in Figure 4E). For the Xist cloud signal, not only the intensity, but also the area of Xist cloud, have to be taken into account.

The 0 h in Figure 7 is after overnight Dox treatment, and different from the time point in Movies S1-7 (maximum 3 hour Dox treatment, figure legend of Figure 4B-D). The discrepancy can be explained by that knockdown of Ssb only slows down the formation of Xist clouds. After overnight forced expression, the Xist RNA still shows an accumulation in the cells. Figure 7 shows the forced accumulation of Xist RNA after prolonged Dox treatment disappears faster after Dox withdraw.

4.In figure 4A the data is from female XX cells, this should be highlighted to limit confusion with the male iXist data shown below in 4B-E. It would also be helpful to have the male/female icons (as in figure 3B), for each figure that has images of cells. Currently Figure 4, 5, 7, S5 and S8 are lacking these icons.

We have revised the labelling on Figure 3, 4, 5, 7 S6 and S9 (S5 and S8 before revision).

5.No explanation of the Flag-Ssb expression is given for figure S2. Furthermore, is it really necessary to express Flag-Ssb? There are reasonably good antibodies out there for Ssb as this was how it was originally found in Systemic Lupus patients. Also, no data showing the amount of Ssb being overexpressed is shown. This may have big implication to the validity of the RIP-qPCR analysis.

We could perform qRT-PCR to quantify the overexpression level of Flag-Ssb. If required, we could use Ssb antibody to do Western blot to show the amount of Flag-Ssb protein.

*- Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

Most of the data is presented reasonably well, but the robustness of the data somewhat retracts from their conclusions. I feel the certainty of their conclusion regarding Xist specific La binding and RNA chaperone activity is still presumptive and should be rewritten unless more robust data can confirm Xist interaction. I would also suggest deciding on the nomenclature for the protein of interest and use either La or Ssb, the continued use of both through the figures and text can get a little confusing to the reader.

In the current literatures, Ssb seems to be commonly used as a gene name and La is used as a protein name. We have revised the manuscript to use one name “Ssb” to describe both the gene and the protein.

Reviewer #1 (Significance (Required)):

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

It was a good trial to use PBSb-PUFb system to purify Xist RNA binding proteins, compared to previous reports had used anti-sense oligo purification using complementary sequence to Xist RNA sequences. But currently the purification still needs further validation and repeats to confirm its use. A potential complementary technique could be to isolate Xist directly by using biotinylated probes against the PBSb sequence.

The authors further claim the identification of a novel Xist RNA chaperone (La/Ssb) which they say facilitates XCI progression. This would be a novel finding in the field; however, the data is currently not robust enough to support this

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

This work has focused on the development of a milder methodology for purifying Xist RNA during XCI. Others have published similar methodologies predominantly focusing on purifying Xist RNA directly with biotinylated probes (McHugh et al. 2015; Minaji et al. 2015; and Chu et al. 2015). Although this method boasts a milder purification method, it seems to be low yielding in Xist specific proteins. Others have shown a more robust identification of bona fide Xist binding proteins which are currently missing in this manuscript. A recent preprint from the Plath lab has identified new factors involved in XCI during differentiation and their tethering/rescue experiments are far more convincing than the ones shown in this manuscript https://www.biorxiv.org/content/10.1101/2020.03.09.979369v1. The candidate protein Ha et al. have identified has multiple roles in developing cells and has shown to be important during mouse development. However, Ha et al do not robustly show that the knockdown of Ssb causes X-linked cell mortality. Alternatively, as would be presumed from Ssb's essential role in many housekeeping short non-coding RNAs, the cell death seems more ubiquitous upon shRNA KD. Therefore, the link the authors are making here are relatively weak.

Ssb KD rescues cell death caused by forced induction of Xist in male ESCs. In addition, Ssb KD leads to cell death in differentiating female ESCs, while it has a negligible effect on cell death in differentiating male ESCs. These data clearly demonstrated X-linked cell survival/mortality by Ssb KD.

Plath lab’s work is different from ours. In their manuscript, the authors report the observation of a protein condensation which is assembled by Xist but sustains in absence of Xist. TDP-43 (a.k.a. Tardbp) happens to be one protein factor involved in the protein condensation and also one candidate protein selected for further validation in our study. In our study, Tardbp KD did not rescue cell death caused by induced XCI in male cells. Thus, Tardbp is not further studied. In the manuscript, we have discussed the possibility that low efficiency of knockdown and redundancy might contribute to the failure in validation of Tardbp

*- State what audience might be interested in and influenced by the reported findings.*

The audience may be interested in the novel technique and the finding of a novel Xist binding protein.

*- 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.*

RNA biochemistry and developmental biology

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

**Summary:**

This manuscript describes a novel "FLAG-out" system, where the authors sought to identify Xist RNA binding proteins. The authors focused on a specific protein found in their screen and also identified in several other screens for Xist RNA binding proteins, Ssb/La, and further characterize the role of this protein in XCI. This manuscript describes the loss of Ssb/La and suggest that it predominately impacts the canonical 'cloud' formation of Xist RNA on the X chromosome during XCI initiation. Further, they determine that loss of Ssb/La decreases Xist RNA half-life and alters folding of Xist RNA transcripts. Based on their findings, the authors propose that Ssb/La functions to directly bind and fold Xist RNA transcripts in a manner that stabilizes Xist RNA, allowing for proper 'cloud' formation and successful initiation of XCI.

**Major comments:**

The authors made an interesting findings that the SLE-relevant autoantigen Ssb/La stabilizes Xist RNA transcripts, and there is some evidence that this occurs by binding and maintaining proper folding of Xist RNA. Despite these intriguing observations, there are many parts of the manuscript that need to be addressed in order to support the authors main conclusions.

The most troubling aspect of this manuscript is the persistent use of an artificial XCI system in male cells to draw strong conclusions about the function of Ssb in XCI. This issue is prevalent throughout the manuscript, and I question why the authors chose to perform most of their experiments in male cells when the same experiments can be (and have previously been by other groups) performed in female cells. Using male ESCs and then making conclusions for XCI, which is a female-specific process, is a major concern.

In addition to iXist male ESC line, many experiments, such as cell death/survival (Figure 3B, C), allelotype (Figure 3E), Xist could formation (Figure 4A), H3K27me3 and H2AK119ub IF (Figure 5), were performed in female ESC. We chose to do SHAPE and Xist RNA stability assays in iXist male ESC line, because the onset of XCI is much more synchronized in this system. Moreover, in female cells, Xa causes additional layers of complication/noise in the ATAC-sequencing which may not be fully cleared up by data analysis. On the other hand, inducible Xist expression in male ESCs can be used as an experimental system to recapitulate the silencing step of XCI (Ha et al. 2018; Wutz et al. 2002).

• Out of the 138 identified binding proteins, the authors chose to only validate three: Mybbp1a, Tardbp, and Ssb/La. The logic for choosing these candidates is weak, and the authors are only able to validate 1 out of 3 of these proteins.

In theory, all candidate proteins in the list are possibly involved in XCI. There is no method which can help to make accurate prediction. We did not follow a clear-cut logic in selecting candidates for validation, but we do consider the candidate gene’s knockout phenotype, “early embryonic lethality”, as a phenotype consistent with a critical role of the candidate gene in XCI. Meanwhile, in the manuscript, we have discussed why we chose the three proteins for validation as the following:

“……From the candidate proteins, we shortlisted three proteins for individual validation. Myb-binding protein 1A (Mybbp1a, Q7TPV4) and TAR DNA-binding protein 43 (Tardbp, Q921F2) were selected because they are known transcription repressors (11, 12). The Lupus autoantigen La (P32067, encoding-gene name: Ssb) was selected because systemic lupus erythematosus (SLE) is an autoimmune disease characterized by a strikingly high female to male ratios of 9:1 (13). Moreover, its autoimmune antigen La is a ubiquitous and versatile RNA-binding protein and a known RNA chaperone (14). All the three selected candidates have also been identified as Xist-binding proteins in previous studies (2, 4). Moreover, the knockout of these three genes all lead to early embryonic death. Tardbp knockout causes embryonic lethality at the blastocyst implantation stage (15). Mybbp1a and Ssb knockout affect blastocyst formation (16, 17). Early embryonic lethality is a mutant phenotype consistent with a critical role of the mutated gene in XCI (1)** ……”

We used cell death/survival assay to further validate the role of Xist binding protein candidates in XCI. This is a stringent assay. It requires not only that Xist binding protein candidates bind to Xist, but also that the candidates have to be functionally important in XCI.

Indeed, it has been demonstrated by Plath lab (the BioRxix manuscript mentioned by reviewer 1) that Tardbp (also named TDP-43), together with other RBPs, bind to the E repeat of Xist to form a condensate and create an Xi-domain. Yet, Tardbp KD did not rescue cell death caused by forced XCI in male cells in our studies. Thus, only 1 out of 3 of these candidates is validated and further studied. In the manuscript, we also discussed that low efficiency of knockdown and redundancy might contribute to the failure in validation of Tardbp and Mybbp1a.

• Use of the cell death assay is not strong enough to "confirm that La is involved in induced XCI" as stated by the authors. This is a huge overstatement.

Given the diverse functions of Ssb in cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation, one would expect less surviving Ssb knockdown cells. In contrast, more Ssb knockdown cells survives in the presence of Dox, suggesting that Ssb plays an important role in XCI. Considering the reviewer’s comment, we revised the sentence to “further suggest that Ssb is involved in induced XCI”.

While the authors observed differences in X-linked gene expression after Ssb KD, they did not examine expression of these genes in after KD of either Mybbp1a or Tardbp. Are the changes observed in these genes specific to Ssb KD? Or could there still be alterations of X-linked gene expression in the non-validated KDs? This experiment should be performed and included in the manuscript, either within Fig 2 or in the supplemental. As well, inclusion of a well characterized positive control, for example Hnrnpu, as comparison to Ssb should be included.

