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

Learn more at Review Commons

Reply to the reviewers

We thank the reviewers for their positive comments on our manuscript. To address their criticisms, we propose to do the following experiments:

Reviewer 1 (mi__nor comments)__:

1. In Fig. 1, the authors show that Btz-WT, but not Btz-HD, localizes to the posterior pole of the oocyte. Do the authors see Btz-WT and/or Btz-HD localized to MNs/muscles/glia at the NMJ? We have had difficulty detecting the expression of our Btz-GFP transgenes at the NMJ. In case this was due to competition with endogenous wild-type Btz, we will repeat the staining in a btz mutant background. If the protein is still undetectable, we can include data showing the localization of UAS-Btz-GFP when overexpressed in muscles or motor neurons.

The mitochondrial phenotypes observed in Btz mutants are striking. But it seems possible that there are defects in overall mitochondrial levels in muscle in addition to defects in their localization. Overall, mitochondrial levels seemed reduced in Btz mutants. Is it possible to do a ATP5A immunoblot in Btz mutants to test whether overall mitochondrial levels are altered?

We will do a Western blot to compare ATP5A levels in btz2/+ and btz2/Df(3R)BSC497 larval carcasses.

ECM proteins are known to be critical for regulating TGFB signaling. That, taken with the multi-tissue genetic requirement for Btz, suggests that Btz might directly regulate either Ltl or Frac RNA, given that these ECM proteins are likely deposited by multiple cell types.

We agree that this is a possibility and we will mention it in the Discussion.

Reviewer 2 (major comments):

1. In Figure 1, regarding the validation of rescue constructs: the EJC interaction-defective mutant is based solely on conservation, as all structural/interaction studies cited with Btz bound to EJC have been with human proteins. They use Vasa localization as a readout of EJC-dependent function, but this is indirect and only assesses one aspect of EJC function (localization). Since many of the main conclusions in the paper are predicated on this mutant being EJC-independent, they should validate this with the Drosophila orthologs using immunoprecipitation. They demonstrate the capability of expressing GFP-tagged versions of Casc3 WT and mutant in S2 cells, so this should not be a cumbersome control experiment to include. We will express tagged Btz-WT and Btz-HD proteins in S2 cells and test whether they can be co-immunoprecipitated with Myc-tagged Drosophila eIF4AIII.

Regarding Figure 3, it could be postulated that the number of boutons would be influenced by the length of axons. Is axon outgrowth accounted for in these experiments? This would influence number of synaptic boutons. Panel F looks very different from panel A in terms of axon length (could this be due to axon outgrowth defect and/or impacted muscle size?) Can quantitation be done also by normalizing to axon length (bouton number/axon length)? Or perhaps this is accounted for in muscle size? If so, this should be explained.

• *

The NMJ grows during development by adding both axonal branches and synaptic boutons, so its size can be measured by counting the number of boutons or branches or measuring branch length. These measures are usually well correlated. In this paper we used bouton number normalized to muscle surface area as our measure of NMJ size, but we did observe corresponding changes in the number and length of branches, as the reviewer points out. We will explain this more clearly in the text.

In Figure 3 quantification: n's vary between genotypes significantly, and this should be explained (e.g. was there a recovery issue between genotypes or just fewer needed for WT-like?).

• *

The btz mutant larvae are more difficult to dissect due to muscle fragility, and some crosses in this genetic background may have yielded fewer usable filets than desired. We believe the numbers we obtained are sufficient to show which differences are significant.

In Figure 4 panels B and F (mutants), there appears to be reduced axon outgrowth (see point above). This should be taken into account when expressing bouton number.

• *

As explained in our response to point 2, axon length and bouton number are correlated measures of synapse size and vary together in this figure as expected.

The RNA-seq data (Figure 5) has a potential issue in that they used larvae with a balancer chromosome (Df), which yields a 50% reduction in any genes on that chromosome. They acknowledge this and removed these genes from the analysis, but the concern remains that this still might be a confounding variable (for example, if reduction in any of these genes might disrupt a signaling pathway). We do not think that the RNA-seq needs to be repeated, but we propose that the authors validate these targets using qPCR in their MN-specific btz knockdown system (this way, they can also include magoh and eif4aIII knockdowns for comparison).

