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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.

Preprint Review

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This manuscript is in revision at eLife.

Summary:

This paper by Thacker et al. describes the use of lung-on-a-chip microfluidic devices to study early interactions during M. tuberculosis infection under conditions meant to mimic the alveolar environment in vivo. The authors use time-lapse microscopy to study host cell-Mtb interactions in macrophages and alveolar epithelial cells and the impact of surfactant on Mtb infection. This study suggests that organ-on-a-chip systems might be able to reproduce elements of host-microbe physiology during infection, which is difficult to reproduce ex vivo using single cells, air-liquid interface, organoids or organ explants.

This is an exciting approach which has the potential to expand the ability to study host-pathogen interactions. However, the reviewers all agree that the manuscript requires a major revision and additional data. Specifically, the manuscript requires improvement in the cell identification/classification, co-localization of Mtb with epithelial cells and macrophages, and distinction between intracellular and extracellular growth in order for the authors to provide convincing data to support their interpretations and conclusions.

While the reviewers recognize that it is challenging to use live cell imaging in this system, much of the data of the paper, such as comparisons between infection of AECs and macrophages, rests on the ability to determine the precise localization of bacteria. However, neither AECs nor macrophages are specifically identified with high enough resolution to give confidence that the Mtb are associated with those cells specifically, and more importantly, that the bacteria are growing intracellularly rather than extracellularly. Many of the images are of such low resolution that only tiny dots of bacteria are observed.

In addition, the findings of attenuated growth of Mtb after exposure to surfactant in macrophages and alveolar epithelial cells, changes in the Mtb cell wall after exposure to surfactant, and the finding that exposure to surfactant does not alter the extracellular viability of M. tuberculosis have been reported by others using other in vitro models and should be discussed in manuscript.

Reviewer #3:

The properties and mechanism of DNA transformation by Streptococcus pneumoniae have been intensively studied for nearly a century. This elegant and insightful paper develops a powerful new set of quantitative assays based on recombining out stop codons of fluorescent protein fusions to reprise several issues that have largely been addressed by conventional antibiotic resistance selection. This approach leads to new answers for a number of fundamental questions about pneumococcal transformation, thereby re-setting the paradigm in this area. This is an extremely well-written, complete study that answers interesting and important questions about bottlenecks and recombination during transformation of this genetically plastic pathogen.

This is a rigorous study that represents a substantial amount of work and creative thinking. The results will be of interest to a large audience concerned with genome evolution by transformation in different bacteria, points of limitation, or not, in the different steps in transformation, and mechanisms of recombination. The conclusions of this paper are well supported by extensive, often corroborative data, and provide new insights that go way beyond traditional genetic approaches. Rather complicated assay schemes are presented in highly effective diagrams and descriptions. Some of the new findings include that: all pneumococcal cells become competent and express the competence machinery in response to added competence stimulatory peptide or during natural competence; confirmation of brief non-genetic inheritance of phenotypes during transformation by single-cell tracking of recombination through lineage trees; a ≈50% limitation of transformation through RecA-dependent recombination that is unaffected by mismatch repair or restriction/modification; cell-cycle independence of recombination, regardless of reading strand or distance to the origin of replication; quantitation of direct multiple recombination (up to three was tested); and reduction of transformation recombination by non-homologous DNA.

Many of these conclusions overturn and/or refine previous results that were obtained by less precise genetic methods. Together, this paper shows that any site or orientation with regard to DNA replication can be transformed in pneumococcal cells, including multiple chromosomal insertions; however, there is an intrinsic limitation to the efficiency of recombination, possibly related to the level of off-marker recombination. This limitation may have implications to pneumococcal evolution.

Reviewer #2:

In this work Kurushima et al. use recently developed fluorescent labelling techniques to study natural transformation in the human pathogen Streptococcus pneumoniae. Previously, genetic marker analyses have been used to study the different aspects of this process, but with these new techniques the process can now be studied at the single cell level. The authors used the single cell analysis to identify new transformation bottlenecks and tried to determine why some cells are genetically transformed and others are not. Related experiments have been performed in the past using classic genetics and Kurushima et al. were able to confirm these studies. In that sense, in my opinion, the novelty is limited and no important new molecular insights are provided. They found that the number of cells that are ultimately transformed is plateauing at approximately 50%, despite the fact that most cells bind DNA. This is partially the result of the heteroduplex formed after recombination followed by separation by strand replication, combined with the fact that the DNA binding sites on cells are limited so that there is a competition between DNA markers at saturating DNA concentrations. The authors argue that this mechanism entails a "fail-safe strategy for the population as half of the population generally keeps an intact copy of the original genome". I find this conclusion far-fetched for two reasons.

Firstly, the DNA recombination event followed by DNA replication will automatically assure that only half the population will inherit the mutation, and to speak of a strategy implies that the organism has specifically evolved this system, but we are dealing here with a well-known and general recombination system found in many organisms that will generally result in a 50/50 distribution. Maybe more importantly, under natural conditions it is highly unlikely that cells encounter saturating levels of tDNA. In their experiments the authors use 3.2 nM DNA for transformation. If my calculation is correct, this would amount to 19xE11 DNA molecules per ml, which seems a bit high when assuming tDNA comes from lysed bacteria. In nature, this number will be much (much) smaller therefore there is no need for the bacterium to come up with a dedicated strategy to assure that not all cells in a population are being transformed.

Finally, the results are very well presented and the paper makes easy reading.

Reviewer #1:

Overall I thought this to be an extremely compelling story, both in terms of general scientific interest and the overall high degree of experimental rigor. Overall, the data provides strong experimental evidence to support the authors conclusions.

Overall, I found it very interesting the maximal efficiency is capped at 50%, as this makes for a very intriguing evolutionary hedge betting strategy for a naturally competent bacterial pathogen that frequently undergoes both intra and inter-species recombination events. In addition, this study provides a very elegant experimental framework for understanding the finer points of pneumococcal recombination through both clever genetic approaches and rigorous experimental design. The data was presented in a clear, concise manner and the overall manuscript followed a clear and logical progression.

Preprint Review

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Summary:

All three reviewers felt that the manuscript was experimentally sound and praised the authors for their use of single cell analysis to tackle the question of why some cells are transformed and others are not in a population of genetically competent Pneumococci. The thoughtful presentation of complicated and extensive data was appreciated by all. Two reviewers were enthusiastic about the study's conclusions regarding bet hedging and the potential for an intrinsic limit on recombination efficiency. The latter reduces the potential for off-marker recombination which, as Reviewer #3 notes, might have implications to pneumococcal evolution. At the same time, Reviewer #2 had some reservations about the significance of the data in light of previous studies of Pneumococcus and other naturally competent organisms. Most importantly, this reviewer questions whether the finding that only a portion of bacteria incorporate exogenous DNA is a particularly novel one and, regardless, whether the saturating DNA concentrations used in the study are representative of a "natural" environment.

Reviewer #3:

The manuscript by Abdulhay, McNally and colleagues presents an effort to combine DNA modification detection and Pacbio sequencing to contribute to the growing body of methods designed to gain epigenome information at the chromatin fiber level, i.e. beyond existing short read NGS-chemistry constraints. They do so by leveraging micrococcal nuclease to cleave and help solubilize DNA, which they then treat with adenine methyltransferases to footprint nucleosomes; single-molecule adenine methylated oligonucleosome sequencing assay - SAMOSA. Fiber-level epigenetic information will be of great use to the field and is expected to answer many open questions that remain unanswered.

However many of the claims made about the potential of the method are insufficiently supported by the data provided. It appears that additional data is required to support the conclusions made from SAMOSA with respect to existing chromatin information, such as signal differences as a function of transcription factor binding (see below).

1) The authors should make an attempt to investigate where sequence bias influences a methylation call in their datasets. Clearly the pattern on the in vitro chromatinized template suggests that on average their methylated calls are correct. However, there appear to be clear positions in their chromatinized template datasets where this is not the case, i.e. lines in sup fig 5a representing methylation calls in unmethylated template DNA and unmethylated calls on fully methylated template DNA. Upon close examination, this also seems the case in the chromatinized template, with certain positions inflexibly methylated/unmethylated and at odds with the surrounding linker/nucleosome patterning (Fig1D). The authors should use Kmer analysis of methylated A's genome-wide to detect sequence bias in either the methyltransferase or sequencing platform.

2) It seems reasonable that the clustered data by NRL estimate (fig 3) should correlate with existing measurements (i.e. MNase-seq). The authors should identify regions of the genome with strong enrichment for the seven clusters and compare this to nucleosome repeat length as can be estimated using conventional MNase measurements, i.e. the average distance between 5' mapping read positions across the genome (Valouev et al., 2011, Teif et al., 2012). Some agreement (for at least a few of these clusters with very regular nucleosomes) would strengthen the conclusions made by this approach, especially where there are irregular positioning patterns. Additionally, for these clusters the authors should display raw read alignment/methylation calls for SAMOSA at a few representative loci, where a sense of the raw data can be gleaned.

3) The comparisons of SAMOSA at different TF bound regions is likely influenced by the fraction of actually TF-bound molecules present in the original cellular sample. For example, CTCF is known to occupy it's strong motifs in the majority of cells, while few other factors have such regular binding/residency (Kelly et al., 2012 NomeSeq data at CTCF sites). It seems reasonable that some cluster fractions should scale with the enrichment for the factor (for at least CTCF and REST, the strong binding/nucleosome positioners), especially those associated with chromatin accessibility at the motif (i.e. A-accessible, HA-hyper-accessible). The authors should try to illustrate this, as well as representative read alignments/methylation calls at a few loci where these signals are prevalent.

4) The meta-plotted data seems noisy for most TFs profiled (Fig 4 A-L) and the authors should show that their replicates agree with each other in terms of the relative size of clusters and at the metaplot level. Similarly, the data shown in Figure 5 should be broken into replicates. It is difficult to know to what extent the differences quoted are quantifiable/reproducible. For example, in panel A the reported deviation seems quite large around the median to make strong claims: e.g. "In specific cases, we observed small effect shifts in the estimated median NRLs for specific domains-for example, a shift of ~5 bp (180 bp vs. 185 bp) in H3K9me3 chromatin with respect to random molecules..." This should also apply to the analysis done in Figure 5B and C, where it is difficult to get a sense of reproducibility from cluster size and the heatmap of Odds ratio and q-values.

Reviewer #2:

The authors describe SAMOSA, a novel method for mapping accessibility on single chromatin fibers, using a non-specific adenine methyltransferase and taking advantage of the long-read high-accuracy capability of the PacBio platform. The method allows for chromatin arrays to be precisely mapped for nucleosomal and non-nucleosomal footprints on single chromatin fibers. When combined with light MNase treatment, the method provides two orthogonal readouts of the chromatin landscape for single molecules, with advantages over other single-molecule long-read methods. Proof-of-concept application of this new method to human K562 cells reveals global heterogeneity, with surprisingly little distinction in nucleosome array patterns between regions distinguished by various active or repressive histone modification patterns. The heterogeneity observed using the unbiased approach represented by SAMOSA highlights the fact that the most common chromatin profiling methods favored by both large projects such as ENCODE and individual researchers are dominated by features such as histone modifications and hyper accessible sites. The method itself and insights into global nucleosomal heterogeneity are of substantial interest to the fields of chromatin and gene regulation. The data are of high quality and the methods are well-described. I have only one suggestion and a couple of minor issues.

In Figure 5, controls are randomly chosen nucleosomes, but it would be interesting to see what unmarked nucleosomes show. For example, unmarked alpha-satellite should be dominated by highly regular arrays with a 171-bp repeat length present in higher-order repeats corresponding to active centromeres, which consist of nucleosomal complexes that lack Histone H3 (CENP-A instead). The authors speculate that satellite irregularity might result from dynamic restructuring by HP1, and this predicts that other (H3-containing) unmarked satellites that lack H3K9me3 and presumably lack HP1 will be in regular arrays.

Reviewer #1:

The authors validate the method on a reconstituted array of 9 nucleosomes, and convincingly show that m6dA is found in linker DNA, and not (or greatly reduced) at positions bound to nucleosomes.

They then apply the approach to chromatin fibers released from K562 cells. Long read patterns were clustered to identify 7 clusters. The idea is that because the fragments are released by mild MNase digestion, there will be a positioned nucleosome at one end. The 7 clusters differ in nucleosomal spacing. I am not familiar with Leiden clustering, it would be good if the authors can confirm these clusters with alternative clustering methods. These clusters appear differentially represented in domains that differ in histone modifications.

Aggregation of data around TF binding sites further reveals a range of different states that show variable nucleosome positioning. This section is interesting but seems rather shallow in analysis. The authors have the ability to look at specific sites and determine the variation in nucleosome positioning in the cell population. However, they look only at aggregated data.

Overall the approach works well and promises to address important questions, but the current work does not yet take full advantage of the single molecule nature of the assay and as such falls a bit short compared to very related methods that have recently been published (the works cited in the ms, and recently published work from the Stamatoyannopoulos lab). Also, the use of mild MNase is presented as an advantage, but is it really necessary? Adding EcoGII to isolated nuclei may work as well as shown in the recent Stamatoyannopoulos paper in Science.

Preprint Review

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Summary:

This manuscript describes a method, named SAMOSA, to identify nucleosome positions along chromatin segments that can be over 10 Kb in size. The approach employs EcoGII-modulated m6dA deposition on accessible non-nucleosomal DNA (inkers, nucleosome free regions) released from nuclear after mild MNase cleavage. The DNA modification is then read-out using PacBio sequencing. Mapping nucleosome positions along longer DNA stretches can provide information on variation in nucleosomal arrays, and how that relates to chromatin state and factor binding etc. The assay is validated using a reconstitute chromatin template and then applied to K562 cells, revealing significant variation in nucleosome positioning and nucleosome repeat lengths at transcription factor binding sites, and throughout domains with various histone modifications.

Reviewer #3:

In this study, the authors present data aimed at supporting their conclusion that microbiota-derived SCFA resulting in increased AD pathology, including microglial activation, ApoE upregulation, and A-beta deposition.

First and foremost, the biggest issue with this study is the lack of male versus female comparisons and the very small sample sizes of the mice. Especially given the past literature of microbiome effects on AD pathology, e.g. with antibiotic cocktails, it is essential to look at sufficient numbers of both female and male mice, individually, and not just group them. Moreover, the average number of mice used in each experiment (N=5) are relatively small for making any firm conclusions.

Specific concerns:

Figure 1D: Based on the observation of more smaller plaques in SPF mice vs GF mice, the authors conclude, "This result highlights the impact of bacterial colonization on early amyloid plaque deposition rather than plaque growth," The problem here is that these mice are 5 months old. It is well-known that SPF APPPS1 mice start depositing at only 6 weeks old. So, they would need much earlier (and later) time points to support this conclusion. In addition, N=5 animals/group is very small and not appropriate for making conclusions.

The authors also need to show total plaque burden distribution in each group and level of variability?

Figure 2: Again, N=5/group is very small for high impact paper. They also need to show plaque burden distribution, especially since there is much more variability in 3 month old animals.

Figure 2D: The authors claim SCFA brings up plaque load to a "significant increase", i.e., 2X GF levels. But what are these values compared to SPF animals? They would need to have data on the 3 month SPF group for comparison sake to make the claim that SCFAs are driving pathology. Otherwise, this is just not convincing.

Figure 3: Westerns should also include 3 month SPF animals. The small differences in CTFalpha and CTFbeta are not convincing. Even if there were a change, how does it account for elevated Abeta?

Figure 4: SCFA trigger microglial activation: the data in this figure fail to support this conclusion:

Fig 4B: Why did the authors perform in situ for CX3CR1 instead of Iba 1 ICC for microglia? The quantification is unconvincing. There should be other CX3CR1 microglia that are not plaque associated, but we don't see these in the field. This brings into question the sensitivity of the in situ analyses? They need to also do Iba1ICC.

Fig 4 C/D: Regarding the statement, "we directly investigated the influence of bacterial colonization on microglial reactivity in the WT background. To this end, we injected brain homogenates from 8 months old APPPS1 mice containing abundant Ab into the hippocampus of GF or SPF WT mice (Fig. 4C) and subsequently analyzed microglial abundance and activation by smFISH. We observed a significant increase in overall microglial cell counts at the peri-injection site of SPF compared to GF WT mice (Fig. 4D)."

This experiment does not support the conclusion since one would expect microglial reactivity to increase in this experimental paradigm. The authors claim more activation in SPF mice, thus "gut microbiome triggers microglial activation and reactivity towards an exogenous insult containing Ab". But, this is unfortunately not supported by the experiments, as performed.

Figure 4F: The ex vivo amyloid clearance assay is not useful or convincing since cultured microglia lose their transcriptional phenotype after 6 hrs in culture (Gosselin et al, Science 2017).

Reviewer #2:

This is an interesting and well-written paper on the relationship between gut microbiota metabolites and AB production. Although previous studies have documented a link between the gut microbiome and Ab pathology, the underlying mechanisms and molecular mediators remain elusive. Here the authors use a germ-free Alzheimer's Disease mouse model to examine the role of short chain fatty acids on amyloidogenesis and neuroinflammation.

The studies thus add another welcome piece to the puzzle of how the microbiota affects the brain.

My comments are relatively minor:

How do the behaviour of GF animals compare with non-GF animals given that cognitive deficits have been reported in them (Gareau et al., 2011)?

I am somewhat surprised that more metabolite differences were not observed between GF & SPF mice as all microbial metabolites should be only in the latter.

Fig 2B should include all metabolites tested individually

Were the concentrations of the metabolites increased in the plasma following administration in drinking water? The physiological relevance of the doses used in the rescue experiments could be better supported with experimental data

If acetate is most important then it is not clear why they used a pooled cocktail in rescue experiments.

The analysis of transcriptome of brain samples from control- and SCFA-supplemented GF APPPS1 mice is a nice addition but the molecular targets for SCFAs on microglia remains unresolved.

The comments about modulating dietary fibre to reduce central SCFA concentrations are provocative and although beyond the scope of the current study are clearly studies that would be very welcome for the field to test.

The potential effects of SCFAs on HDACs is completely left as a cliff-hanger...

Reviewer #1:

The authors do a good job of citing the prior literature; however, Harach et al., 2017 did diminish my enthusiasm as it covers much of the same ground as this study, limiting the novelty of the current findings.

Essential Revisions:

1) Experimental perturbation of the proposed pathway. The manuscript leads to a nice model; however, the data is descriptive in nature with any experiments using either genetic or pharmacological approaches to test the proposed mechanisms. The impact of this study would be increased substantially if at least one link between SCFAs and AB, microglia, or ApoE were experimental validated. While most of the text avoids making causal claims based on correlative evidence, the one sentence summary states that SCFAs impact disease "via activation of microglial cells and upregulation of ApoE."

2) Identify which SCFA matters. The experiments all rely on a mixture of 3 SCFAs making it impossible to determine which compound is responsible. There is also high salt in this mixture which confounds the interpretation further. At a minimum, each individual compound needs to be tested using an equimolar amount of salt as a negative control. The authors should also note issues with oral delivery of SCFAs, which does not necessarily mimic production in the colon. Ideally, tributyrin, or a similar ester for acetate or propionate should be used. Another key missing control is the administration of SCFAs to SPF mice. It is also important to be clear that while SCFAs are sufficient to impact AB, there is no evidence in the paper to suggest that they are necessary, the full scope of "key microbial metabolites" remain to be determined. If the authors want to claim necessity, they would need to deplete specific SCFAs in the presence of a complex gut microbiome.

3) Be more cautious in discussing the role of the microbiome in Alzheimer's disease. The background discussion includes studies that show correlations in humans and phenotypic differences in germ-free mouse models, which in my opinion are insufficient to claim a causal role in human disease. The authors should discuss the level of evidence in humans for a causal role of the microbiome and its relative impact relative to other risk factors, including any prospective or intervention studies that have been conducted. They should also take care not to extrapolate differences in intermediate phenotypes in mice (plaque levels, microglial activation, and ApoE expression) to human disease. For example, the one sentence summary says, "contributing to AD disease progression". The authors should also discuss whether or not cognitive performance was evaluated in response to SCFAs.

Preprint Review

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Summary:

Colombo et al. present an intriguing set of findings from the amyloidosis mouse model (APPS1). Rederivation of this model under germ-free conditions led to both decreased plaque load and impaired cognitive performance. Administration of a cocktail of SCFAs and salt (sodium propionate, butyrate, and acetate) significantly increased plaque levels, microglial activation, and ApoE expression. Together, these findings suggest a potential pathway through which the microbiome could impact cognitive performance. The paper is well-written, with a clear description of the current results and a logical flow to the text and figures. These data are a good starting point for further mechanistic dissection and add another welcome piece to the puzzle of how the microbiota affect the brain.

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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.

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

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.

Learn more at Review Commons

---

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.

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 3 of the manuscript. The major points agreed by the reviewers are included below, as well as the separate reviews.

Summary:

The work presented is a major scientific achievement. This is the first functional reconstitution of any CO2 concentrating mechanism (CCM). The work has major implications for engineering of CCMs into crops for increasing yields: the authors have definitively identified a set of components that confer CCM activity in a heterologous host. As a bonus, the authors demonstrate a new way of generating a Rubisco-dependent E. coli.

Major points:

1) The EM images shown in Figure 5-figure supplement 1 should be presented as a main figure, not a supplement. The negative control is too dark and difficult to compare with the other micrographs. Moreover, it is concerning that the positive control (WT:pHnCB10) failed. It should be repeated as it would allow comparison of the putative carboxysomes to a native carboxysome and would greatly improve the quality and value of this figure.

2) For the benefit of a non-expert reader, the names of the 20 proteins and corresponding genes should listed in a Table, together with their function and the relevant references.

3) In Figure 3-figure supplement 1A, the authors should discuss why the gene csos1D is present in both pCB and pCCM.

4) In Figure 4B, the large variance in the OD600 after 4 days for CCMB1:pCB'+pCCM' cultures was explained as being due to genetic effects or non-genetic differences (line 1064). However, in Figure 3 - figure supplement 2B the measured growth kinetics did not show such big differences. Authors please explain.

5) Would be nice if the authors can demonstrate that Rubisco localizes to the putative carboxysomes by performing an experiment such as immunogold labeling. It would improve the claim that the observed polyhedral bodies are in fact carboxysomes. We leave the decision of such an experiment to the authors.

Reviewer #3:

In this manuscript, Urchs and colleagues use transductive conformal prediction (TCP) applied to rsfMRI functional connectivity data to predict autism in a subset of cases. The approach is novel for applying to autism research and also is pinpointed at a topic that is very much needed in autism - the problem of heterogeneity. The logic applied is that only a subset of autism cases will have powerful biomarker differences in terms of resting state functional connectivity and TCP is utilized to isolate that subset. Thus, while the approach is novel and maps onto similar kinds of logic in the realm of genetics of autism, the utility is somewhat limited, as TCP will not be able to tell us much about the majority of cases. This is the same problem with many highly penetrant genetic mechanisms that lead to high risk for autism. However, it is still an issue that the approach can only make statements about a very small percentage of the total autism cases in the population. Could the authors comment more on this issue/limitation? For instance, what does this biomarker in a small percentage of cases tell us? Are there powerful, specific, and homogeneous biological mechanisms behind such cases, whereas for the rest of the population the underlying mechanisms are highly diverse and not powerful enough to penetrate up into macroscale functional connectivity phenotypes? The result could help to generate new hypotheses focused on such a group. However, I think the authors should try to lead readers in discussing how to take such results further for new discoveries.

Besides this main issue noted above about the utility or meaning behind the novel findings, the following are comments about how to make the introduction more readable, and how to potentially better facilitate a reader's understanding of the analyses.

1) Introduction: I would suggest that some modifications need to be done to the introduction in order to make the ideas flow a bit better. The problem is that the authors are introducing a variety of complex and not necessarily easily linked information - e.g., risk from a variety of different types of genetic mechanisms, failure of neuroimaging classifier studies, and TCP. With a bit of effort and a couple re-readings it is clear that the logic the authors are using is that we have some understanding of how much risk there is from different types of genetic mechanisms, and we would like to understand how neuroimaging data might match up to that. Using TCP would hopefully allow you to do that, hence the goals of the study. This logic is not clearly spelled out as one reads the introduction however, because the different topics are either mixed together within a paragraph with little linking text to help the reader follow the logic, or the bits of information for each topic are segregated into their own paragraphs with little linking text and the beginning or ends of the paragraphs to help the ideas flow from one paragraph to the next. A good example of this is that the background paragraph to start with has these topics mixed together within the very first paragraph, and then the subsequent 3 paragraphs solely focus on each topic, without helping the reader understand why they are jumping from very different topics. By the time the reader gets to line 120 of the Objectives, then things are spelled out a little better, but the reader has to then go back and connect the ideas about how the authors are trying to compare how a TCP approach to identify a high risk imaging marker would match up against more well known risk markers at the genetic level. It may be the case that the manuscript here will get readers of various different backgrounds (e.g., autism researchers, those with expertise in genetics, neuroimaging, or machine learning). Few have expertise in all those areas, and for those individuals, it may be hard to understand how these different topics flow together and are linked in a specific logical way. The logic is there, but even for this reviewer, it required a couple readers to see how all this information lined up in a logic way to justify the study. Thus, I would suggest that the authors make changes to the writing so that the reader can clearly follow the logic without too much extra effort to connect what isn't written about how these topics are supposed to line up.

2) Methods: The methods and analysis are fairly complex. Can the authors make a figure that clearly lays out the analysis pipeline? It would help to have a visual that clearly outlines how the authors selected the subset of individuals from the larger ABIDE datasets, how the preprocessing was done, how the features were estimated, and how the TCP analysis was implemented with all the associated added aspects like the bootstrapping, etc. Furthermore, to facilitate understanding of the complexities of the analysis, can the authors create a GitHub repo that has all the reproducible analysis code that generates the results and figures produced in the paper, along with tidy data files that have the features used by the TCP model? Although in the data availability statement the authors write that a GitHub repo exists, having had a look through this, no tidy data files are available that the code can load up to have readers reproduce the analysis or figures. In addition, the code consists of only 4 brief R scripts. That code isn't easily readable with regards to how the analysis was done. The R code could be done in another way that is more in line with literate programming, such as an Rmd file, that has the analysis code, along with plain text to describe the different steps, and then the figures embedded within the html or pdf report that it creates when it is knitted in R Studio. There are also some Jupyter notebooks that show how the figures were generated. This was helpful to see and is what is needed for the R code too. In those Jupyter notebooks, it seems like there are certain tidy data files that those notebooks load, but they are absent in the repository and therefore, the readers cannot reproduce the analysis.

Reviewer #2:

This work represents an investigation into autism(s). For this purpose, multi-network inputs to transductive conformal prediction are used. This approach provides a measure for how much an individual resembles a pattern linked to autism(s) or healthy controls. The resulting predictions are translated to the population prevalence. The authors state correctly that their models are in the ballpark of what has previously been reported. However, they claim that their improvements with respect to predictions in the general population are a major improvement, achieved by a bias towards specificity of their model. While machine learning papers often do not report this translation it is also apparent that they easily could. Therefore, the novelty of this approach is not clear to me as it may be to the authors. This requires clarification in the context of the literature in addition to addressing the major concerns below.

1) The paper would benefit from a more in depth discussion of the literature. There have been more than 50 papers published using different pattern recognition approaches on ASD. It is important that the authors evaluate their work in the context of those findings. There are a bunch of reviews on pattern classification approaches in psychiatry in general and ASD in particular.

2) A slightly longer and more in-depth description of the methods section would help the reader, especially a description of the method used to calculate the relevant score.

3) Based on Figure 3 it is a bit unclear to me if the small number of individuals identified with higher HRS score indeed also show higher symptoms. This should be statistically tested.

4) The strongest confounding effects are usually induced by scanner differences, as both the discovery as well as the replication sample are multi-site samples. It would be important to investigate the effect of scanners on the proposed models. This is particularly problematic should there be disbalances between the groups across scanners.

5) Probabilistic predictive approaches have already been applied to ASD using for instance gaussian process regression (e.g. Ecker et al. 2010, Neuroimage). The paper would benefit by stating clearly how their method improves above the approach mentioned in this referred paper as well as other approaches in ASD. The adjustments of the prediction to the population prevalence is a minor achievement.

6) The authors discuss: "Although our model made only few predictions, those predictions carry a much higher risk of an ASD diagnosis for the identified individuals. The result is a prediction with a much higher specificity (99.5% compared to 72.3% and 63% for traditional approaches, Heinsfeld et al., 2018; Abraham et al., 2017) and much lower sensitivity (4.2%, compared to 61% and 74% respectively). It is thus important to point out that here we have not proposed a better prediction learning model, but rather addressed a different objective." However, sensitivity and specificity are always a trade-off and dependent on the decision threshold. You can bias this for either of the two. For probabilistic models this is easy to do by adjusting the decision threshold to the population prevalence of a disorder. It is also possible to determine a decision margin which will naturally lead to higher performance, similar to the approach presented here and has been done and proposed earlier.

Reviewer #1:

This is a well-written manuscript examining prediction of ASD diagnosis from resting-state fMRI data. The primary innovation is the application of Transductive Conformal Prediction (TCP), which quantifies the confidence with which one can accurately make a prediction. The authors show that they can identify a functional connectivity (FC) signature with high PPV for a subset of patients.

The approach is certainly interesting, but it also seems circular. As I understand it, predictions are limited only to individuals who can be classified with high accuracy. A priori, we might expect that these people would be patients with severe illness, and the results show that the subset of patients who are correctly identified do have more severe symptoms. It therefore seems unfair to compare the high PPV of this method with other approaches, when the current method, by construction, focuses only on those cases who are easier to classify (whereas others don't). Could the authors please clarify whether this interpretation is accurate?

Related to the above, the PPV of the test is high, but this is only one side of the coin. The sensitivity is very low and I imagine the NPV is also low. Given its low sensitivity, It does not seem correct to speak of the FC signature as a risk marker, since many people at risk (indeed with a diagnosis) do not show it. In practical terms, it seems like a positive result with this FC marker is conservative, relatively accurate indicator of someone's risk for a severe form of ASD, but a negative result carries almost no information at all. What is the practical utility of such a marker, given that severe autism should be evident from clinical observation? That is, how could the current results add value to clinical decision-making? If the FC signature could be detected in newborns, it would be of value, but this analysis is conducted in adults after diagnosis has been established.