Mybbp1a and Tardbp were not validated by the cell death assay. Thus, compared with Ssb, Mybbp1a and Tardbp are less important for XCI functionally. We only focused on Ssb in the subsequent studies. Mybbp1a and Tardbp KD could be additional negative controls. Yet, we have used empty vector as a negative control. We do not need so many controls.

As mentioned, Tardbp indeed binds to Xist RNA. It is very likely that Tardbp KD might alter some X-linked gene expression. This rules out Tardbp KD as a good negative control.

If we do not see any effect of Ssb KD on X-linked gene expression, a positive control is absolutely required. However, we have detected that Ssb KD compromises the silencing of several X-linked gene. A positive control might not be essential.

• The authors perform RIP to validate the interaction of Ssb with Xist, but this is performed in male ES cells with induced Xist RNA and with FLAG-tagged Ssb. Aside from these cells being male, in this system Xist RNA expression is much higher than would be found endogenously. RIP should have been done in female differentiated ESCs if there is in fact a role for XCI.

• The authors need to include more details in the methods section to explain how the FLAG-Ssb is expressed in these cells, and why the authors chose to use a tagged contrast over endogenous Ssb. Due to these issues the result from this experiment is essentially meaningless and is not convincing of Ssb interaction with Xist RNA. There is no reason RIP cannot be performed in female cells, and the authors should repeat this experiment in the relevant experimental condition. As well, if a validated Ssb antibody exists the authors should perform RIP using the endogenous protein.

If required, we could try to perform RIP and/or CLIP using Ssb antibody in female cells.

The authors state in Fig 3A-C that the results of the cell death and differentiation experiments "...support a functional role of La in XCI". The authors state earlier that Ssb is a ubiquitous protein that is embryonic lethal (in both female and males). Based on this, the cell death results shown do not support a functional role of La in XCI as the Ssb KD could be having an indirect affect due to its other developmental functions. This manuscript lacks a direct functional link between Ssb and XCI; more data is necessary.

Given the diverse functions of Ssb in cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation, one would expect less surviving Ssb knockdown cells. In contrast, more Ssb knockdown cells survives in the presence of Dox, suggesting that Ssb plays an important role in XCI.

For the data in Fig 3A-C, Ssb KD causes the death of female differentiating cells, but not male differentiating cells. Therefore, it rules out that the death of female cells is due to the general function of Ssb. Rather, the specific role of Ssb in XCI contributes to the female specific cell death.

In Fig 3D, the authors perform ATAC-seq in inducible male ES cells. The authors claim that the extremely slight reduction in chromatin compaction of the Ssb KD compared to control iXist "directly connect La to the heterochromatinization of Xi, supporting a functional role of La in XCI". This is also an overstatement based on the minimal, and possibly indirect, change in compaction. The positive control i-detaA-Xist sample has significantly less compaction (and thus significantly higher compaction defect) than the Ssb KD again disputing the claim stated above. It is unclear why performing ATAC-seq is even necessary, as Ssb isn't stated to have a function in regulating chromatin architecture. In addition, why the authors performed ATAC-seq in the artificial male XCI system and not in the F1 female cells, and the N of the experiment is unclear. If the authors want to include the ATAC-seq in further revisions it should be repeated n=3 in the female system.

The male induced XCI system provides a more synchronized onset of XCI. More importantly, in the male induced XCI system, only one X chromosome exists, avoiding the interference from the active X chromosome in female cells. If ATAC-seq was performed in female cells, only loci with SNPs can be distinguished. The sequencing reads from Xa will create additional layers of complication/noise which may not be cleared up fully by data analysis

“i-delat-Xist” is a positive control to show the experimental system works. It is not justified to compare the chromatin accessibility of the mutant, which is only a Ssb “knockdown” mutant, and the control “i-delat-Xist”, in which the Repeat A is “deleted”. We admit that ATAC-Seq results did not reveal a drastic difference in chromatin accessibility between the wild type sample and the mutant sample. However, as what we discussed in the manuscript, clear difference can still be seen at the 14 h time point. This is shown clearly by the heatmap (Fig. 3E) and the sequencing coverage profile (Fig. S4A).

• In Fig 6, the authors state in their methods that "The shRNA construct, which worked efficiently against Ssb, was not designed against the 3' UTR of the RNA. Therefore, the shRNA is against some of the rescue plasmid constructs. Nonetheless, transfecting the Ssb knockdown cells with the rescue plasmids should compensate the effect of Ssb knockdown and serve as a rescue assay to study the functional domains of La.". This is troubling and seems like a major experimental issue; the specific rescue constructs that may be impacted by this issue are not stated and should be explicitly mentioned. This becomes more confusing when examining the data from rescue experiments.

We pointed out this issue in the original manuscript. We agree that the experiment was not perfectly designed. In the revision, we added in the information on the shRNA target site. Our shRNA targets the LAM domain, so the expression of ∆LAM is not affected by the shRNA. We agree that the detected GFP expression levels of ∆RRM1 and ∆RRM2 are too low to be conclusive. In the revision, we have removed the data point of ∆RRM1 and ∆RRM2. Meanwhile, it is clear that ∆RRM1&2 has a better rescuing effect than ∆NLS, when ∆RRM1&2 and ∆NLS are expressed at similar levels. Ssb is a well-known RNA chaperone/RNA helicase. Identifying Ssb is an Xist-binding protein already suggests the functional role of Ssb in XCI. The data of the plasmid rescue experiments further suggests that Ssb is involved in XCI as a RNA chaperone/RNA helicase.

If it is necessary, we could redo this experiments using a shSsb targeting 3’-UTR or expressing GFP-Ssb immune to shSsb.

In Figure S7, the expression of the rescue constructs deltaRRM1 and deltaRRM2 is extremely low, yet the authors observe a rescue of the cloud phenotype (fig 6D) from those constructs that reaches almost the level of full length Ssb. This is confusing, and the authors need to address this by performing a western blot to show the protein levels of these rescue constructs and discuss further how such a low level of expression can show a rescue phenotype. The results would also be stronger if the authors examined H3K27me3 and H2AK119ub1 enrichment since they observed decreased overlap of these marks with Xist RNA after Ssb KD. Finally, the authors state that "...all three RNA-binding domains are required for the functionality of La in XCI..." however I have trouble coming to this conclusion based on the above issues. As well, if the authors want to support direct function, they should repeat the RIP experiments with these rescues constructs to show that the domains capable of rescue can still bind to Xist RNA.

Reviewer 1 raised similar concerns. In Figure 6C, the live cell counts of ∆RRM1 and ∆NLS are about the same. It might be due to the low expression level of ∆RRM1 (Figure S7). It is clear that ∆RRM1&2 has a better rescuing effect than ∆NLS, when ∆RRM1&2 and ∆NLS are expressed as similar levels. To make the data more straight forward, we removed the data point of ∆RRM1 and ∆RRM2, because of their low expression levels.

As for the Western blot and GFP fluorescence (IF), we have tried both. Neither of them detected GFP signal, reflecting the low expression level of these GFP fusion proteins. The shSsb is not targeting the 5’ or 3’-UTR region, therefore interfering the exogenous Ssb as well. This might be a reason for the low expression of these GFP fusion proteins. If it is necessary, we could redo this experiments using a shSsb targeting 3’–UTR or expressing GFP-Ssb immune to shSsb.

We deleted the sentence "all three RNA-binding domains are required for the functionality of La in XCI".

**Minor comments:**

The authors may want to consider better highlighting the strengths of their "FLAG-out" system. As written, is it difficult to tell how this system sets them apart from the previously published studies referenced in the text, especially as some of these studies used similar crosslinking conditions and cell types. Additionally, the logic and questions the authors pose in the introduction as to why they performed this project are too general and not very strong. For example, the authors mention how might protein machinery may assemble on Xist RNA, and how might Xist RNA may spread on the X chromosome. However neither of these topics are actually addressed in their experiments or discussion. These are interesting questions, but the authors should either discuss them further within the context of their results or take these questions out. It would also be helpful if the authors could better label Figure 4, as it is unclear in the figure itself that Fig 4A is in reference to female cells, but remaining panels are in male cells.

The inducible XCI in male cells is a valid system to recapitulate the silencing step of XCI. It also provides unique advantages in many experiments, such as ATAC-seq. Meanwhile, we did perform extensive functional analysis on the endogenous XCI process using female cells. However, we do realize that presenting the data of induced XCI in male cells together with the data from female cells is confusing to many readers. We have revised the labelling on Figure 3, 4, 5, 7 S6 and S9 (S5 and S8 before revision).

To understand “how the protein machinery is assembled by Xist” and “how Xist spreads along its host chromosome territory” are not specifically the initial aims of this study. We removed the sentences from the introduction section. However, we believe Ssb may provide clues for the future studies to fully address these questions, and we did provide the following thoughts in the discussion section:

“……Secondly, as Ssb is able to utilize ATP to unwind RNA-RNA and RNA-DNA duplex, it may play a more active role in controlling the structural dynamics of Xist in living cells (14, 23). These structural dynamics may be important for recruiting proteins onto the RNA and spreading of the RNA along its host chromosome territory……”

Reviewer #2 (Significance (Required)):

I am not convinced the this manuscript, as written, has sufficient novelty. Ssb/La has been previously identified to be an Xist RNA binding protein with older/different approaches. However, there are some interesting observations in this manuscript. Major revisions are necessary.

We agree with the reviewer that identification of Ssb as an Xist RNA binding protein is not novel. The novelty of our discovery lies in: 1) we developed a new method for isolating lincRNA associated proteins; 2) we confirmed that Ssb is an important player involved in XCI; 3) we showed that Ssb regulates the folding of Xist RNA, consequently the stability of Xist and the formation of Xist cloud.