• *

Because only one btz allele was available, we used transheterozygotes with a deficiency for the region to avoid homozygosing other mutations that might be present on the btz2 chromosome. As a consequence, we did observe reduced expression of genes located within the deficiency (which covers a small region, not an entire chromosome), and it is possible that this might contribute to the phenotype. However, we have seen a similar reduction in NMJ size in btz2 homozygotes. We do not think that motor neuron-specific btz knockdown is a useful genotype to validate the RNA-Seq results because ltl and frac levels do not change significantly in the CNS, only in muscle, and knockdown only in motor neurons would be unlikely to change daw levels measured in the whole CNS. Knocking down mago or eIF4AIII in muscle is lethal before the third larval instar stage, preventing us from comparing their effects on gene expression to those of btz. However, we will do qRT-PCR to measure daw, ltl and frac mRNA levels in btz2 homozygous mutant muscles.

Reviewer 2 (minor comments):

1. *Some statements made in the introduction that are not entirely accurate: **

   "A fourth core subunit, known as Barentsz (Btz), Cancer susceptibility candidate gene 3 (CASC3), or Metastatic lymph node 51 (MLN51), associates with the complex following the completion of splicing, and is required for the effects of the EJC on translation, NMD and mRNA localization (Chazal et al., 2013; Palacios et al., 2004; Shibuya et al., 2006; van Eeden et al., 2001)."

   A recent study indicates that Casc3 is not required for EJC-dependent NMD targets in human cells, but rather enhances NMD on a subset of targets (Gerbracht et al. 2020 NAR). Perhaps "is required" should be changed to "plays a role in cytoplasmic EJC-mediated processes, such as...". It has also been shown that EJC core can assemble without Casc3 (e.g. Ballut et al 2005 NSMB, Gehring et al 2009 PLoS Biol). Previous work from the authors show that Casc3 (Btz) is not necessary for EJC function in pre-mRNA splicing (Roignant et al, 2010 Cell). Further, there exists a population of Casc3 lacking EJCs in human cells (Mabin et al 2018 Cell Reports). Collectively, all this evidence points to Casc3 not being a core EJC subunit. *

• *

We will change the text so that we do not refer to Btz/Casc3 as a core subunit.

• • "In the mouse brain, haploinsufficiency for Magoh, Rbm8a or Eif4a3 causes severe microcephaly, but complete loss of Casc3 has a much milder effect that can be attributed to developmental delay (Mao et al., 2017; Mao et al., 2016; Mao et al., 2015; Silver et al., 2010)."

  From Mao et al 2017: complete loss and hypomorphic mutants were embryonic and perinatally lethal (contrary to what the authors are stating here), while compound mutants and heterozygotes exhibited neurodevelopmental delay. By "milder effects" the authors could also be referring to brain size being proportional to body size in the complete loss homozygotes; either way, this should be clarified. *

• *

    By “milder effects” we meant the effect on brain size. We will clarify this in the revised text.
  

Fly-specific nomenclature could be made more accessible to a broader audience, as the full readership will likely not have expertise in Drosophila genetics. For example, w118, btz2 labels used in figures are not explained anywhere in the manuscript. While the authors do a good job of describing various mutants in a more accessible fashion in the results section, the genotype labels in figures can be better explained in the legends.

We apologize for this and will clarify the genotype labels in the figure legends.

Fig 2 L-N panels might warrant more explanation. Can the mitochondria be counted here? Is there also a difference in volume/morphology that could be quantitated? In Figure 2N, muscle fibers are more densely packed in mutant vs. control; can this be explained?

• *

We are hesitant to quantify mitochondria or comment on muscle fiber packing based on the EM images, because only one individual of each genotype was examined. We prefer to simply use these images to provide a higher resolution view of the change in mitochondrial distribution that we observed and quantified using light microscopy. However, we do plan to do a Western blot to determine whether there are changes in the number of mitochondria in btz mutants (see Reviewer 1 point 2).

In Fig 2, to draw parallels between panels A-K and L-N, it might also be helpful to use the red/yellow arrow system on panel A for comparison.

This is a good suggestion that we will follow.

In Figure 3, it might be helpful for a general audience to include zoomed-in picture of boutons (as in Fig 5B), as some panels appear to have less defined bouton shape.