The methods section indicates that the approach prioritises specificity, but the reasons for this decision are unclear.

How were site differences addressed in the analysis?

It would be useful to see how results vary as the 5% threshold is varied.

The evidence for cluster structure in Fig 1b seems quite weak.

The Figure 1 caption requires greater detail explaining what is actually shown in the plots.

Were any of the participants taking psychotropic medications? to what extent could this have impacted the findings?

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

Summary:

The reviewers shared a number of concerns in common, as outlined in their detailed reviews. In addition, the following points were raised upon further discussion between the reviewers:

-A comprehensive analysis of the potentially confounding effect of site differences is required

-The potential circularity of the method - classifying only cases that can be confidently classified - and practical limitations of this approach should be discussed in greater detail. The algorithm is biased towards specificity. This could also be achieved using probabilistic machine learning approaches by, for instance, adjusting the decision threshold to the population prevalence or by defining a margin for cases for which you do not make a decision.

-The findings are considered in relation to population prevalence rates, but the algorithm is not applied to a population sample. It seems likely that the classifier would not detect cases with the same accuracy in a population sample. If this claim is made, it needs to be explicitly tested.

-The passage "The result is a prediction with a much higher specificity (99.5% compared to 72.3% and 63% for traditional approaches, Heinsfeld et al., 2018; Abraham et al., 2017) and much lower sensitivity (4.2%, compared to 61% and 74% respectively)." seems problematic. If you calculate the balanced accuracy for the current approach, of Specificity + Sensitivity/2, you end up slightly above chance accuracy. The other papers actually perform better.

Reviewer #3:

The article by Delabouglise and collaborators presents a longitudinal analysis of farms in Southeast Asia to understand farmer behaviours in response to disease outbreaks in poultry. The study is original in its design and the results are important for the prevention of avian flu epidemics in the region, as they suggest that smallholder farmers are more likely to sell their poultry to traders following outbreaks, which could contribute to the rapid disease spread. There are important differences in terms of response to outbreaks (harvest, vaccination, etc.) between large and small farms, which suggests that targeted sensitization campaigns and programs are necessary to modify these behaviours. The article is well written, although the discussion needs some work to lay out the limitations of the study and to expand the practical implications of the study in terms of policies or interventions to put in place.

I have a few comments to improve the manuscript:

Introduction:

-I am uncertain about whether the term cohort applies to their study, as the unit of follow-up are farms but they're not following the same individuals (chickens) over time. I would suggest changing the term to longitudinal study.

Methodology:

-How reliable is the classification of outbreaks with and without sudden deaths? Are farmers able to recognize fast the onset of symptoms and then a death within 24h after the onset of those symptoms? I imagine that misclassification can happen, so I would mention this as a potential limitation.

Results:

-Table 2: it would be much more easily interpretable if variables are described fully, with the function used for transformation in brackets. For example, instead of "square root of Nbc", I would include "Number of broiler chickens in the farm (sqrt)", and so on.

Discussion:

-I think the different limitations of the study should be explained and discussed. For instance, 1) the use of a proxy for weight instead of weight itself, 2) potential misclassification of outbreaks (see above), 3) some behaviours may depend on events happening in longer time frames, for example the previous year, but this is not accounted for in the models.

-Also, the harvest of chickens could be greatly influenced by economic needs of the household (a family event, an economic shock, disease, etc.), especially for smallholder farmers in the developing world who may use chicken as a form of cash savings. I am actually surprised that this was not included in the questionnaires, and I think it's an important limitation that should be discussed (and appropriate literature referenced).

-I feel that the discussion lacks insights into practical implications/solutions coming from this study (policies, interventions, etc.). Given the results, what can the government or NGOs or international organizations implement in order to reduce the risk of future outbreaks? This part should be expanded and be more specific.

Reviewer #2:

This manuscript addresses an important gap in knowledge of infectious disease emergence and spread within small-scale poultry production systems. The study design allows for analysis of longitudinal epidemiological and human behavioral data, not commonly found in animal health research; the statistical analysis is well thought out and robust. The authors find that farmers with small flocks respond to disease outbreaks with the rapid sale of sick birds to traders, and that despite government-supported programs, there is little uptake of vaccination in this population. Findings point to future areas of research that could inform policy development or better target activities to reduce disease transmission within similar poultry production systems.

Reviewer #1:

General assessment:

The manuscript is very well written, easy to follow despite the substantial statistics, and has a clear goal that the authors address with strength. The study is highly relevant in the context of emerging infectious diseases, and addresses one of the main understudied candidate drivers of emergence. The study design allows a thorough analysis of the observed patterns, providing highly useful insights into the potential ways in which avian influenza can spread.

Substantive concerns:

I have no substantive concerns. The longitudinal study seems to have been designed and conducted well, allowing the incorporation of potentially important variables in the statistical models. The authors made great and responsible use of MGAMs, and clearly have an excellent background in statistics. I have no reservations or concerns about any aspect of the statistics. In fact I would like to complement the authors on the way in which the methods were described and results were reported, which was done in a clear way despite the large and potentially confusing number of results.

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

Summary:

Your manuscript surveyed 53 poultry farms in Southern Vietnam and identified that small scale farmers with lower sized flocks were more likely to rapidly harvest and sell disease birds to mitigate loss of profit. This finding is of great potential importance for developing prevention efforts for introduction of avian influenza into human populations.

The reviewers were all highly complimentary of this paper. They all felt the manuscript was methodologically sound, clearly written, highly original and of substantial public health and policy relevance. Particular noted strengths were appropriate use and description of mixed-effects general additive models, appropriate study design and the inclusion of all raw data for public use.

Reviewer #3:

PREreview of "The gene cortex controls scale colour identity in Heliconius" Authored by Luca Livraghi et al. and posted on bioRxiv DOI: 10.1101/2020.05.26.116533

Review authors in alphabetical order of last name: Monica Granados, Vinodh IlangovanORCiD, Katrina Murphy, Aaron Pomerantz

This review is the result of a virtual, live-streamed preprint journal club organized and hosted by PREreview and eLife. The discussion was joined by 17 people in total, including researchers from several regions of the world.

Overview and take-home message:

In this preprint, Livraghi et al. present noteworthy advances in evolutionary biology by characterizing the role of cortex gene in multiple Heliconius butterfly species, which is responsible for the wing patterns: yellow bar or the Type I scale cell fates (white/yellow). The authors identified cortex gene’s major role in sympatric speciation, the modulation of convergent wing patterns, and the regulation of scale identity in multiple Heliconius species, which naturally have different niches to help explain different co-mimetic morphology. Livragi’s team provides strong evidence for the cortex gene as one of the earliest regulators and its ability to set the differentiation of scale cells in a molecular switch fashion from yellow to red/black at a particular development stage through distal localization. This important discovery on the role of cortex gene fills a gap in our existing knowledge about the gene’s ability to control scale cell identity and wing color patterns. Since this work is of significant interest in evolutionary biology, we outlined some concerns below that could be addressed in the next version.

Positive feedback:

1) We strongly recommend this preprint to others/for peer review. In addition, we recommend this article to trainees as educational material to learn evolutionary developmental biology through interactive tutorials.

2) The authors have provided a good amount of novel results and have utilized current tools to address their questions.

3) This research fills a gap in our understanding of wing patterning in Heliconius while doing so in a very comprehensive way across multiple species and using techniques that systematically detail the association between gene expression and phenotype.

4) It was interesting to learn that the cortex gene doesn’t follow the typical pattern gene paradigm. We do not have many examples of integrator genes like cortex, which give binary outputs from a network of genes and integrate elements to produce a singular output.

5) This is a textbook example and is important for evolutionary development and mimicry studies. It is hard to find and/or work with a developmentally important gene that is amenable for genetic modification and still be able to work with viable offspring and have it be relevant for evolution.

6) The current cortex protein data as seen in Figure 6 adds novel data to the manuscript.

7) Thanks to the authors for setting a great example of showing modeling information. The graphics are visually appealing and convey complex information well.

8) This preprint sets up a good next step of how cortex evolved in a more broad context. We know the cortex gene is potentially implicated in wing pattern evolution in other distantly related butterflies and moths (e.g. peppered moth Biston betularia) and in possible roles of evolution/speciation by pattern changes due to genomic inversions at cortex locus.

9) The authors did a good job of creating a well-composed manuscript. Yellow bar with one species had a contradiction but did reconcile with further research questions.

10) Definitely, [the results are likely to lead to future research] especially with understanding how a cell cycle regulator affects developmental cell fates in terms of these scale colors and structures.

11) Antibodies can open up future research. This research team figured out three elements and there are possibly more to explore. Future research might investigate how cortex possibly regulates endocycling and what this means for color identity determination.

Major concerns:

1) The use of the term “race” to define butterflies with specific phenotypes needs to be revised to clines or strains or variants. “Race” is a social construct and not a biological reality and we strongly suggest revising this term.

2) The authors state that cortex and dome/wash genes are controlled by inversion (see Line 375, page 19). Does the strain they engineered have/carry the inversion ?

-We are aware that inversion for species is complex - strains, genetic background - starting material for inversion.

-Inversion events occurred millions of years ago in the loci contributing to the wing pattern. Authors describe the first generation of CRIPSR knock-outs in Heliconius sp. and hence we suggest to include further information.

3) We strongly suggest the authors elaborate on their qRT-PCR analysis pipeline. Did the authors follow MIQE guidelines in their quantitative real time PCR assays?

4) More explanation could be provided for cortex protein experiments. Figure 6 could explicitly say what developmental stage/time after pupation (they report this in the Methods section) and the rationale behind presenting data for this stage in development.

-If a systematic developmental time series of cortex protein expression is observed using immunostaining, we suggest adding the data. Otherwise we request the authors to comment on the rationale behind selecting this particular stage of development.

5) We recommend the authors mention institutional or local animal care ethical approval and safety regulations in the field working on Heliconius sp. for setting best practice reporting standards.

6) We suggest to clarify the lack of a clear correlation between in situ stains and the mutational effects of cortex CRISPR knock-outs.

7) Please add statistical analyses in figure legends, e.g. Figure 2 lacks statistical analysis information. Which test was performed and why? A statistical analysis subsection under the Methods section could be useful.

8) Could a sized-down Figure S10 be added to Figure 6 in the manuscript to provide more information about the nuclear ploidy and cortex antibody signal? Even no association is informative and helps the reader think about the connection between color/endopolyploidy.

Minor concerns:

General

1) We request authors to revise the introduction section allowing an easy to comprehend information on gene regulatory complex affecting each patterning region.

2) We strongly recommend minor rephrasing of the on/off switch to guide non-experts in evo-devo biology.

Figures

1) Figure S10 has a couple of typos - ‘localisation’ and ‘punctae’ in the first sentence of the figure caption.

2) It will be helpful to guide the readers, if a high-level phylogenetic tree mapping the related Heliconius’ evolution is presented in Figure 1. We suggest a compass guide to be added in the map of Figure 1b.

3) The scale bar is missing in Figures 6a and 7a.

4) In Figure 4, some of the mosaic KOs are very apparent and others are not especially for researchers unfamiliar with butterfly CRISPR, e.g. H. charithonia. I might suggest highlighting or using arrows to indicate the mKO regions.

5) We request the authors to consider reflecting on the distribution of samples in qPCR data superimposed on box-whisker plots .

Sufficient Detail

1) More information about the genes would be helpful, such as accession numbers and annotated gene information rather than the complete genome data.

-Might not be able to repeat CRISPR from the details in the Methods section. If the gene information is not well annotated as a model system then it is difficult. What about Heliconius? It might be helpful to report the scores for low off-targets.

-Non-standard genetic model systems present a challenge particularly to create genomic resources.

2) Multiple people mentioned not able to repeat in situ hybridization methods from the available information on methods. The hybridization conditions for thicker whole mounts were not fully explained.

3) Please provide more information about the number of animals.

Data Accessibility

1) We appreciate the authors adding supplemental information as figures and we request to report data files associated with the manuscript.

2) R code was used for morphometric analysis - this is difficult to track from pay walled reference mentioned and thus a problem. We request to make this analysis information/pipeline available openly.

3) Please include supplemental information on the microscope settings and metadata of images used for analysis explicitly.

4) High-resolution images of the CRISPR mutants could be provided in a supplemental/data repository.

5) Providing gene sequences used in this study will be very helpful rather than the SRA repository, especially probes used for in situ and sequences targeted for CRISPR.

Acknowledgments:

We thank all participants for attending this preprint journal club. We especially thank those that engaged in the discussion. Their participation contributed to both a constructive and lively discussion.

Below are the names of participants who wanted to be recognized publicly for their contribution to the discussion:

-Monica Granados | PREreview | Leadership Team | Ottawa, ON

-Vinodh Ilangovan | Labdemic - Founder |Postdoc | @I_Vinodh

-Katrina Murphy | PREreview | Project Manager | Portland, OR

-Aaron Pomerantz | UC Berkeley/Marine Biological Laboratory | Ph.D. Candidate | Berkeley, CA/Woods Hole, MA

Reviewer #2:

This manuscript explores the role of the gene cortex in the specification of wing scales in the butterfly genus Heliconius. Species of Heliconius butterflies are notorious for their reciprocal mimicry of wing color patterns. Several genes are known to control variation of specific color pattern elements within and between species, cortex is one of them. The authors combine RNAseq analysis across wing development, in situ hybridizations, antibody stainings and analysis of crispr somatic mutations to dissect the role of cortex in the specification of scales. Their main claim is that cortex imparts scale identity (color, morphology), namely type II and type III identity.

Although this paper includes a substantial amount of work and a number of interesting observations, I am not sure what can really be concluded in the end, and several results would need follow-up experiments to reach a stable conclusion.

The strongest part, in my opinion, is the analysis of somatic mutant clones of cortex in the wings of different species. The authors show that the lack of cortex consistently results in the conversion of type II and type III scales into type I scales, and thereby demonstrate the necessity of this gene for type II & III identity. This is solid, interesting, but not a novel concept from a genetic or developmental biology point of view. There are countless examples in the 1990s literature of genes whose mutations results in such shifts in cell identity (e.g., poxn and cut in the peripheral nervous system of flies).

From this result, two questions emerge: how and when does cortex assign this identity during development? And how does cortex explain the variation in color pattern among Heliconius morphs and species? Although the paper discusses these two questions, I find the answers unclear and the results confusing.

The authors first examine the expression dynamics of cortex. They re-annotated the 47-gene genomic interval where cortex maps and analyzed the differential expression of all genes in the interval, across developmental stages, across species and morphs and also compared wing compartments. Their main conclusion is that cortex is the most likely candidate in this interval to explain color pattern variation. I am not sure why the authors did this. I thought this was already clearly established from a previous paper (Nadeau et al. 2016, Nature). Moreover, the explanations of the differential gene expression (DGE) analysis are often too shallow to really understand what the authors really did, including the method description. The figures are poorly annotated and it's difficult to understand if there are replicates in the RNA-seq analysis (see minor comments). One striking result from this part, is that the DGE suggests that cortex is differentially expressed in the the 5th instar larvae between 2 morphs of Heliconius erato and 2 morphs of Heliconius melpomene, but the differential expression goes into opposite directions between this 2 species. How could the same phenotypic variation between morphs of 2 species be caused by opposite DGE? They authors note that it is interesting but do not comment or analyze further.

They pursue their investigation with in situ hybridization on 5th larval instar wings and mitigate the notion of a spatial correlation between cortex transcripts spatial distribution and color patten elements proposed by Nadeau et al., 2016. Here again, the figure would benefit from better annotation. The authors indicate subtle differences in the local distribution of cortex transcripts between morphs but do not really conclude anything from their observation. They also give no indication of sample size or replicates, which I find unsettling given the noise associated with this experiment. I am not sure what this figure really adds to the published work, or to the present manuscript.

Finally, the authors examine the distribution of Cortex protein in late (2-day pupa) developing wings with a polyclonal antibody. They find, surprisingly, that the protein is distributed more or less uniformly in the wing epithelium and localizes to the cell nuclei. While this is very different from the patterned transcript distribution, it is consistent with the somatic mutant clone analysis that showed that any mutated cell at any position of the wing displayed a phenotype. But this opens many questions: what is the origin of the apparent difference in expression between protein and transcripts? Is cortex secreted and it diffuses across the wing? Or is the transcript expression spatially dynamic and the protein distribution revealed by the authors reflects the temporal integration of this expression? And if Cortex is present and functional across the wing, how does it produce discrete pattern elements?

The authors conclude their paper with a figure suggesting that cortex specifies typeII/III scale identity early during wing disc development and that the distinction between type II and type III is subsequently governed by the gene optix at a later stage. But what substantiates the idea that cortex imparts cell type identity early on? What does Cortex larval (5th instar) distribution look like? Is it as uniform as that of later stages? The data presented here do not offer the temporal or functional resolution to support this conclusion.

In conclusion, this paper shows that the mutation of the gene cortex results in scale type transformation, but fails to explain or suggest how this may happen during development. It also does not suggest how cortex may control the "fantastically diverse" pattern variation in Heliconius.

Reviewer #1:

This is an interesting but complex study that examines the role of a few genes in a previously mapped interval in being the "switch" gene that regulates the presence or absence of a yellow band in the wings of Heliconius butterflies. The study first examines whether there is a correlation between expression level of several (47) genes in the mapped interval in developing wings (or parts of wings) in two separate species of Heliconius each having a race with the yellow band and a race without the yellow band. This part of the study highlights three genes (among others) that show some pattern of differential regulation but shows that there is no simple correlation between the expression level of these three genes in either larval or pupal wings and the presence of the yellow band. The authors then examine the function of one of the genes in the interval, cortex, in scale color development by using CRISPR. They find that cortex crispant individuals display color changes across the whole wing, not just in the region of the yellow band. In particular the black scales (Type II) become white or yellow (Type I), and the red scales (Type III) also become white or yellow (although this last transformation is not documented at the SEM level). The authors examine, once again, the expression domain of the cortex gene, this time during pupal development with an antibody, and they find that the gene is expressed across the whole wing, supporting its functional effects also across the whole wing. They observe that cortex is expressed in multiple punctate domains in the nuclei of scale building cells, which are polyploid cells, and in a single punctate domain in the nucleus of non-scale building epidermal cells, which are not polyploid. They then test whether perhaps there are more of these punctate nuclei in the region of the yellow band, but they find no such correlation.

In the end the authors try to argue that either 1) cortex is the yellow-band switch gene they are after but that the switch is not in the form of a typical spatially expressed gene (in the shape of the yellow band) but perhaps in the form of some threshold or heterochronic mechanism (not clearly explained), or that 2) another gene in the mapped interval, not examined for function in this study, is instead the switch genes they are after, and which may (or may not) interact with cortex in the differentiation of the yellow band.

I believe the authors are trying hard to implicate cortex in some way, as the yellow band switch locus, but the data just does not support this. Instead the authors implicate cortex in scale color identity (the title of the manuscript). However, given that cortex (alone) cannot control a specific color either, because the effect of cortex on color is different in different parts of the wing, their model for how cortex acts is too simple and does not fit their data. A combinatorial genetic code for both scale color and morphology (see below), where cortex is simply one of the players (rather than a major switch/homeotic gene) is required to explain the data in this manuscript.

Furthermore there are several data missing from the manuscript that need to be added to support some of the conclusions drawn, and several other data that would be important to add for purposes of data replication across labs.

1) The authors claim that cortex converts Type II (black) scales into Type I (white/yellow) scales but their SEM data and scale morphological measurements presented in the supplement don't fully support this conclusion. These transformations vary from species to species (e.g. H. melpomene and H. erato show different degrees of transformation) and only some features of the scale are actually transformed (e.g., cross rib periodicity in both species, and scale width and length and ridge periodicity in H. melpomene). The remainder of the measurements show that cortex is not sufficient to convert scale Type II into scale Type I.

2) I suggest that the definition of the scale types presented should be made more explicit. What are scale types I, II and III really? In line 87 it is mentioned that these scale types are based on scale color and on scale morphology but what follows is just a description of the pigments found in each scale and not their morphology. Furthermore, the data presented in the manuscript suggests that color and morphology can be uncoupled with genetic perturbations of cortex, so is it even useful to stick to this scale type nomenclature going forward? Something to consider.

3) There is a need for a new figure showing how the scale morphological measurements were actually conducted. There is no scale bar in the SEM images of yellow and black scales and this should be added. The SEM images used to represent a typical yellow WT scale and a transformed yellow scale of H. melpomene (in Figure 7) show very different densities of cross-ribs (but I am not even sure what exactly is being considered a cross-rib), yet the graph indicates that there is no difference between these scale types. This is confusing and needs clarification. Make sure you look up scale morphology nomenclature in Ghiradella 1991 (Applied Optics) to make sure you designate ribs (crossribs) and microribs appropriately. There seem to be quite a lot of differences in microrib density across Wt scales and transformed yellow scales in H. melpomene.

4) The authors claim that cortex converts Type III (red) scales into Type I (white) but they only describe conversions of Type II (black) into Type I (yellow) scales at the SEM level and don't provide any SEM images or quantitative data for the red to yellow, red to white, and black to white scale transformation. Adding these data is important to support the conclusions of the study.

5) I suggest the authors remove the dome-t and dome/washout gene data from the manuscript as 1) nothing about these genes is mentioned in the abstract; 2) the expression of these genes doesn't correlate with presence of the yellow band; 3) the genes are not investigated at the functional level; 4) the whole gene duplication issues surrounding these genes make the whole manuscript more difficult to read and does not, in the end, contribute to the main story that yielded results - which is the function of cortex in scale development. The function of these genes might still be worthy of investigating using CRISPR at a later date, and perhaps it would be useful to include the expression pattern data in that subsequent paper. This is merely a suggestion that I believe will make this manuscript less heavy and easier to read by focusing the reader's attention on the main points of this story.

6) Pigmentation and scale morphology is most likely controlled at the pupal stages of wing development and by measuring RNA levels of candidate switch genes at just two time points during pupal development (36hrs and 60-70 hrs after pupation) you may not have sampled the correct time window for yellow band differentiation. Several genes are expressed only during the first 16-30 hrs of pupal development, in species that need 7 days for pupal development (see Monteiro et al. 2006 for genes such as Wg, pMad and Sal) so sampling wings (for RNA-seq and antibody stains) at 36hrs and 60-70 hours may not be an ideal sampling strategy going forward.

7) The authors mention that because cortex causes changes in both scale color and morphology this suggests "that cortex acts during early stages of scale cell fate specification rather than during the deployment of effector genes". This conclusion needs more discussion. Matsuoka and Monteiro (2018) showed that knockout of the gene yellow, an effector gene at the end of a gene regulatory network for melanin pigment production, also led to both changes in scale color and morphology. These authors proposed instead that absence of certain pigments on the wing, such as dopa melanin, caused chitin to polymerize differently and form an extra lamina that prevent the windows from forming in the scales (just as seen in cortex mutants). The authors should consider and evaluate this alternative explanation in their discussion.

8) Did the authors examine whether there were protein coding changes between the 47 genes in the mapped interval between the yellow and black races? Please mention whether this was done. Please also upload the sequences of the genes that were studied and provide accession numbers for these sequences.

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

Summary:

The topic of your work is timely and intriguing, but the reviewers raise several issues with the study. For example, the reviewers propose that the major conclusions of the manuscript are not supported by the data presented, and that a full set of SEM data across all scale type color transformations should be presented. Given the results presented, the relationship between cortex expression and the actual pigmentation remains unclear, and the sole phenotypic analysis is insufficient to make conclusions about the role of a gene in producing pigmentation pattern variation.

Reviewer #3:

In this manuscript Hashmi et al describe the emergence of an endoderm population in a gastruloid model. They observe that endoderm cells are positive for E-Cad, likely express E-Cad continuously from an epiblast state, initially form small islands, and finally coalesce into a larger endoderm region at the pole of the gastruloid. There are several issues with this manuscript in its current form.

1) No evidence is provided that there is a relationship between how endoderm forms in this gastruloid model and in vivo. In fact, endoderm is believed to derive from a restricted area in the anterior primitive streak. This is evident from the mouse imaging data of Mcdole et al Cell 2018 as well as from more recent genetic labeling experiments (Probst et al bioRxiv 2020). It is well known that cells of different germ layers may self-segregate and this may drive the behavior observed here downstream of heterogeneous differentiation in the gastruloids, but that is not necessarily the mechanism which occurs in vivo. The authors suggest that their experiments show something about endoderm formation in vivo without addressing this point which substantially diminishes from the interest of the manuscript.

2) The authors suggest that this view of endoderm differentiation, which doesn't require full EMT is novel, however, much of the observations here are already known. It is known that future endoderm cells do not down regulate E-Cadherin but instead must continue to express it. They also are known to migrate collectively rather than as single cells in a cadherin-dependent way (Montero et al Development 2005; reviewed in Nowotschin et al Development 2019). The authors should discuss this literature and make clear which aspects of the proposed mechanisms are novel.

3) The authors are assessing the status of EMT based on a single marker, E-Cad. If this is a major point of the manuscript other markers e.g. Snail, N-Cad should be examined.

4) It is well known that embryoid bodies form an outer layer of visceral endoderm, e.g. Concouvanis & Martin Cell 1995, Doughton et al PLOS ONE 2010. None of the markers here are exclusive to definitive endoderm (including Sox17 which is used throughout, see Artus et al Dev Biol 2011). The authors should address the possibility that their observations may be consistent with a similar mechanism and may not reflect definitive endoderm differentiation.

Reviewer #2:

In this manuscript, the authors proposed a new mechanism of endoderm formation in 3D gastruloid models based on cell migration and fragmentation. Specifically, they found that E-cad is first uniformly expressed inside mESC aggregates. After exposure to Wnt agonist Chiron (Chi), a gradual repression of E-cad and an increase of T-Bra were detected. Cells in the core are tightly packed and express E-cad. T-Bra expressing cells are sparsely wrapped around the core. A directed flow of E-cad expressing cell islands surrounded by T-Bra expressing cells help to accumulate E-cad expressing cells to the tip of the aggregate and form endoderm domain. I think the dynamical expression of E-cad and T-Bra and the directed cell flow reported in this manuscript are interesting. The results and videos have shown that the elongation and formation of endoderm region is a collective cell behavior rather than single cells undergo epithelial-to-mensenchymal transition. But I am not convinced that the process is done based on the three-step mechanism proposed by the authors. Moreover, I am not sure if this phenomenon really happened in mouse embryo development, giving the considerable differences between gastruloid model and embryo. Since there are methods culturing mouse embryo in vitro up to the early organogenesis stage, I would suggest the authors provide more evidence showing that the proposed mechanisms might also happen in vivo.

In addition, the manuscript provides too little information to understand the phenomenon. And they did not clearly introduce experimental and computational methods they used to acquire the results. I listed some of my comments below.

Major comments:

1) Did all 3D aggregates become elongated shape in the presence of Chi? If not, what do E-cad and T-Bra expressions and cell migration dynamics look like in those spherical aggregates? Without Chi, inside the spherical aggregates, do they also have cell migration since the aggregates keep growing larger?

2) When did the collective cell migration start? Right after exposing to Chi? Or after some percentage of cells become T-Bra positive cells? Did the gastruloid keep elongating with directed cell flow inside it when cultured for a long time?

3) Are the collective cell migration driven by the T-Bra cells? Is it a spontaneous property of E-cad cells when the E-cad cell density exceed some critical threshold (e.g. glassy dynamics)?

4) Does the elongation and migration dynamics depend on the concentration of Chi, size of the aggregates? I noticed the authors used different initial seeding densities.

5) For the elongated cell aggregates, one side of cells express E-cad. How about the other side of cells? Did they all become mesoderm-like (T-Bra+) cells?

6) Many results are only based on several (3 or 4) gastruloids. For example, figure 1 (d) (e), figure 2 (b), figure 3(c). And in Figure 4 (b), the authors only quantify 13 junctions, probably in the same gastruloid. Due to the heterogeneity among the gastruloids, I am not sure how repeatable the experiments are. Can those observations really reflect phenomenon happened inside the majority of gastruloids? I think the authors should provide some quantifications of the percentage of observing the reported results among a large number of gastruloids.

Unclear results or experimental descriptions:

1) Can the authors show a schematic of the experimental process, such as the time of adding Chi and fixation?

2) 'We find that 30/37 ... set to 0.125.' How did the authors define and calculate the elongation ratio and E-cadherin polarization ratio? How did the authors define the elongation threshold?

3) Figure 1 (a): what is the y axis? 1 (d): how did the author measure the E-cad and T-Bra expression? Fixing at different time points or live imaging? If it is live imaging, is the acquisition process influenced by adding and removing Chi? 1(e) how can the authors get continuous results for polarization?

4) Figure 2 (b) Are those dots represents the nuclear position? Can the authors provide the 3D view of the whole gastruloid? (c) What information the authors are trying to get from the connectivity graph?

5) Figure 3 (a) What are those white dots in the images, also in movie 6? Can the authors replace t1, t2, t3, t4 with the real time, such as 24h, 36h? (d) How did the authors calculate the intensity? How did the authors normalize the intensity? The schematic in (b) is hard to understand. What do the light and dark colors represent? How did the authors measure theta_1 and theta_2, especially in 3D situation? More quantitative information should be acquired from (a).

6) I am not able to identify islands of E-cad expressing cells in Figure 3 (a) and movie 6.

Reviewer #1:

General assessment:

The manuscript by Hashmi et al describes the emergence of endoderm-like cells in a stem cells based embryo model. The particularity of the protocol is that it stimulates transition through an epiblast-like state, then differentiation towards mesoderm after a pulse of Chiron, a Wnt agonist. In those conditions, islets of E-cadherin positive cells emerge, surrounded by Ecad+Brachyury+, then Brachyury positive cells. Those islets fuse together at the tip, possibly due to distinct surface tension and directed cell movements, and express endoderm markers such as Sox17 and FoxA2.

It is an original approach and concept, raising new questions and possibilities about the mechanisms of endoderm emergence in the mouse embryo. The manuscript is well written and clear.

Concerns:

1)The data would benefit from increased clarity in stating, for each experiment, the proportion of aggregates in which a given phenomenon was observed, as well as the number of cells counted in each aggregate, in particular in supplementary figures.

2) For the migration analysis, it could be interesting to distinguish each cell trajectory in order to distinguish behaviours of the subpopulations.