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

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Referee #2

Evidence, reproducibility and clarity

Summary:

This manuscript describes a novel "FLAG-out" system, where the authors sought to identify Xist RNA binding proteins. The authors focused on a specific protein found in their screen and also identified in several other screens for Xist RNA binding proteins, Ssb/La, and further characterize the role of this protein in XCI. This manuscript describes the loss of Ssb/La and suggest that it predominately impacts the canonical 'cloud' formation of Xist RNA on the X chromosome during XCI initiation. Further, they determine that loss of Ssb/La decreases Xist RNA half-life and alters folding of Xist RNA transcripts. Based on their findings, the authors propose that Ssb/La functions to directly bind and fold Xist RNA transcripts in a manner that stabilizes Xist RNA, allowing for proper 'cloud' formation and successful initiation of XCI.

Major comments:

The authors made an interesting findings that the SLE-relevant autoantigen Ssb/La stabilizes Xist RNA transcripts, and there is some evidence that this occurs by binding and maintaining proper folding of Xist RNA. Despite these intriguing observations, there are many parts of the manuscript that need to be addressed in order to support the authors main conclusions.

• The most troubling aspect of this manuscript is the persistent use of an artificial XCI system in male cells to draw strong conclusions about the function of Ssb in XCI. This issue is prevalent throughout the manuscript, and I question why the authors chose to perform most of their experiments in male cells when the same experiments can be (and have previously been by other groups) performed in female cells. Using male ESCs and then making conclusions for XCI, which is a female-specific process, is a major concern.

• Out of the 138 identified binding proteins, the authors chose to only validate three: Mybbp1a, Tardbp, and Ssb/La. The logic for choosing these candidates is weak, and the authors are only able to validate 1 out of 3 of these proteins.

• Use of the cell death assay is not strong enough to "confirm that La is involved in induced XCI" as stated by the authors. This is a huge overstatement.

• While the authors observed differences in X-linked gene expression after Ssb KD, they did not examine expression of these genes in after KD of either Mybbp1a or Tardbp. Are the changes observed in these genes specific to Ssb KD? Or could there still be alterations of X-linked gene expression in the non-validated KDs? This experiment should be performed and included in the manuscript, either within Fig 2 or in the supplemental. As well, inclusion of a well characterized positive control, for example Hnrnpu, as comparison to Ssb should be included.

• The authors perform RIP to validate the interaction of Ssb with Xist, but this is performed in male ES cells with induced Xist RNA and with FLAG-tagged Ssb. Aside from these cells being male, in this system Xist RNA expression is much higher than would be found endogenously. RIP should have been done in female differentiated ESCs if there is in fact a role for XCI.

• The authors need to include more details in the methods section to explain how the FLAG-Ssb is expressed in these cells, and why the authors chose to use a tagged contrast over endogenous Ssb. Due to these issues the result from this experiment is essentially meaningless and is not convincing of Ssb interaction with Xist RNA. There is no reason RIP cannot be performed in female cells, and the authors should repeat this experiment in the relevant experimental condition. As well, if a validated Ssb antibody exists the authors should perform RIP using the endogenous protein.

• The authors state in Fig 3A-C that the results of the cell death and differentiation experiments "...support a functional role of La in XCI". The authors state earlier that Ssb is a ubiquitous protein that is embryonic lethal (in both female and males). Based on this, the cell death results shown do not support a functional role of La in XCI as the Ssb KD could be having an indirect affect due to its other developmental functions. This manuscript lacks a direct functional link between Ssb and XCI; more data is necessary.

• In Fig 3D, the authors perform ATAC-seq in inducible male ES cells. The authors claim that the extremely slight reduction in chromatin compaction of the Ssb KD compared to control iXist "directly connect La to the heterochromatinization of Xi, supporting a functional role of La in XCI". This is also an overstatement based on the minimal, and possibly indirect, change in compaction. The positive control i-detaA-Xist sample has significantly less compaction (and thus significantly higher compaction defect) than the Ssb KD again disputing the claim stated above. It is unclear why performing ATAC-seq is even necessary, as Ssb isn't stated to have a function in regulating chromatin architecture. In addition, why the authors performed ATAC-seq in the artificial male XCI system and not in the F1 female cells, and the N of the experiment is unclear. If the authors want to include the ATAC-seq in further revisions it should be repeated n=3 in the female system.

• In Fig 6, the authors state in their methods that "The shRNA construct, which worked efficiently against Ssb, was not designed against the 3' UTR of the RNA. Therefore, the shRNA is against some of the rescue plasmid constructs. Nonetheless, transfecting the Ssb knockdown cells with the rescue plasmids should compensate the effect of Ssb knockdown and serve as a rescue assay to study the functional domains of La.". This is troubling and seems like a major experimental issue; the specific rescue constructs that may be impacted by this issue are not stated and should be explicitly mentioned. This becomes more confusing when examining the data from rescue experiments.

• In Figure S7, the expression of the rescue constructs deltaRRM1 and deltaRRM2 is extremely low, yet the authors observe a rescue of the cloud phenotype (fig 6D) from those constructs that reaches almost the level of full length Ssb. This is confusing, and the authors need to address this by performing a western blot to show the protein levels of these rescue constructs and discuss further how such a low level of expression can show a rescue phenotype. The results would also be stronger if the authors examined H3K27me3 and H2AK119ub1 enrichment since they observed decreased overlap of these marks with Xist RNA after Ssb KD. Finally, the authors state that "...all three RNA-binding domains are required for the functionality of La in XCI..." however I have trouble coming to this conclusion based on the above issues. As well, if the authors want to support direct function, they should repeat the RIP experiments with these rescues constructs to show that the domains capable of rescue can still bind to Xist RNA.

Minor comments:

The authors may want to consider better highlighting the strengths of their "FLAG-out" system. As written, is it difficult to tell how this system sets them apart from the previously published studies referenced in the text, especially as some of these studies used similar crosslinking conditions and cell types. Additionally, the logic and questions the authors pose in the introduction as to why they performed this project are too general and not very strong. For example, the authors mention how might protein machinery may assemble on Xist RNA, and how might Xist RNA may spread on the X chromosome. However neither of these topics are actually addressed in their experiments or discussion. These are interesting questions, but the authors should either discuss them further within the context of their results or take these questions out. It would also be helpful if the authors could better label Figure 4, as it is unclear in the figure itself that Fig 4A is in reference to female cells, but remaining panels are in male cells.

Significance

I am not convinced the this manuscript, as written, has sufficient novelty. Ssb/La has been previously identified to be an Xist RNA binding protein with older/different approaches. However, there are some interesting observations in this manuscript. Major revisions are necessary.

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Referee #1

Evidence, reproducibility and clarity

Summary:

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

Here Ha et al. has further developed their Pumilio RNA tagging methodology for the isolation of UV-crosslinked proteins that are suggested to associate with Xist RNA in mouse embryonic stem cells (mESCs). Within this study the authors claim to have found the Lupus antigen RNA binding protein (La) as a novel Xist interacting partner that influences the efficacy of X-chromosome inactivation (XCI). The authors use a number of different techniques such as qPCR, fluorescent imaging, ATAC-SEQ and SHAPE to show aberration of XCI upon La shRNA knockdown. However, this study has significant flaws in the efficient isolation and validation of Xist associated proteins using their FLAG-out methodology. Furthermore, later experiments predominantly focus on cell death/survival assays, which is somewhat troubling given the essential roles La plays in processes such as cell differentiation and proliferation, ribosome biogenesis, transcriptional control and tRNA maturation. I feel the authors need to robustly address the potential effects La knockdown may be having on their mESCs.

Major comments:

-Are the key conclusions convincing?

My major concern is in their Xist RNA purification. First of all, I couldn't find any data on proving the enrichment of Xist RNA itself in their Pumilio pull-down experiment. It would have been useful to show Xist RNA enrichment before benzonase step. Secondly, it is hard to imagine the protocol would successfully isolated Xist RNA-protein complexes from the cell. An earlier report by Clemson et al., (J Cell Biol., 1996) has shown that majority of Xist RNA is still stuck in the nucleus after nuclear matrix prep protocol using detergent, which is not so different from the authors' protocol. Moreover, the authors used UV crosslink, which would have made even harder to purify Xist RNA without sonication. Thirdly, as the tag is located on 5' of Xist RNA, it is rather surprising to see that Spen is not detected in their pulldown. Spen is one of the main functional interactors with Xist, robustly detected by several previous reports. Similarly, other high-affinity binders of Xist such as hnRNP-K and Ciz1 were also lacking from this screen. Finally, the peptides found associated with FLAG-out Xist are extremely low in comparison with other data using glutaraldehyde or formaldehyde crosslinking. For example, HnRNP-M found in Chu et al 2015 has 1120 peptide counts in differentiated cells. The authors here use HnRNP-M as a baseline for specific interactions and show a total of 6 peptide counts in Xist expressing cells and 5 in i-Empty cells (Supplementary excel sheet 1). Similarly, the La protein of interest in this study has 8 counts in i-FLAG-Xist and 6 counts in i-Empty. I struggle to see how this result indicate specific Xist binding. Worryingly this is the starting rationale for the rest of their experiments, it is hard to therefore accept the rest of their conclusions either.