• *

We do observe that boutons tend to be less well separated from each other in btz mutants, and will include zoomed-in pictures to document this.

Is the bouton size different in the mutant in Figure 3? Can this be quantified?

We do not think that there is a significant difference in bouton size in btz mutants, but we will measure this and include a quantification.

Fold changes are modest and not very apparent in staining (we acknowledge that this could be due to early developmental time point). Images could better point out differences in WT vs. mutant that are not readily apparent to those outside the fly neurodevelopment audience.

Because of the inherent variability in synapse shape, it can be difficult to appreciate changes in bouton number from a single image. However, our quantifications show that the changes are consistent and significant.

Fig 4 NMJs are shown on different scale (more zoomed in) than in Figure 3, and differences are bit easier to see at this scale. Presenting Fig 3 on this scale might help the reader with visualizing the differences in WT versus mutant.

• *

We will crop the images in Figure 3 so as to show them at the same scale as in Figure 4.

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

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

Summary

Ho et al. describes the developmental functions of the Drosophila Casc3 ortholog, Barentsz (Btz) using in vivo loss-of-function and rescue experiments in Drosophila larvae. In this study, the authors find that loss of Casc3 contributes to neuromuscular defects in the larval fly. Utilizing transgenics of WT and EJC interaction-defective mutants, they demonstrate that Btz has both EJC-dependent and independent functions in the larval neuromuscular junction, wherein muscle defects are EJC dependent and synaptic defects are EJC-independent. Using RNA-seq, they find that upregulated mRNAs include those that belong to the Activin signaling pathway. They go on to find that the neuromuscular defects in Btz mutants can be attributed to dysregulation of Activin signaling, and are rescued with loss of the Activin ligand, Dawdle (Daw).

Major Comments

Overall, the paper presents well-controlled experiments that support the main conclusions. We propose achievable validation experiments that we believe will strengthen the conclusions of the paper. There is some concern that the magnitude of the effects are overstated, or could be made more apparent to a broader audience (i.e. those in the mRNA regulation field beyond Drosophila geneticists).

• In Figure 1, regarding the validation of rescue constructs: the EJC interaction-defective mutant is based solely on conservation, as all structural/interaction studies cited with Btz bound to EJC have been with human proteins. They use Vasa localization as a readout of EJC-dependent function, but this is indirect and only assesses one aspect of EJC function (localization). Since many of the main conclusions in the paper are predicated on this mutant being EJC-independent, they should validate this with the Drosophila orthologs using immunoprecipitation. They demonstrate the capability of expressing GFP-tagged versions of Casc3 WT and mutant in S2 cells, so this should not be a cumbersome control experiment to include.

• Regarding Figure 3, it could be postulated that the number of boutons would be influenced by the length of axons. Is axon outgrowth accounted for in these experiments? This would influence number of synaptic boutons. Panel F looks very different from panel A in terms of axon length (could this be due to axon outgrowth defect and/or impacted muscle size?) Can quantitation be done also by normalizing to axon length (bouton number/axon length)? Or perhaps this is accounted for in muscle size? If so, this should be explained.

• In Figure 3 quantification: n's vary between genotypes significantly, and this should be explained (e.g. was there a recovery issue between genotypes or just fewer needed for WT-like?).

• In Figure 4 panels B and F (mutants), there appears to be reduced axon outgrowth (see point above). This should be taken into account when expressing bouton number.

• The RNA-seq data (Figure 5) has a potential issue in that they used larvae with a balancer chromosome (Df), which yields a 50% reduction in any genes on that chromosome. They acknowledge this and removed these genes from the analysis, but the concern remains that this still might be a confounding variable (for example, if reduction in any of these genes might disrupt a signaling pathway). We do not think that the RNA-seq needs to be repeated, but we propose that the authors validate these targets using qPCR in their MN-specific btz knockdown system (this way, they can also include magoh and eif4aIII knockdowns for comparison).

Minor comments

Some statements made in the introduction that are not entirely accurate:

• "A fourth core subunit, known as Barentsz (Btz), Cancer susceptibility candidate gene 3 (CASC3), or Metastatic lymph node 51 (MLN51), associates with the complex following the completion of splicing, and is required for the effects of the EJC on translation, NMD and mRNA localization (Chazal et al., 2013; Palacios et al., 2004; Shibuya et al., 2006; van Eeden et al., 2001)."