3) In terms of the surface tension analysis, performing a similar analysis at different timepoints might be helpful to understand how the islets come to fuse at the tip.

4) I am not sure about the specificity of the gata6 staining, not that it adds a lot to the story.

5) The authors might want to discuss how those aggregates evolve, and whether the endoderm-like cells have a potential for further differentiation.

Conclusion:

Overall it is an interesting and original observation, well substantiated. More details on the quantification methods would help convince about the solidity of the model: chance of obtaining those cells, amount of cells of each subpopulation including those described in supplementary figures, technical possibility of sorting them for transcriptome analysis etc.

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

Summary:

The reviewers agree that the manuscript reports an interesting and original observation in gastruloids. However there is currently no evidence to propose that such a mechanism would be present in embryos. Additionally, there is a consensus that the methods are not sufficiently explained, the reproducibility is not clearly quantified, and some claims would require a larger number of aggregates/cells to be solid.

Reviewer #3:

This is an interesting study addressing a very relevant and exciting topic. The study investigates the contribution of auditory subcortical nuclei and the cochleae using physiological recordings while listeners differentiated words in different noisy-speech conditions. It is a valuable approach to consider contiguous measures along the auditory pathway during a single behavioral measurement.

However, I have several substantial concerns with the design, conceptualization, data analysis and interpretation of the results. I have had challenges to understand the hypotheses and rationale behind this study. A number of experimental paradigms have been employed, including peripheral/brainstem physiological measure, as well as cortical auditory responses during active versus 'passive' listening. Different noise conditions were tested but it is not clear to me what rationale was behind these stimulus choices. The authors claim that "our data comparing active and passive listening conditions highlight a categorical distinction between speech manipulation, a difference between processing a single, but degraded, auditory stream (vocoded speech) and parsing a complex acoustic scene to hear out a stream from multiple competing and spectrally similarly sounds" (lines 401-403). This seems like too much of a mouthful. I cannot see that the data support this pretty broad interpretation.

Despite maintaining iso-difficulty between vocoded vs speech-in-noise (SIN) conditions, the authors neither address (a) the fundamental differences in understanding vocoded vs. SIN speech nor (b) any theoretical basis for how the noise manifests in vocoded speech. If the tasks are indeed so obviously 'categorically' different - then it should not be surprising they engage different processing (the 'denoising' may not be comparable). I would prefer much more clearly defined and targeted hypotheses and a justification of the specific stimulus and paradigm choices to test such hypotheses. It appears to me that numerous measures have been obtained (reflecting in fact very different processes along the auditory pathway) and then it has been attempted to make up some coherent conclusions from these data - but the assumptions are not clear, the data are very complex and many aspects of the discussion are speculative. To me, the most interesting element is the reversal of the MOCR behavior in the attended vs ignored conditions. However, ignoring a stimulus is not a passive task! It would have been interesting to also see cortical unattended results.

Overall, I'm struggling with this study that touches upon various concepts and paradigms (efferent feedback, active vs. passive listening, neural representation of listening effort, modeling of efferent signal processing, stream segregation, speech-in-noise coding, peripheral vs cortical representations...) where each of them in isolation already provides a number of challenges and has been discussed controversially. In my view, it would be more valuable to specify and clarify the research question and focus on those paradigms that can help verify or falsify the research hypotheses.

Reviewer #2:

This is a highly ambitious study, combining a great number of physiological measures and behavioral conditions. The stated aim is to investigate the role of the descending auditory system in (degraded) speech perception. Unfortunately, the study was not designed with a clear a priori hypothesis, but instead collected a large number of measures, which were fitted together post-hoc into a particular interpretation, based on a selective subset of the data. Even more problematically, the experimental design is based on a fundamentally flawed premise, which undermines the validity of the interpretation. A final practical problem is that the most important comparison is made between conditions that were measured in separate experiments, with different participants. Given the notoriously poor reproducibility of across studies of these measures in this research field (suggesting large inter-individual variations), this casts a serious doubt on the interpretability of the observed difference.

Specific comments:

1) A core premise of the experiment is that the non-invasive measures recorded in response to click sounds in one ear provide a direct measure of top-down modulation of responses to the speech sounds presented to the opposite ear. This is not acknowledged anywhere in the paper, and is simply not justifiable. The click and speech stimuli in the different ears will activate different frequency ranges and neural sources in the auditory pathway, as will the various noises added to the speech sounds. Furthermore, the click and speech sounds play completely different roles in the task, which makes identical top-down modulation illogical. The situation is further complicated by the fact that the clicks, speech and noise will each elicit MOCR activation in both ipsi- and contralateral ears via different crossed and uncrossed pathways, which implies different MOCR activation in the two ears.

2) The vocoded conditions were recorded from a different group of participants than the masked speech conditions. Comparing between these two, which forms the essential point in this paper, is therefore highly confounded by inter-individual differences, which we know are substantial for these measures. More generally, the high variability of results in this research field should caution any strong conclusions based on comparing just these two experiments. A more useful approach would have been to perform the exact same task in the two experiments, to examine the reproducibility.

3) The interpretation presented here is essentially incompatible with the anti-masking model for the MOCR that first started of this field of research, in which the noise response is suppressed more than the signal, which is contradictory to the findings and model presented here, which suggest no role for the MOCR in improving speech in noise perception.

4) The analysis of measures becomes increasingly selective and lacking in detail as the paper progresses: numerous 'outliers' are removed from the ABR recordings, with very uneven numbers of outliers between conditions. ABRs were averaged across conditions with no explicit justification. The statistical analysis of the ABRs is flawed as it does not compare across conditions (vocoded vs masked) but only within each condition separately (active v passive) - from which no across-condition difference can be inferred. The model simulation includes only 3 out of 9 active conditions. For the cortical responses, again only 3 conditions are discussed, with little apparent relevance.

5) The assumption that changes in non-invasive measures, which represent a selective, random, mixed and jumbled by-product of underlying physiological processes, can be linked causally to auditory function, i.e. that changes in these responses necessarily have a definable and directional functional correlate in perception, is very tenuous and needs to be treated with much more caution.

Reviewer #1:

This preprint investigates neural mechanisms for processing degraded speech, in particular regarding efferent feedback. The authors thereby study two main types of speech degradations: noise vocoded speech and speech in background noise. Efferent feedback is assessed by recording click-evoked otoacoustic emissions as well as click-evoked brainstem responses, and the measurements are taken when the degraded speech is attended as well as when it is ignored. In addition, the authors also measure cortical responses to speech onsets. They find that these measures are affected by the two types of speech degradation in very different ways. In particular, for the noise vocoded speech, the click-evoked otoacoustic emissions are reduced when the speech is attended than when it is ignored. The opposite behaviour emerges when subjects listen to speech in background noise. The authors rationalise these different mechanisms through a computational model, which, as they show, can exhibit similar properties.

Unfortunately, many of the obtained results suffer from a lack of proper controls, which renders them rather inconclusive. In addition, important details of the experimental methodology are not properly described.

1) An important aspect of assessing the efferent feedback through the CEOAEs and ABRs is to ensure that different stimuli have equal intensity. The authors write in the methodology that the speech stimuli were presented at 75 dB SPL. However, it is not stated if this applies to the speech stimuli only, such that the stimuli that include background noise would have a higher intensity, or to the net stimuli. If the intensity of the speech signals alone had been kept at 75 dB SPL while the background noise had been increased, this would render the net signal louder and influence the MOCR. In addition, it would have been better to determine the loudness of the signals according to frequency weighting of the human auditory system, especially regarding the vocoded speech, to ensure equal loudness. If that was not done, how can the authors control for differences in perceived loudness resulting from the different stimuli?

2) Many of the p-values that show statistical significance are actually near the threshold of 0.05 (such as in the paragraph lines 147-181). This is particularly concerning due to the large number of statistical tests that were carried out. The authors state in the Methods section that they used the Bonferroni correction to account for multiple comparisons. This is in principle adequate, but the authors do not detail what number of multiple comparisons they used for the correction for each of the tests. This should be spelled out, so that the correction for multiple comparisons can be properly verified.

3) Line 184-203: It is not clear what speech material is being discussed. Is it the noise vocoded speech, the speech in either type of background noise, or these data taken together?

4) Line 202-203: The authors write that "the ABR data suggest different brain mechanisms are tapped across the different speech manipulations in order to maintain iso-performance levels". It is not clear what evidence supports this conclusion. In particular, from Figure 1D, it appears plausible that the effects seen in the auditory brainstem may be entirely driven by the MOCR effect. To see this, please note that absence of statistical significance does not imply that there is no effect. In particular, although some differences between active and passive listening conditions are non-significant, this may be due to noise, which may mask significant effects. Importantly, where there are significant differences between the active and the passive scenario, they are in the same direction for the different measures (CEOAEs, Wave III, Wave V). Of course, that does not mean that nothing else might happen at the brainstem level, but the evidence for this is lacking.

5) The way the output from the computational model is analyzed appears to bias the results towards the author's preferred conclusion. In particular, the authors use the correlation between the simulated neural output for a degraded speech signal, say speech in noise, and the neural output to the speech signal in quiet with the efferent feedback activated. They then compute how this correlation changes when the degraded speech signal is processed by the computational model with or without efferent feedback. However, the way the correlation is computed clearly biases the results to favor processing by a model with efferent feedback. The result that the noise-vocoded speech has a higher correlation when processed with the efferent feedback on is therefore entirely expected, and not a revelation of the computational model. More surprising is the observation that, for speech in noise, the correlation value is larger without the efferent feedback. This could due to the scaling of loudness of the acoustic input (see point 1), but more detail is needed to pin this down. In summary, the computational model unfortunately does not allow for a meaningful conclusion.

6) The experiment on the ERPs in relation to the speech onsets is not properly controlled. In particular, the different acoustics of the considered speech signals -- speech in quiet, vocoded speech, speech in background noise -- will cause differences in excitation within the cochlea which will then affect every subsequent processing stage, from the brainstem and on to the cortex, thereby leading to different ERPs. As an example, babble noise allows for 'dip listening', while with its flat envelope speech-shaped noise does not. Analyzing differences in the ERPs with the goal of relating these to something different than the purely acoustic differences, such as to attention, would require these acoustic differences to be controlled, which is not the case in the current results.

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

Summary:

The authors address a very important and timely research question, namely whether, and if so, how, efferent feedback contributes to the neural processing of degraded speech. However, the reviewers have identified significant problems with the experimental design and the data analysis, as well as with the conceptualization and the interpretation of the findings.

Reviewer #3:

This manuscript by Yu et al. explores the potential predictive value of Hyperpolarized 13C MRI and DCE-MRI in detecting early response of ICB. 2 mouse tumor models with different sensitivities to ICB were used in the study. Early changes in tumor glycolysis and necrosis were evaluated via [1-13C] pyruvate and [1,4-13C2] fumarate MRI, and perfusion/permeability state could be reflected by DCE-MRI. While the paper describes several intriguing pieces of data concerns below limit enthusiasm:

1) Figure 1A-C. Tumor growth curves in the anti-PD-L1 and anti-PD-L1 plus anti-CTLA4 groups appear to be steadily increasing, albeit at a slightly delayed pace than the control treated tumors. Most reports showed that PD-1/PD-L1 blockade results in tumor clearance in MC38 model, particularly when the treatment was started at small tumor sizes as presented in Figure 1A. The combination of anti-PD-L1 and anti-CTLA4 antibodies displayed more effective clearance of MC38 tumors. That is not the case here, where tumors are growing progressively throughout.

2) Figure 2A. I am surprised that CD4 T cells were barely detected in MC38 and B16 tumors. An example gating strategy must be shown, including an isotype or FMO control for each of the antibodies used.

3) Figure 3A. Given the therapeutic effect relies on the blockad of the binding between PD-1 and PD-L1, a careful assessment of the contribution of PD-1 binding to the metabolic change of tumor cells should be performed to provide clarity.

4) The microenvironment and structure of transplanted tumors are quite different from spontaneous tumor models, which are similar to human tumors. To demonstrate the clinical relevance of these findings, the authors will need to show more results in spontaneous tumor models or human tumors.

Reviewer #2:

Saida et al. performed multi-modal imaging to detect the early response to immune checkpoint blockade (ICB) therapy in murine models. This non-invasive method is attractive to monitor ICB response, thus it is valuable to discover relevant biomarkers in preclinical animal models before potential application in clinics. In ICB sensitive MC38 model, the authors identified increased cell death and intratumor perfusion/permeability, by 13C fumarate MRI and DCE MRI, respectively. While these descriptive results are interesting, this referee has a concern for the limited conceptual advance brought by this manuscript.

Major comment:

In recent years, new MRI technology has been shown to be promising to study pathophysiological changes, particularly in the metabolic field. To identify the efficacy of ICB, particularly at the early stage of treatment, is an important issue in immunotherapy. Thus the authors have chosen two murine models with a purpose to discover potential biomarkers with MRI. For metabolism, the author focused on glycolysis. In ICB sensitive MC38 model, no glycolytic changes were observed. Can the authors further clarify the role of glycolytic changes in ICB sensitive models? For instance, by using another ICB sensitive model; checking ECAR by using ex vivo digested tumor cells.

Reviewer #1:

The identification and validation of non-invasive imaging biomarkers for early response to cancer immunotherapy is a research hotspot. Dr. Krishna and colleagues proposed a potential combination of [1-13C] pyruvate-based detection of glycolysis, [1,4-13C2] fumarate-based analysis of necrosis, and Gd-DTPA-based quantification of perfusion/permeability with MRI technologies. To make the conclusions more convincing, some major issues should be carefully addressed.

1) It is a bit unfair to compare two different tumor models (MC38 colon cancer versus B16 melanoma). Reasonable solutions can be: 1) to compare good responders versus bad responders in the same type of cancer; 2) to compare ICB resilient tumor cell clones versus ICB sensitive clones, which originate from the same parental cell lines. To test whether these potential biomarkers can be generalized to multiple cancer types. Several tumor models should be tested.

2) It seems that the authors didn't test these parameters at different time points. As delayed response can be frequently observed in ICB, it is recommended to monitor tumor-bearing mice at different time points. These recorded parameters can be correlated to the therapeutic outcomes, once the whole tumor growth kinetics is finalized.

3) It is not accurate to consider the area of necrosis as the equivalent of immunogenic cell death.

4) Were any of these findings validated in a small cohort of cancer patients?

5) With radioactive probe-based analysis of glycolysis, it is difficult to judge whether metabolic changes were from tumor cells or from tumor-infiltrating immune cells. Ex vivo seahorse-based analyses of ECAR and OCR do not resemble in situ metabolic status of tumor cell.

6) Once glycolysis is reduced, OXPHOS and fatty acid oxidation may be switched on. A systemic analysis of the metabolic programs may be necessary. Mechanistic explorations on why these parameters correlate with late therapeutic outcomes is weak.

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

Summary:

A major concern is that the two transplantable murine tumor models used in this study may not be appropriate: it is odd to compare the therapeutic efficacy of ICB in colon cancer versus that in melanoma; the responsiveness of MC38 tumors seem to be much less than a series of reports; the composition of immune cells in the tumor microenvironment seems to be a bit abnormal; and the reviewers also have concerns on whether these transplantable tumor models can mimic human cancer patients.

Reviewer #3:

The manuscript by Gann et al., investigates whether theta-burst TMS stimulation (TBS) of the DLPFC can alter hippocampal and striatal activity during a sequence-learning task (SRTT). Across two experiments, the authors provide a well-powered investigation of this question using MRS and fMRI. The first experiment describes a nice approach for selecting an anatomically accurate brain region, whilst the second experiment uses a robust 4-session within-subject design. The study is clearly written and has some very interesting results, with the authors concluding that it provides the first experimental evidence that brain stimulation can alter motor learning-related functional responses in the striatum and hippocampus. As described below, I believe the interpretation of these results is overstated and would be better framed in the context of the other changes across the brain (it was not a specific effect between DLPFC and hippocampus/striatum) and also several clear negative effects (behaviour, MRS, fMRI).

1) Interpretation of the results given the lack of a behavioural effect: I do appreciate the authors discussion of this, however the lack of a behavioural effect makes me wonder whether the imaging results are over interpreted. Generally, I found the negative results (behaviour, MRS, aspects of connectivity) to be understated and the positive results to be overstated, even though the positive results, linking DLPFC stimulation to changes in striatum and hippocampus, required a far more nuanced analysis of the data (Figure 6). The results seem to suggest that TBS has a general (and somewhat inconsistent) effect on connectivity across the brain rather than a specific effect on DLPFC-hippocampus/striatum connectivity. Given these issues, I believe a more cautious interpretation of the results are required in which it is not concluded multiple times that DLPFC brain stimulation can alter motor learning-related functional responses in the striatum and hippocampus without making clear this was in the context of other changes across the brain and also several clear negative effects. The manuscript would be improved if a more balanced picture were provided throughout.

2) Use of iTBS vs cTBS: By using these TMS conditions, we do not know what the normal brain activation pattern is for the sequence task. Although the authors have provided a good attempt at trying to interpret these results, as a reader it was difficult to comprehend the somewhat inconsistent results across the two TMS conditions. The manuscript would have benefited from a sham/null TMS condition.

3) Consistency of results for experiment 1: Although this experiment provides a nice mechanism to determine TMS location, the details of these results need to be more substantial. In particular, for the conjunction analysis, the reader needs to understand how consistent this effect (Figure 1B) was across participants. Does every participant show this conjunction map or what percentage of participants show this map? This feels like an important thing to report, and then possibly base a power analysis on for Experiment 2 i.e. how many participants do we expect to see the predicted TMS results given the % of participants who show this conjunction map?

4) Clarity of results for experiment 2: I found it difficult to follow the results for experiment 2, especially from page 17. I appreciate the authors refer to the methods but I think a little more explanation of the methodology within the results is warranted. In addition, it got pretty difficult to follow the results relating task (seq vs random), stimulation (iTBS vs cTBS) and their interaction. Maybe more informative sub-titles would help?

5) Interpretation of BOLD activity: Given recent work (https://elifesciences.org/articles/55241 ) could the authors discuss what increases and decreases in BOLD activity represent within a learning context? Is a decrease or increase beneficial?

-Why do the authors keep using the term 'proof of concept'?

-Figure 7B: which line is cTBS and iTBS?

Reviewer #2:

Gann and colleagues report the effect of iTBS and cTBS of DLPFC on GABA+, BOLD-activity, and functional connectivity during sequence learning (SRTT). Despite finding no difference between the brain stimulation conditions on behavioral performance, the authors report widespread differences in BOLD-activity and functional connectivity between intermittent and continuous TBS. The key result (reported in Figure 6), is a complex difference between the stimulation conditions and the GABA+ change in DLPFC with i) the learning-related activity in hippocampus and ii) the learning-related changes in functional connectivity between DLPFC and putamen. The authors affirm that these results are important, as they are the first to show an interaction between DLPFC stimulation, learning-related changes in MRS, and BOLD-activity change in the hippocampus and striatum.

The authors have undertaken a mammoth effort in running this study. The targeted brain stimulation appears to have been conducted in an exemplary fashion, and the integration of multiple MR modalities is impressive. Nonetheless, I feel that the lack of a control group in the study design is a major concern and makes interpreting the study's results challenging. In addition, I have several reservations regarding the analysis that should be addressed before this work is suitable for publication.

General comments:

1) This study does not include a control group, and all conclusions are drawn based on the comparison between inhibitory and excitatory TBS protocols. A control condition is necessary to put the difference between the i/cTBS differences in perspective. Without this perspective, it is challenging to interpret the directionality and magnitude of the effects reported in this study.

2) The authors stress that the major contribution of this work is revealing the effects of DLPFC stimulation on fMRI/MRS signals during/after learning (e.g., Abstract: line 43-45; Introduction: lines 102-104; Discussion: lines 498-500, 741-743). As it is written, this work is primarily interesting to the brain stimulation community. The article would be of substantially broader interest if the authors discussed their results with respect to the contribution of DLPFC to sequence learning, rather than as an exploratory investigation into the effect of brain stimulation.

Methodological/Analytical comments:

3) The small volume correction analysis and reporting has several issues. Throughout the results, the authors report analyses plotted on the whole brain, and do not make reference to any small volume correction being used (except for the results reported in Table 2). However, in the methods section, the authors report that analyses were conducted using small volume corrections (10mm spheres drawn around the points reported in Supplementary table 7). There are several problems here.

i) Most importantly, the authors use (by my count) 87 separate small volumes, and reconstructing the spheres from the coordinates in Supplemental table 7 shows that this mask covers a substantial portion of the brain. This seems highly unusual to me. However, it is not clear whether all of these small volumes were considered together, or whether they were each considered as an independent volume. In either case, the authors should report whether any of their results survive whole brain correction. Additionally, if the authors tested 87 regions independently, multiple corrections should be applied to account for all regions tested (0.05/87 = 0.00057 for the new FWE-corrected p-threshold). Alternatively, if the authors used this single mask for correction, they should provide a justification for using an analytical mask restricted in this way, which, again, seems highly unusual.

ii) In the main text and figures, the authors should note when small volume correction is being applied.

iii) In the whole brain figures, it should be made clear what voxels were considered for the analysis (e.g., by shading the brain that was outside the small volume).

4) The authors appear to have done two instances of spatial smoothing (8mm before fitting the GLM (line 1184), and 6mm on the resulting statistical map (line 1224)). Again, this seems highly unusual, and given that the majority of results are conducted within small volumes, it seems smoothing to this extent would introduce unwanted levels of spatial blurring. The authors should report the total smoothness of the image for all subjects (AFNI's 3dFWHMx can do this) and consider performing the group analyses without additional smoothing applied to the statistical maps.

5) Although Study 1 is obviously important to the manuscript, I think it is perhaps overstated and makes the present work difficult to parse. Specifically, it does not seem to be important for interpreting the effects of the stimulation during learning; rather, Study 1 is a means to localize a brain stimulation target (i.e., a methodological point). Further, in the methods section, the authors reveal that they constrained their search for a conjunctive target (connected to both Hippocampus and Striatum) to the superior frontal gyrus and middle frontal gyrus. Thus, the authors seemed to have "found what they were looking for", because they restricted their search to a fairly well circumscribed region before running this study. Taken together, the authors might consider moving these details entirely to the Methods section, and removing Study 1 and associated figure from the main text.

6) Are there any relationships between Glx levels and fMRI effects?

Reviewer #1:

This is a very ambitious and interesting study that uses a state-of-the art combination of multiple methods to provide new insights into functional network interactions during motor learning. However, I have several major concerns against the design and analyses that may have contributed to the overall very weak effects that are reported (mainly null effects in standard measures at the behavioural and neural network level). I also think that some of the conclusions are not justified given the partly non-significant and overall weak effects.

1) My main concern is that no baseline stimulation condition (sham TBS) was included. The authors address this in the discussion but I cannot agree with their argumentation. Without a baseline, it is impossible to assess whether each stimulation protocol had a significant impact on the outcome measures. For instance, it would be plausible that both protocols had opposite effects (which is also hypothesized by the authors) which were, however, only slightly or not significant from baseline. If cTBS slightly decreases connectivity and iTBS slightly increases it, this could result in a difference between both protocols that might not be observed when contrasting each protocol against baseline. Put differently, how do we know that these changes are meaningful and significantly different from zero (baseline)? I think this is especially important in the present study since the overall effects are weak and there is no significant modulation of behavior - so the functional / behavioral relevance of the observed modulation remains unclear. I think that without the inclusion of a baseline (sham), it is very hard to interpret the data.

2) Another main concern is that the reported effects are very weak and not properly corrected for multiple comparisons. I don't think that it is justified to apply small volume corrections for large-scale network effects and it seems that some of the results are at threshold. Given the weak effects in these analyses, in combination with the absence of any modulation in the "standard" analyses (fMRI, connectivity, behaviour, MRS only significant in exploratory post-hoc tests which are not well justified), I am not sure if the reported results are really reflecting any stimulation-induced modulations at all or mainly show some noise added by the TMS protocols. This of course affects the conclusions that can be drawn from the study.

3) There are a number of issues with the design that might have contributed to the weak findings. These include data loss (e.g. no MRS data for the hippocampal voxel) and somehow arbitrary sample sizes that are not well justified. I am also not sure why cortical excitability measures (MEPs) were performed after TBS because this is of minor importance and delayed the start of the fMRI sessions. Given that TBS effects are expected to decrease over time, I am not sure if this was necessary. Was the potential change of the TBS effects across session taken into account (e.g., by using a parametric modulation of the TMS effect)?

4) Given the overall weak effects the conclusions should be toned down. The discussion would further benefit from including additional work that demonstrated changes in remote subcortical regions and effective connectivity after TMS over a frontal area (e.g. Herz et al., J Neurosci 2014).

5) There is no modulatory effect of TBS on behaviour, which is surprising in light of previous neurostimulation studies on motor learning. I think the way this is sold in the discussion is a bit odd. I guess that initially, one would have expected a behavioural modulation that should ideally be correlated with any TBS induced changes in functional connectivity (or with the MRS data). If not, how would you be able to claim behavioural relevance? In the discussion, the absence of a behavioural modulation is sold as an advantage, I think this is not justified and should be toned down. Moreover, since the authors speculate about potential influences of TBS on motor consolidation, I was wondering if consolidation was assessed (which seems to be a relevant parameter here)?

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

Summary:

The main concerns with the study were three-fold. First, the absence of a control group makes it hard to draw conclusions about the effects of inhibitory and excitatory TBS protocols separately, limiting the appeal of the study. Secondly, the control for multiple statistical tests is not adequate - conducting 87 independent "small volume corrected" tests will lead to an inflated family-wise error rate. Finally, all reviewers were unanimous in judging the effects to be somewhat interesting, but rather weak - with the absence of an effect on behaviour and simpler standard fMRI and connectivity measures being as remarkable as the positive effects reported.

Reviewer #3:

Short-summary:

Children (wide range) and adults participated in 3 MEG-experiments with auditory oddball paradigms varying in task demands. The focus is on the child N250m. Results show that although the N250m was not attenuated by task differences, increased activation in the left hemisphere (for at least the standard stimulus in the gng-task) was associated with better performances in inhibition tasks. Since the N250m in children was mainly located in the temporal cortices, whereas activation in adults was present in other areas, notable the ACG, this suggests that children differ from adults in the mechanisms required for cognitive control, and rely longer on sensori-motor areas.

Positive: well-written, great pictures.

Major comments:

1) The proposed link between auditory skills and inhibition is poorly explained and requires more elaboration. They want to relate auditory processing to " cognitive skills" (line 22, 77). Yet cognitive skills are a broad construct, encompassing many skills and abilities, including for instance reading, but also executive functions, which includes inhibition (Miyake et al., 2000) . Actually why zoom in on inhibition? Is it as one of the three components of executive function (Miyake et al., 2000) or is it viewed as part of self-regulation (Nigg, 2017)? You refer to cognitive control (46/47)- which suggests the latter explanation - but this remains unclear. It would help the reader to use consistent terminology (e.g., inhibition, cognitive control, executive control, inhibitory control are used now), or to highlight the links between the related concepts. Moreover, there are many paradigms to test inhibition, why did you chose the one you chose (Littman & Takacs, 2017)? And you also examine the behavioral performance sin the inhibition-MEG task (line 303)? Why the additional focus on attention in the results?

2) Your summary of the two child components (N1m and N250m) predicts different findings with the relationships with inhibition. That is, the link between the child N1m and inhibition should be smaller/non-existent, while it is mainly in the N250m that you expect it. While your results prove evidence towards the latter, your analyses do not concern the first component. It would be stronger if you do.

3) The logic of having three different auditory-listening tasks, and one behavioral outcome measure was not entirely clear to me. It seems that the paper is addressing various aims (e.g., replicate earlier work showing that the N250m is unaffected by task parameters in children while it is only present in adults for active tasks; another is linking the N250 m across all three tasks, or only for one of the three, if so, which? related to inhibition, but only for children, or also adults?) it would help to be explicit about this. For instance, there is an MEG-inhibition task and a behavioral task. Were you going to relate performances to both?

4) Why is age a significant predictor in explaining SSRT performance when adding left hemisphere OB-deviant, but not when adding LH GNG-S? Moreover, isn't it surprising that increase in activation yields better SSRT scores when this component disappears in adults?

5) From an association one cannot infer causal relationships - such limitations need to be discussed in more detail. Results do not allow for concluding that a sustained response ' aid inhibitory performance' (line 624).

Reviewer #2:

In this study the authors sought to explore the relationship between a child-specific auditory evoked response (N250) and cognitive control, using a classic auditory oddball paradigm. Here the cognitive control was manipulated by varying the tasks that participants were instructed to do while listening to the oddball tone sequence: (a) ignore all sounds ("passive listening"), (b) respond to the standard tones ("go/no-go") and respond to the deviant tone (which was called "oddball task" in this study).

Using the combined MEG and EEG as well as MRI, they reported an association between the strength of N250 in the left hemisphere and the behavioural performance in the go/no-go task and a separate inhibition task. Based on this observation as well as the fact that N250 was only visually observed in the children's brain response, the authors claimed that when doing sound involved cognitive tasks, different neural mechanisms were employed by adults and children. Considering the difficulty in operating a MEG and EEG combined experiment on children (6-14 years), the large sample size (n=78) is very, very impressive and the task was carefully embedded in a children-friendly game, which showed that the authors did a lot of work for this project.

However, it is hard to generalise such a conclusion based on the current paradigm and the results reported here. I also found it's a bit hard to follow the logic in the original manuscript, not because of the language but I think it might need more thorough revision to better explain what exactly the authors hypothesize, why use this specific paradigm, and why analyse the data in these ways.

Major comments:

1) While the task "press a button to standard tone" is called go/no-go task, the task "press a button to deviant tone" is called an oddball task. Why is the latter not a go/no-go task? It's definitely ok to have different names for different blocks, and also to analyse the data in these two blocks separately to double check. However, I would not expect fundamental difference in these two blocks. (However, as mentioned later, the authors reported divergence between these two tasks).

2) The main finding "Left hemisphere auditory responses at 250ms predicts behavioural performance on inhibition tasks" was based on the observation that, the brain activity in the left hemisphere (independent of the task) was negatively correlated with the within-individual variance in RT and the error rate in the go/no-go task (where subjects were instructed to respond to the standard tones) and the reaction time in a separate inhibition task done outside of the scanner. Why smaller within-individual variance in RT means better performance? Someone could be consistently very slow and this should not be a better performer. I found this is confusing, especially that based on Table 3, there was no correlation between RT and the brain response strength.