The other key conclusion the authors make is from the use of numerous cell death/survival assays for both male and female cell lines. This is extremely troubling in the context of assessing their target protein La. La is involved in multiple RNA maturation events of rRNAs, tRNAs and other polIII transcripts. Furthermore, La has been implicated in binding to the mRNA for Cyclin D1 in both human cells and mouse fibroblasts (NIH/3T3 - male) which show a significant effect on cell proliferation upon siRNA knockdown https://www.nature.com/articles/onc2010425. This, along with the observation that La knock-out blastocysts fail to develop any mice or ES cell lines (male or female) show the effect observed in the authors results is most likely not X-linked cell death https://mcb.asm.org/content/mcb/26/4/1445.full.pdf. The authors need to show that their shRNA KD isn't affecting the proliferation and general fitness of their mESC lines.

- Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

As discussed above, I feel the authors have not clearly demonstrated Xist specific protein enrichment and haven't proven X-linked cell death. Due to the lack of necessary control experiments as discussed below, I feel the notion that La is involved directly in XCI as an RNA chaperone is currently preliminary/speculative.

- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

I would suggest them to show RT-qPCR results of Xist RNA enrichment from the sample after flagIP before benzonase treatment.

Also, it would have been more convincing if their negative control construct (i-Empty) would contain 25 copies of PBSb RNA at least.

In Fig1b, the total amount of proteins loaded on the gel is not equivalent between two lanes. The gel should show equivalent amounts of proteins on the gel. It looks like if the negative control sample had been loaded at the same amount as the one with Xist, the band pattern wouldn't be distinguishable between the two samples. Furthermore, as these samples were used in the following mass spectrometry screen it may suggest that the minimal increase in peptide counts observed in the iXist FLAG-out were due to an increased amount of sample being loaded? No controls are conducted to account for this.

The authors quantify cell death in figures 2C - E. It seems clear that shSsb 1 and 2 have an effect on cell count even in the absence of Dox. The rescue effect seen upon Dox addition is minimal when compared to Empty + Dox 2D. The authors ∆A-iXist line with and without Ssb KD/Dox would be an informative control on whether the increase in cell survival that they see is X-linked.

The qPCR results used to validate silencing defects show minor changes in expression and also don't show significant silencing of X-linked genes sufficient for cell death. Could this be because only ~ 50 - 60% of Male iXist cells seem to be expressing in the movies and that this will have an effect on the observed qPCR results? Furthermore, it seems counterintuitive that expression in the Empty male cells increases in 48h compared to 14h. Is this due to cell death and positive selection of cells less able to silence their X-chromosome? How would these data look in the female XX line? How would the data look in a ∆A-iXist line in the presence and absence of shSsb/Dox?

Confusingly, the male line in Fig 3C shows a drop in live cell count at day 6 of differentiation? Surely given their previous results in Fig 2 the Ssb KD should increase cell viability with +Dox? Ssb KD seems to have an adverse effect on ES cells during extended differentiation protocols. In Figure S1 the authors show ~ 8 - 10% survival of male lines during differentiation. Could the recombination of the Xist sequence around the loxP sites enable the cells to outcompete the dead cells? How would iEmpty and ∆A-iXist cells compare here? Have the differentiated cells been tested for their expression of Xist? Additionally, how are there similar live cell counts for male vs female lines when ~90% of male cells die during differentiation? Were more cells plated at day 4? If so, this would bias the competition of male cell survival and therefore make the male line an inappropriate control. Given the essential role of La during development a control is needed to prove that this death is X-linked in the female 3F1 line. For example, an XO cell line retaining the Cast allele and shSsb expression could show the amount of death caused from shSsb alone independent of X-linked cell death.

If I understood correctly, the RNA FISH used dsDNA probes ("Sx9") against 40 kb of the X-inactivation centre (Xic). Surely Tsix or other Xic transcripts will also be visible? Can the authors use their RNA FISH to determine the XX or XO status of their cells? In Figure S5 a number of cells appear to show a single pinpoint of transcription. This could either be low levels of Xist transcripts or Xic transcription from an XO line in which the 129 chromosome is missing. It would be best to solely quantify cells which have two x chromosomes and if a significant amount of X chromosomes have been kicked out, this should be discussed and controlled for.

In Fig6, the authors generated a number of Ssb constructs for a rescue assay. However, these results complicate the matter and raise more questions than they address. It seems odd that the ∆RRM1 does not rescue based on comparison with their putative negative control, ∆NLS. However, the ∆RRM1 + 2 and ∆LAM do rescue the phenotype better than the full length Ssb? This makes no logical sense and highlights the inherent variation in cell viability these generated cell lines seem to show. Following on from this, figure S7 quantifies the GFP tag mRNA levels, depicting all ∆RRM mutants with expression below ~30%? How can ∆RRM1 or 2 be rescuing in this scenario? Have these lines been tested for their XX or XO status? The loss of an X chromosome would lead to a rescue of the cell death phenotype, which is a process known to occur in XX lines that have been cultured for extended periods of time. Could it also be that the cell lines derived are more or less sensitive to exogenous shRNA expression? Also, further validation is needed to assess the efficiency of KD in these lines as theoretically most of these constructs will be targeted by shRNA? What is the endogenous Ssb expression level in these lines? Where in the mRNA sequence are the shRNAs targeted to? Does this make sense on the relative expression levels of ∆RRM1/2 for example? Further testing of GFP expression could also be assessed by quantitative western blot of GFP or even visualised in their RNA FISH/IF samples (Figure S8), currently neither are shown. In addition, some kind of information of stability of each Ssb protein constructs has not been demonstrated.

For the data shown in Figure 7A and B the authors quantify the % of cells with Xist signal. The authors have already shown a defect in Xist visualisation in Ssb KD. Surely it is plausible to assume a faster loss of Xist signal below background in weaker expressing cells. A more appropriate quantification would be the % loss of Xist signal per cell over time.

With Figure 7C and D, the samples have been treated with actinomycin D which globally affects the transcription of cells even the PolIII associated genes Ssb is needed to mature. This treatment could have an added effect on cell mortality and function. Data confirming that actinomycin D doesn't affect the cells disproportionately is needed. The difference in half-life could be attributed to such a treatment.

In summarising the authors claim that La binds Xist to facilitate folding and appropriate spreading of Xist along the X-chromosome. No direct interaction has been shown, CLIP-seq data would resolve this, however I do understand this is a challenging technique. The authors have instead opted for RIP followed by qPCR (Figure S2). However, this process has a greater potential for non-specific recovery of RNAs via indirect binding. Furthermore, qPCR may also amplify the relative abundance of the RNA detected. As multiple nucleolar proteins came down in the mass spec screen and FLAG-Ssb is being over expressed, it is plausible to assume some transient Xist interactions may arise from nucleolar association at which La will be in high abundance. Positive and negative nuclear RNA controls (e.g. 7SK and U1 snRNA respectively) could be used so to determine the amount of non-specific Protein-RNA interactions in their RIP pull downs. Cytoplasmic actin is not an appropriate control as it is cytosolic.

Other than this the authors may want to probe (via IF) for the presence of La accumulation on the X? Many other know factors such as Ciz1, hnrnpK and PRC1/2 complexes show clear accumulation on the X. If I understand correctly, there are many La antibodies on the market and endogenous levels on the X could be assessed. These antibodies may be useful in IP's and pull downs also.

-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 experiments suggested above are centrally focussed on the cell lines that are currently in the authors possession with maybe exceptions with the ∆A-iXist-shSsb line suggested. However, this should be reasonably quick to obtain given their previous work for this paper. Most experiments suggested will focus on the validation of karyotype, Xist expression, rescue construct expression, further RNA FISH classification and repeating more appropriate positive and negative controls for a number of experiments. In theory this can be obtained relatively simply and quickly from current resources. But with the sheer volume of further experiments that are required here, this may take a significant amount of time. One vital improvement needed is the replication of mass spec data and the validation of Xist specific recovery and protein enrichment. As it stands this manuscript seems to not have any replicates of the FLAG-out methodology and mass spec data. This is troubling given the poor recovery and specificity of the protein samples obtained. Repeating these experiments would be costly in time and also financially. As it stands, I feel this is essential to conclusively validate their target of interest.

- Are the data and the methods presented in such a way that they can be reproduced?

The data is presented relatively well, however, it would be beneficial if deailed methods were in the main text and not in a supplementary file. Similarly, more information about the process of differentiation and how cell death/survival was quantified and validated is needed.

- Are the experiments adequately replicated and statistical analysis adequate?

In the most part yes, however there seems to be no replicates of the FLAG-out mass spec screen which is worrying given the minimal specificity observed in the current data.

Minor comments:

- Specific experimental issues that are easily addressable.

Unfortunately, the majority of experimental issues need to be addressed with more robust data which are highlighted above. However, some image analysis, quantification and classification can be amended relatively easily. For example, the live-cell imaging data should be quantified as loss of signal as discussed and RNA FISH should be used to classify XX positive cells and the XO cells can be discarded from analysis.

- Are prior studies referenced appropriately?

Most papers regarding Xist pull down and biology are discussed and referenced appropriately. However, the role in which La plays during development and its aberrant affects upon KD are seemingly downplayed. I would like to see more discussion of potential defects that could be caused due to globally altering cellular RNA folding.

- Are the text and figures clear and accurate?

For the most part, lots of the figures are clear and accurate. Apart from these exceptions.

1.The Y-axis of Figure 2D is confusing. What does 0.3 as a "sum of area" equate to? 30% of the area was ES cells? This doesn't look to be the case from Fig 2C. Also, how does the intensity of the signal compare? The area may not be a good quantification due to ES cells growing in colonies.

2.In the Movies S1-7 there are boxes around certain cells and marked with "Figure 5a - c". This seems to be incorrect as figure 5 is currently the IF staining of polycomb marks. I assume this is in relation to Figure 4b-d?

3.Similarly, in Movies S1-7, the intensities of Xist foci seem by eye to be similar. In the paper it is claimed that the Xist clouds that do form are lower in intensity. Are the Movies depicting the same range of pixel intensities? If not, this should be amended. Similarly, figure 7 seems to show relatively equivalent RNA signal at 0 h?