A recent study indicates that Casc3 is not required for EJC-dependent NMD targets in human cells, but rather enhances NMD on a subset of targets (Gerbracht et al. 2020 NAR). Perhaps "is required" should be changed to "plays a role in cytoplasmic EJC-mediated processes, such as...". It has also been shown that EJC core can assemble without Casc3 (e.g. Ballut et al 2005 NSMB, Gehring et al 2009 PLoS Biol). Previous work from the authors show that Casc3 (Btz) is not necessary for EJC function in pre-mRNA splicing (Roignant et al, 2010 Cell). Further, there exists a population of Casc3 lacking EJCs in human cells (Mabin et al 2018 Cell Reports). Collectively, all this evidence points to Casc3 not being a core EJC subunit.

• "In the mouse brain, haploinsufficiency for Magoh, Rbm8a or Eif4a3 causes severe microcephaly, but complete loss of Casc3 has a much milder effect that can be attributed to developmental delay (Mao et al., 2017; Mao et al., 2016; Mao et al., 2015; Silver et al., 2010)."

From Mao et al 2017: complete loss and hypomorphic mutants were embryonic and perinatally lethal (contrary to what the authors are stating here), while compound mutants and heterozygotes exhibited neurodevelopmental delay. By "milder effects" the authors could also be referring to brain size being proportional to body size in the complete loss homozygotes; either way, this should be clarified.

General minor comments:

• Fly-specific nomenclature could be made more accessible to a broader audience, as the full readership will likely not have expertise in Drosophila genetics. For example, w118, btz2 labels used in figures are not explained anywhere in the manuscript. While the authors do a good job of describing various mutants in a more accessible fashion in the results section, the genotype labels in figures can be better explained in the legends.

• Fig 2 L-N panels might warrant more explanation. Can the mitochondria be counted here? Is there also a difference in volume/morphology that could be quantitated? In Figure 2N, muscle fibers are more densely packed in mutant vs. control; can this be explained?

• In Fig 2, to draw parallels between panels A-K and L-N, it might also be helpful to use the red/yellow arrow system on panel A for comparison.

• In Figure 3, it might be helpful for a general audience to include zoomed-in picture of boutons (as in Fig 5B), as some panels appear to have less defined bouton shape.

• Is the bouton size different in the mutant in Figure 3? Can this be quantified?

• Fold changes are modest and not very apparent in staining (we acknowledge that this could be due to early developmental time point). Images could better point out differences in WT vs. mutant that are not readily apparent to those outside the fly neurodevelopment audience.

• Fig 4 NMJs are shown on different scale (more zoomed in) than in Figure 3, and differences are bit easier to see at this scale. Presenting Fig 3 on this scale might help the reader with visualizing the differences in WT versus mutant.

Significance

Overall, this paper contributes conceptually to understanding EJC-mediated mRNA regulation during development. The contribution here is incremental, but meaningful in terms of defining the scope of regulation by the EJC and its peripheral factors in various contexts. These findings will likely be of interest to the fields of RNA metabolism and neurodevelopment. It also adds to the existing work suggesting Casc3 may have additional functions outside of the EJC (e.g. Mao et al. 2017 RNA, Baguet et al 2007 J Cell Sci, Cougot et al. 2014 J Cell Sci); while these previous studies have suggested Casc3 roles in development and mRNA localization/granule formation that are different from the EJC core proteins, this study more directly tests an EJC-independent role in mRNA regulation of specific targets. Further addressing the molecular basis of this regulation will be outside the scope of this article but will be of interest to the field.