3) The scatterplots in Figure 7 shows the correlation reported in Table 3. However, the correlation seems to be largely driven by a few outliers: about 5 subjects whose source amplitude was much more negative than the rest of the population. Why did these subjects have a particularly strong source amplitude? After excluding these five subjects' data, will the correlation remain significant?

4) This point is related to points (1) and (2). In table 3, left hemisphere response during passive listening was strongly correlated with ICV, ERR and SSRT, but in table 6, there were no more correlations for passive listening. Can the authors explain why there is a difference between two passive listening sessions which in theory should be the same? Again, if there is no difference between "press a button to standard tone" and "press a button to deviant tone" and the correlation observed in table 3 was robust, then we would expect to see the significant correlations in table 6 for ICV, ERR too, but it is not true. These together make the finding less convincing. If there is any misunderstanding, the authors still need to justify these clearly in the manuscript and further analysis would be helpful.

5) Interpretation of the result: Even if the association between the amplitude of N250 and the behavioural performance is proven robust and true, this doesn't mean that this activity "aid the inhibitory performance in children" (line 624). Correlation does not imply causation. The authors need to provide more direct evidence to support such a claim or consider re-wording.

6) Design of the task: a. The block order: Why was the task order fixed for all participants? b. It is unclear why the passive listening block has a different number of trials (300) compared to the other two active blocks (360).

7) Sample size: Although the large sample size used in this study is very impressive, it is unclear how this sample size was determined and why the sample size of two groups (children vs adults) was so different (children n=78, adults n=16). It is crucial to justify this difference in this study because the motivation of this study is based on the hypothesis that N250 is present in children but not in the adult.

8) The unequal number of samples in statistical analysis: Related to the last two points, it is unclear whether the number of trials/participants was equalised before running the statistical analysis in this study.

9) Handedness: As the authors themselves mentioned in the discussion, the effect in left hemisphere observed here could be related to the handedness. Then the authors should also report the handedness amongst the participants.

10) Analysis: It is generally unclear why the children's data were divided into two groups: above or below 10 years old. The authors need to explain their rationale behind this clearly before doing so.

11) Figure 3: Each sub-figure includes 6 different conditions and makes it very hard to visualise. Please consider plotting the results in pairs for comparison, also show error-bar and run proper time-series statistics on the result. Also, it is unclear which channels are selected based on the black and white cap image at the centre. Please visualise it differently, for example, colours.

Reviewer #1:

This study by van Bijnen et al. used MEG/EEG recordings to examine the behavioral relevance of auditory processing in children, with a specific focus on an auditory cortical component around 250 ms, which only occurs in children but not in adults. They demonstrate that this component, particularly in the left auditory regions, covaries with several behavioral measurements in children. They conclude that the results suggest a shift in cognitive control function from sensorimotor regions in children to prefrontal involvements in adults.

The study addresses an important question in both auditory neuroscience and developmental science and is carefully performed. The modified design for children is interesting. However, I am not quite convinced that the findings constitute a great breakthrough. Moreover, I have major concerns about the correlation analysis that would support the central claim in the paper, that is, the attentional inhibition relevance of the M250 component in the left auditory cortex.

Major:

1) The child-specific M250 component occurring in the auditory cortex is interesting, but as mentioned throughout the paper, this is a well established observation. This study provides some evidence for the behavioral correlates of the component, but I am not convinced that the correlations supports the unique function of the component in attentional inhibition and its development trajectory from children to adults. First, the author only focused on the M250 component and calculated its correlation to behavior and thus could not exclude that other components might also be involved in the process. I would suggest the authors to do the correlation throughout the time course to thoroughly seek the behavioral-related components. Second, even for the passive listening conditions when inhibition is not required, the M250 also correlated with some behavioral measurements in certain task (Table 3) ? Third, only the to-be-inhibited sound was analyzed so how could the results support that the component only correlates with attentional inhibition? I understand that the authors might want to avoid the motor confounding factors associated with attended sound, but without comparison or control analysis, the conclusion could not firmly hold. Finally, behavioral relevance analysis was only done on children but not adults.

2) The authors calculated correlations between the M250 component with a series of behavioral measurements. Is there any way to do multiple comparison correction? Moreover, the results are inconsistent across different comparisons (e.g., Table 3, Table 6). For example, several behavioral indexes correlated with neural component for both PL and GN conditions (Table 3) while only SSRT showed correlation with the component under OB condition. I would suggest a more fair analysis by performing a GLM analysis using all the behavioral measurements (not just select factors that showed significant partial correlation which I think is double dipping to some extent, e.g., table 4, 7) as predictors.

3) To support the developmental trajectory of the M250 component, the authors could also perform the behavioral correlation for adults and compare it to that for children, even the behavioral-relevant component might be different in time and occur in distinct brain regions.

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

Summary:

All the reviewers acknowledged the importance of the question the paper aims to address, the big sample size, and the interesting experimental design for children. The paper is also clear and well written. However, the reviewers raised several critical concerns that question the main conclusions, including the interpretation of the results (i.e., relation to inhibition in cognitive control), inconsistency across experiments (i.e., differences in the results between the two experiments), and analysis details (i.e., behavioral-neural correlations). The paper could also be improved by a reframing in which the hypotheses and rationale behind the experiments are better explained.

Reviewer #3:

Summary:

The authors evaluate functional implications of two B9D2 missense variants identified in an individual with Joubert syndrome, by engineering the variants into a C. elegans model system. Few studies have evaluated the functional consequences of patient variants in model systems (rather than null alleles). Overall, the experiments are elegant and rigorous. The functional defects evaluated include decreased and altered localization of the variant proteins at the TZ, altered TZ function, altered cilium function (dye filling and behavioral assays), and reduced TZ protein localization, especially for TMEM216. The functional effects of homozygous null, homozygous missense variants, and compound heterozygous missense variants are compared. While most of the conclusions are well-supported, the work does not connect the functional consequences in C. elegans to phenotypic severity in humans, a critical validation of methods to test pathogenicity of human variants.

Major comment:

The authors introduce the concept of using C. elegans for genotype-phenotype correlations in the Abstract and Introduction, but do not interrogate an allelic series from humans with more and less severe phenotypes. The claims of genotype-phenotype correlation could be de-emphasized (eliminated? restricted to C. elegans), or the work could be strengthened by including more of an allelic series including variants predicted to be more deleterious (p.Ser101Arg identified in families segregating a Meckel syndrome phenotype. and p.His5Gln identified in a family segregating a possible Meckel syndrome phenotype), less deleterious (the p.Leu36Pro variant in a possibly less severely affected person with JBTS, also published in the Bachmann-Gagescu paper), and benign (i.e. common variants that are found homozygous in population databases like gnomAD and are unlikely to impair B9D2 function). It seems that this would be a lot of additional work; however, the Discussion highlights "it should be possible to generate hundreds of alleles in a relatively short time frame at relatively low cost and manpower compared to other multicellular systems. The workflow to generate and characterize ciliopathy associated variants described here can also be extended to other conserved cilia genes and ciliopathies."

Other comments:

1) Important considerations for data presentation and statistical analysis:

-Use dot (or violin) plots rather than bar graphs to show data structure for length, intensity, and other measurements (see PMID 32346721). For the curves of linescan intensities, it would be helpful to include supplemental figures with all of individual curves to see their shapes and variability.

-t-tests on all data points together may over estimate statistical significance; consider whether it would be more appropriate to compare mean measurements for each animal (or median if the data are not normally distributed). At a minimum, list the number of cilia and the number of animals for each experiment.

2) Could the lower levels of mutant protein in the TZ be due to lower levels of total mutant protein? Although there is no MKSR-2 antibody, this could be evaluated by Western blots of mNG::MKSR-2/mNG::MKSR-2(P74S) and mNG::MKSR-2/mNG::MKSR-2(G155S) animals.

Reviewer #2:

In this manuscript, the authors analyzed the function of two pathogenic missense variants (P74S, G155S) of Joubert Syndrome protein B9D2/ mksr-2 using a C. elegans model. The data shows that both P74S and G155S mutations change the distribution of MKSR-2 on TZ and disrupt the structure and function of cilia in C. elegans, indicating that both mutations are pathogenic.

Characterizing the function of pathogenic mutations associated with ciliopathies is important for us to understand the function of ciliopathy genes and the pathogenesis of ciliopathies, therefore, the topic of the manuscript is very important and interesting.

The manuscript is well organized, and the data is of high experimental quality. However, there is a lack of new insights about the function of MKSR-2 protein or the formation of TZ.

Major concerns:

What are the possible mechanisms by which P74S and G155S mutations affect the function of MKSR-2? Do these mutations affect the interaction between MKSR-2 with other TZ proteins? I do think some (even a little) new insights into the function of MKSR-2 are needed.

Reviewer #1:

The experiments are elegant, take advantage of the strengths of the model and the conclusions are mostly supported by the results, even if the discussion should address potential limitations a little more. Overall, this is thorough work of potential high impact.

Major comments:

1) The authors test the localization of the mutant B9D2 protein at the base of the cilium, show decreased fluorescent signal and conclude that the patient mutations affect the TZ localization of the protein. It seems important to me to also demonstrate that the overall protein stability is not affected by measuring the protein levels by western blot if possible. The competition assay between wildtype and mutant alleles with and without transgene somewhat supports the presence of product from the mutant alleles, but an objective measure of the amount would further strengthen their point. (One could imagine that the G155S mutation leads to decreased protein stability with increased degradation and this may explain why it is more similar to the knock-out than the P74S allele?).

2) One major claim made by the authors is that the experiments allow to classify the severity of the effect caused by the individual mutations, showing that the G155S is more severe than P74S. What I find puzzling, is that the ultrastructural consequences on the TZ appear to be similar in both mutants, whereas the TZ gating function is affected only in the G155S mutant. How do the authors explain this discrepancy? If the morphology of the gate is affected similarly, why is the function not affected similarly? Maybe some quantification of the ultrastructural defects would show that the ZT is more disrupted in the G155S mutant?

3) A more detailed discussion of the differences between C.elegans and mammalian cilia appears necessary to me, since these difference may prove to limit the applicability of the proposed assays (for example differences in the basal body of C.elegans may limit this approach for basal body resident proteins? Even for the TZ, in humans mutations in only on TZ component cause phenotypes, while in worms, double mutants are necessary for most genes, suggesting differences in the function of the individual proteins or the structure of the TZ). Beyond the species differences, evidence is appearing for cell-type specific roles of ciliary proteins, so that results from one type of cilium (including those shown here in worms), do not necessarily guarantee that this holds true in all cilia types, which would limit the interpretation for patients with many different cilia types. This being said, I still support the relevance of the current work, but just think that these potential limitations should be mentioned in the discussion in a more detailed manner.

4) Figure 2D: the curve for the P74S mutant overlays with the WT curve with respect to height (fluorescence intensity) and length (x-axis). Does this not contradict panels A-C where the signal is weaker and shorter?

5) Figure 6C: my impression from the graphs is that nphp4 and cep290 are just as much affected as mks14 and mks6 in both P74S and G155S mutants? The text does not mention that mksr2 mutants have any effect on nphp4, cep290 or mks5 whereas the graphs do show a mild effect? Wouldn't this contradict the model of how the TZ is built? Figure 6D again seems to show a different result than Figure 6C (mainly for mks6)?

6) Statistics: correction for multiple testing should be performed everywhere (no pair-wise t-tests).

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

Summary:

The manuscript by Lange et al describes how C. elegans can be used to generate functional assays to interpret the significance of missense variants in known human ciliopathy genes. This work thus aims at being a proof-of-principle for a way to address the major problem of VUSs (variants of unknown significance) faced by human geneticists today and is therefore of high relevance to the field (even if no major novel biological insights with respect to ciliary biology are described). The reviewers agreed that characterizing the function of pathogenic mutations associated with ciliopathies is important for us to understand the function of ciliopathy genes and the pathogenesis of ciliopathies. The manuscript is well organized and the data are of high experimental quality.

Author Response:

All three reviewers agreed that establishing a link between a proteasome activator and heterochromatin stability was novel and intriguing. However, limited insight into the PA28-gamma mechanism of action (or possibly a new heterochromatin compaction mechanism) dampened reviewer enthusiasm. The reviewers offered many suggestions, including additional experiments, new controls, and structural changes to the Discussion, that we hope you find useful.

We would like to thank the reviewers for their suggestions and comments, which we will take into account to improve our manuscript as much as possible. As stressed by reviewers, our manuscript highlights a new and unexpected function of a proteasome regulator, PA28γ, in the regulation of heterochromatin compaction. We also provide evidences that this unexpected function of PA28γ is independent of its proteasome regulatory function. Moreover, we can show that PA28γ is required at least for proper maintenance of heterochromatin regions dependent on HP1 proteins, thereby providing a clear insight into the PA28γ mechanism of action on chromatin.

Reviewer #1:

In the manuscript entitled, "The 20S proteasome activator PA28γ controls the compaction of chromatin," Fesquet et al. establish a functional link between PA28γ and chromatin compaction in human cells. Previous work established a role for PA28γ in DNA repair and in chromosome stability through mitotic checkpoint regulation; however, a role, if any, for PA28γ in heterochromatin establishment/maintenance was not known. The authors use an elegant LacO-GFP system combined with PA28γ knockdown to support the possibility that this nuclear activator contributes to DNA packaging of repetitive DNA. A nucleosome proximity assay offers additional support that the most compacted chromatin is most sensitive to loss of PA28γ. Using a truncated version of PA28γ, the authors show that this chromatin function appears to be independent of its interaction with the 20S proteasome. ChIP-qPCR suggests that PA28γ binds repetitive DNA and ChIP-qPCR of PA28γ knockdown cells lose H3K9me and H4K20me, two silent heterochromatin marks. In addition to these data, the authors also attempt to establish that PA28γ and HP1β may work together to support heterochromatin formation/maintenance. The manuscript reports several intriguing pieces of data that have the potential to open new areas of inquiry into proteasome components and accessory factors in chromatin organization and remodeling. The potency of these key experiments, however, were diluted by unconvincing co-localization assays, poorly controlled PLA and ChIP-qPCR assays, and a highly speculative Discussion. Moreover, key controls were missing for several experiments (detailed below) that would have otherwise established the heterochromatin-specificity of PA28γ. Finally, important potential functional consequences of heterochromatin disruption, including chromosome segregation defects, transposable element proliferation, and accumulation of DNA damage, were not addressed while there was a focus instead on cell cycle without clear interpretations.

Major Comments:

1) Figure 2: The co-localization experiments were unconvincing - HP1β and PA28γ foci decorate most of the nucleus, making inferences about significant overlap difficult to grasp.

We fully agree with this criticism for Fig. 2A, in which classical indirect immunofluorescence (IF) and widefield microscopy were used. This is why in Fig. 2B, we set up a pre-extraction protocol of cells with a Triton X100 treatment, to remove almost all soluble proteins before cells fixation and IF. Then images were acquired as Z-stacks with an Airyscan confocal microscope, followed by a 3D reconstruction and analysis of the co-localization between PA28γ and PA28γ using Imaris co-localization software. This experiment highlighted that only a fraction of both endogenous proteins co-localize in the nucleus. Furthermore, we noted that the number of co-localization sites evidenced in Fig 2B (~32) was in the same range than the number of dots (~37) detected by another approach (is-PLA), suggesting that we can be confident with these results.

I also found the significance of the PLA assays difficult to discern. When both factors are so abundant in the nucleus, it seems inevitable to observe loss of proximity when one 'partner' is depleted. How do these data demonstrate the specificity of this potential proximity? A clearer explanation would be helpful.

PLA technique imposes numerous constraints to obtain a signal (i.e. distance less than 30-40 nm between the two epitopes, formation of a closed circular DNA template). As a control, we verified that the is-PLA approach gives specific signals between PA28γ and the 20S proteasome. Like PA28γ, the 20S proteasome is very abundant in the nucleus but only a small fraction of PA28γ interacts with the 20S proteasome by Co-IP (Jonik-Nowak, 2018). Consistent with this, less than 60 distinct PLA spots were detected in the nucleus between PA28γ and the 20S proteasome rather than a global nuclear PLA labeling suggesting that it is probably not the abundance of each protein tested that is responsible for PLA signal but rather, as suggested by this kind of techniques, their ability to interact.

Note that the PIP30 data were a distraction from the main thread - I recommend removing or explaining more clearly.

PIP30 is currently the only known regulator of PA28γ, for which we have previously shown a critical role in PA28γ interaction with different partners and localization (Jonik-Nowak, 2018). This is why we examined the potential requirement of PIP30 in this new chromatin regulatory function of PA28γ.

2) The ChIP-qPCR data were certainly exciting but the absence of a negative control locus made me wonder how specific this result was to DNA repeats.

As a control, we already used cyclin E2 promoter in our ChIP-qPCR for PA28γ. This led us to show that the detection of PPA28γ on heterochromatic repetitive DNA sequences is enriched by a factor of 2-3 as compared to this euchromatic loci bound by PA28γ. In a modified version of this manuscript, we will add other control loci located in euchromatin. We will also test these new loci as negative controls in ChIP-qPCR for H3K9me3 and H4K20me3.

3) The LacO-GFP data are really cool. Why didn't the authors not attempt to rescue compaction with a PA28γ transgene as was done for the FLIM-FRET?

Since, we could not obtain stable U20S-LacO-KO-PA28γ and -KO/KI-PA28γ cell lines, we decided to analyze the impact of PA28γ absence, using siRNA approaches. As it was shown that overexpression of PA28γ is sufficient to cause a disruption of Cajal Bodies (Cioce et al., 2006) and a decrease in the number of PML bodies (Zannini et al., 2009), and we also noticed in FRET-FLIM experiments that PA28γ expression level is critical for chromatin compaction, it is difficult to consider to overexpress a RNAi-resistant PA28γ protein in order to rescue the effect of the depleted endogenous protein.

4) Cell cycle data would be much more interesting if the authors set up a priori predictions based on Figures 1-5.

We agree with the comment, and we will correct it in the modified version of the manuscript.

5) The absence of any report of PA28γ KD/KO on genome instability was surprising.

As indicated in the manuscript, the potential effect of PA28γ depletion on genome stability has already been reported in the literature showing an increase in chromosomal instability (Zannini, 2008).

Loss of heterochromatin integrity is expected to compromise chromosome transmission/transposable element expression or insertions. Do the repeats to which PA28γ localizes upregulate upon PA28γ KD or KO? Does DNA damage signaling increase at the loci? These functional consequences would be rather more explicable that the S-phase result reported.

We did not detect any upregulation at these specific loci by RT-qPCR experiment using KO-PA28γ U2OS cells. Concerning the potential accumulation of DNA damage signaling at these loci in the absence of PA28γ, we have not studied this aspect because PA28γ depletion was reported to induce only a marked delay in DSB repair and not a DNA damage accumulation (Levy-Barda, 2011).

6) The histone mark ChIP-qPCR, like the PA28γ ChIP-qPCR, lacks a negative control locus/loci, again undermining the inference of specificity of PA28γ on heterochromatin.

We agree and these different control loci would be added in the modified version.

7) The LLPS paragraph in the discussion was weak - consider removing.

Yes, potentially. To be defined in the context of the modified version.

8) The speculation of 20S into foci does not add and, to my mind, detracts from the focus of the Discussion.

In the modified version of the manuscript, we will focus our study on endogenous proteins and therefore this aspect of the discussion concerning the 20S proteasome, and related to the overexpression of alpha4, will no longer be discussed.

Reviewer #2:

In this manuscript, Fesquet and colleagues describe an important role of the proteasome activator PA28-gamma in the compaction of chromatin. The authors first demonstrate that PA28-gamma colocalizes HP1-beta at nuclear foci induced by the ectopic expression of alpha-4 subunit of the 20S proteasome. They further show that a fraction of PA28-gamma colocalizes also with HP1-beta in cells without ectopic expression of the alpha-4. The authors then show that PA28-gamma is associated with heterochromatic regions and is required for the compaction of lacO array integrated at a pericentromeric region. They also performed the quantitative FLIM-FRET and demonstrate that PA28-gamma controls chromatin compaction in living cells, independently of its interaction with 20S proteasome. Finally, the authors show that PA29-gamma depletion leads to a decrease of heterochromatin marks, H3K9me3 and H4K20me3, at representative heterochromatic regions. From these findings they conclude that PA28-gamma contributes to chromatin compaction and heterochromatin formation.

Although PA28-gamma has been identified as an alternative component associated with 20S proteasome, its physiological roles remain obscure. The present study demonstrates that PA28-gamma is involved in chromatin compaction and heterochromatin formation. The results presented are in most cases of high quality and convincingly controlled. I have the following concerns that should be addressed by the authors.

Major points: 1) For the localization study (Fig. 1), the authors first show the colocalization of alpha-4, PA28-gamma, and HP1-beta in the nuclear foci induced by ectopic expression of alpha-4-GFP. While the authors point out the similarity of cell-cycle dependent patterns between the alpa-4 induced foci and HP1-beta foci (lines 135-138), this argument seems to be poorly reasoned.

We omitted to mention that we also tested the potential co-localization of alpha-4-GFP with different proteins associated with nuclear foci (SC35, PML PCNA, γH2AX) or BrdU-labelled replication foci without success, before to find a correlation with the accumulation of newly synthesized GFP-HP1β in nuclear foci.

The authors previously showed that ectopically expressed CFP-tagged alpha-7, another core component of 20S, accumulates into discrete nuclear foci, and the foci are colocalized with SC35, a well-characterized member of nuclear speckle (Baldin et al. MCB 2008). Considering that both alpha-4 and alpha-7 are core components of 20S proteasome, it is highly likely that the alpha-4-GFP- accumulating nuclear foci are corresponding to the nuclear speckles. If so, HP1-beta foci should be distinct from that of alpha-4-GFP foci. The authors should test the relationship between alpha-4-GFP foci and nuclear speckles, and if this would be the case, it might be better to omit the colocalization data using cells expressing alpha-4-GFP (Fig. 1) and start by potential colocalization of PA28-gamma and HP1-beta in cells without expressing alpha-4-GFP (Fig. 2).

As mentioned above alpha4-GFP did not co-localize with SC35, a marker of the nuclear speckles. When different alpha subunits of the 20S proteasome are overexpressed, only alpha7 and alpha4 show an accumulation in specific nuclear foci. This remains unclear but a possible explanation could be an alternative composition of alpha subunits in the 20S as previously reported for alpha4 (Padmanabhan A. et al., Assemnbly of an evolutionarily conserved alternative proteasome isoform in human cells, , 2016, Cell Reports). As this part of our study appears to confuse readers and to dilute the essential message of the manuscript, we are considering to exclude these data in the modified version.

2) Although the functional link between PA28-gamma and chromatin compaction seems quite interesting, it remains unclear how it contributes to the establishment of repressive histone marks such as H3K9me3 and H4K20me3. While the authors clearly show that 20S-binding-deficient PA28-gamma mutant (PA28-gamma ∆C) could restore the chromatin compaction defect caused by PA28-gamma KO, it is also possible that PA28-gamma controls the stability of factors involved in heterochromatin assembly. To exclude this possibility the authors should test whether PA28-gamma KD/KD does not affect the protein levels of core histone modifying enzymes and HP1 proteins by immunoblotting.

During this study we performed numerous immunoblots using anti-HP1 antibodies and we did not observe any significant variation of these proteins in KO-PA28γ cells. Furthermore, in an atempt to identify proteins whose stability could be controlled directly or indirectly by PA28γ, we performed a SILAC-based quantitative proteomic analysis comparing nuclear extracts from U2OS or HeLa wild type cells to U2OS- or HeLa KO-PA28γ cells. Under the tested conditions, we could not identify variation of the amount of factors involved in chromatin assembly, suggesting that the impact of PA28γ on chromatin organization is not driven by changes in the level of the important histone-modifying enzymes, nor core components of chromatin such as HP1 proteins.

Reviewer #3:

This manuscript explores the localization and function of a previously studied proteasome activator, PA28gamma. This protein is a nuclear activator of the 20S proteasome and is widely conserved during evolution, although largely absent in fungi. The authors report that (1) subunits of the 20S proteasome (alpha4 and alpha6) and GFP-tagged or endogenous PA28gamma colocalize with each other and with HP1beta in the nucleus, with HP1beta required for the localization of PA28gamma to nuclear foci, (2) depletion of PA28gamma results in decompaction of pericentromeric heterochromatin, and (3) use a FLIM-FRET based microscopy assay to show a broad role for PA28gamma in chromatin compaction, a function that PA28gamma shares with HP1beta. They also show that the C terminus of PA28gamma, which is required for its interaction with the 20S proteasome, is not required for its subnuclear localization or compaction functions, and that PA28gamma KO cells have reduced levels of H3K9me3 and H4K20me3 heterochromatin-associated histone modifications.

The identification of a role for PA28gamma in heterochromatin compaction and heterochromatin maintenance is interesting and raises intriguing possibilities about the role of this protein and the 20S proteasome in heterochromatic domains. The study is largely descriptive and does not provide new mechanistic insight into heterochromatin or PA28gamma. Although the experiments in the paper are of high quality and well-executed, they basically amount to identification of a new factor that affects heterochromatin stability. The fact that PA28gamma is a proteasome activator provides no mechanistic insight since the 20S proteasome does not seem to be required for the heterochromatin compaction function of PA28gamma.

The following suggestions may be helpful to the authors in preparing their manuscript for publication (in order of appearance).

1) The IP experiments in Figure 1D should be performed in the presence of nuclease (DNase/RNase A or benzonase) to test whether the interactions are bridged by RNA or DNA.

We actually tried several times to perform this IP experiments using notably benzonase. However, despite several attempts under various conditions, we could not obtain a clear and consistent answer to this question.

2) Figure 2. What percentage of PA28gamma and HP1beta foci overlap in the absence of alpha4 overexpression?

As indicated in the text, on average of 32 spot of co-localization between the two proteins were detected in Figure 2B and on average of 37 spots in is-PLA experiment (Figure 2C).

3) Figure 3. Does decompaction result in loss of silencing of heterochromatin targets such as HERV-K, LINE1, alpha satellite etc? Ideally, the authors should perform RNA-seq to provide a more complete picture of changes in gene expression as a result of PA28gamma depletion.

RT-qPCRs were performed on heterochromatin loci used for ChIP (HERV-K, L1 Line, SatII and alpha-sat) and no significant variation was observed. In order to determine whether the absence of PA28γ could affect gene expression, we performed a trancriptomic analysis using Affymetrix® Human Gene 2.1 ST Array Strip comparing mRNA expression in U2OS -WT and KO-PA28γ cells. This experiment revealed only very little variation between the two samples tested: 11 genes were up in KO-PA28γ (MFAP5 (Microfibrillar-associated protein 5), GLIPR1 (Glioma pathogenesis-related protein 1) and 9 that are still unannotated), and only 2 genes were significantly down: PSME3 (PA28γ) and MAGE-C1(Melanoma-associated antigen C1). These experiences led us to consider that PA28γ probably does not directly affect the level of transcription.

4) Based on experiments with PA28gamma-deltaC, which does not interact with the 20S proteasome, the authors conclude that the 20S proteasome is not required for the PA28gamma-mediated chromatin compaction. Although their IP data (Figure 4E) seem persuasive, a more convincing experiment would be to also perform the FRET assay for compaction with knockdown of subunits of the proteasome.

Knockdown of 20S proteasome subunits was not performed since in that condition all the proteasome family will be affected, and we already know that depletion of these proteins has several and pleiotropic effects (i.e. cell cycle progression), which could indirectly affect chromatin compaction.

5) Figure 6. It is critical that the effects on histone modifications are evaluated using siRNA KD (or other transient KD methods) of PA28g to complement the KO results. PA28gamma KOs have many defects including genome instability and aneuploidy that may affect K9me3 and K20me3 indirectly.

This is indeed a hypothesis that cannot be ruled out. But considering that these modifications (H3K9me3, H4K20me1/3) are crucial for the establishment of chromatin compaction and that the elimination of PA28γ (siRNA treatment) induces chromatin decompaction within 48h, it is reasonable to consider that the variation of these marks does not result from genome instability.

6) In general, the manuscript would benefit from the addition of genome-wide approaches such as ChIP-seq to gain broader insight into PA28gamma localization and general compaction functions.

We agree that the mapping of PA28γ distribution on non-repeated DNA sequences will be useful for the subsequent studies of PA28γ functions in DNA–related processes such as gene regulation. However, because of the difficulty to map HP1 proteins and heterochromatin regions by ChIP-seq, we do not believe that this approach will necessarily reinforce the current message of this first manuscript on the role of PA28γ in the regulation of heterochromatin compaction.

Reviewer #3:

This manuscript explores the localization and function of a previously studied proteasome activator, PA28gamma. This protein is a nuclear activator of the 20S proteasome and is widely conserved during evolution, although largely absent in fungi. The authors report that (1) subunits of the 20S proteasome (alpha4 and alpha6) and GFP-tagged or endogenous PA28gamma colocalize with each other and with HP1beta in the nucleus, with HP1beta required for the localization of PA28gamma to nuclear foci, (2) depletion of PA28gamma results in decompaction of pericentromeric heterochromatin, and (3) use a FLIM-FRET based microscopy assay to show a broad role for PA28gamma in chromatin compaction, a function that PA28gamma shares with HP1beta. They also show that the C terminus of PA28gamma, which is required for its interaction with the 20S proteasome, is not required for its subnuclear localization or compaction functions, and that PA28gamma KO cells have reduced levels of H3K9me3 and H4K20me3 heterochromatin-associated histone modifications.

The identification of a role for PA28gamma in heterochromatin compaction and heterochromatin maintenance is interesting and raises intriguing possibilities about the role of this protein and the 20S proteasome in heterochromatic domains. The study is largely descriptive and does not provide new mechanistic insight into heterochromatin or PA28gamma. Although the experiments in the paper are of high quality and well-executed, they basically amount to identification of a new factor that affects heterochromatin stability. The fact that PA28gamma is a proteasome activator provides no mechanistic insight since the 20S proteasome does not seem to be required for the heterochromatin compaction function of PA28gamma.

The following suggestions may be helpful to the authors in preparing their manuscript for publication (in order of appearance).

1) The IP experiments in Figure 1D should be performed in the presence of nuclease (DNase/RNase A or benzonase) to test whether the interactions are bridged by RNA or DNA.