4.In figure 4A the data is from female XX cells, this should be highlighted to limit confusion with the male iXist data shown below in 4B-E. It would also be helpful to have the male/female icons (as in figure 3B), for each figure that has images of cells. Currently Figure 4, 5, 7, S5 and S8 are lacking these icons.

5.No explanation of the Flag-Ssb expression is given for figure S2. Furthermore, is it really necessary to express Flag-Ssb? There are reasonably good antibodies out there for Ssb as this was how it was originally found in Systemic Lupus patients. Also, no data showing the amount of Ssb being overexpressed is shown. This may have big implication to the validity of the RIP-qPCR analysis.

- Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

Most of the data is presented reasonably well, but the robustness of the data somewhat retracts from their conclusions. I feel the certainty of their conclusion regarding Xist specific La binding and RNA chaperone activity is still presumptive and should be rewritten unless more robust data can confirm Xist interaction. I would also suggest deciding on the nomenclature for the protein of interest and use either La or Ssb, the continued use of both through the figures and text can get a little confusing to the reader.

Significance

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

It was a good trial to use PBSb-PUFb system to purify Xist RNA binding proteins, compared to previous reports had used anti-sense oligo purification using complementary sequence to Xist RNA sequences. But currently the purification still needs further validation and repeats to confirm its use. A potential complementary technique could be to isolate Xist directly by using biotinylated probes against the PBSb sequence. The authors further claim the identification of a novel Xist RNA chaperone (La/Ssb) which they say facilitates XCI progression. This would be a novel finding in the field; however, the data is currently not robust enough to support this.

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

This work has focused on the development of a milder methodology for purifying Xist RNA during XCI. Others have published similar methodologies predominantly focusing on purifying Xist RNA directly with biotinylated probes (McHugh et al. Minaji et al and Chu et al.). Although this method boasts a milder purification method, it seems to be low yielding in Xist specific proteins. Others have shown a more robust identification of bona fide Xist binding proteins which are currently missing in this manuscript. A recent preprint from the Plath lab has identified new factors involved in XCI during differentiation and their tethering/rescue experiments are far more convincing than the ones shown in this manuscript https://www.biorxiv.org/content/10.1101/2020.03.09.979369v1. The candidate protein Ha et al have identified has multiple roles in developing cells and has shown to be important during mouse development. However, Ha et al do not robustly show that the knockdown of Ssb causes X-linked cell mortality. Alternatively, as would be presumed from Ssb's essential role in many housekeeping short non-coding RNAs, the cell death seems more ubiquitous upon shRNA KD. Therefore, the link the authors are making here are relatively weak.

- State what audience might be interested in and influenced by the reported findings.

The audience may be interested in the novel technique and the finding of a novel Xist binding protein.

- 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.

RNA biochemistry and developmental biology

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Reply to the reviewers

General comments

We thank all three reviewers for providing their thoughtful and insightful review comments of our manuscript. We appreciate that the reviewers recognized the significance and impact of our work - “Very little imaging has been done on CAR synapses and to our knowledge this is the first live cell imaging study describing CAR microclustsers” (Reviewer 2); “This is an evolving field and little is known to date. Hence, this study could represent an insightful and important advance to the field” (Reviewer 3). A broad audience from both basic and clinical research sides will be interested in this work: “_This study will have a broad audience. Both scientists that study basic T cell signaling as well as clinicians that use CAR Ts will be interested in this study” (_Reviewer 2); “Audience is to both basic immunologist and cancer biologists” (Reviewer 3).

Meanwhile, we understand that the reviewers have raised a few major and minor issues, which we attempted to address. Most importantly, as suggested by both reviewer 1 and 3, we performed new experiments showing that LAT is not required for microcluster formation of the 1st generation of CAR (new Fig 4 and EV5). This finding suggests that the CAR-independent signaling is due to the intrinsic CAR architecture, and is not dependent on the co-signaling domains of CD28 and 4-1BB.

With the successful solutions to other issues, we believe the manuscript has been significantly improved and is ready for publication. Below we will provide point-to-point responses to each reviewer’s comments.

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

The authors compare the TCR alone to a CAR that contains signaling modules from three receptors- TCR, CD28 and 41BB. The data quality if good and the experiments done are. The difference is quite clear, and I would even like to see a little more of the evidence related to failure of the TCR system.

We appreciate the general positive comment of this reviewer.

More specifically:

Su and colleagues show that a third generation CAR with TCR zeta, CD28 and 41BB signal transduction pathways can activate a T cell for microcluster formation and Gads/SLP-76 recruitment, but not IL-2 production, without LAT. This is surprising because LAT is generally considered, as is up held here, as an essential adapter protein for T cell activation. However, this is not a "fair" experiment as the CAR has sequences from TCR, and two co-stimulatory receptor- CD28 and 41BB. It would be important and very straight-forward to test first and second generation CARs to determine if LAT independence is a function of the CAR architecture itself, or the additional costimulatory sequences. If it turns out that a first generation CAR with only TCR sequences can trigger LAT independent clustering and SLP-76 recruitment then the comparison would be fair and no additional experiment would be needed to make the point that the CAR architecture is intrinsically LAT independent. If the CD28 and/or 41BB sequences are needed for LAT independence then the fair comparison would be to co-crosslink TCR, CD28 and 41BB (an inducible costimulator such that anti-CD27 might be substituted to have a constitutively expressed receptor with this similar motifs) should be cross-linked with the TCR to make this a fair comparison between the two architectures.

We agree with the reviewer that it is critical to make a “fair” comparison between TCR and CAR by testing the 1st generation CAR, which only contains the TCR/CD3z domain. Our new data showed that LAT is not required for microcluster and synapse formation of the 1st generation of CAR, in both Jurkat and primary T cells (new Fig 4 and EV5). This result is similar to our previously reported result from the 3rd generation CAR, although the 1st generation CAR induced less IL-2 production and CD69 expression in LAT null cells than the 3rd generation CAR did (new Fig 6). This suggests that the LAT-independent signaling is intrinsic to the CAR architecture, as the reviewer suggested. The co-signaling domains from CD28 and 4-1BB contribute to, but are not required for bypassing LAT to transduce the CAR signaling.

The authors may want to cite work from Vignali and colleagues that even the TCR has two signaling modules- the classical ZAP-70/LAT module that is responsible to IL-2 and a Vav/Notch dependent module that controls proliferation. Its not clear to me that the issue raised about distinct signaling by CARs is completely parallel to this, but its interesting that Vignali also associated the classical TCR signaling pathway as responsible for IL-2 with an alterive pathways that uses the same ITAMs to control distinct functions. See Guy CS, Vignali KM, Temirov J, Bettini ML, Overacre AE, Smeltzer M, Zhang H, Huppa JB, Tsai YH, Lobry C, Xie J, Dempsey PJ, Crawford HC, Aifantis I, Davis MM, Vignali DA. Distinct TCR signaling pathways drive proliferation and cytokine production in T cells. Nat Immunol. 2013;14(3):262-70.

We appreciate the reviewer’s mentioning this paper from Vignali’s group. It provides insights into understanding LAT-independent signaling in CAR T cells. We cited this paper and added a discussion about the mechanism of LAT-independent signaling.

I would be very interested to see a movie of the LAT deficient T cells interacting with the anti-CD3 coated bilayers in Figure 2A. Since OKT3 has a high affinity for CD3 and is coated on the surface at a density that should engage anti-CD3 I'm surprised there is no clustering even simply based on mass action. The result looks almost like a dominant negative effect of LAT deficiency on a high affinity extracellular interaction. It would be interesting to see how this interface evolves or if there is anti-adhesive behavior that emerges.

We now presented a movie showing the detailed process of LAT deficient GFP-CAR T cells landing on the bilayers coated with OKT3 (new Movie EV5), in which the bright field images delineate the locations of the cells, the OKT3 signal marks TCR, and the GFP signal marks CAR proteins on the plasma membranes. No TCR clusters (as indicated by OKT3) were formed during the landing process. We think the binding of bilayer-presented OKT3 to TCR is not sufficient to trigger TCR microclusters. However, TCR microclusters could form in LAT-deficient cells if OKT3 is presented by glass surface. This point is raised by reviewer 2. We added a discussion on the difference between bilayer and glass-presented OKT3 in inducing microcluster formation.

Reviewer #1 (Significance (Required)):

While it interesting that the CAR is LAT independent, its obvious that the signalling networks are different as the CAR has two sets of motifs that are absent in the TCR, so the experiments as presented are not that insightful about the specific nature of the differences that lead to the different outcomes. At present its not a particularly well controlled experiment as the third gen CAR is changing too many things in relation to the TCR for the experiment to be interpreted. It would be easy to address this is a revised manuscript. To publish as is the discussion would need to acknowledge these limitations. The work is preliminary as science, but it might be useful to T cell engineering field to have this information as a preliminary report, which might be an argument for adding discussion of limitations, but going forward without more detailed analysis of mechanism.

This is an excellent point and we have addressed it. See our response above on the new data of the 1st generation CAR.

Reviewer #2 (Evidence, reproducibility and clarity):

Summary:

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

In this study, the authors have interrogated CAR signaling by imaging CD19-CAR microclusters as well as T cell signaling molecules recruited to CAR microclusters. They report differences spatial assembly between CAR and TCR microclusters that form on a lipid bilayer containing ligand. They also report that LAT is not required for CAR microcluster formation, recruitment of downstream signaling molecules or IL-2 production in Jurkat cells, while in primary T cells IL-2 production by CARs show more of a LAT dependence. From these observations, they conclude that CAR T cells have a rewired signaling pathway as compared to T cells that signal through the TCR.

Major comments:

• Are the key conclusions convincing?

The conclusions made by the authors about CAR microclusters are convincing. However, the conclusion that there is a "rewired signaling network" different from TCR microclusters needs to be more convincingly demonstrated in side-by-side comparisons of TCR and CAR microclusters and synapses.

1. One of the key conclusions in this study is that CAR microclusters form in the absence of LAT, but TCR microclusters require LAT (in JCam2.5 cells in Fig. 2 and primary T cells in Fig. 4B). The requirement of LAT for formation of TCR microclusters is surprising, given multiple reports (one of which the authors have cited) that TCRz and ZAP70 clusters form normally in the absence of LAT (pZAP microclusters form normally in JCam2.5 cells Barda-Saad Nature Immunology 2005 Figure 1; TCRz clusters form normally in LAT CRISPR KO Jurkat cells Yi et al., Nature Communications, 2019 Figure 5). The authors should carefully evaluate TCRz and ZAP70 clusters (that form upstream of LAT) in their assays.

We thank the reviewer for raising this excellent point. LAT-independent TCR clusters were reported in the two papers mentioned by the reviewer, which we think is convincing. However, there is a key difference in the experimental settings between these two papers and ours. We use supported lipid bilayer to present MOBILE TCR-activating antibody to activate T cells, whereas these two papers used IMMOBILE TCR-activating antibody attached to the cover glass. We reasoned that the mobile surface of supported lipid bilayer more closely mimics the antigen-presenting cell surface where antigens are mobile on the membrane. We added a new discussion about the difference between supported lipid bilayer and cover glass-based activation.

We agree with the reviewer on the careful evaluation of TCR and ZAP70 clusters. We had showed the data of TCR clusters as marked by TCR-interacting OKT3 (Fig 3A). We performed new experiments on ZAP70 clusters (new Fig EV3). Our data suggest that, similar to TCR clusters, ZAP70 clusters are not formed in LAT-deficient T cells, if activated by OKT3, but are formed if activated by CD19.

1. The authors make major conclusions about LAT dependence and independence of TCR and CAR microclusters respectively, by using JCam2.5 Jurkat cells and CRISPR/Cas9 edited primary cells. Of relevance to this conclusion, differences in the phosphorylation status of ZAP70 and SLP76 have been described between JCam2.5 cells lacking LAT (in which LAT was found to be deleted by gamma radiation) and J.LAT cells (in which LAT was specifically deleted by CRISPR/Cas9 in Lo et al Nature Immunology 2018). Of importance, pZAP and pSLP76 appeared fairly intact in J.LAT cells, but absent in JCam2.5 cells (Lo et al., Nat Immunol. 2018, Supp Fig 2). Therefore, the authors should evaluate TCRz, ZAP70, Gads and SLP76 in TCR and CAR microclusters in J.LAT cells. This may partly explain the discrepancy in LAT requirement for IL-2 production in JCam2.5 cells and primary cells with LAT CRISPRed out.

Jcam2.5 is a classical well-characterized LAT-deficient cell line that has been continuously used in the T cell signaling field (Barda-Saad Nature Immunology 2005, Rouquette-Jazdanian A, Mol. Cell, 2012; Balagopalan L, J Imm. 2013; Carpier J, J Exp Med, 2018; Zucchetti A, Nat. Comm. 2019). We agreed with the concern that the reviewer raised on the absence of pZAP70 and pSLP76 in JCam2.5 cells. As the reviewer suggested, we obtained J.LAT, which is LAT null but has intact pZAP70 and pSLP76. We introduced CAR into J.LAT and the wild-type control and performed the clustering assay as we did for Jcam2.5. Our results showed that, similar to Jcam2.5, CAR forms robust microclusters in J.LAT cells (new Fig EV2). More importantly, we presented data confirming the LAT-independent CAR clustering, SLP76 phosphorylation, and IL-2 production in human primary T cells (Fig 7). Therefore, the data from three independent cell sources support our conclusion on LAT-independent CAR signal transduction.

1. Since the authors are reporting differences between CAR synapses and TCR synapses, the authors should show side by side comparison of CAR and TCR synapses in Figure 1F.

We focused on characterizing CAR synapse in this manuscript and did not make any conclusion on the difference between TCR and CAR synapse. We are cautious about comparing CAR synapse to TCR synapse for technical reasons: it is critical to use antigen-specific TCRs (e.g. mouse OTI as a common model) to study the TCR synapse pattern so that the study will be physiologically relevant. However, we use human T cell line and human primary T cells for the CAR study. The technical barrier to introduce an antigen-specific TCR complex into these cells, and to activate these cells by purified peptide-MHC complex, is very high. And the result is interesting, but beyond the scope of the current work.

1. The authors should evaluate Gads microcluster formation in response to TCR stimulation via OKT3 (in Figure 4A). Given that it has been reported that TCRz, Grb2 and c-Cbl are recruited to microclusters in Jurkat cells lacking LAT by CRISPR deletion (Yi et al., Nature Communications, 2019), it is important to establish the differences between TCR microclusters and CAR microclusters in side by side comparisons in their assay system.

As the reviewer suggested, we evaluated Gads microcluster formation with TCR stimulation and found that Gads did not form microclusters in LAT-deficient cells (new Fig 5A). Because we only made conclusions on the Gads-SLP76 pathway, we think investigating Grb2 and c-Cbl microcluster, though interesting, is beyond the scope of this manuscript.

1. Similar to the comment about Gads above, the authors should evaluate pSLP76 microcluster formation in response to TCR stimulation via OKT3 in primary T cells lacking LAT in Figure 4C, i.e. side by side comparisons of pSLP76 in TCR and CAR synapses (with and without LAT) should be shown.

We totally agree and performed new experiment on pSLP76 in human primary T cells. Our data suggested that, similar to Jurkat, pSLP76 microclusters remain intact in LAT null primary cells (new Fig 7D and 7E).

• Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

1. The data shown in Figure 3C shows a reduction in conjugate formation from 80% (WT) to 30% (LAT -). This is a severe reduction and does not support the authors' claim in the corresponding Figure legend that "LAT is dispensable for cell conjugate formation between Jurkat T cells expressing CAR and Raji B cells" and the Abstract that "LAT.....is not required for....immunological synapse formation". Statistical analysis for variance should be shown here.

We agree with the reviewer’s judgement. This cell conjugation analysis was performed using Jcam2.5 cells. As pointed by the reviewer, Jcam2.5 has additional defects in ZAP70 and SLP76 in addition to the lack of LAT. Therefore, we performed the same analysis again using J.LAT cells, which was recommended by the reviewer. Our new data showed that J.LAT cells form conjugates with Raji B cells in a similar rate as the wild-type cells do, as evaluated by statistical analysis (new Fig 6A). Therefore, we think these new data support the claim that LAT is dispensable for cell conjugate formation.

1. In a similar vein, based on data from Movie S5 (where in a single cell, CAR microclusters translocate from cell periphery to center), and Figure 3C where (as described above in point 1) conjugate formation appears to be severely reduced, the authors conclude in the Results and Abstract that "LAT....is not required for actin remodeling following CAR activation". This conclusion is not supported by the data and the authors should remove this claim. Alternatively, actin polymerization in CAR expressing cells (that are LAT sufficient and deficient) can be easily evaluated using phalloidin or F-Tractin.

As suggested by the reviewer, we evaluated actin polymerization in TCR or CAR stimulated cells using a filamentous actin reporter F-tractin. Our data showed that LAT is required for TCR-induced but not CAR-induced actin polymerization (new Fig 5C). Therefore, our results support the claim that LAT is not required for actin remodeling following CAR activation.

• Would additional experiments be essential to support the claims of the paper?<br> Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

Yes. Please see major comments 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. It should take 3 months to complete these experiments, since reagents and experimental systems to do these experiments already exist.

• Are the data and the methods presented in such a way that they can be reproduced?<br> Yes. Methods are clearly explained.

We appreciate the reviewer’s recognition of the clarity of the methods part.

• Are the experiments adequately replicated and statistical analysis adequate?

There is no statistical analysis to evaluate differences between samples in Figures 3 and 4. These must be included.

We now added statistical analysis in Fig 5B and 6A (old figure 3 and 4).

Minor comments:

• Specific experimental issues that are easily addressable.

Please see Major Comments above. We believe that the recommended experiments are not difficult to execute since reagents exist and experimental systems are already set up.

• Are prior studies referenced appropriately?

Authors reference 13 and 14 for the following sentence in Results section 2: "Deletion or mutation of LAT impairs formation of T cell microclusters". However, in Reference 14 Barda-Saad et al., actually show that pZAP clusters are intact in JCam2.5 cells lacking LAT. Perhaps authors should clarify that LAT (and downstream signaling molecule) microclusters are impaired when LAT is deleted or mutated.

As the reviewer suggested, we now clarified that clustering of LAT downstream binding partners is impaired when citing reference (Barda-Saad et al).

• Are the text and figures clear and accurate?

Yes. But would be helpful if authors specify what "control" is in Fig. 3B and C. In Figure 3B it is lipid bilayers without CD19, while in 3C it is K562 cells that do not express CD19.

We now specified “control” in the figure.

• Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

Would be helpful if authors specify in every Figure or at least Figure legend the experimental bilayer system/ligand used, since they use both OKT3 and CD19 as ligands in the paper.

We now specified the ligand in the figure or legend.

Reviewer #2 (Significance):

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

If CAR microclusters and synapses are appropriately compared in a side by side comparison with TCR microclusters and synapses (as described in comments above), this study will be a conceptual advance in the field of CAR signaling. CAR microclusters have not been studied previously.

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

Very little imaging has been done on CAR synapses and to our knowledge this is the first live cell imaging study describing CAR microclusters.

We appreciate this reviewer’s comment on our work as a conceptual advance in understanding CAR signaling.