2) Figure 2. What percentage of PA28gamma and HP1beta foci overlap in the absence of alpha4 overexpression?

3) Figure 3. Does decompaction result in loss of silencing of heterochromatin targets such as HERV-K, LINE1, alpha satellite etc? Ideally, the authors should perform RNA-seq to provide a more complete picture of changes in gene expression as a result of PA28gamma depletion.

4) Based on experiments with PA28gamma-deltaC, which does not interact with the 20S proteasome, the authors conclude that the 20S proteasome is not required for the PA28gamma-mediated chromatin compaction. Although their IP data (Figure 4E) seem persuasive, a more convincing experiment would be to also perform the FRET assay for compaction with knockdown of subunits of the proteasome.

5) Figure 6. It is critical that the effects on histone modifications are evaluated using siRNA KD (or other transient KD methods) of PA28g to complement the KO results. PA28gamma KOs have many defects including genome instability and aneuploidy that may affect K9me3 and K20me3 indirectly.

6) In general, the manuscript would benefit from the addition of genome-wide approaches such as ChIP-seq to gain broader insight into PA28gamma localization and general compaction functions.

Reviewer #2:

In this manuscript, Fesquet and colleagues describe an important role of the proteasome activator PA28-gamma in the compaction of chromatin. The authors first demonstrate that PA28-gamma colocalizes HP1-beta at nuclear foci induced by the ectopic expression of alpha-4 subunit of the 20S proteasome. They further show that a fraction of PA28-gamma colocalizes also with HP1-beta in cells without ectopic expression of the alpha-4. The authors then show that PA28-gamma is associated with heterochromatic regions and is required for the compaction of lacO array integrated at a pericentromeric region. They also performed the quantitative FLIM-FRET and demonstrate that PA28-gamma controls chromatin compaction in living cells, independently of its interaction with 20S proteasome. Finally, the authors show that PA29-gamma depletion leads to a decrease of heterochromatin marks, H3K9me3 and H4K20me3, at representative heterochromatic regions. From these findings they conclude that PA28-gamma contributes to chromatin compaction and heterochromatin formation.

Although PA28-gamma has been identified as an alternative component associated with 20S proteasome, its physiological roles remain obscure. The present study demonstrates that PA28-gamma is involved in chromatin compaction and heterochromatin formation. The results presented are in most cases of high quality and convincingly controlled. I have the following concerns that should be addressed by the authors.

Major points:

1) For the localization study (Fig. 1), the authors first show the colocalization of alpha-4, PA28-gamma, and HP1-beta in the nuclear foci induced by ectopic expression of alpha-4-GFP. While the authors point out the similarity of cell-cycle dependent patterns between the alpa-4 induced foci and HP1-beta foci (lines 135-138), this argument seems to be poorly reasoned. The authors previously showed that ectopically expressed CFP-tagged alpha-7, another core component of 20S, accumulates into discrete nuclear foci, and the foci are colocalized with SC35, a well-characterized member of nuclear speckle (Baldin et al. MCB 2008). Considering that both alpha-4 and alpha-7 are core components of 20S proteasome, it is highly likely that the alpha-4-GFP- accumulating nuclear foci are corresponding to the nuclear speckles. If so, HP1-beta foci should be distinct from that of alpha-4-GFP foci. The authors should test the relationship between alpha-4-GFP foci and nuclear speckles, and if this would be the case, it might be better to omit the colocalization data using cells expressing alpha-4-GFP (Fig. 1) and start by potential colocalization of PA28-gamma and HP1-beta in cells without expressing alpha-4-GFP (Fig. 2).

2) Although the functional link between PA28-gamma and chromatin compaction seems quite interesting, it remains unclear how it contributes to the establishment of repressive histone marks such as H3K9me3 and H4K20me3. While the authors clearly show that 20S-binding-deficient PA28-gamma mutant (PA28-gamma ∆C) could restore the chromatin compaction defect caused by PA28-gamma KO, it is also possible that PA28-gamma controls the stability of factors involved in heterochromatin assembly. To exclude this possibility the authors should test whether PA28-gamma KD/KD does not affect the protein levels of core histone modifying enzymes and HP1 proteins by immunoblotting.

Reviewer #1:

In the manuscript entitled, "The 20S proteasome activator PA28γ controls the compaction of chromatin," Fesquet et al. establish a functional link between PA28γ and chromatin compaction in human cells. Previous work established a role for PA28γ in DNA repair and in chromosome stability through mitotic checkpoint regulation; however, a role, if any, for PA28γ in heterochromatin establishment/maintenance was not known. The authors use an elegant LacO-GFP system combined with PA28γ knockdown to support the possibility that this nuclear activator contributes to DNA packaging of repetitive DNA. A nucleosome proximity assay offers additional support that the most compacted chromatin is most sensitive to loss of PA28γ. Using a truncated version of PA28γ, the authors show that this chromatin function appears to be independent of its interaction with the 20S proteasome. ChIP-qPCR suggests that PA28γ binds repetitive DNA and ChIP-qPCR of PA28γ knockdown cells lose H3K9me and H4K20me, two silent heterochromatin marks. In addition to these data, the authors also attempt to establish that PA28γ and HP1β may work together to support heterochromatin formation/maintenance. The manuscript reports several intriguing pieces of data that have the potential to open new areas of inquiry into proteasome components and accessory factors in chromatin organization and remodeling. The potency of these key experiments, however, were diluted by unconvincing co-localization assays, poorly controlled PLA and ChIP-qPCR assays, and a highly speculative Discussion. Moreover, key controls were missing for several experiments (detailed below) that would have otherwise established the heterochromatin-specificity of PA28γ. Finally, important potential functional consequences of heterochromatin disruption, including chromosome segregation defects, transposable element proliferation, and accumulation of DNA damage, were not addressed while there was a focus instead on cell cycle without clear interpretations.

Major Comments:

1) Figure 2: The co-localization experiments were unconvincing - HP1β and PA28γ foci decorate most of the nucleus, making inferences about significant overlap difficult to grasp. I also found the significance of the PLA assays difficult to discern. When both factors are so abundant in the nucleus, it seems inevitable to observe loss of proximity when one 'partner' is depleted. How do these data demonstrate the specificity of this potential proximity? A clearer explanation would be helpful. Note that the PIP30 data were a distraction from the main thread - I recommend removing or explaining more clearly.

2) The ChIP-qPCR data were certainly exciting but the absence of a negative control locus made me wonder how specific this result was to DNA repeats.

3) The LacO-GFP data are really cool. Why didn't the authors not attempt to rescue compaction with a PA28γ transgene as was done for the FLIM-FRET?

4) Cell cycle data would be much more interesting if the authors set up a priori predictions based on Figures 1-5.

5) The absence of any report of PA28γ KD/KO on genome instability was surprising. Loss of heterochromatin integrity is expected to compromise chromosome transmission/transposable element expression or insertions. Do the repeats to which PA28γ localizes upregulate upon PA28γ KD or KO? Does DNA damage signaling increase at the loci? These functional consequences would be rather more explicable that the S-phase result reported.

6) The histone mark ChIP-qPCR, like the PA28γ ChIP-qPCR, lacks a negative control locus/loci, again undermining the inference of specificity of PA28γ on heterochromatin.

7) The LLPS paragraph in the discussion was weak - consider removing.

8) The speculation of 20S into foci does not add and, to my mind, detracts from the focus of the Discussion.

Preprint Review

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Summary:

All three reviewers agreed that establishing a link between a proteasome activator and heterochromatin stability was novel and intriguing. However, limited insight into the PA28-gamma mechanism of action (or possibly a new heterochromatin compaction mechanism) dampened reviewer enthusiasm. The reviewers offered many suggestions, including additional experiments, new controls, and structural changes to the Discussion, that we hope you find useful.

Reviewer #3:

The paper "Morphology and local connectivity of the plis de passage in the superior temporal sulcus" is an interesting and thoughtful paper which uses modern tractography methods quite skillfully to examine whether the finer features of gyrification are related to connectivity patterns. This is an understudied area of research in human MRI because it deals with inter-individual variability, and so is time consuming, not well-suited to existing analysis pipelines, and requires a high level of neuroanatomical expertise. The methods, which included impressive inter-rater reliability and a nice control condition, were well-suited to the question. I also very much appreciated the discussion section, which covered anatomical, historical, evolutionary, and developmental considerations quite effectively and with clarity of language. Overall, the paper is well thought out and executed and a joy to read!

Below are some comments that could improve clarity for the reader:

-Figure 2 is a bit difficult to understand - to clarify that a and b are from the first brain, and b and c are from the second brain, the authors might consider labeling them or putting them in boxes. It might be helpful to add some arrows to help illustrate the "pinching" they describe.

-End of results, p. 19, final paragraph. The authors write "Second, there was generally an increasing number of U-shape fibers from the anterior to the posterior part of the STS." I don't think the authors tested this, so I would rephrase this to say "on visual inspection, it appears that there was an increase in...". I would also replace the word "fibers" with "streamlines".

Reviewer #2:

A new characterization of the "plis de passage"(PP) is proposed. The interest of this new definition is demonstrated in a cortical area where a huge amount of variability exists, hence it is very difficult to study. The results shown are convincing. The new connection established between PP and U-fibers contributes to the understanding of the link between gross anatomy and connectivity.

Several questions for clarification:

1) The distribution of the number of PP in STS is given in the results. Did you try to match the PP across the subjects, to try to define a stable model? In terms of the location of the PPs, is a model possible or their positions span the whole main branch of STS in a continuum? Did you try to study the relationships between PP and sulcal pits?

2) Did you try to clarify whether all dense clusters of U-fibers correspond to PP across subjects? Due to the random selection of the extremities of the control PPs, such clusters with different trajectories (not necessarily facing each other) could be missed by your procedure in controls?

3) Is there a link found between the superficiality of a PP and the extent of the shift of the two extremities along the sulcus (the S and C shapes)?

Reviewer #1:

The study aims to improve the anatomical characterisation of STS plis de passage (PPs). Morphologically, the authors use the geometry of the surrounding surface to reveal deep PPs, which might be buried. Structurally, they explore associations with short-range u-shape connectivity across the two banks of the STS. This methodological advancements follow from previous work on the central sulcus (e.g. Zlatkina et al., 2016, European JNS; Catani et al., 2012, Cortex). The authors provide detailed characterisation of these anatomical features in 90 individuals from the HCP dataset, and focus their analysis in differences across the two hemispheres. But the study stops short of showing how this impacts functional organisation or behaviour. Overall, the methodological advancement offered here is incremental relative to other studies, and very little insight is provided about the impact of these morphological features and their variations on STS functional organisation. Considering the HCP offers high quality functional data, including tasks specifically relating to STS function, as well other highly related data (e.g. twins), I thought the present manuscript missed numerous precious opportunities to leverage the present findings into more significant impact and innovation.

Major Comments:

1) The abstract and introduction highlight the importance of studying inter-individual variability in PPs. But the results do not address this variability, as all the results are dedicated to inter-hemispheric differences. What is the significance of inter-individual structural variance, and how is it informed by experience (the introduction suggests that these folding patterns are determined in utero?)? For example, are they more similar in twins? Is there any clear evidence that it determines functional organisation and behaviour? Clinical symptoms? Also, are the inter-individual variations observed specific to the STS? Or do they reflect 'trait' like foldiness of the cortex? None of these questions are explored empirically, and as such, the present findings offer very little advancement to our understanding of STS function and functionality.

2) I'm not sufficiently qualified to determine, but I have to wonder if the 'control PPs' are suitable, considering they are much smaller than the 'true PPs'? Considering the probabilistic nature of the analysis and the fact that the experimenters were not blinded as to which aspect of the sulcus was 'true' or 'control', some more consideration should be given to the appropriateness of this control.

3) The u-shape analysis requires histological confirmation, as demonstrated by Catani et al. for the central sulcus.

4) Nonsignificant results (e.g. between hemispheres) require further consideration - are the two hemispheres truly similar, or is the study underpowered to find such differences? Bayesian statistics can inform this question.

5) I found the discussion to be overly speculative, and in particular the part relating to functional implications to be overly speculative, considering the very modest innovative contribution the current study offered.

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

Summary:

The reviewers very much appreciated the careful analysis, including very laborious manual segmentation. They agreed that the study provides a new level of detail of STS morphology that hasn’t been available to date. They also agreed that this characterisation has potential to support future research focusing on the inter-individual variability that is so common in this brain region. However, the study has not yet delivered on this promise, as the analysis is focused on inter-hemispheric differences across the group, without illuminating the impact of inter-individual morphological variability on the area's functional organisation or function.

Reviewer #3:

This is a very interesting paper that describes a novel zebrafish cardiac phenotyping pipeline consisting of high frequency echocardiography, cardiac magnetic resonance imaging (CMR) and micro-computed tomography (micro-CT). The work presented provides proof-of-principle that this suite of elegant techniques provides high resolution images of the adult fish heart. The concerns raised relate mainly to the adoption of this pipeline as a routine phenotyping method and the extent to which the data presented can be considered as a reference set for the field.

Pulsed wave Doppler tracings obtained from high frequency echocardiography are noted for their clarity and reproducibility. The authors took advantage of this, together with color Doppler, to identify abnormal blood flow jets in the alk5 mutant fish. These findings nicely show that echocardiography is a useful screening tool for evaluating mutants with unknown phenotypes or for those in which structural defects are anticipated.

Echocardiography is an established method for quantification of ventricular size and function in adult zebrafish when performed by experienced operators. As noted by the authors, echocardiographic images from a single fish can be collected within minutes and this technique can be used to evaluate large numbers of fish. The real question here is when to implement the complete pipeline and in which situations echocardiography (or one of the other techniques) might be adequate. The authors might consider making some recommendations about this. For example, echocardiography would be sufficient to evaluate adult fish that are expected to have cardiomyopathy phenotypes and completion of the extended imaging pipeline may not be necessary. Procedural tolerance, potential for serial assessment, throughput and cost also need to be considered, especially when substantial numbers of fish need to be evaluated. In order to better compare echocardiography and CMR for assessment of ventricular size and contractile function, echo data for these parameters needs to be included and a comparative analysis undertaken.

The wide range of heart rates in the echo data (58-143 bpm) is a concern and suggests that anesthetic and environmental factors are contributing to variability. The lower value of 58 bpm is notably unphysiological. These extremes of heart rate would confound cardiac assessment, particularly for ventricular size. The causes of these heart rate differences need to be identified, and at the very minimum, greater numbers of fish would need to be studied to be able to identify any biological differences between groups.

A major limitation is the relatively small numbers of fish that have been included in this study. Although looking at 10 WT fish and 12 mutants was sufficient to demonstrate the utility of these imaging methods, there was considerable variability for many of the cardiac parameters measured and the number of WT fish, in particular, is far too small to be robust as a reference data set. If this is an important goal of the paper, then more male and female WT fish of different ages need to be studied. Data also need to be provided for reproducibility, and inter- and intra-observer variability for measurement of cardiac parameters using the different methods.

Reviewer #2:

In this manuscript, the authors conducted phenotypic studies of a zebrafish adult alk5a/tgfr1 mutant by integrating different technologies, including echocardiography, MRI and microCT. They selected 10 WT and 12 alk5a mutants for their studies, and identified some mild phenotypes in OFT. They conducted correlation analysis among different parameters, and then selected fish with more severe phenotypes for further morphological characterization. The strength of the manuscript is optimization of novel technologies including MRI and microCT for cardiac studies, and their integration. However, there are some notable concerns as described below.

Major concerns:

1) There is excessively high variation in almost all parameters among different fish in the same group. For example, heart rate ranges from 58-143 bpm. It appears that adult zebrafish naturally exhibit high phenotypic variation in cardiac functions. However, the authors need to more carefully control their experimental conditions before reaching this conclusion. It has been reported that anesthesia and water temperature might affect cardiac functions in this animal model.

2) The experiments were not designed to deal with the excessively high variation. Fish from three different ages are phenotyped together as a single group, and the size of the group is small. This is a main weakness of the manuscript.

3) Fig. 3-figure supplement 1: contraction of the ventricle appears rather weak (difference between F' vs F" is small). Can you calculate ejection fraction? Is the EF significantly lower than EF in wild type fish that were obtained from high frequency echo or other technologies? Low EF might indicate that the fish is far from normal physiological condition, suggesting that the technology is premature for assessing cardiac function. Moreover, there is a huge difference in heart size between WT and mutant fish (F' vs G').

Reviewer #1:

This study by Benisimo-Brito and colleagues describes a comprehensive integration of functional imaging approaches for adult zebrafish cardiovascular phenotyping. The authors describe combined use of echocardiography, MRI and (ex vivo) micro-CT with light- and transmission electron microscopy to study alk5a-mutant zebrafish. They were able to identify multiple altered phenotypic parameters including abnormal hemodynamics (retrograde blood flow), compromised functional output, and morphological defects, including expanded outflow tract and altered atria and aortae. The authors were also able to nicely correlate the extent of morphological defects with function, across a highly variable range in severity of phenotypes.

This is an informative and elegant use of combined imaging platforms to study adult zebrafish; which has thus far been very challenging, given their opaque nature and the need for specialised adaptation of available clinical modalities. That said, use of some of these platforms has been applied previously for imaging adult zebrafish; for example, echocardiography and MRI (Gonzalez-Rosa et al., 2014; Koth et al., 2017) and micro-CT (most recently, Ding et al., 2019). The authors acknowledge this, but it remains the case that the technical novelty, as applied to functional cardiovascular imaging, is compromised. Instead, the strength here is in the combined, integrated use of multiple platforms. This study on the whole provides a very nice proof-of-principle, but it is unclear how this will be widely adopted by zebrafish laboratories elsewhere, given the need for significant high-end imaging facilities and appropriate in-house expertise. Moreover, the methodologies to adapt the platforms for zebrafish studies are not sufficiently well described herein to enable others to readily adopt.

Other specific comments:

1) The statement on page 8 needs qualifying. MRI has been used previously beyond generating static images in adult zebrafish: Koth et al., (2017) documented longitudinal imaging of live adult zebrafish during heart regeneration.

2) The monitoring of heart rate using self-gating (Figure 3, figure supplement 1C-C') is a nice addition - did the authors explore the use of telemetry probes to record the ECG, as this would be a novel addition to what has gone before?

3) Regarding the correlation analysis on page 10 the authors note 32 parameters. What are the prospects for applying machine learning/AI (eg. automated image analysis algorithms) here to enhance the number of parameters that can be recovered? This in turn would increase the depth of phenotyping and further inform the phenotypic-functional association.

4) The inherent variability of phenotypes between individuals is potentially a significant issue for basic studies, despite mapping to human variation in disease progression/outcome. Given the assumed relatively in-bred nature of the mutant background, why is there such variability and does this reflect on the sensitivity of the imaging? The authors note age and body size (page 11) as influencing variation; if they were to image fish of the same age (and sex) and within a narrow body size range is this variability reduced?

5) As under the general comment above, the methodology is insufficient in places for others to adopt the described imaging platform(s); for example, under echocardiography (page 19) the authors loosely describe a "bed made of modelling clay"- more details are required here and elsewhere to facilitate others utilising similar platforms.

6) Under the MRI procedure the authors decided to analyze specimens in a container without water flow, to reduce the imaging time to less than 20 minutes and consequently to maintain survival. This relatively short imaging time is reflected in the low resolution and somewhat suboptimal images shown in Figure 3C-D. Moreover, in the absence of water flow and gill perfusion it is unclear how any functional parameters obtained are physiologically meaningful? This approach renders the use of MRI more for 3D live imaging than for interrogating function. In the previous MRI study, by Koth and co-workers (2017), live adult zebrafish were placed under anaesthesia and physiological conditions, i.e. upright in water and with gills suitably perfused. This enabled imaging for several hours and with a 100% recovery rate and consequently, the resolution and image quality were higher and the functional parameters more physiologically relevant. The current MRI approach ought to be at least comparable in terms of quality of outputs as that which has gone before.

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

Summary:

The reviews highlight the value of imaging adult zebrafish to evaluate cardiovascular structure and function. However, they point out that each of the three imaging technologies has been reported before, and suggest that the manuscript would be strengthened by a more critical comparison between the imaging modalities. The reviewers also raised concerns about the value of the data as a reference for cardiac function parameters, given the small numbers of WT fish, variability in WT fish, and the lack of data for reproducibility, and inter- and intra-observer variability for the various cardiac parameters. Lastly, they felt that the platforms are highly technical and require significant resource and specialist insight into adaptation for use on zebrafish, thus making it unclear how it will be applicable more broadly and within other laboratories in the field.

Reviewer #3:

The article by Youssef G et al, focused on developing a Machine Learning system to use immunofluorescence data to detect metastatic cells in tumor stroma, which might be responsible for metastasis in case of OSCC. To detect single cells in the transition of EMT to MET they focused on EMT-Stem cells rather than only EMT phenotypes. They have shown that retention of epithelial marker EpCAM and stem cell marker CD24 and upregulation mesenchymal marker Vimentin can identify disseminating EMT stem cells in the tumor stroma. It is very well presented, well written and has high implication.

Comments to improve:

1) Strongly recommended to add the distribution of tumor status vs. proposed marker expression pattern. That is to show the distribution of EpCAM, CD24, Vimentin +/- in metastatic vs. other tumor status as mentioned in Supplementary figure 2. This might help you to establish these markers combination to follow a pattern in disease progression.

2) In all cell and tissue images add the scale.

3) For figure 3f, show enlarged picture of the single cell staining on the inset or add a separate panel to show only single cell staining.

4) Figure 4, the panel name or the font is too small to read, enlarge the font size (a, b, c, d, f).

5) Same problem with figure 6a, font size too small. In addition, in the heat maps, is it possible to add cluster names horizontally? Also for figure 6c, the cluster names are too small.

6) The EMT sub-populations are not associated with a spectrum of epithelial/mesenchymal genes expression (supplementary figure 5). The explanation is not very clear.

Reviewer #2:

The authors tackle the important and intractable question of the mismatch between the primacy of EMT in cell culture studies versus the rarity with which EMT is morphologically apparent in resected tumour tissues.

The early part of the study is convincing and well conducted, with identification of subpopulations of EMT cells with the ability to undergo MET, and associated marker profiles in flow cytometry.

They then develop an impressive multiplex assay for the identification of cells with the same profile in resected tumour material- a really promising approach bringing molecular findings into the context of primary tumour tissue.

The major issue that I have is in the application of this assay to tissues, and the subsequent AI analysis. Only one example of the putative invading population is shown (Fig 4C) and the stromal 'infiltrative' subpopulation is adjacent to a very flat and 'pushing' tumour/stroma boundary, with no apparent budding into the stroma. This would need to be addressed with several more examples and high-magnification H&E images. Furthermore, this is a major claim- namely that occult infiltrating EMT cells are commonly encountered in peritumoural stroma but can only be differentiated from somatic stroma by multiplex immunofluorescence- and it needs major evidence to back it up. What do these cells look like on H&E? Are they mesenchymal in their appearances on H&E? Can they be conclusively differentiated from other stromal constituents (eg myofibroblasts, plasma cells) immunohistochemically and/or morphologically? It could be that the power to predict metastatic status power is related to somatic stromal factors rather than EMT.

The AI prediction of metastatic status is compelling, but this fundamental point would need to be persuasively addressed in order to support the author's major claims. I do not feel qualified to comment upon the AI strategies used later in the study.

Reviewer #1:

This manuscript follows previous studies describing the existence of a subpopulation of mesenchymal-like cells (expressing Vimentin) that also express EpCAM and/or CD24 concomitant with the ability to undergo MET. These subpopulations appear to exist within oral squamous cell carcinoma (OSCC) cell lines and within primary tissues. The paper demonstrates that CD24 expression is requisite for plasticity and suggests that the presence of CD24+/EpCAM+/VIM+ cells in the stroma of OSCC tumors may be indicative of metastasis. Some whole genome transcriptome analysis was also done to determine differences between bulk, EMT restricted and EMT stem populations. Overall, the notion that specific cells have the plasticity needed to move between epithelial and mesenchymal states is intriguing, and the presumption that these cells contribute to metastasis seems logical. However, the work is still rather preliminary. Accordingly, it is difficult to make solid conclusions regarding the prognostic utility of this state or of what may regulate it.

Major comments:

The study uses a very small sample size (24 patients) for the test and validation cohorts. The study should be expanded to use a different set of patient samples for test and validation sets. Moreover, the utility of the stem-EMT signature should be tested using multivariate analyses.

In figure 4, it looks like CD24 is positive in the bulk of tumors (regardless of stage) and in skin. Is this specific? Also, there appear to be VIMENTIN/EPCAM/CD24 positive cells in the bulk of non-metastatic tumours. Can this be seen using sequencing? Overall, the images as presented are not overly convincing.

EMT stem versus restricted signatures should be validated using additional models. Also, greater evidence is required to determine how these cell fractions may differ. Are they sitting in different epigenetic states? Can trajectories be detected in human cancers, using single cell sequencing, for example? Finally, do they have different metastatic potentials?

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

Summary:

This manuscript was reviewed by experts in the areas of cancer stem like cells, EMT events and pathology. Overall, all of the reviewers were intrigued by the concepts underlying this paper. However, it seems that the work is validating the existence of an EMT-stem like population, whilst also attempting to formulate a clinical prognostic application for the existence of these cells. The function of these cells as metastatic drivers requires further exploration. Moreover, the pathological assessments must be improved upon. We hope that these comments are helpful.

Reviewer #3:

General assessment:

Futai and colleagues present an extremely elegant study in hippocampal CA1 slice cultures which combines cell type-specific expression of inhibitory synapse markers and conditional deletion of neurexins (Nrxn), knockdown of neuroligin3 (Nlgn3) and rescue experiments with defined splice variants of Nrxn by biolistic transfection with paired patch-clamp recordings. They find that synaptic transmission between inhibitory cholecystokinin(&VGT3)-positive interneurons (VGT3+) and CA1 pyramidal neurons depends specifically on the combination of presynaptic Nrxn1α with insert in splice site #4 and postsynaptic Nlgn3 without A1&A2 splice inserts.

Major concerns:

1.) The role of neurexins for transmission at the VGT3+ interneuron-to-CA1-pyramidal cell synapse remains unclear: The authors claim that Nrxn are important for the transmission at the VGT3+ synapse. However, I do not see the necessary experiment to substantiate such a general claim, for example, by comparing VGT3+synapses of control/undeleted to deleted NrxnTKO slices. Figure 5 rather shows that Nrxn is required to mediate the effect of overexpression of transfected Nlgn3Δ in CA1 neurons but this might be due to the overexpression itself. Thus, this effect would be more convincing if lack of Nrxn at VGT3+ synapses caused the opposite result on uIPSCs.

2.) The idea of "A Specific Neuroligin3-αNeurexin1 Code ..." implies to most readers that a direct interaction between these two molecules is involved because numerous biochemical papers in the field have used the term splice code to refer to a hierarchy of binding affinities between Nrxn and Nlgn variants. However, such preferential binding of αNrxn1+AS4 to Nlgn3Δ is neither shown in the manuscript, nor is it likely: The authors report that „αNrxn1+AS4 and βNrxn3-AS4 are the unique Nrxn genes expressed in VGT3+ neurons compared with PV+ and Sst+ neurons" (Figure 6 & 7) but demonstrate that of these two isoforms, only αNrxn1+AS4 transfected into VGT3+ interneurons mediated the effect of Nlgn3Δ overexpressed in pyramidal neurons (Figure 8). If binding or direct physical interaction was involved in the mechanism, βNrxn3-AS4 should have performed better than αNrxn1+AS4 because both the LNS5-EFG-LNS6 cassette and the insert in AS4 reduced the affinity. The surprise is shared by the authors themselves in the last paragraph of the Results section (p.12). At least to me, it appears that additional pre- or postsynaptic molecules, or a different mechanism altogether, are involved in mediating the effect of αNrxn1+AS4&Nlgn3Δ on VGT3+ synapses.

3.) To actually prove the specificity of the impact of Nlgn3Δ splice variant on inhibitory transmission from VGT3+ interneurons (Figure 2), an important control is missing: Another Nlgn3 variant, in which the A inserts are present, should be tested in the overexpression experiment. I do acknowledge that the authors compared different Nlgn3 variants in a recent paper (Uchigashima et al., 2020, JBC) in a related setting but no data exist for the proposed specificity of the Nlgn3Δ splice variant at VGT3+ synapses as far as I can see.

Reviewer #2:

Motokazu et. al., identified a specific Nlgn-Nrxn pair that regulates GABAergic synapse function in a subset of interneurons. This is a really interesting study, in which they use complicated techniques to dissect NLGN3 and NRXN function. The authors performed elaborate experiments from a single cell level to a tissue level that support their conclusions. Overall the data appear of high quality and reliable, but the study would benefit from some clarification of text and figures.

1) They are doing overexpression experiments on a WT background, so it's impossible to know if this effect is from homodimers of NLGN3 or heterodimers of NLGN3 with either NLGN1 or NLGN2. Perhaps the authors could discuss this caveat in the manuscript.

2) The authors see an increase in IPSCs when o/e NLGN3 in pyramidal neurons when Sst+ neurons were stimulated using optogenetics, whereas Horn and Nicoll did not see any changes. Horn and Nicoll used NLGN3A2 (including A2 insert) and in this study the construct doesn't have A1 or A2 insert. Perhaps they can discuss if the A2 insert can potentially be the culprit for the discrepancy if this is a potential confounding factor.

Additional comments:

1) In Fig. 1, please indicate Fig. 1C in the legends and make a box for the enlarged region in the lower magnificent image. It would be better to take out the busy alphabetic labels (E1, E2, E3, etc.).

2) Please increase text font size for the sample traces in all figures.

3) Authors showed quantitative graphs showing connectivity but definition of the connectivity is not well explained. More detailed explanation for how they quantified the connectivity should be addressed in methods.

4) In Fig. 5A, it would be more accurate to normalize KO Nrxns to WT Nrxns (set WT Nrnx as 100 %). The current Fig. 5A graph looks like Nrxn3 is not dominant in WT mice (~ 0.1 %) but the Fig. 7B graph shows Nrxn3 is dominant. Is there a discrepancy or perhaps add an explanation?

5) In Fig. 6J, the graphs for βNrxn1, αNrxn2, and βNrxn2 can go together with the image data in Figure S3.