• State what audience might be interested in and influenced by the reported findings.<br> This study will have a broad audience. Both scientists that study basic T cell signaling as well as clinicians that use CAR Ts will be interested in this study.

We appreciate this reviewer’s recognition of the broad audience of this manuscript.

• 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.

T cell signaling and imaging of proximal T cell signaling responses.

Reviewer #3 (Evidence, reproducibility and clarity):

This manuscript by Dong and colleagues characterizes the molecular requirements and consequences of engaging a third-generation chimeric antigen receptor (CAR) directed to CD19. Utilizing a biological system of JCaM2.5, a Jurkat T cell mutant with dramatically low levels of LAT, expressing a CAR directed to CD19 fused to the cytoplasmic tails of CD28, 4-1BB and CD3z that is activated by CD19/ICAM1 reconstituted lipid bilayers, the authors demonstrate LAT is not required for microcluster formation, immunologic synapse formation or recruitment of GADS and pSLP76 to the plasma membrane. In contrast, LAT was required for anti-CD3 mediated microcluster formation and pSLP76 recruitment to the plasma membrane. However, LAT does appear to contribute to efficient synapse formation, PIP2 hydrolysis and IL-2 secretion when CAR+ JCaM2.5 or primary T cells are presented with Raji B cells, respectively. These data provide intriguing insights into the molecular requirements for third-generation CAR-T cell functions. The authors have developed quite a nice system to understand the molecular contributions for CAR-T function. A few suggestions are provided here to further enhance the accuracy and significance of the findings:

1. The authors can address whether the LAT-independent effects are due to the attributes of third generation CAR-Ts with inclusion of CD28 and 4-1BB cytoplasmic domains or whether these differences are intrinsic to all CAR-Ts (e.g., first and second generation CARs).

This is an excellent point. We have included new data showing LAT-independent cluster formation of the 1st generation CAR in both Jurkat and primary T cells (new Fig 4 and EV5). Therefore, we favor the second possibility as pointed by the reviewer that LAT-independent effects are intrinsic to CAR architecture.

1. Since a first-generation CAR-T forms non-conventional synapses (Davenport, et al., PNAS 2018), the authors should consider more detailed kinetic analysis to understand the formation and dissolution of the constituents of the synapse with their third generation CAR. This should include measurements of the duration of microcluster and synapse formation as well as further analysis of c- and p-SMAC constituents (e.g., LFA-1, TALIN, LCK and pSLP76) over time.

We agree with the reviewer on a more detailed characterization of the CAR synapse. We measured the duration of the unstable CAR synapse and time from cell landing to the start of retrograde flow (new Fig 2C). We also determined the localization of CD45, a marker for d-SMAC (new Fig 2D). We found that the formation of dSMAC is also not common in CAR T synapse, strengthening our conclusion that CAR forms non-typical immunological synapse.

1. The authors utilize two different activation platforms. While using CD19/ICAM1 reconstituted bilayers, CAR+ JCaM2.5 or CAR+ primary T cells demonstrate no differences compared to wildtype JCaM2.5 cells in the parameters studied. However, when using Raji B cells, the CAR+ JCaM2.5 cells or CAR+ primary T cells demonstrate a more intermediate phenotype with respect to cell conjugate formation (Figure 3C) and IL-2 production (Figure 4D). The authors should analyze whether the differences attributed to the different outcomes may be due to the stimulation mode. For example, is c-SMAC assembly and GADS or pSLP76 recruitment to the plasma membrane still LAT-independent when activated with Raji B cells?

As the reviewer suggested, we examined c-SMAC assembly in Raji B cells conjugated with CAR T cells. We found that the majority of CAR do not form cSMAC (new Fig EV4), which is consistent with the result from the bilayer activation system. Since both Gads and SLP76 are cytosolic proteins, they keep largely in the cytosolic pool which obscures their recruitment and clustering on the plasma membrane when imaged by confocal microscopy at the cross-section of cell-cell synapse.

1. The authors should consider whether CAR expression level affects their observations. For example, do lower levels of CAR expression make the system LAT-dependent? Further, what is the level of the CAR relative to endogenous TCR expression on their primary T cells.

We agree with the reviewer that it is informative to determine if LAT-independent signaling is dose dependent. We tried to measure the CAR concentration relative to the endogenous TCR/CD3z. By western blot using two different antibodies against CD3z, we detected TCR/CD3z expression, but found no bands corresponding to CAR. We believe this reflects a low expression of CAR in our system, which is confirmed by FACS. The general low expression of CAR makes it challenging to sort an even lower CAR-expressing population. Therefore, we sought alternative ways to determine the dose-dependence; we titrated the CD19 concentrations on the bilayer. As shown in the new Figure EV1, CAR formed microclusters similarly in the wild-type versus LAT-deficient cells in a wide range of CD19 concentration. Therefore, we conclude that the LAT-independent cluster formation is robust at low antigen density as well.

Minor comment:

1. Since JCaM2.5 has differences when compared to the parental Jurkat E6.1 T cell line, the authors should utilize JCaM2.5 reconstituted with wildtype LAT as a comparator.<br> Agreeing with this reviewer, we recognized that Jcam2.5 was generated by mutagenesis which may result in protein expression difference for genes besides Lat. As suggested by reviewer1, we used J.LAT, a genuine LAT knockout cell line that is generated by CRISPR-mediated gene targeting, to perform the clustering assay (new Fig EV2). Our results showed that, similar to Jcam2.5, CAR but not the TCR formed microclusters in J.LAT cells.

Reviewer #3 (Significance):

The mechanism(s) by which CAR-Ts function is of high significance from both scientific and clinical viewpoints. From a scientific viewpoint, it provides important basic mechanistic information of how T cells are being activated to kill tumor cells. By understanding the molecular requirements, additional generations of CARs can be designed to provide greater efficacy, overcome resistance and possibly less toxicity.

This is an evolving field and little is known to date. Hence, this study could represent an insightful and important advance to the field.

Audience is to both basic immunologist and cancer biologists.

We appreciate this reviewer’s comments on the high significance of our work to the field of both basic immunology and clinical application.

My expertise is in T cell signaling, T cell biology and immunotherapy.

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Referee #3

Evidence, reproducibility and clarity

This manuscript by Dong and colleagues characterizes the molecular requirements and consequences of engaging a third-generation chimeric antigen receptor (CAR) directed to CD19. Utilizing a biological system of JCaM2.5, a Jurkat T cell mutant with dramatically low levels of LAT, expressing a CAR directed to CD19 fused to the cytoplasmic tails of CD28, 4-1BB and CD3 that is activated by CD19/ICAM1 reconstituted lipid bilayers, the authors demonstrate LAT is not required for microcluster formation, immunologic synapse formation or recruitment of GADS and pSLP76 to the plasma membrane. In contrast, LAT was required for anti-CD3 mediated microcluster formation and pSLP76 recruitment to the plasma membrane. However, LAT does appear to contribute to efficient synapse formation, PIP2 hydrolysis and IL-2 secretion when CAR+ JCaM2.5 or primary T cells are presented with Raji B cells, respectively. These data provide intriguing insights into the molecular requirements for third-generation CAR-T cell functions.

The authors have developed quite a nice system to understand the molecular contributions for CAR-T function. A few suggestions are provided here to further enhance the accuracy and significance of the findings:

1. The authors can address whether the LAT-independent effects are due to the attributes of third generation CAR-Ts with inclusion of CD28 and 4-1BB cytoplasmic domains or whether these differences are intrinsic to all CAR-Ts (e.g., first and second generation CARs).
2. Since a first-generation CAR-T forms non-conventional synapses (Davenport, et al., PNAS 2018), the authors should consider more detailed kinetic analysis to understand the formation and dissolution of the constituents of the synapse with their third generation CAR. This should include measurements of the duration of microcluster and synapse formation as well as further analysis of c- and p-SMAC constituents (e.g., LFA-1, TALIN, LCK and pSLP76) over time.
3. The authors utilize two different activation platforms. While using CD19/ICAM1 reconstituted bilayers, CAR+ JCaM2.5 or CAR+ primary T cells demonstrate no differences compared to wildtype JCaM2.5 cells in the parameters studied. However, when using Raji B cells, the CAR+ JCaM2.5 cells or CAR+ primary T cells demonstrate a more intermediate phenotype with respect to cell conjugate formation (Figure 3C) and IL-2 production (Figure 4D). The authors should analyze whether the differences attributed to the different outcomes may be due to the stimulation mode. For example, is c-SMAC assembly and GADS or pSLP76 recruitment to the plasma membrane still LAT-independent when activated with Raji B cells?
4. The authors should consider whether CAR expression level affects their observations. For example, do lower levels of CAR expression make the system LAT-dependent? Further, what is the level of the CAR relative to endogenous TCR expression on their primary T cells.

Minor comment:

1. Since JCaM2.5 has differences when compared to the parental Jurkat E6.1 T cell line, the authors should utilize JCaM2.5 reconstituted with wildtype LAT as a comparator.

Significance (Required)

The mechanism(s) by which CAR-Ts function is of high significance from both scientific and clinical viewpoints. From a scientific viewpoint, it provides important basic mechanistic information of how T cells are being activated to kill tumor cells. By understanding the molecular requirements, additional generations of CARs can be designed to provide greater efficacy, overcome resistance and possibly less toxicity.

This is an evolving field and little is known to date. Hence, this study could represent an insightful and important advance to the field.

Audience is to both basic immunologist and cancer biologists.

My expertise is in T cell signaling, T cell biology and immunotherapy.