6) Authors should explain and provide more information about the parameters of the Fig. 7A plot in the main text and legends. Correct the missed indication of Fig. 2B in the results text.

7) What if presynaptic αNrxn1+AS4 couples with Nlgn3 KD or NlgnTKO condition? What would be the expectation?

Reviewer #1:

Gaining insights into synapse-type specific regulatory mechanisms is of significant general interest. Yet, substantial concerns need to be addressed to improve this study.

Major points:

1) The authors state that Nlgn3 is particularly enriched at VGT3+ synapses. This is based on the colocalization of immunolabeling for Nlgn3 and each of the markers for three interneurons types as well as with the general inhibitory synapse marker VIAAT in Figure 1. If the intensity of marker labeling is not similar across interneuron types and if imaged fields are not comparable, the use of Nlgn3 co-labeling to assess a preferential localization is compromised. This concern is apparent e.g. in the example image for SST+ synapses with its weak labeling (Fig 1J) and needs to be addressed.

2) The data in Figure 5 support that the lack of presynaptic Nrxns 1/2/3 abolishes the potentiation effect of VGT3+ inhibitory synaptic transmission by Nlgn3Δ. To interpret these data, the authors also need to show whether the Nrxn triple KO in VGT3+ cells affects the amplitude of uIPSCs in postsynaptic control neurons expressing Nlgns at endogenous level.

3) The physiological findings are based on paired recordings where genetically labeled VGT3+ interneurons are stimulated. These cells are sparse and heterogeneously distributed in CA1 (Pelkey et al., Physiological Reviews 2017). Given the issues with Cre driver lines, a more thorough analysis is needed to establish that bona fide VGT3+ interneurons contribute to the reported findings. The scattered distribution of the individual RFP+ cells in single-cell RNAseq data (Fig 7a) adds to this concern about cell identity. The only relevant evidence presented is the IHC analysis in Fig S1A-C but it does not include probes for other interneuron types in the CA1 that shows the specificity of the VGT3+ label.

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

Summary

This study by Uchigashima et al. investigates to what extent Neurexin-Neuroligin interactions define synapse functions in an inhibitory microcircuit in the hippocampal CA1. The authors propose that Neuroligin-3 and Neurexin-1α regulate inhibitory synaptic transmission at synapses formed by vGluT3-positive (VGT3+) interneurons on CA1 hippocampal pyramidal neurons. This is based on (1) immunohistochemical data that localize Nlgn3 to synapses of VGT3+ interneurons, (2) the regulation of unitary inhibitory postsynaptic currents by Nlgn3 overexpression or knockdown in postsynaptic pyramidal neurons in organotypic slices from VGT3+ reporter mice, and (3) the finding that Nrxn deletion in VGT3+ interneurons prevents the effect of Nlgn3 overexpression in postsynaptic neurons. Single-cell RNA sequencing and in situ hybridization are presented to show that Nrxn1α and Nrxn3beta mRNAs are prominent Nrxn isoforms in VGT3+ interneurons, and Nrxn1α SS4 rescued Nrxn deletion effects. With some additional critical controls and a more careful interpretation of the presumed mechanism, the manuscript would make a highly interesting contribution to the field of synapse specification by synaptic cell adhesion molecules.

Author Response

Reviewer #1:

The manuscript by Mitchell et al. finds that the NAIP-NLRC4 inflammasome in mice is a critical host factor that controls intestinal infection with the human specific bacterial pathogen Shigella flexneri. The work suggests that Shigella is actively suppressing the human NAIP-NLRC4 inflammasome possibly using an T3SS effector protein, which does not recognize its substrate in mouse cells. The authors use this information to determine that B6 mice lacking the NAIP or NLRC4 inflammasome components are susceptible to Shigella infection and observe disease symptoms similar to Shigellosis in humans. In addition, 129 mice exhibit additional disease symptoms, and the authors suggest that loss of Caspase-11 in 129 mice is responsible for this phenotype.

The strengths of this manuscript include the introduction of a new mouse model that mimics Shigellosis, the demonstration that NAIP/NLRC4 activation is important for epithelial cell defense, and the potential of these findings to clarify aspects of human infectious disease caused by this pathogen. The manuscript is well presented, and the experiments are conducted with a high degree of rigor. Overall, this is an important contribution to the Shigella field and also has significant implications on our understanding of inflammasomes in host defense against pathogens.

Response: We thank the Reviewer for recognizing the impact and rigor of our work.

There are some weaknesses that should be addressed. Experimentally, it has not been directly demonstrated that IECs from NLRC4-/- mice undergo cell death (using biochemical markers). This is a critical aspect of the model.

Response: Prior work in the field (e.g., Sellin et al, 2014; Rauch et al, 2017) has already established that inflammasome activation in IECs results in their death and expulsion from the intestinal epithelium. We are currently working on showing this also occurs with Shigella but we have no reason to doubt that it does; our preliminary data indicate that Shigella-infected propidium iodide (PI)-positive cells are expelled from IEC monolayer cultures in an NLRC4-dependent manner. We intend to provide these data in a revised version of the manuscript.

In addition, it would be useful for the authors to evaluate bacterial burden over the time course in Figure 6. Although this is not absolutely necessary to support the manuscript conclusions, this information would greatly benefit the community that intends to use these mice in the future.

Response: This is indeed an experiment we plan to complete in the future. At present we are constrained by the numbers of available mice. We agree with the reviewer that the timecourse is not essential to establish the main conclusions of the present manuscript, and have thus prioritized other experiments.

There are also some discussion points about the mouse model that would enhance the overall impact of the work. For example, a more in depth discussion about the differences between human Shigella infection and the new model would be helpful. It is important to emphasize that the mouse model requires a much greater inoculum of the pathogen to induce disease and requires microbiota-deficiency to be effective. What are the implications of this finding on our understanding of human disease?

Response: Although it is often (correctly) stated that as few as 10-100 bacteria can infect humans with Shigella, there is actually considerable heterogeneity in the infectious dose. DuPont et al 1989 summarizes several human challenge studies in their Table 1, which shows that while 25-39% of humans exhibit symptoms after low dose infection (<200 CFU), 36-44% of humans are resistant to high doses (10^4-10^8 CFU). Therefore we do not consider the infectious dose in our mouse model to be out of the range of what is ‘normal’ in humans. Indeed, our new model may help us understand some of the factors that confer resistance to certain humans. We used a dose of 5x10^7 in our manuscript to ensure reproducible infection of all mice. However, in limited studies, we have observed disease in oral route infected, antibiotic pre-treated NAIP–NLRC4-deficient mice with 10^6 CFU (4/4 mice) and 10^5 CFU (2/3 mice). We are currently repeating these experiments, which we intend to include in a revised manuscript. We also agree with the reviewer that the infectious dose in humans vs. mice merits more discussion in a revised manuscript.

In lines 274-285 the authors present an either/or scenario in which either macrophage pyroptosis is required for IEC infection or inhibition of NAIP/NRLC4 pyroptosis in IECs is required for IEC infection. However, these scenarios are not mutually exclusive. For example, it is plausible that the extremely low burdens of Shigella required to infect humans (<100 CFUs) is due to the pathogen initially crossing the epithelial barrier (e.g. through M-cells) to infect macrophage, and then re-infection of IECs after macrophage pyroptosis. In this scenario, the NAIP/NLRC4 inflammasome could prevent further expansion of bacterial in IECs by eliminating the cell-to-cell spread that have been described by others. Importantly, the macrophage lifecycle stage may not be necessary in mice in which the microbiota has been removed and Shigella is delivered at a very high inoculum. While, additional ideas could be, and should be, put forth since the mouse model provides new insights or challenges an existing dogma in the field.

Response: We do clearly state in our manuscript (line 277) that our results do not directly address the question of whether Shigella might benefit from inflammasome activation in macrophages. In a revised version of the manuscript we will further expand on the discussion of the role of inflammasomes in macrophages and IECs to acknowledge multiple, non-mutually exclusive scenarios.

Reviewer #2:

Mitchell et al explore the role of NLRC4 in defending against Shigella infection by demonstrating that NLRC4 contributes to resistance to shigellosis in mice. Using in vitro assays, they first show that mouse but not human macrophages undergo NLRC4-mediated pyroptosis in response to Shigella infection despite an ability for both species to successfully detect Shigella NLRC4 agonists. They then demonstrate that C57BL/6 background mice, which normally resist shigellosis, become susceptible to infection when deficient in NAIPs or NLRC4. In parallel, 129 background mice develop more significant infection including intestinal bleeding. Furthermore, using a mouse line in which NLRC4 expression is restricted to intestinal epithelial cells (IECs), they show that IEC expression of NLRC4 is sufficient to resist shigellosis. Finally, using a known attenuated Shigella mutant, they demonstrate that their shigellosis model can mimic kinetics seen in humans.

Mitchell et al convincingly demonstrate both the importance of NLRC4 in protecting mice against Shigella and the utility of their mouse model for studying Shigella infections, both of which are significant and will push the Shigella field forward. There are mechanistic questions to be addressed in future studies beyond the current manuscript, attesting to the importance of the paper in opening up new areas in the field of research. In some places, the authors draw conclusions that reach beyond what is proven in the data, which should be addressed in text edits to the manuscript. In summary, this article presents an important new model for Shigella infection. The impact of the manuscript is the development of a mouse model with which to study Shigella infection in vivo.

Response: We thank the Reviewer for emphasizing the importance of our new shigellosis model for the field. We have addressed their comments below.

Major comments:

Many questions remain concerning why NLRC4-deficient THP1 cells still undergo pyroptosis. The authors provide evidence that Shigella activates PYRIN and/or AIM2 inflammasomes in humans, and that somehow mouse macrophages would fail to have this same detection. At face value, the data would suggest that humans are able to detect Shigella by Pyrin and AIM2, but for some reason these two inflammasomes are insufficient, and instead NLRC4 is required for in vivo defense. Then in mice, it would imply that everything is flipped - for some reason detection by Pyrin and AIM2 is not important, but now the bacteria can be detected by NLRC4 and this is important. The NLRC4 focused conclusions are consistent with the in vivo data, that NLRC4 in humans fails to detect, but NLRC4 in mice succeeds in detecting Shigella. However, the data that Pyrin and AIM2 in human cells successfully detect Shigella are inconsistent with the overall conclusions of the paper. I suspect that this is an artifact of THP1 cells, and that the in vivo situation in humans is that these two inflammasomes will fail to detect Shigella. There is published precedent from other infections where in vitro detection belies in vivo lack of detection (e.g. Listeria is detected by AIM2 in vitro, but probably not in vivo). It may be difficult to make direct comparisons between how inflammasomes act in THP1 cells as compared to BMMs, due to artifacts arising from the different origins and passage levels of the two cell types. It may be that the inflammasomes response is most important in IECs, as proposed by the authors, and that IECs may not express Pyrin or AIM2. There is evidence from publicly available IEC transcriptional profiles that IECs do not express Pyrin (Mefv) (Reikvam, doi: 10.1371/journal.pone.0017996), although this profile does show Aim2 expression in IEC. It is my understanding that BMMs do not express Pyrin unless they are strongly stimulated with some TLR agonist. As it stands, the in vitro data appear to contradict one of the main conclusions of the paper, because it would seem that human Pyrin and AIM2 inflammasomes can detect Shigella, and so these should compensate for NLRC4. The explanation as to why Pyrin and AIM2 are insufficient to compensate for NLRC4 evasion in human infection should be addressed at least in discussions of the data to explain the apparent discrepancy.

Response: The reviewer states that our claim that human PYRIN and AIM2 inflammasomes can detect Shigella in THP1 cells is “inconsistent” with the overall conclusion of our paper, which is that the NLRC4 inflammasome provides necessary defense of mouse intestinal epithelial cells. We do not agree that there is an inconsistency and indeed many of the points the reviewer makes in their comments fit with our view, so perhaps there is less disagreement than it might seem.

As the reviewer discusses, differences in inflammsome expression in humans vs. mice, and in IECs vs. macrophages vs. THP1 cells, and the kinetics of inflammasome responses, as well as several other factors, can easily account for the results we obtain. It appears that PYRIN is not well expressed in mouse IECs (Price et al. 2016), at least not uniformly at levels in all cells that are sufficient to confer protection. AIM2 is expressed in colonic IECs (Price et al. 2016), but it is not clear that it would be engaged in every infected IEC. For example, AIM2 detects bacterial DNA, which might only be released if the Shigella bacteria lysed in the cytosol. As noted by the reviewer, this may be a relatively rare event, as previously documented for AIM2 activation by Listeria-infected macrophages (Sauer JD et al, 2010). AIM2 activation may also be kinetically delayed in IECs. It appears instead that NLRC4 is the main inflammasome that can respond to Shigella in mouse IECs; thus loss of NLRC4 is sufficient to lead to susceptibility of mice. It remains possible that there is some functional AIM2 or PYRIN (or CASP11 or NLRP1B) in mouse IECs; thus, the further removal of these inflammasomes might lead to even greater susceptibility. Alternatively, a low level of activation mediated by these additional inflammasomes (perhaps in macrophages instead of in IECs) might even be necessary to produce the inflammation that causes disease symptoms.

In humans, consistent with our data in Fig. 1, we propose that the NLRC4 inflammasome is antagonized or otherwise evaded by Shigella. The reviewer wonders why PYRIN or AIM2 cannot compensate for NLRC4, and is suspicious that the activation of PYRIN/AIM2 we observe in THP1 cells is not representative of what would occur in vivo. Certainly we agree that THP1 cells are non-physiological and we do not attempt to make claims in the manuscript that our observation of AIM2/PYRIN activity in these cells means anything for human shigellosis.

The reviewer states: “the in vitro data [in THP1 cells] appear to contradict one of the main conclusions of the paper, because it would seem that human Pyrin and AIM2 inflammasomes can detect Shigella, and so these should compensate for NLRC4.” For all the reasons discussed above, we do not agree there is a contradiction. There are many reasons why PYRIN and AIM2 might function in THP1 cells (and possibly even human macrophages) but would not compensate for NLRC4 in IECs.

In sum, we agree that there is more to learn about which inflammasomes, if any, are activated by Shigella in human IECs, but given the many uncertainties, we do not feel it is fair to say that our results are internally contradictory. We will endeavor to discuss some of these points in a revised manuscript.

Reviewer #3:

Mitchell et al describe the development of a mouse model for shigella gastroenteritis, the lack of which has been a serious impediment to Shigella research. They identified a difference in recognition of shigella between human and mouse Naip/NLRC4 which contributes to the resistance of mice to Shigella gastroenteritis. They suggest that Shigella specifically inhibits human Naip/NLRC4 activation and that the difference between mice and human susceptibility to infection is due to differential inhibition. This was confirmed by the ability of NLRC4-/- mice can recapitulate human infection. Furthermore they show that it is inhibition of NAIP-NLRC4 in IEC that is required for infection to occur. This manuscript therefore describes a number of important findings and uses these to develop a very useful animal model of shigellosis.

We are grateful for the Reviewer’s comments and suggestions, and provide point-by-point responses below:

I have three suggestions that I believe would improve the manuscript:

1) Determine the inflammasome that causes cell death in Shigella-infected THP1's. WT Shigella infection did not induce pyroptosis of colchicine-treated (PYRIN inhibitor) AIM2-/- THP1 cells, indicating one or both of these inflammasomes is responsible for the cell death observed in shigella infected THP1 cells. Why not test these separately to determine which?

Response: We have now made AIM2/MEFV–/– THP-1 cells. Our preliminary finding is that cell death and IL-1B levels in these cells are impaired in response to Shigella infection. We intend to include these data in a revised manuscript.

2) Markers of inflammation during disease. Clinical features of the disease (diarrhoea, weight, CFU/organ, fecal blood) are described well. But since Shigellosis is an inflammatory disease, it would have been nice to have seen some inflammatory molecules/cytokine levels measured, in addition to clinical features. The authors did measure levels of MPO, but that was as a marker for neutrophil recruitment.

Response: We agree that additional readouts of inflammatory disease are warranted. We are planning to repeat our experiments and measure cytokines in the blood. We intend to provide these data in a revised manuscript.

3) Further refinement of the mouse model. The authors present the inhibition of human NAIP/NLRC4 as the main factor that affects the difference in infection between humans and mice but a high innolcum (5 x 10(7) cfu/mouse compared to approx. 100 cfu for humans) is still required in addition to streptomycin treatment. It is not discussed whether any refinement of these procedures was attempted or why such a high inoculum and streptomycin treatment is still required. Presumably microbiota differences in addition to naip-/nlrc4 is an important species specific determinant of infection, hence the streptomycin treatment. Why is such a high innoculum required?

Response: this comment is similar to one of the comments of Reviewer 1. As we state above, it is actually not entirely clear that the infectious dose for humans is consistently ~100 CFU. Indeed, there appears to be great variation, with some humans exhibiting resistance to doses more than 10^5 CFU. Although we used high inoculums in our experiments, this was just to ensure consistent infection of all mice. Preliminary experiments in which we reduce the dose suggests that, like some humans, some mice are also susceptible to lower doses (e.g., 10^5 CFU). Thus our model exhibits an infectious dose within the range of what is observed in humans and we do not feel there is a large discrepancy here, though it appears that we do not recapitulate the extreme susceptibility seen in some humans. We don’t find this particularly surprising as Shigella is a human-specific pathogen and it is likely that at least some of its virulence factors may not work well in mice. Instead, we think what is most surprising is that loss of one host defense component (NLRC4) is sufficient to produce disease symptoms that are strikingly similar to what is seen in humans. We acknowledge that one difference is the need for streptomycin in our model. Clearly this suggests, as the reviewer states, that the microbiota can influence susceptibility. This is a well-described phenomenon with many enteric pathogens and it will be of interest in future studies to determine what components of the microbiota afford protection in our model.

Reviewer #3:

Mitchell et al describe the development of a mouse model for shigella gastroenteritis, the lack of which has been a serious impediment to Shigella research. They identified a difference in recognition of shigella between human and mouse Naip/NLRC4 which contributes to the resistance of mice to Shigella gastroenteritis. They suggest that Shigella specifically inhibits human Naip/NLRC4 activation and that the difference between mice and human susceptibility to infection is due to differential inhibition. This was confirmed by the ability of NLRC4-/- mice can recapitulate human infection. Furthermore they show that it is inhibition of NAIP-NLRC4 in IEC that is required for infection to occur. This manuscript therefore describes a number of important findings and uses these to develop a very useful animal model of shigellosis.

I have three suggestions that I believe would improve the manuscript:

1) Determine the inflammasome that causes cell death in Shigella-infected THP1's. WT Shigella infection did not induce pyroptosis of colchicine-treated (PYRIN inhibitor) AIM2-/- THP1 cells, indicating one or both of these inflammasomes is responsible for the cell death observed in shigella infected THP1 cells. Why not test these separately to determine which?

2) Markers of inflammation during disease. Clinical features of the disease (diarrhoea, weight, CFU/organ, fecal blood) are described well. But since Shigellosis is an inflammatory disease, it would have been nice to have seen some inflammatory molecules/cytokine levels measured, in addition to clinical features. The authors did measure levels of MPO, but that was as a marker for neutrophil recruitment.

3) Further refinement of the mouse model. The authors present the inhibition of human NAIP/NLRC4 as the main factor that affects the difference in infection between humans and mice but a high innolcum (5 x 10(7) cfu/mouse compared to approx. 100 cfu for humans) is still required in addition to streptomycin treatment. It is not discussed whether any refinement of these procedures was attempted or why such a high inoculum and streptomycin treatment is still required. Presumably microbiota differences in addition to naip-/nlrc4 is an important species specific determinant of infection, hence the streptomycin treatment. Why is such a high innoculum required?

Reviewer #2:

Mitchell et al explore the role of NLRC4 in defending against Shigella infection by demonstrating that NLRC4 contributes to resistance to shigellosis in mice. Using in vitro assays, they first show that mouse but not human macrophages undergo NLRC4-mediated pyroptosis in response to Shigella infection despite an ability for both species to successfully detect Shigella NLRC4 agonists. They then demonstrate that C57BL/6 background mice, which normally resist shigellosis, become susceptible to infection when deficient in NAIPs or NLRC4. In parallel, 129 background mice develop more significant infection including intestinal bleeding. Furthermore, using a mouse line in which NLRC4 expression is restricted to intestinal epithelial cells (IECs), they show that IEC expression of NLRC4 is sufficient to resist shigellosis. Finally, using a known attenuated Shigella mutant, they demonstrate that their shigellosis model can mimic kinetics seen in humans.

Mitchell et al convincingly demonstrate both the importance of NLRC4 in protecting mice against Shigella and the utility of their mouse model for studying Shigella infections, both of which are significant and will push the Shigella field forward. There are mechanistic questions to be addressed in future studies beyond the current manuscript, attesting to the importance of the paper in opening up new areas in the field of research. In some places, the authors draw conclusions that reach beyond what is proven in the data, which should be addressed in text edits to the manuscript. In summary, this article presents an important new model for Shigella infection. The impact of the manuscript is the development of a mouse model with which to study Shigella infection in vivo.

Major comments:

Many questions remain concerning why NLRC4-deficient THP1 cells still undergo pyroptosis. The authors provide evidence that Shigella activates PYRIN and/or AIM2 inflammasomes in humans, and that somehow mouse macrophages would fail to have this same detection. At face value, the data would suggest that humans are able to detect Shigella by Pyrin and AIM2, but for some reason these two inflammasomes are insufficient, and instead NLRC4 is required for in vivo defense. Then in mice, it would imply that everything is flipped - for some reason detection by Pyrin and AIM2 is not important, but now the bacteria can be detected by NLRC4 and this is important. The NLRC4 focused conclusions are consistent with the in vivo data, that NLRC4 in humans fails to detect, but NLRC4 in mice succeeds in detecting Shigella. However, the data that Pyrin and AIM2 in human cells successfully detect Shigella are inconsistent with the overall conclusions of the paper. I suspect that this is an artifact of THP1 cells, and that the in vivo situation in humans is that these two inflammasomes will fail to detect Shigella. There is published precedent from other infections where in vitro detection belies in vivo lack of detection (e.g. Listeria is detected by AIM2 in vitro, but probably not in vivo). It may be difficult to make direct comparisons between how inflammasomes act in THP1 cells as compared to BMMs, due to artifacts arising from the different origins and passage levels of the two cell types. It may be that the inflammasomes response is most important in IECs, as proposed by the authors, and that IECs may not express Pyrin or AIM2. There is evidence from publicly available IEC transcriptional profiles that IECs do not express Pyrin (Mefv) (Reikvam, doi: 10.1371/journal.pone.0017996), although this profile does show Aim2 expression in IEC. It is my understanding that BMMs do not express Pyrin unless they are strongly stimulated with some TLR agonist. As it stands, the in vitro data appear to contradict one of the main conclusions of the paper, because it would seem that human Pyrin and AIM2 inflammasomes can detect Shigella, and so these should compensate for NLRC4. The explanation as to why Pyrin and AIM2 are insufficient to compensate for NLRC4 evasion in human infection should be addressed at least in discussions of the data to explain the apparent discrepancy.

Reviewer #1:

The manuscript by Mitchell et al. finds that the NAIP-NLRC4 inflammasome in mice is a critical host factor that controls intestinal infection with the human specific bacterial pathogen Shigella flexneri. The work suggests that Shigella is actively suppressing the human NAIP-NLRC4 inflammasome possibly using an T3SS effector protein, which does not recognize its substrate in mouse cells. The authors use this information to determine that B6 mice lacking the NAIP or NLRC4 inflammasome components are susceptible to Shigella infection and observe disease symptoms similar to Shigellosis in humans. In addition, 129 mice exhibit additional disease symptoms, and the authors suggest that loss of Caspase-11 in 129 mice is responsible for this phenotype.

The strengths of this manuscript include the introduction of a new mouse model that mimics Shigellosis, the demonstration that NAIP/NLRC4 activation is important for epithelial cell defense, and the potential of these findings to clarify aspects of human infectious disease caused by this pathogen. The manuscript is well presented, and the experiments are conducted with a high degree of rigor. Overall, this is an important contribution to the Shigella field and also has significant implications on our understanding of inflammasomes in host defense against pathogens.

There are some weaknesses that should be addressed. Experimentally, it has not been directly demonstrated that IECs from NLRC4-/- mice undergo cell death (using biochemical markers). This is a critical aspect of the model. In addition, it would be useful for the authors to evaluate bacterial burden over the time course in Figure 6. Although this is not absolutely necessary to support the manuscript conclusions, this information would greatly benefit the community that intends to use these mice in the future.

There are also some discussion points about the mouse model that would enhance the overall impact of the work. For example, a more in depth discussion about the differences between human Shigella infection and the new model would be helpful. It is important to emphasize that the mouse model requires a much greater inoculum of the pathogen to induce disease and requires microbiota-deficiency to be effective. What are the implications of this finding on our understanding of human disease? In lines 274-285 the authors present an either/or scenario in which either macrophage pyroptosis is required for IEC infection or inhibition of NAIP/NRLC4 pyroptosis in IECs is required for IEC infection. However, these scenarios are not mutually exclusive. For example, it is plausible that the extremely low burdens of Shigella required to infect humans (<100 CFUs) is due to the pathogen initially crossing the epithelial barrier (e.g. through M-cells) to infect macrophage, and then re-infection of IECs after macrophage pyroptosis. In this scenario, the NAIP/NLRC4 inflammasome could prevent further expansion of bacterial in IECs by eliminating the cell-to-cell spread that have been described by others. Importantly, the macrophage lifecycle stage may not be necessary in mice in which the microbiota has been removed and Shigella is delivered at a very high inoculum. While, additional ideas could be, and should be, put forth since the mouse model provides new insights or challenges an existing dogma in the field.

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

Summary

In this manuscript, the authors introduce a new mouse model of Shigellosis, provide evidence for NAIP/NLRC4 activation as being important for epithelial cell defense, and apply these findings to observations made in humans infected by this pathogen. These are important findings and provide an opportunity to further advance the field in ways not previously possible. However, there are areas where the in vitro and in vivo data presented contradict each other, and there are inconsistencies with previously published work by the authors. In addition, with the development of the new mouse model being a major highlight of this manuscript, significantly more detail and discussion must be added to explain this mouse model.

Reviewer #3:

Luo and colleagues use a combination of viral tracing and targeted neuronal manipulation tools to dissect the role of GABAergic retinal ganglion cells (RGC) in mediating aversive responses to looming visual stimuli. This paper reports the existence of a population of GABAergic RGCs projecting to the superior colliculus (SC). These conclusions were based on a set of retrograde and anterograde tracing experiments in two mouse lines that putatively label GABAergic neurons (vGAT-Cre and GAD2-Cre). Targeted ablation of the GABAergic RGCs compromised looming-triggered escape behavior, which suggests a possible involvement of the superior colliculus projecting GABAergic RGCs in mediating the looming-evoked flight response. Although these findings could provide important insights into the neural circuitry that mediates aversive behaviors, tracking the origin of these circuits back to the retina, we have major concerns with the paper in its current format.

The current data set raises three major concerns that need to be addressed.

-First, the specificity of the labelling strategies is insufficient to make the current claims. The viral tools used to kill or manipulate RGCs are not specific for either RGCs (shRNA experiment), or SC-projecting RGCs (DTA experiments), and hence do not support the pathway specific claims in the manuscript. Also, the presented data raises questions as to whether all labeled neurons in these mouse lines are functionally inhibitory in the adult retinal.

-Second, the behavioral tests performed do not check for effects of killing GABAergic RTGCs on thalamic dependent visual processing.

-Third, the presentation of the data makes it very difficult for the reader to ascertain the claims made by the authors. In addition, the writing needs major revision.

Major concerns:

1) Lack of specificity of the labelling strategy. The authors claim that GABAergic RGCs mediate escape responses via GABA release in the SC. The current data does not support this claim since none of the used tools is specific enough. To maintain these claims, the authors need to either test if the SC-projecting GABAergic RGCs have no (or minimal) collateral projections to other targets or if the ganglion cells that collaterally project to other brain areas are not involved in the aversive behavior being tested. Further, it is necessary to narrow down the cell populations affected by their manipulations and to show that GABA-release by RGCs is involved in the tested escape behavior.

a. We are concerned that the mouse lines used, in particular the vGAT line, do not label cells that are functionally GABAergic in an adult animal. The gene coding for vGAT, Slc32al, is found in amacrine cells, but not in RGCs (Siegert et al. 2011). In Figure 1, the GABAergic positive cell bodies are not confirmed as being RGCs, i.e. having an axon. In Figure S2 the authors do not differentiate between GAD2 and vGAT.

b. The viral tools used for ablating SC-projecting GABAergic neurons are not specific for the SC, but will also label very common collaterals to brain targets other than the SC including the thalamus (e.g. Ellis et al., 2016).

c. The shRNA approach to knock down vGAT expression removes vGAT from all types of vGAT-expressing retinal neurons, not only RGCs. It is very likely that vGAT-expressing amacrine cells are affected by this manipulation.

d. Please remove the claims that the behavior is mediated through the PBGN pathway. The transsynaptic HSV approach to label multisynaptic targets of GABAergic RGCs does not support this claim. It is unknown whether the axon terminals in the PBGN labelled in these experiments stem from the SC. Also, other SC-targets that are known to mediate escape, such as the PAG (Evans et al. 2018), are also labelled in these experiments.

2) Inadequate test of visual circuitry function. The approaches used by the authors to test for different aspects of visual processing focus on a few known reflex pathways, but do not directly test for thalamic/cortical dependent visual behaviors, i.e. image formation. This needs to be changed in the description of the tests and the interpretation of the results. The description in the figure itself is good, but the conclusions drawn are misleading. The authors use four different approaches to test for visual function and draw the following conclusions:

a. Looming-evoked escape is decreased in two experimental conditions where vGAT-expressing neurons are impaired. The authors conclude that GABAergic RGCs projecting to the SC mediate escape. As described in point 1, these experiments are not specific enough to make those claims.

b. Electroretinograms (ERG) have unchanged a- and b- waves, which leads to the claim that GABAergic RGCs are not necessary for normal retina function. This is misleading as the largest contributors to an ERG signal are the photoreceptors and bipolar cells (e.g. Smith, Wang ... and Trembley, 2014).

c. Optokinetic reflex is unchanged after vGAT ablation. The authors conclude from these experiments that image formation is independent of GABAergic RGCs. This approach only tests for very specific retinal projections, probably to the AOS, but does not test for the function of the LGN-cortex projections, which form the major image formation pathway.

3) Insufficient data presentation. The currently presented histological sections are not sufficient to draw the same conclusions as the authors. In addition, several of the experiments lack clear quantifications to back up the claims in the text. The authors need to provide complete and readable pictures, and add quantifications to support their claims.

a. The role of PV+ SC neurons in the present study is unclear. The authors do not provide adequate evidence that GABAergic RGC-recipient neurons in the colliculus are PV+ (line 196). The referenced figure (Fig. 4B) shows only a single example of a staining. Quantification of the % of labeled neurons that are PV positive is required to strengthen this claim.

b. Please provide complete images of the histology at a readable size for all figures and add outlines of brain areas where necessary. Importantly, in Fig. 1G it looks as if the RGC axon terminals are only present in the intermediate layers of the medial SC. Since it is claimed that the GABAergic RGCs include many different types, one would expect axon terminals across all depths of the superficial SC.

c. The re-analysis of the published scRNA results from Rheaume et al 2018 should be made clear in the manuscript. Currently there is no information in the main text about the underlying data set or analysis.

d. It is not clear in the text what the differences/similarities are between vGAT- and GAD2-Cre mouselines. Please make clear in the narrative and conclusion how each mousseline was used.

Reviewer #2:

In this study, Luo et al. report that GABAergic retinal ganglion cells projecting to the superior colliculus (spgRGCs) drive defensive responses to looming. Previous studies demonstrated that neurons in the superior colliculus (SC) mediate behavioral responses to looming. Most of the >40 ganglion cell types in mice project to SC. Which of these convey the relevant looming signals from the retina is unclear. Most ganglion cells express VGLUT2 and release glutamate from their axon terminals, but anatomical evidence suggested that a subset of ganglion cells may contain GABA. Furthermore, a recent study (Sonoda et al., 2020) showed that some intrinsically photosensitive retinal ganglion cells are labeled in Gad2-Cre mice and reduce the light sensitivity of photoentrainment and pupillary light responses.

Here, Luo et al. find that intravitreal injections of Cre-dependent adeno-associated virus (AAVs) reporters in Vgat-Cre and Gad2-Cre transgenic mice label ganglion cell axons in many retinorecipient brain areas, including SC. The authors reanalyze a published single-cell RNA sequencing dataset (Rheaume et al., 2018), which suggest that subsets of ganglion cells of most types express Gad2. Using optogenetics, the authors confirm that activation of Gad2-Cre- and Vgat-Cre-positive ganglion cells elicit inhibitory postsynaptic currents (IPSCs) in SC neurons. Based on retrograde tracing, the authors suggest that these ganglion cells belong to a variety of types. Next, Luo et al. demonstrate that the deletion of spgRGCs abolishes defensive responses to looming stimuli and looming-evoked cFos expression in SC. The authors illustrate the selectivity of these effects by showing that electroretinograms, optomotor responses, and pupillary light responses are unaffected by spgRGC deletion. Finally, the authors use an AAV-based shRNA strategy to knock down Vgat in the retina and show that this abolishes looming responses.

Overall, this study reports a surprising and potentially transformative finding (i.e., that a small subset of many RGC types uses GABA to drive looming responses in SC and behavior). The authors leverage a wide range of techniques to study spgRGCs, but some results and interpretations are confusing, and some conclusions are insufficiently supported evidence.

Specific comments:

1) The Vgat knockdown experiments are critical to show that GABAergic transmission matters. The current strategy targets all Vgat-expressing neurons in the retina. The vast majority of these are amacrine cells. Silencing amacrine cells will likely have widespread effects among ganglion cells. The authors should use a dual AAV strategy similar to the one they employed for DTA to restrict Vgat-shRNA expression to spgRGCs and show that ganglion cells' responses to looming are unchanged.

2) Previous studies show that activation of SC neurons (particularly PV+cells) promotes defensive responses to looming, and the cFos labeling in this study suggest that spgRGCs activate SC neurons. Yet, optogenetics experiments indicate that spgRGCs elicit IPSCs in SC neurons. These findings seem at odds. Although the authors show that some spgRGCs elicit a mixture of EPSCs and IPSCs, the Vgat knockdown experiments suggest that the GABAergic transmission mediates looming signals and elicits behavioral responses. The authors should characterize looming responses in SC by electrophysiology or optical recordings (as they have done in previous studies) to clarify the contributions of spgRGCs.

3) Details of the cFos experiments were missing. The authors should compare cFos labeling and changes in cFos labeling after spgRGC ablation between looming and other visual stimuli, to discern the specificity of these effects.

4) The characterization of spgRGC types is superficial. The authors should show patch-clamp recordings from a small number of RGCs, which seem to encompass a variety of types. The authors should record light responses characterization (incl. responses to looming stimuli) and reconstruct the morphology of a larger number of ganglion cells to classify types in line with other studies (e.g., Bae et al., 2018, Reinhard et al., 2019).

Reviewer #1:

The goal of this study is to identify the retinal ganglion cells that mediate the flight response of mice to a looming stimulus. The candidate they focus on are a subset of RGCs that release GABA at synapses with a particular subtype of neuron in the SC that was previously implicated in this behavior.

The impact of the paper is limited for two main reasons. First, there was a paper using a similar mouse line that was just published (Sonoda et al, Science 2020) that revealed that GABAergic intrinsically photosensitive RGCs shape several of the non-vision forming behaviors in mice (such as photoentrainment of circadian rhythms and the pupillary light reflex). The authors may not have been aware of this work when they submitted this paper but now that it is published, the authors need to more rigorously compare their results to that study.

Second, it is not at all clear that the authors have identified a subtype of RGCs that mediate the looming responses. Rather their data (particularly Figure 3) seems to argue that a lot of different RGC subtypes have GABA. So the model is that the 13% of multiple RGC subtypes project to PV cells in the SC and together they mediate the looming response?

Finally, there is a lack of quantitative description of many of the experiments that undermines many of the conclusions the authors want to make. These are described explicitly below.

1) The authors make use of both a GAD2-Cre and vGAT-Cre to label cells in the retina, apparently the results from the two lines are combined throughout the paper. The authors need to verify that the same cells are labeled for each line.

2) In Figure 1, the authors show beautiful images of their labeling but the quantification of RGCs that express GABA is not described. In the mouse retina 50% of the cells in the ganglion cell layer are amacrine cells. They do show some labeling in the optic nerve so clearly some RGCs are labeled but there is no way to know how many. Rather the authors rely on a re-analysis of an RNA-seq data set to estimate that 13% of RGCs across all subtypes! Of course, protein levels and not transcripts are what matter for this sty so at least a co-stain for a RGC marker would strengthen the finding. This would also resolve the issue brought up in point 1 above.

3) Figure 2. The authors use optogenetics to characterize the synaptic connections with target neurons in the SC. Again, there is a surprising lack of quantification. In Figure 2C they show light activation ChR induces an inward current but they don't say how many times they do that experiment. Most experiments are done in TTX + glutamate receptor blockers to isolate GABAergic currents but there is a subset in which the igluR blockers are absent and excitatory currents were detected. The authors need to clarify in what percent of neurons did they see GABAergic currents and in what percentage excitatory currents and in what percentage did they see both? Basically, the authors need to clarify if these RGCs are releasing both GABA and glutamate. This is critical for interpreting the experiments in which they kill this population of neurons and inhibit the behavior.

4) Figure 3. The authors use retrograde labeling to begin to identify the GABAergic RGC subtypes that project to SC. Again, the quantification is lacking - what percent of SC projecting cells were positive for GABA? It is really a confusing result because they find an array of RGC subtypes that seem to express GABA. Hence it does not appear that one type of RGC projects to SC but rather all types - but just the subset that release GABA along with glutamate. 5) Figure 4AB appears quite impressive but the logic is not clear. Does Herpes simplex virus work as an anterograde transsynaptic virus? More explanation is required. Again, there is no quantification of the results - just examples of single neurons found in several retinorecipient brain regions.

(Note: Figures 4C-G are impressive - killing this subpopulation of cells seems to eliminate the response to looming stimuli while other visually guided behaviors are retained.)

6) Figure 5H - this is a difference with the Sonoda paper - they find an effect on PLR when they reduce GABA release from ipRGCs.

7) Figure 6 - the authors use RNAi to reduce vGAT expression in spgRGCs and this also impacts behaviors. There are many controls that need to be done here primarily showing the glutamate release is normal. Otherwise this could just be a synaptic transmission deficit.

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 2 of the manuscript.

Summary:

This study reports that a small subset of different RGC neuron types that release GABA at synapses with a particular class of neurons on the super colliculus mediate the looming responses in mice. This result is potentially highly significant and as noted by one reviewer, potentially transformative in our thinking of how RGC cell types mediate behaviors. However, all three reviewers agree that many of the conclusions are insufficiently supported by the evidence. The reviewers offer many details for necessary experiments and clarifications that need to be made in order for the authors to be able to reach their conclusion.

Reviewer #3:

The bHLH transcription factor Atoh7 has been studied as a critical regulator of retinal ganglion cell (RGC) generation in several species but, as the authors detail in the Introduction, it is not clear how or at what stage it acts. For example, some (most) data suggest it is required to specify the RGC fate in precursors, while other data suggest it may be required for RGC differentiation and/or survival following their generation. Here, Brodie-Kommit et al. use a well-established method to distinguish these possibilities, deleting Atoh7 in the absence of Bax, a powerful proapoptotic gene. Both naturally occurring and genetically-provoked apoptosis of many neuronal types, including RGCs, have been shown to be prevented in Bax mutants, which are viable and generally healthy.

The authors show that RGCs survive and are functionally normal in the absence of both Atoh7 and Bax, but few axons leave the retina, so of course light-induced behaviors are greatly decreased. Single cell RNAseq demonstrates a delay in RGC differentiation in the absence of Atoh7 (with or without Bax) and a variety of gene expression changes.

As expected for a paper from two superb laboratories, the work is done to the highest standard and uses the best available methods. The result that blocking apoptosis rescues RGCs in the absence of Atoh7 is important and should help resolve controversies about its role, providing a strong argument against what is likely still the best-accepted model.

On the other hand, the paper does not go far beyond that simple result: it shows what Atoh7 does not do, but not what it does do, either to the RGCs that express it or to the RGCs that do not express it but nonetheless require it for survival. The physiological and histological data largely back up the survival result; the behavioral defects are sort of trivial once one knows that RGC axons fail to reach the brain; and the RNAseq data do not lead to substantial novel insights that shed light on either the presumably cell-autonomous or the clearly cell-nonautonomous mechanisms.

Reviewer #2:

In the present manuscript the authors reveal that RGC differentiation is largely rescued in the absence of Atoh7 when the pro apoptotic gene Bax is also removed in the developing retina. These rescued RGCs show some proper physiological responses but fail to develop proper connections to the brain. Retina vasculature is also affected by the absence of Atoh7 even when RGCs are "rescued". Finally by single cell analysis they reveal that Atho7 is required for proper timing of RGC differentiation but the expression of major markers for RGC can be independent from Aoh7 transcriptional activity. The paper is based on a series of very elegant genetic experiments and the single cell analysis is particularly illuminating in this context.

Major Points:

Cell death is only one of the RPC possible fates in the absence of Atoh7. Indeed the author and a vast amount of literature showed that in the absence of Atoh7 more adopt photoreceptor precursor fate among others. Is the block of apoptosis by Bax inactivation reducing this "ectopic differentiation phenotype" in addition to RGC fate restoration?

Linked to the previous point, does the single cell data reveal why some progenitors die in the absence of Atho7 while others change fate?

The authors should discuss this point in more detail.

Reviewer #1:

This manuscript challenges the notion that the transcription factor Atoh7 is required to confer neurogenic retinal progenitors the competence of generating retinal ganglion cells (RGCs), the first-born neurons of the retina. This idea is based on the evidence that Atoh7 inactivation in mice causes the loss of the majority of RGCs. Here the authors have generated a Atoh7Cre/Cre;Bax-/- mouse line to ask what happens if apoptotic cell death is prevented in Atoh7 null mice. Using a number of RGC markers, they show that in the adult retina a large number of RGCs are no longer lost and are functionally connected with other retinal cell types as the retinas generate light driven photic responses. However, the RGCs of the Atoh7Cre/Cre;Bax-/- mice cannot connect with brain targets as the axons (when present) do not exit the optic disk but grow in a disorganized manner within the fibre layer. As an additional feature, the hyoloid artery does not regress. In Atoh7Cre/Cre;Bax-/- embryos, RGC generation is delayed as determined by analysis of single cell RNA-seq. The authors conclude that Atoh7 is required for RGC survival but dispensable for their specification.

This is an interesting study that adds up to the existing literature related to the role of Atoh7 in RGC generation/differentiation. However, the conclusion seems rather stretched: do the cells generated in the absence of Atoh7 and Bax really have a (full) RGC identity as claimed in the title? Is the specification of ALL RGC really independent of Atoh7? Conclusions should be toned down and alternative interpretations should be offered. Indeed, preventing apoptosis does rescue the full number of RGCs (see for example melanopsin positive cells). The lack of Isl1 in Fig. 4 and the low number of Brn3a+ cells in Fig. S6 is rather striking and suggests more than a delay. Thus, at least a subset of RGCs seems to require Atoh7, likely early born RGCs. There are several studies indicating that RGCs secrete factors that regulate their own number (GDF11, Kim et al., 2005, Science, as an example). Lack of this feed-back at early stages may favour the generation of RGCs that are not full Atoh7-dependent, creating an imbalance between Atoh7-dependent (early) and Atoh7-independent (late) RGCs.

The second problem that remains unanswered is related to the "identity" of the RGCs present in Atoh7Cre/Cre;Bax-/- embryos. Are they really bona fide RGCs? These cells cannot connect properly with their brain target nor secrete the putative factors needed to induce hyaloid artery regression. These defects could perhaps be explained by asynchrony (cells are generated late to read the axon guidance cues, for examples) but they may also be interpreted as lack of full identity.

The authors need to consider these possibilities and further address the related points below:

1) The difference in cell number detected with RBPMS and Isl1 is puzzling (Fig. 1). Isl1 recognises RGCs but also amacrine cells, which should be increased in absence of Atoh7. How do the authors explain that Isl1+ cells are less than the RBPMS+ ones in Atoh7Cre/Cre;Bax-/- mice.

2) The sentence "Brn3b-positive ipRGCs differentiate normally in the absence of Atoh7" is an overstatement. Only 35% of them do, the others are presumably lost. Furthermore, the presence of a cell specific marker does not ensure that the cells are fully differentiated.

3) Line 435. Presumably a sentence describing the response of RGC in Atoh7Cre/Cre;Bax-/- is missing.

4) Lines 506-509. Failed vasculature regression: the authors state "...implies that Brn3b and Isl1 may activate expression of secreted factors that drive vascular regression". If this is the case why in Atoh7Cre/Cre;Bax-/- retinas the hyaloid artery is still present? The retinas do express levels of Isl1 and Brn3b so that these factors should be present.

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

Summary:

The three reviewers agree that the study is very elegant and well performed. However, they also find that the conclusions are rather stretched and there is no clear demonstration of what Atoh7 is needed for. Major concerns relate to the real identity of the rescued cells and the claimed independence of RGC specification from Atoh7. Unfortunately, the RNAseq data do not illuminate this issue or solve the cell-autonomous and non cell-non-autonomous mechanisms that are at the basis of the present observations.

Reviewer #2:

The focus of this paper is the zinc metalloprotease ADAMTS-5. This protein has received attention as a therapeutic target for the treatment of degenerative joint diseases such as osteoarthritis. The primary effort is devoted to the development of non-zinc chelating exosite type inhibitors. The authors have previously identified exosites in hypervariable loops that are required for or proteolysis of both aggrecan and versican. Targeting these sites with the hope of selectivity is certainly a good approach. The authors used the glycosaminoglycan (GAG) feature of substrates to build in exosite affinity. To this end the authors probed ADAMTS-5 with a small library of GAG-mimetic glycoconjugated arylsulfonamides.

With some minimal SAR, the authors were able to achieve some selectivity of ADAMTS-5 over ADAMTS-4 and some increase in potency over other inhibitors they have developed. They report IC50 values with the most potent (molecule 4b) at 9.4±2.8 µM. Some further SAR to more fully understand exosite binding (4b did not inhibit a peptide cleavage assay) did not lead to a more potent inhibitor. Further characterization of 4b inhibitory activity was carried out looking at synergism with a known zinc chelating inhibitor and some molecular docking studies. The docking studies led to experiments mutating residues that were thought to involve inhibitor binding. The results largely supported the in silico predictions.

Overall the reported results advance the idea that selective inhibitors of ADAMTS enzyme that are not dependent on zinc coordination are possible; however, in the absence of more detailed studies of inhibition in cells and potentially in animals it is not possible to say how important and influential molecules such as those described here will be on sorting out complicated in vivo physiology. The potency reported for 4b suggests significant optimization would be needed before in vivo significance could be assessed.

Reviewer #1:

This study pursues the development of ADAMTS-5 protease inhibitors by screening compounds linking a glycan (GlcNAc) with an arylsulfonamide, using click chemistry as the tether contains a triazene. ADAMTS-5 is a metalloprotease that has been implicated as a drug target for osteoarthritis. In prior work, this lab has identified exosites in ADAMTS-5 that can contribute to substrate recognition and processing. Here they identify a hybrid compound, 4b, that can block the protease activity of ADAMTS-5 with 9 µM potency. Using docking, they implicate several Lys residues that might confer interaction and show that potency of 4b is reduced with ADAMTS-5 mutants. Overall, compound 4b may bind as predicted although no additional experimental structural studies are performed to validate binding mode. While the study is a solid but limited medicinal chemistry effort, it is not felt that this manuscript will be of broad interest.

-The compound 4b potency is still rather weak relative to other previously published agents which show sub-µM potency. 1) Biochem J paper (2016) from this lab and BBRC (2016) from a Japanese lab reported antibodies that blocked ADAMTS-5 in the low nM range and worked in human chondrocytes. 2) Thiazolidine-diones (sub µM) were reported as cell active ADAMTS-5 inhibitors (Eur J Med Chem 2014). 3) Acylthio-semicarbazides are sub-µM ADAMTS-5 inhibitors (Eur J Med Chem 2013) although also target ADAMTS-4 more weakly, and showed selectivity against other metallohydrolases.

-Compound 4b was not used in a cell-based let alone animal model to analyze its pharmacological effects or promise. It is thus not clear how compound 4b stacks up to earlier agents. Compound 4b is a rather large compound for advancing clinically.

-No new insight into ADAMTS-5 biological function was gained here.

Preprint Review

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

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Reply to the reviewers

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.

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 #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.

Author Response

Reviewer #1:

In this manuscript, Cobb and colleagues report on the biochemical and functional characterization of redox active ER proteins in the malaria parasite Plasmodium falciparum. They studied a protein called PfJ2, which contains HSP40 J and Trx domains and is homologous to human ERdj5. Using the TetR-PfDOZI aptamer system to tag PfJ2 and conditionally regulate its expression, they show that PfJ2 is localized in the parasite ER and is essential for parasite growth during the asexual blood stages. Using co-immunoprecipitation combined with mass spectrometry, they identify partner proteins of PfJ2 including other ER proteins such as PDI and BIP. Using a chemical biology approach based on DVSF crosslinker, they document the redox activity of PfJ2 and identify redox substrates of PfJ2, which include PDI8 and PDI11 protein disulfide isomerases. They further functionally characterized PDI8 and PDI11 using the glmS ribozyme for conditional knockdown. These experiments confirm that PDI8 and PDI11 are partners of PfJ2 and show that knockdown of PDI8 impairs parasite blood stage growth. Finally, the authors show that inhibitors of human PDI inhibit parasite growth (at best in the micromolar range) and block the redox activity of PfJ2 and parasite PDI.

This is an interesting study combining genetic and chemical biology approaches to investigate an understudied compartment of the malaria parasite. The manuscript is clearly written and the work technically sound. In summary, this study illustrates that ER redox proteins in the malaria parasite perform similar functions as in other organisms. The main limitation of this study is that evidence showing that redox ER parasite proteins are druggable is rather weak. PfJ2 is very similar to human ERdj5 in terms of active redox site and function, and the authors used inhibitors that are active on human PDI. It thus remains uncertain whether an antimalarial strategy targeting such conserved pathways is achievable.

RESPONSE: We thank the reviewer for their appreciation of our work. While PfJ2 shares some similarity to human ERdJ5, we disagree that they are functionally similar. Our data show that, unlike ERdJ5, PfJ2 substrates are primarily other redox chaperones. In terms of the redox active site, our data clearly identifies a pathway that is targeted by a small molecule inhibitor. There is a lot of precedence for targeting conserved pathways as an antimalarial strategy. For example, anti-translational and anti-proteasomal inhibitors are being widely studied for their potency as antimalarials (Baragana et al 2015 Nature; Li et al 2016 Nature; Wong et al 2017 Nat. Microbiol.; Kirkman et al 2018 PNAS; Stokes et al 2019 PLoS Path.), several proteases (with conserved active sites) are well known antimalarial targets (Sleebs et al 2014 PLoS Biol.; Nasamu et al 2017 Science; Pino et al 2017 Science; Favuzza et al 2020 Cell Host Microbe), and effective inhibitors targeting a parasite chaperone has been repurposed for antimalarial drug development (Lu et al 2020 PNAS). We thank the reviewer for recognizing that there is a long road ahead of us to develop a more specific inhibitor for PfJ2, however, that is beyond the scope of this study.

In addition, a number of specific points should be addressed to improve the quality of the manuscript:

Although PDI8 and PDI11 gene edition were performed in the PfJ2apt line, the authors did not attempt to knockdown both PfJ2 and PDI8/11 simultaneously (because PfJ2 is essential). Therefore, referring to "double conditional mutants" is misleading.

RESPONSE: We are open to alternative ways to refer to these mutants. Since we have orthogonal systems for knockdown of two proteins, we refer to these as double conditional mutants.

The authors should provide details on the parasite lysis conditions used for the co-IP experiments to identify interacting proteins (Table 1) and redox partners (figure 3). In their proteomic analysis, the authors considered proteins with a 5-fold increase in the specific versus control conditions. A more stringent analysis would retain only proteins identified exclusively in the modified J2apt line.

RESPONSE: We will include this in a new version. We agree that a more stringent analysis would lead to fewer proteins being identified, however, it also runs the risk of missing real interactors. We chose to use a 5-fold cutoff based on previously published work (Boucher et al 2018 PLoS Biol; Florentin et al 2020 PNAS).

In figure 6, the authors should probe the blots for a control protein that is not co-immunoprecipitated with PfJ2 or PDI8. In Supplementary fig 4, control untreated parasites should be analyzed in parallel to GlcN-treated parasites.

RESPONSE: We will do this once our labs reopen after the pandemic.

The partial reduction of protein levels (Fig S4) shows that the glmS system is not very efficient here, which might explain why there is no phenotype in the PDI11 mutant (Fig5B). This questions the conclusion that PDI11 is dispensable.

RESPONSE: We agree and we state that “These results...suggest that PfPDI11 may be dispensable... conclusions are supported by a genome-wide essentiality screen performed in P. falciparum” (Lines 319-322). We will add more discussion to explain this result.

Reviewer #2:

The claim here is of having discovered a druggable cellular process in P. falciparum, one that opens the door to therapeutic intervention in the most deadly form of malaria.

The study commences with a focus on what appears to be the Pf homologue of a eukaryotic protein disulphide isomerase, known to many as ERdJ5 and referred to here as PfJ2. Its cellular contingents were identified by cross-linking and pull down, it’s (predicted) thiol reactivity explored with agents that react with reduced thiols and it’s functional importance to parasite fitness (in the lab) explored by gene knockout. These experiments provide evidence that PfJ2 and it’s associated Pf PDIs engage in thiol redox chemistry in the ER of the parasite and that integrity of this biochemical process is important to viability of the parasite.

Lacking all expertise in molecular parasitology, this reader is unable to judge the specific significance of these findings to the field nor indeed the extent to which these are hard-won discoveries.

RESPONSE: We are gratified to note that the reviewer is cognizant of their limitations and their ability to judge the significance of this work.

However, from the perspective of the fundamentals of ER redox chemistry the findings represent a modest advance, showing that what is true of yeast and mammals is also true of Apicomplexa. The important mystery related to the juxtaposition of a J-domain and thioredoxin domains in PfJ2, remains.

The most important claim however is the one with translational potential, namely that one might be able to discover (electrophilic) compounds that, despite the monotony of shared chemical features of thiol chemistry, will nonetheless possess sufficient specificity towards this or that malarial protein to be converted one day to a useful drug. However, in regards to this important point the authors offer very little in the way of evidence how and if this might be achieved.

RESPONSE: We disagree. The work does not reconfirm the ‘fundamentals of ER redox’ chemistry. There is no work, in any system, that has shown that PfJ2-like proteins act as reductases for PDIs. In fact, as we state in the paper, in other model systems, there is a lot of redundancy built in the ER redox systems and PfJ2-like proteins work with specific clients like SERCA pumps or LDL receptor. Thioredoxin domain proteins in the ER of other eukaryotes have not been shown to work with each other or other chaperones. Furthermore, our data actually does suggest a reason why the J-domain is juxtaposed to thioredoxin domains. It recruits BiP to the mixed disulfides formed by PDIs. This insight would not have been possible in other systems because of the redundant redox mechanisms. In terms of the translational aspect, this work identifies an essential, pathway and a starting point for developing better inhibitors. As the reviewer may be aware, once a starting drug-like molecule has been identified, one has to embark on a medicinal chemistry program to develop more potent inhibitor. However, this is beyond the scope of this manuscript.

Therefore, the main conclusions to draw from this paper are that ER-localised thiol chemistry is also important in malaria parasites and that, assuming one were able to explore localised context-specific features of thiol reactivity in malarial proteins, it may one day be possible to develop anti-malarial drugs that exploit this as a mechanism of action. The generic nature of these considerations limits the significance of the conclusions one might draw from this paper.

RESPONSE: We are disappointed that we were unable to satisfy the reviewer’s need for ‘a giant leap for mankind’ insights.

Reviewer #3:

This paper describes redox-active proteins in the ER of malaria parasites. The authors start out with PfJ2, a J- and Trx-domain containing protein. They find that it is an essential ER protein that interacts with other chaperone and Trx domain proteins. Using a crosslinker with specificity for redox-active cysteines they identify PfPDI8 and PfPDI11 as redox-partners that together may aid folding of other proteins in the secretory pathway. Finally the authors use inhibitors that act on human PDIs and show that they inhibit parasite growth, albeit at rather high concentrations. This may be fortunate as this suggests different specificities for host and parasite PDIs. However, it also means that from this work it is difficult to judge if the parasite PDIs can be specifically targeted.

RESPONSE: We thank the reviewer for recognizing the important insights gained from this work. We agree that the specific inhibitor identified is not an ideal antimalarial. There is a lot of precedence in the field for antimalarial inhibitors that target conserved mechanisms such as protein translation (Baragana et al 2015 Nature; Wong et al 2017 Nat. Microbiol.), aspartic proteases (Sleebs et al 2014 PLoS Biol.; Nasamu et al 2017 Science; Pino et al 2017 Science; Favuzza et al 2020 Cell Host Microbe), the proteasome (Li et al 2016 Nature; Kirkman et al 2018 PNAS; Stokes et al 2019 PLoS Path.), the TRiC chaperone complex (Lu et al 2020 PNAS) etc. We are starting a medicinal chemistry program to identify more potent inhibitors of these redox chaperones. However, that is beyond the scope of this paper.

This is an interesting paper and rightly emphasises that it addresses a much understudied process and organelle in the parasite. The DVSF-crosslinking and the knockdown cell lines are highlights (although the knockdown cell lines were not fully exploited). The paper covers a lot of ground. However, this comes at the cost of depth. The actual function of the studied proteins on folding of other proteins and on the state of the ER was not evaluated and it is also not clear if the human PDI inhibitors indeed target the parasite enzymes. The high concentrations of inhibitors needed to show an effect on DVSF-crosslinking might indicate a secondary effect due to loss of parasite viability. As a result it is not fully clear if the studied proteins are indeed critical for folding of relevant substrates and if this process is druggable. More work is needed to support the main conclusions of the paper.

RESPONSE: We thank the reviewer for appreciating the diverse toolsets used here to gain important insights into the ER of malaria-causing parasites. Due to the short time-frame of the DVSF-crosslinking experiment (30 mins vs 48h life cycle), we are able to conclude that the effect of the drug is not secondary. A new version will clarify this.

Major points:

1) The authors describe conditional knockdown lines and find that PfJ2 and PfPDI8 are essential but these lines are not further exploited for functional studies. Did the knock downs have any effect on proteins they mention as potential substrates (Table 1)? Did it affect the state/morphology of the ER? Did knock down of PfPDI8 remove/shift one of the PfJ2 bands after DVSF-crosslinking, as would be expected? Is there an effect on BiP? A general folding problem in the ER with such a lethal phenotype might have profound effects on the morphology of the organelles receiving protein from the ER. What happens to other cellular markers after knock down of these proteins? Were the knock down cells analysed by EM? Was there an effect on protein export? As it stands the knock down data does not show a role of the complex in the folding of any type of substrate and the function in oxidative folding, as indicated in the title, remains tentative.

RESPONSE: The morphology of the ER is difficult to address due the fact that in these lifecycle stages the ER is quite condensed. Further, the ER is not clearly identifiable via EM. The knockdown of PDI8 is not complete, therefore, it is not possible to perform the suggested experiment as we will always see the residual PDI8 crosslinked with PfJ2. We are not sure what or if there’s any effect on BiP upon knockdown of PfJ2. BiP does not crosslink with PfJ2 and its expression levels do not change. We are not sure what other effect the reviewer expects on BiP. The co-IP data show that BiP is part of a complex with PfJ2 and PDI8, this complex has not been previously observed in the ER of any organism. Since the parasites die during the trophozoite/early schizont stages, several of these organelles such as Rhoptries, micronemes etc probably do not form. Once the lab reopens after the pandemic, we will test for the presence of these organelles via immunofluorescence microscopy as well as EM. Similar experiments could show an effect on protein export. However, since we didn’t identify any exported proteins to be putative substrates of PfJ2 (despite the expectation that chaperones are sticky and bind everything), and therefore, any effect we observe is likely to be indirect. Given the published data establishing the function of PDIs as oxidative folding chaperones, their high degree of conservation, and in vitro characterization, we conclude that they function in oxidative folding. Furthermore, we show that PfJ2 regulates the function of Plasmodium PDIs as well as recruits BiP to the mixed disulfide complex. BiP is a highly conserved chaperone that has clear function in protein folding. Based on this and the data presented here, we conclude that PfJ2 functions as a regulator of oxidative folding in P. falciparum.