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

Learn more at Review Commons

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Referee #2

Evidence, reproducibility and clarity

Summary:

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

In this study, the authors have interrogated CAR signaling by imaging CD19-CAR microclusters as well as T cell signaling molecules recruited to CAR microclusters. They report differences spatial assembly between CAR and TCR microclusters that form on a lipid bilayer containing ligand. They also report that LAT is not required for CAR microcluster formation, recruitment of downstream signaling molecules or IL-2 production in Jurkat cells, while in primary T cells IL-2 production by CARs show more of a LAT dependence. From these observations, they conclude that CAR T cells have a rewired signaling pathway as compared to T cells that signal through the TCR.

Major comments:

Are the key conclusions convincing?

The conclusions made by the authors about CAR microclusters are convincing. However, the conclusion that there is a "rewired signaling network" different from TCR microclusters needs to be more convincingly demonstrated in side-by-side comparisons of TCR and CAR microclusters and synapses.

1. One of the key conclusions in this study is that CAR microclusters form in the absence of LAT, but TCR microclusters require LAT (in JCam2.5 cells in Fig. 2 and primary T cells in Fig. 4B). The requirement of LAT for formation of TCR microclusters is surprising, given multiple reports (one of which the authors have cited) that TCR and ZAP70 clusters form normally in the absence of LAT (pZAP microclusters form normally in JCam2.5 cells Barda-Saad Nature Immunology 2005 Figure 1; TCR clusters form normally in LAT CRISPR KO Jurkat cells Yi et al., Nature Communications, 2019 Figure 5). The authors should carefully evaluate TCR and ZAP70 clusters (that form upstream of LAT) in their assays.
2. The authors make major conclusions about LAT dependence and independence of TCR and CAR microclusters respectively, by using JCam2.5 Jurkat cells and CRISPR/Cas9 edited primary cells. Of relevance to this conclusion, differences in the phosphorylation status of ZAP70 and SLP76 have been described between JCam2.5 cells lacking LAT (in which LAT was found to be deleted by gamma radiation) and J.LAT cells (in which LAT was specifically deleted by CRISPR/Cas9 in Lo et al Nature Immunology 2018). Of importance, pZAP and pSLP76 appeared fairly intact in J.LAT cells, but absent in JCam2.5 cells (Lo et al., Nat Immunol. 2018, Supp Fig 2). Therefore, the authors should evaluate TCR, ZAP70, Gads and SLP76 in TCR and CAR microclusters in J.LAT cells. This may partly explain the discrepancy in LAT requirement for IL-2 production in JCam2.5 cells and primary cells with LAT CRISPRed out.
3. Since the authors are reporting differences between CAR synapses and TCR synapses, the authors should show side by side comparison of CAR and TCR synapses in Figure 1F.
4. The authors should evaluate Gads microcluster formation in response to TCR stimulation via OKT3 (in Figure 4A). Given that it has been reported that TCR, Grb2 and c-Cbl are recruited to microclusters in Jurkat cells lacking LAT by CRISPR deletion (Yi et al., Nature Communications, 2019), it is important to establish the differences between TCR microclusters and CAR microclusters in side by side comparisons in their assay system.
5. Similar to the comment about Gads above, the authors should evaluate pSLP76 microcluster formation in response to TCR stimulation via OKT3 in primary T cells lacking LAT in Figure 4C, i.e. side by side comparisons of pSLP76 in TCR and CAR synapses (with and without LAT) should be shown.

Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether?

1. The data shown in Figure 3C shows a reduction in conjugate formation from 80% (WT) to 30% (LAT -). This is a severe reduction and does not support the authors' claim in the corresponding Figure legend that "LAT is dispensable for cell conjugate formation between Jurkat T cells expressing CAR and Raji B cells" and the Abstract that "LAT.....is not required for....immunological synapse formation". Statistical analysis for variance should be shown here.
2. In a similar vein, based on data from Movie S5 (where in a single cell, CAR microclusters translocate from cell periphery to center), and Figure 3C where (as described above in point 1) conjugate formation appears to be severely reduced, the authors conclude in the Results and Abstract that "LAT....is not required for actin remodeling following CAR activation". This conclusion is not supported by the data and the authors should remove this claim. Alternatively, actin polymerization in CAR expressing cells (that are LAT sufficient and deficient) can be easily evaluated using phalloidin or F-Tractin.

Would additional experiments be essential to support the claims of the paper?<br> Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation.

Yes. Please see major comments 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. It should take 3 months to complete these experiments, since reagents and experimental systems to do these experiments already exist.

Are the data and the methods presented in such a way that they can be reproduced?

Yes. Methods are clearly explained.

Are the experiments adequately replicated and statistical analysis adequate?

There is no statistical analysis to evaluate differences between samples in Figures 3 and 4. These must be included.

Minor comments:

Specific experimental issues that are easily addressable.

Please see Major Comments above. We believe that the recommended experiments are not difficult to execute since reagents exist and experimental systems are already set up.

Are prior studies referenced appropriately?

Authors reference 13 and 14 for the following sentence in Results section 2: "Deletion or mutation of LAT impairs formation of T cell microclusters". However, in Reference 14 Barda-Saad et al., actually show that pZAP clusters are intact in JCam2.5 cells lacking LAT. Perhaps authors should clarify that LAT (and downstream signaling molecule) microclusters are impaired when LAT is deleted or mutated.

Are the text and figures clear and accurate?

Yes. But would be helpful if authors specify what "control" is in Fig. 3B and C. In Figure 3B it is lipid bilayers without CD19, while in 3C it is K562 cells that do not express CD19.

Do you have suggestions that would help the authors improve the presentation of their data and conclusions?<br> Would be helpful if authors specify in every Figure or at least Figure legend the experimental bilayer system/ligand used, since they use both OKT3 and CD19 as ligands in the paper.

Significance (Required)

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

If CAR microclusters and synapses are appropriately compared in a side by side comparison with TCR microclusters and synapses (as described in comments above), this study will be a conceptual advance in the field of CAR signaling. CAR microclusters have not been studied previously.

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

Very little imaging has been done on CAR synapses and to our knowledge this is the first live cell imaging study describing CAR microclusters.

State what audience might be interested in and influenced by the reported findings.

This study will have a broad audience. Both scientists that study basic T cell signaling as well as clinicians that use CAR Ts will be interested in this study.

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.

T cell signaling and imaging of proximal T cell signaling responses.

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

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Referee #1

Evidence, reproducibility and clarity

The authors compare the TCR alone to a CAR that contains signaling modules from three receptors- TCR, CD28 and 41BB. The data quality if good and the experiments done are. The difference is quite clear, and I would even like to see a little more of the evidence related to failure of the TCR system.

More specifically:

Su and colleagues show that a third generation CAR with TCR zeta, CD28 and 41BB signal transduction pathways can activate a T cell for microcluster formation and Gads/SLP-76 recruitment, but not IL-2 production, without LAT. This is surprising because LAT is generally considered, as is up held here, as an essential adapter protein for T cell activation. However, this is not a "fair" experiment as the CAR has sequences from TCR, and two co-stimulatory receptor- CD28 and 41BB. It would be important and very straight-forward to test first and second generation CARs to determine if LAT independence is a function of the CAR architecture itself, or the additional costimulatory sequences. If it turns out that a first generation CAR with only TCR sequences can trigger LAT independent clustering and SLP-76 recruitment then the comparison would be fair and no additional experiment would be needed to make the point that the CAR architecture is intrinsically LAT independent. If the CD28 and/or 41BB sequences are needed for LAT independence then the fair comparison would be to co-crosslink TCR, CD28 and 41BB (an inducible costimulator such that anti-CD27 might be substituted to have a constitutively expressed receptor with this similar motifs) should be cross-linked with the TCR to make this a fair comparison between the two architectures.

The authors may want to cite work from Vignali and colleagues that even the TCR has two signaling modules- the classical ZAP-70/LAT module that is responsible to IL-2 and a Vav/Notch dependent module that controls proliferation. Its not clear to me that the issue raised about distinct signaling by CARs is completely parallel to this, but its interesting that Vignali also associated the classical TCR signaling pathway as responsible for IL-2 with an alterive pathways that uses the same ITAMs to control distinct functions. See Guy CS, Vignali KM, Temirov J, Bettini ML, Overacre AE, Smeltzer M, Zhang H, Huppa JB, Tsai YH, Lobry C, Xie J, Dempsey PJ, Crawford HC, Aifantis I, Davis MM, Vignali DA. Distinct TCR signaling pathways drive proliferation and cytokine production in T cells. Nat Immunol. 2013;14(3):262-70.

I would be very interested to see a movie of the LAT deficient T cells interacting with the anti-CD3 coated bilayers in Figure 2A. Since OKT3 has a high affinity for CD3 and is coated on the suface at a density that should engage anti-CD3 I'm surprised there is no clustering even simply based on mass action. The result looks almost like a dominant negative effect of LAT deficiency on a high affinity extracellular interaction. It would be interesting to see how this interface evolves or if there is anti-adhesive behavior that emerges.

Significance

While it interesting that the CAR is LAT independent, its obvious that the signalling networks are different as the CAR has two sets of motifs that are absent in the TCR, so the experiments as presented are not that insightful about the specific nature of the differences that lead to the different outcomes. At present its not a particularly well controlled experiment as the third gen CAR is changing too many things in relation to the TCR for the experiment to be interpreted. It would be easy to address this is a revised manuscript. To publish as is the discussion would need to acknowledge these limitations. The work is preliminary as science, but it might be useful to T cell engineering field to have this information as a preliminary report, which might be an argument for adding discussion of limitations, but going forward without more detailed analysis of mechanism.

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---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

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Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

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Transparent Peer Review

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Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

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Transparent Peer Review

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Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

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Transparent Peer Review

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Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

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Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

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Transparent Peer Review

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Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

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Transparent Peer Review

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Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

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Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

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Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

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Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

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Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

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Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>

Transparent Peer Review

---

Download the complete Review Process [PDF] including:

• reviews
• authors' reply
• editorial decisions

</br>