2) While I like the idea to use established commercial drugs as novel potential antimalarials, those used here are specific for non-infectious human diseases and target the host which is not a desirable property. Considering this, their rather low activity against the parasite can be taken as a positive result. However, the low activity is less convincing to establish the folding pathway in the parasite ER as a drug target. Beside the issue that it is unclear if indeed oxidative folding is the essential function of the PfJ2 complex (see previous point), the data in Fig. 7 does not clearly establish that this function is targeted by the inhibitors used. The effect is only seen at concentrations of 5xIC50. It is possible that this severely reduced viability which could be a non-specific reason for the lack of DVSF-crosslinked products. This needs to be examined in more depth. For instance, is the crosslink still seen after equivalent treatment of cultures with 5xIC50 of other unrelated drugs? Were other, unrelated processes unaffected? What was the effect of exposure to the drug on the ER and parasite morphology? Was the appropriate parasite stage affected? Can it be tested how fast exposure to 5xIC50 of the drug kills the parasites (at least morphologically, but preferably also by more specific means)?

RESPONSE: We agree that the drugs identified here are not ideal antimalarials but rather they are starting molecules for a larger medicinal chemistry program, that is beyond the scope of this manuscript. While we see significant loss of DVSF crosslinking (for PfJ2) even at the IC50, the relationship between protein activity inhibition and parasite death isn’t always linear. We are testing analogs of 16F16 to identify more potent inhibitors of these proteins. We thank the reviewer for the suggested experiments, and when the pandemic is no long limiting access to the lab, we will perform some of these.

3) While generally sound, a few experiments would have benefitted from more controls. A reducing sample from the same parasites for Fig. S7 (loaded a couple of lanes away to avoid interference of the reducing agent) would have been nice for comparison to show specificity of the higher molecular weight adducts. Detection of a control protein not expected to co-purify (for instance a cytosolic protein or a membrane-bound protein to control for residual parasite material) would have been appropriate for the co-immunoprecipitations (e.g. Fig. 6A,D, Fig. S9).

RESPONSE: We show that there are no non-specific bands for PDI11, because when we mutate the cysteines, we do not observe any cross-linking. We will include the control proteins for the co-IPs, they were not included for the sake of clarity.

Reviewer #3:

This paper describes redox-active proteins in the ER of malaria parasites. The authors start out with PfJ2, a J- and Trx-domain containing protein. They find that it is an essential ER protein that interacts with other chaperone and Trx domain proteins. Using a crosslinker with specificity for redox-active cysteines they identify PfPDI8 and PfPDI11 as redox-partners that together may aid folding of other proteins in the secretory pathway. Finally the authors use inhibitors that act on human PDIs and show that they inhibit parasite growth, albeit at rather high concentrations. This may be fortunate as this suggests different specificities for host and parasite PDIs. However, it also means that from this work it is difficult to judge if the parasite PDIs can be specifically targeted.

This is an interesting paper and rightly emphasises that it addresses a much understudied process and organelle in the parasite. The DVSF-crosslinking and the knockdown cell lines are highlights (although the knockdown cell lines were not fully exploited). The paper covers a lot of ground. However, this comes at the cost of depth. The actual function of the studied proteins on folding of other proteins and on the state of the ER was not evaluated and it is also not clear if the human PDI inhibitors indeed target the parasite enzymes. The high concentrations of inhibitors needed to show an effect on DVSF-crosslinking might indicate a secondary effect due to loss of parasite viability. As a result it is not fully clear if the studied proteins are indeed critical for folding of relevant substrates and if this process is druggable. More work is needed to support the main conclusions of the paper.

Major points:

1) The authors describe conditional knockdown lines and find that PfJ2 and PfPDI8 are essential but these lines are not further exploited for functional studies. Did the knock downs have any effect on proteins they mention as potential substrates (Table 1)? Did it affect the state/morphology of the ER? Did knock down of PfPDI8 remove/shift one of the PfJ2 bands after DVSF-crosslinking, as would be expected? Is there an effect on BiP? A general folding problem in the ER with such a lethal phenotype might have profound effects on the morphology of the organelles receiving protein from the ER. What happens to other cellular markers after knock down of these proteins? Were the knock down cells analysed by EM? Was there an effect on protein export? As it stands the knock down data does not show a role of the complex in the folding of any type of substrate and the function in oxidative folding, as indicated in the title, remains tentative.

2) While I like the idea to use established commercial drugs as novel potential antimalarials, those used here are specific for non-infectious human diseases and target the host which is not a desirable property. Considering this, their rather low activity against the parasite can be taken as a positive result. However, the low activity is less convincing to establish the folding pathway in the parasite ER as a drug target. Beside the issue that it is unclear if indeed oxidative folding is the essential function of the PfJ2 complex (see previous point), the data in Fig. 7 does not clearly establish that this function is targeted by the inhibitors used. The effect is only seen at concentrations of 5xIC50. It is possible that this severely reduced viability which could be a non-specific reason for the lack of DVSF-crosslinked products. This needs to be examined in more depth. For instance, is the crosslink still seen after equivalent treatment of cultures with 5xIC50 of other unrelated drugs? Were other, unrelated processes unaffected? What was the effect of exposure to the drug on the ER and parasite morphology? Was the appropriate parasite stage affected? Can it be tested how fast exposure to 5xIC50 of the drug kills the parasites (at least morphologically, but preferably also by more specific means)?

3) While generally sound, a few experiments would have benefitted from more controls. A reducing sample from the same parasites for Fig. S7 (loaded a couple of lanes away to avoid interference of the reducing agent) would have been nice for comparison to show specificity of the higher molecular weight adducts. Detection of a control protein not expected to co-purify (for instance a cytosolic protein or a membrane-bound protein to control for residual parasite material) would have been appropriate for the co-immunoprecipitations (e.g. Fig. 6A,D, Fig. S9).

Reviewer #2:

The claim here is of having discovered a druggable cellular process in P. falciparum, one that opens the door to therapeutic intervention in the most deadly form of malaria.

The study commences with a focus on what appears to be the Pf homologue of a eukaryotic protein disulphide isomerase, known to many as ERdJ5 and referred to here as PfJ2. Its cellular contingents were identified by cross-linking and pull down, it’s (predicted) thiol reactivity explored with agents that react with reduced thiols and it’s functional importance to parasite fitness (in the lab) explored by gene knockout. These experiments provide evidence that PfJ2 and it’s associated Pf PDIs engage in thiol redox chemistry in the ER of the parasite and that integrity of this biochemical process is important to viability of the parasite.

Lacking all expertise in molecular parasitology, this reader is unable to judge the specific significance of these findings to the field nor indeed the extent to which these are hard-won discoveries. However, from the perspective of the fundamentals of ER redox chemistry the findings represent a modest advance, showing that what is true of yeast and mammals is also true of Apicomplexa. The important mystery related to the juxtaposition of a J-domain and thioredoxin domains in PfJ2, remains.

The most important claim however is the one with translational potential, namely that one might be able to discover (electrophilic) compounds that, despite the monotony of shared chemical features of thiol chemistry, will nonetheless possess sufficient specificity towards this or that malarial protein to be converted one day to a useful drug. However, in regards to this important point the authors offer very little in the way of evidence how and if this might be achieved.

Therefore, the main conclusions to draw from this paper are that ER-localised thiol chemistry is also important in malaria parasites and that, assuming one were able to explore localised context-specific features of thiol reactivity in malarial proteins, it may one day be possible to develop anti-malarial drugs that exploit this as a mechanism of action. The generic nature of these considerations limits the significance of the conclusions one might draw from this paper.

Reviewer #1:

In this manuscript, Cobb and colleagues report on the biochemical and functional characterization of redox active ER proteins in the malaria parasite Plasmodium falciparum. They studied a protein called PfJ2, which contains HSP40 J and Trx domains and is homologous to human ERdj5. Using the TetR-PfDOZI aptamer system to tag PfJ2 and conditionally regulate its expression, they show that PfJ2 is localized in the parasite ER and is essential for parasite growth during the asexual blood stages. Using co-immunoprecipitation combined with mass spectrometry, they identify partner proteins of PfJ2 including other ER proteins such as PDI and BIP. Using a chemical biology approach based on DVSF crosslinker, they document the redox activity of PfJ2 and identify redox substrates of PfJ2, which include PDI8 and PDI11 protein disulfide isomerases. They further functionally characterized PDI8 and PDI11 using the glmS ribozyme for conditional knockdown. These experiments confirm that PDI8 and PDI11 are partners of PfJ2 and show that knockdown of PDI8 impairs parasite blood stage growth. Finally, the authors show that inhibitors of human PDI inhibit parasite growth (at best in the micromolar range) and block the redox activity of PfJ2 and parasite PDI.

This is an interesting study combining genetic and chemical biology approaches to investigate an understudied compartment of the malaria parasite. The manuscript is clearly written and the work technically sound. In summary, this study illustrates that ER redox proteins in the malaria parasite perform similar functions as in other organisms. The main limitation of this study is that evidence showing that redox ER parasite proteins are druggable is rather weak. PfJ2 is very similar to human ERdj5 in terms of active redox site and function, and the authors used inhibitors that are active on human PDI. It thus remains uncertain whether an antimalarial strategy targeting such conserved pathways is achievable.

In addition, a number of specific points should be addressed to improve the quality of the manuscript:

Although PDI8 and PDI11 gene edition were performed in the PfJ2apt line, the authors did not attempt to knockdown both PfJ2 and PDI8/11 simultaneously (because PfJ2 is essential). Therefore, referring to "double conditional mutants" is misleading.

The authors should provide details on the parasite lysis conditions used for the co-IP experiments to identify interacting proteins (Table 1) and redox partners (figure 3). In their proteomic analysis, the authors considered proteins with a 5-fold increase in the specific versus control conditions. A more stringent analysis would retain only proteins identified exclusively in the modified J2apt line.

In figure 6, the authors should probe the blots for a control protein that is not co-immunoprecipitated with PfJ2 or PDI8.

In Supplementary fig 4, control untreated parasites should be analyzed in parallel to GlcN-treated parasites.

The partial reduction of protein levels (Fig S4) shows that the glmS system is not very efficient here, which might explain why there is no phenotype in the PDI11 mutant (Fig5B). This questions the conclusion that PDI11 is dispensable.

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

Reviewer #3:

In this manuscript, Bohr et al examine how the pluripotent stem cell system of planarians responds to organ-specific damage. If and how the differentiation of specific cell types is dynamically regulated is a conceptually fascinating problem in planarians and in general stem cell research. The authors address this problem by comparing the stem cell response between a single-organ amputation (the pharynx) versus broad tissue loss (decapitation). Their findings indicate that only the removal of the pharynx triggers the increased differentiation of pharyngeal cell types, while the loss of non-pharyngeal tissues upregulates the differentiation of progenitors of multiple organs, but not the pharynx. Further, the authors implicate temporally restricted ERK signaling as a regulatory component in the differentiation of pharyngeal cell types. These observations are also important because they contrast with the previously proposed "target blind" model (LoCascio et al., 2017) that posits the differentiation of different cell types at constant relative proportions, with the rate of stem cell divisions as global production rate regulator. In contrast, the observations by Bohr et al provide further evidence for more flexibility and specificity within the planarian stem cell system ("target consciousness") in the sense of lineage-specific adjustments in the production rates of specific cell types.

That said, the manuscript generally suffers from an overly narrow focus. Important questions remain regarding the specificity of the stem cell response to pharynx amputation and multiple experiments lack important controls (see below). Moreover, the authors have overlooked that a "target conscious" progenitor response has already been demonstrated by the selective proliferation of protonephridial markers expressing neoblasts in response to protonephridial damage by RNAi (Vu et al., 2015).

Major points:

1) Specificity of the stem cell response:

The central premise of the paper is the selective amplification of pharyngeal progenitors in response to pharynx amputation. The authors conclude this based on i) an increase in the absolute number of foxA+/piwi-1+ cells in a specific area, while ii) de-capitation has no effect on the absolute number of foxA+/piwi-1+ cells in the same area. This approach is an insufficient demonstration of specificity, as the known phenomenon of wound-induced stem cell activation might also change the absolute number of specific neoblast subclasses and might do so in an injury-dependent manner.

To account for this important caveat, the authors need to i) quantify RELATIVE proportions of foxA+/piwi-1+ cells out of total piwi cells (or of total H3P+ cells) and ii) they need to include other organ progenitors in the initial analysis. The latter is also critical because the pharynx is a complex organ comprising descendants of multiple lineages (e.g., muscle, neurons, epidermis) and it is not clear whether the foxA+/piwi-1+ cells indeed serve as a singular origin of all constituting lineages (as assumed by the authors), or if they only provide a subset of pharyngeal cells with a rate-limiting role in pharynx assembly (e.g., pharyngeal muscle). In the face of such uncertainty, iii) the quantification of new cell incorporation into the pharynx versus other tissues via BrdU labeling would be necessary to address this caveat and to provide a global perspective on the specificity of the response independent of incompletely characterized marker genes.

In addition, the following experimental design problems need to be addressed or better documented, including:

• The authors provide insufficient methodological detail on progenitor quantifications, even though the entire manuscript rests on this assay. What are their criteria for scoring a piwi-1+ cell as double-positive for the often weak and noisy lineage labels? If done "by eye", was double-blind scoring used? Were all cells in a given Z-stack counted or only specific planes? If the latter, by which criteria were image planes selected for quantification? How were the specific specimens out of an experimental cohort selected for imaging/quantification? Though not necessarily a unique shortcoming of this particular study, these points simply need to be adequately addressed in order to rigorously support quantitative differences between experimental conditions (e.g., specificity).

• The authors appear not to distinguish at all between technical replicates (e.g., multiple specimens within an experimental cohort) versus biological replicates (independent experimental cohorts). This is significant, because i), the use of the standard error of the mean (SEM) that the authors employ throughout is not really an appropriate measure for a single biological replicate with 3 animals - the standard deviation (SD) would seem a more appropriate measure in this context (SD). ii), the number of worms quantified for each experiment is generally low (n=3 animals in Fig. 1, 3, 5, 6; n=5 animals in the rest of the figures) given the observed variability in the data (e.g., ~25-30 foxA+/piwi-1+ cells 3d after pharynx amputation in Fig. 1E versus 50 cells in figure 1H). Similarly, for kinetic experiments as in Fig. 1H or 2C, it is simply crucial to ensure that the error bars include the variation in response dynamics between multiple replicates due to the drift in the baseline fraction of H3P+ cells or varying staining efficiencies (e.g., different batches of animals on different days), rather than the technical variation in a single experimental cohort only. Please address these concerns by adding more specimens and a thorough description of the experimental design.

2) Timeline of pharynx regeneration: The pharynx regeneration timeline and associated events that the authors present are insufficiently supported by experimental data. The conclusion in line 215 "that proliferation in a critical window of 1 to 2 days after pharynx amputation produces a population of progenitors that are likely essential for pharynx regeneration" rests i) on the diagnosis of a "proliferative peak of FoxA+ stem cells that occurs after pharynx amputation (Figure 2C)" (line 202). However, rather than a "proliferative peak", Fig. 2C shows a broad "proliferative plateau" of FoxA+ stem cells between 6h and 3 days after amputation. Similarly, the foxA+/H3P+ quantification after pharynx amputation in Fig. 4G also displays a lack of a peak of foxA+/H3P+ from 1d to 2d after amputation. ii), the associated nocodazole experiments suffer from the fact that the authors did not quantify the impact of the drug on the abundance of foxA+/piwi-1+ cells during treatment intervals from 0-1d and 2-3d after pharynx amputation. Therefore, the authors cannot rule out that nocodazole treatment might have similar effects on the abundance of foxA+/piwi-1+ cells throughout the 1-3 d post-amputation time interval, with the more severe organ-level phenotype of the 1-2d treatment window being caused by some other effect of the drug (e.g., on the differentiation of another rate-limiting cell type for pharynx regeneration or, conceivably, inhibition of priming neuronal activity ). Similar concerns apply regarding the statement in line 242, "... a window 1-2 days after amputation in which activation of ERK signaling is important for pharynx regeneration." Here, i) the quantification of the end-stage phenotype of drug treatment during the 1-2d time interval (regain of feeding ability) is missing. ii) Similarly, the examination of the consequences of PD treatment on foxA+ expression in piwi-1+ cells in panel 4D-H employs drug soaking for 3 days, yet the corresponding end-stage phenotype of 3-day drug treatment is not shown. iii) the implications of ERK in pharynx regeneration are tentative. Even though the PD compound is initially correctly introduced as "MEK inhibitor", the authors subsequently switch to the factually wrong "ERK inhibitor" designation (e.g., line 358). Further, additional experimental evidence for the assumed Erk inhibition as the cause of the observed phenotypes would be desirable to rigorously support the conclusion.

These caveats need to be addressed if a cell biological timeline is to remain part of this manuscript.

3) Integration with the existing literature:

The authors need to better integrate their findings with the literature. First, they need to cite the findings of Vu et al, which explicitly demonstrated a specific increase in the fractional abundance of piwi-1+/protonephridial marker+ cells in response to RNAi-mediated damage to protonephridia (Vu et al., 2015). As such, this study already demonstrates the main point of Bohr et al., namely that the planarian stem cell system is capable of "target conscious" progenitor provision. At the very least, the authors should credit these results as additional evidence for their model. A further finding that they should discuss is the demonstration by LoCascio et al (LoCascio et al., 2017) that flank region cut-outs cause a significant increase in pharynx cell incorporation over baseline, despite the absence of injury to the pharynx. How do the authors reconcile the discrepancy between these data and their own? In general, the discussion would benefit greatly from a more explicit comparison between the "target blind" model versus their data, as well as a broader perspective on the regulation of stem cell homeostasis.

Reviewer #2:

Bohr, Shiroor, and Adler investigate how stem cells respond to the loss of specific tissues in planarians. The planarian stem cell population (neoblasts) are distributed throughout the planarian body and include pluripotent stem cells and a wide range of lineage-committed progenitor cells. How this heterogenous pool of cells behave post-injury or amputation is incompletely understood. The discovery of markers to label stem cell progeny have opened the door to investigate how stem cells respond to tissue loss. However, the anatomy of planarians makes it difficult to surgically remove or damage specific organs. The PI of this study developed an assay to remove the pharynx by "chemical amputation" to study the mechanisms underlying regeneration of this organ without drastically perturbing or injuring other tissues. Using this approach, this paper investigates how a well-defined population of FoxA+ progenitors respond to pharynx removal at early time-points during the regeneration. Their data suggest that stem cells are able to detect loss of the pharynx and respond by generating significantly more cells fated to become pharynx, whereas amputation of non-pharyngeal tissues does not have an obvious effect on pharynx progenitor specification dynamics. In addition, using pharmacological treatments, the authors show that cell proliferation and ERK signaling are required for the expansion of pharynx progenitors and cell differentiation. In contrast, other cell types in the planarian eye do not appear to require proliferation or ERK signaling, suggesting that stem cell responses "target blind" as suggested in a previous study, but are rather tuned to specific missing tissues.

This work has the potential to make a significant contribution to the field by advancing our understanding of how the planarian heterogenous stem cell population responds to the loss of a specific organ. However, the report is preliminary as presented. It appears that the authors performed many experiments a single time. In addition, description of the methods is insufficient. The authors need to chiefly demonstrate the reproducibility of the data and robustness of the observations.

Concerns:

1) The authors need to replicate experiments to increase the sample size for most experiments.

2) Details for imaging and quantification should be explicitly stated in the methods, and the reported cell count numbers should be normalized as appropriate for each set of experiments.

3) Although the authors mention "lineage-tracing" experiments (see minor comments), they do not perform DNA analog pulse-chase experiments to analyze a temporal progression and spatial localization of stem cells to FoxA+ progenitors after pharynx removal. The authors rely on PH3-staining in conjunction with FoxA, and supplementary experiments using the pluripotent stem cell marker tgs-1 (which was only examined at 1 dpa). Could the authors clarify what they think FoxA+ stem cells represent? Are these self-renewing pluripotent stem cells or lineage-committed progenitors? Can the authors get some insight by scanning their images of PH3+ cells expressing FoxA visibly undergoing metaphase? Are daughter cells uniformly FoxA+ as reflected in the model? At least in one of the cells shown in the nocodazole treated controls it appears that both daughter cells express FoxA (Figure 3). I suggest showing some higher magnification images to support the interpretations/conclusions. Others have posited (e.g., Rink, Chapter 2 of Planarian Regeneration: Methods and Protocols), that not every dividing cell may be a long-term self-renewing stem cell and whether transient amplifying cells exist or contribute to regeneration in planarians is unknown. Adler and Sánchez Alvarado (2015) discuss the role of transient states and how the transcriptional profiles change in response to regeneration. It wasn't clear to me how the authors think about these cells based on the limited number of experiments and analysis, and there are a few places where the terminology is inconsistent, especially in reference to proliferating ovo+ progenitors (P. 14). The authors need to be clear, and it might be helpful to illustrate their model in one of the early figures or to include it in the final model, which omits tgs-1 due to the limited number of experiments performed with this marker gene (Figure 7).

4) The pharynx is complex and there is no data to assess what the contribution of other progenitor populations might be. I don't think or think it is unlikely that FoxA+ progenitors are solely responsible for reconstructing the pharynx. The authors should examine how other progenitor populations behave during the process of pharynx regeneration by extending the timeline of progenitor cell analysis. This would reveal if there is fluctuation in progenitor dynamics as animals regenerate the pharynx or re-scale proportions after pharynx regeneration. For example, can the authors test if they are able to detect a contribution of neural progenitors to regeneration of the pharyngeal nervous system? And if so, when during the regeneration process does it take place in the context of their study?

Reviewer #1:

This manuscript explores how planarian stem cells respond to the loss of a specific organ: the pharynx. The previously proposed "target-blind" model of planarian regeneration (LoCascio et al. 2017) posited that stem cells do not respond directly to missing tissues, but rather replace missing cell types based on their normal rates of homeostatic turnover. In contrast, the authors of this manuscript suggest that planarian stem cells can sense and respond to the loss of specific missing tissues, using the pharynx as a case study. The authors conclude that planarians may use more than one mode of regeneration, depending upon the target being regenerated (eye vs. pharynx).

The question explored in this paper is of fundamental importance, and providing an alternative model by which planarian stem cells regenerate missing tissues should be of interest to a broad readership. Unfortunately, in its current form, the manuscript presents enticing preliminary findings, rather than robust experimental observations. Because the authors are attempting to refute a previously published model, it is critical that the data are clear and convincing. If not, these findings could be summarily dismissed without appropriate debate. If the authors can demonstrate that their results are robust across larger samples sizes and experimental replication, and address the major issues listed below, this manuscript would represent a significant contribution to our understanding of planarian regeneration.

Major Issues:

1) Throughout the manuscript, experiments were either not repeated, or the number of biological replicates was not reported. In most cases, it appears that experiments were done only once (with the exception of the drug treatments). Numbers of biological replicates and sample sizes should be explicitly stated and the data from different replicates reported for Figs. 1D-G, 2B-D, 3C, 3E-F, 4B, 4D-H, 5C-D, and 6A-E.

2) The authors do not sufficiently describe their methods for imaging and quantifying cells (Figs. 1E, 1G, 2C-D, 3F, 4E-H, 5D, 6B). The size of the area covered to collect these data is unclear. High-magnification images are shown: are these the areas that were imaged? If so, their results could be biased by choosing small regions of interest. Ideally, the authors should quantify more than one region per animal. Also, they do not describe the depth of the z-stacks collected or how these stacks were normalized/standardized across conditions. All their conclusions hinge on the quantification of progenitor populations in response to different amputation paradigms or chemical treatments, so the standards for imaging and quantification must be clearly reported.

3) Inappropriate statistical tests were used throughout. The use of multiple t-tests amplifies the chance of a Type I error and is especially problematic when up to 7 comparisons were made! The authors should use one-way ANOVA with multiple comparison corrections for all experiments with more than two groups.

4) Figs. 1D-E show that upon pharynx amputation but not head amputation, FoxA+ piwi+ pharynx progenitors increase. These data suffer from the quantification issues highlighted above: how the data were quantified is not sufficiently described, only 3 data points were taken (one per animal), the experiment appears to have been performed only one time, and the wrong statistical test was used. Rather than reporting the number of FoxA+ piwi-1+ cells counted, the authors should quantify the total number of doubly positive cells as a percentage of piwi-1+ cells, as was previously published (Adler et al. 2014). The authors also fail to specify whether the change observed between "3 dpa phx" and "3 dpa head" is significant, which is a material point.

5) Fig. 2D also suffers from the inadequate quantification practices described above. Ideally, FoxA+ cells should be quantified as a percentage of the H3P+ cells observed.

6) The authors use "stem cell", "progenitor", "stem cell progenitor", and "progenitor stem cells" in a mixed and confusing way throughout the paper. For example, in lines 174-175 the authors state that "proliferation of FoxA+ stem cells precedes the increase in pharynx progenitors." This refers to FoxA+ H3P+ cells vs. FoxA+ piwi-1+ cells, but the only difference is that the former are stem cells in the act of mitosis. Is a distinction being made? Elsewhere in the paper, FoxA+ piwi+ cells are referred to as stem cells. The terminology used needs more clarity and consistency.

Along these lines, what is a FoxA+ PSC (Line 168)? Are the authors suggesting that FoxA is a pluripotency marker? Or are the authors saying that tgs-1 has a more expansive expression pattern and is co-expressed with progenitor markers? If double FISH was performed with the progenitor markers reported (ovo, myoD, gata-4/5/6, six-1/2, and pax6), would they all overlap with tgs-1? These experiments need to be performed to make any claims about FoxA expression in the context of pluripotency.

7) In Fig. 3C, how does pharynx regeneration occur over such a long period of time after nocodazole treatment in the 1-2 day window? Does target-blind regeneration occur once this window is missed? The authors should repeat analysis of FoxA+ progenitors at later time points in this condition, and/or show rates of BrdU incorporation into the pharynx with and without nocodazole treatment in this window.

8) The use of the inhibitor PD in Fig. 4 is problematic. No data are shown to verify that phosphorylation of ERK was inhibited in these experiments. A citation of previous use is not sufficient. The effect of PD on WT uncut animals with regards to FoxA+ cells is not shown and is a necessary control. To address questions of drug specificity, the authors should corroborate their findings with a second inhibitor of ERK signaling like U0126, which has already been shown to work in planarians (Owlarn et al. 2017).

9) The conclusion that ERK signaling functions in regulating differentiation but not proliferation is premature (line 249-264). Figs. 4E-H should be quantified as percentages of piwi-1+ and H3P+ cells, especially since there is decreased proliferation overall with PD treatment.

10) The use of head fragments to compare eye regeneration vs. pharynx regeneration is inappropriate. Previous studies have already shown that the absence of eyes is not required to induce ovo+ progenitor amplification (LoCascio et al. 2017). Thus, this is not a surprising result (line 343-345) and the authors are mis-citing previous observations (in the earlier work, head fragments were never described). The region where ovo+ cells was quantified in Fig. 6A is not justified or explained. The yellow box is placed in a medial region where ovo+ cells do not normally reside. The authors should image within the laterally positioned ovo+ streams that have been previously described (Lapan & Reddien 2012; LoCascio et al. 2017).

11) Rather than using head fragments, the authors should repeat the flank resection experiments shown previously (LoCascio et al. 2017). This previous study showed that increased BrdU incorporation into the pharynx occurred following flank resection even though the pharynx was present. That result may have been 1) an artifact of increased BrdU staining due to stimulation of proliferation upon injury, 2) caused by unintentional damage to cells associated with the pharynx, or 3) a response to the loss of FoxA+ progenitor populations that surround the pharynx rather than the loss of the differentiated organ. The authors have the opportunity to revisit this published observation by quantifying the FoxA+ progenitor response during flank resection +/- pharynx. Without these data, this story is incomplete and therefore the conclusion of a targeted regeneration response is not yet convincing.

12) The negative results that proliferation and ERK signaling are dispensable for eye regeneration in Fig. 6 are weak and unconvincing. The regenerated eyes appear smaller; this should be quantified (number of PRNs per eye). If the small pharynges that form in Figs. 3D and 4C are considered a deleterious phenotype, why is the same standard not applied to the eye? Also, existing eye progenitors could have been sufficient for eye regeneration in these drug treatments. Furthermore, eyes did not regenerate after nocodazole treatment 50% of the time. Is it not more likely that the observations reported are dosage and timing artifacts? How has proliferation been affected? These observations do not live up to the claims made.

13) The authors claim that ovo+ cells are not proliferative (H3P+) even in cases where there is eye progenitor amplification (head amputation), but the data are not shown (line 321). They should be. Indeed, previous publications have never shown that ovo+ cells proliferate. This might mean that there are proliferating eye progenitors that precede expression of ovo. The authors should discuss this alternative.

14) The authors claim that eye regeneration does not require proliferation or ERK signaling but pharynx regeneration does. This conclusion hinges on the gross observation that eyes can regenerate in the presence of nocodazole and PD (see point 12 above). These data are coarse and the interpretations are unconvincing. Instead, their model can be directly tested using BrdU pulse-chase experiments. According to the authors' model, one would predict that following pharynx amputation, the rate of incorporation of BrdU+ cells into the regenerating pharynx should be higher than in uninjured controls. Conversely, the rate of BrdU incorporation into the regenerating eye should remain unchanged between injured (i.e. eye-resected) and control animals (LoCascio et al. 2017). Once the authors have established the prediction above, they have the opportunity to show the effects of nocodazole, PD, and U0126 on BrdU incorporation in the regenerating eye vs pharynx following eye resection and pharynx amputation in the same animal. This way, the authors can directly test the requirement for proliferation and/or ERK signaling in both tissues.

Preprint Review

This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

Summary

All three reviewers recognized the potential significance of this work, but shared concerns about sample sizes, lack of biological replicates, and insufficient details about cell quantification.

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

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Reply to the reviewers

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

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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

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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.

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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.

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 #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.

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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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