We are molecular biologists who study NMD and are thus equipped to address the EJC-related molecular functions and impact on the transcriptome. We do not have expertise in Drosophila genetics or neurobiology, and thus cannot critically evaluate the specific genetic approaches used or anatomy presented to the full extent. We have, however, pointed out areas that need elaboration regarding the genetic approaches and/or presentation of data that may be unfamiliar to a broader audience (i.e. the RNA metabolism field).

$$ \begin{array}{l} a x^{2}+2 b x y+c y^{2} \text { 可以写作 } \\ {\left[\begin{array}{ll} x & y \end{array}\right]\left[\begin{array}{ll} a & b \\ b & c \end{array}\right]\left[\begin{array}{l} x \\ y \end{array}\right]=1} \end{array} $$

更线性代数的形式 $$ \left.\begin{array}{l} {\left[\begin{array}{ll} \boldsymbol{x} & \boldsymbol{y} \end{array}\right]\left[\begin{array}{ll} a & b \ b & c \end{array}\right]\left[\begin{array}{l} \boldsymbol{x} \ \boldsymbol{y} \end{array}\right]=1} \ \boldsymbol{X}=\left[\begin{array}{l} \boldsymbol{x} \ \boldsymbol{y} \end{array}\right] \ \boldsymbol{A}=\left[\begin{array}{ll} a & b \ b & c \end{array}\right] \end{array}\right} \quad \Longrightarrow \boldsymbol{X}^{T} \boldsymbol{A} \boldsymbol{X} $$

SciScore for 10.1101/2021.04.16.440101: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Author Response:

Reviewer #1 (Public Review):

The manuscript by Schrieber et al., explores whether inbreeding affects floral attractiveness to pollinators with additional factors of sex and origin in play, in male and female plants of Silene latifolia. The authors use a combination of spatial sampling, floral volatiles, flower color, and floral rewards coupled with the response of a specialized pollinator to these traits. Their results show that females are more affected by inbreeding and in general inbreeding negatively impacts the "composite nature" of floral traits. The manuscript is well written, the experiments are detailed and quite elaborate. For example., the methodology for flower color estimation is the most detailed effort in this area that I can remember. All the experiments in the manuscript show meticulous planning, with extensive data collection addressing minute details, including the statistics used. However, I do have some concerns that need to be addressed.

Core strengths: Detailed experimental design, elaborate data collection methods, well-defined methodology that is easy to follow. There is a logical flow for the experiments, and no details are missing in most of the experiemnts.

Weaknesses: A recent study has addressed some of the questions detailed in the manuscript. So, introduction needs to be tweaked to reflect this.

Thank you very much for bringing this excellent article to our attention! We adjusted the writing in the introduction and the discussion accordingly. Please consider that this article was first published at the 15th of January 21, while our manuscript was submitted at the 9th of January. Hence, we were not able to account for this study in the first submission. Introduction pp 4-5, ll 48-54: “Although in a few cases inbreeding has been shown to alter single components of flower attractiveness (Ivey and Carr, 2005; Ferrari et al., 2006; Haber et al., 2019), insight into syndrome-wide effects is restricted to a single study. Kariyat et al. (2021) demonstrated that inbred Solanum carolinense L. display reduced flower size, pollen and scent production and receive fewer visits from diurnal generalists. It is necessary to broaden such integrated methodological approaches to other plant-pollinator systems (e.g., nocturnal specialist pollinators) and further floral traits (i.e., flower colour).” Discussion p 19, ll 535-542: “In summary, our research on S. latifolia suggests that in addition to inbreeding disrupting interactions with herbivores by changing plant leaf chemistry (Schrieber et al., 2018) it affects plant interactions with pollinators by altering flower chemistry. Our observations are in line with studies on other plant species (Ivey and Carr, 2005; Kariyat et al., 2012, 2021) and highlight that inbreeding has the potential to reset the equilibrium of species interactions by altering functional traits that have developed in a long history of co-evolution. These threats to antagonistic and symbiotic plant-insect interactions may mutually magnify in reducing plant individual fitness and altering the dynamics of natural plant populations under global change.”

Some details and controls are missing in floral scent estimation. Flower age, a pesticide treatment of plants that could affect chemistry..needs to be better refined.

We clarified this issue at different occasions in the methods section. Previous studies (and our study) on S. latifolia have shown no clear differences in the quality of floral scent between sexes. However, one study found higher total emission of VOC in males, while others found no differences. Hence, females produce no specific VOC that are used as oviposition cues but may be differentiated from males by the total amount of emitted VOC and pronounced differences in spatial flower traits. We highlight this at p 6, ll 111-116: “Silene latifolia exhibits various sexual dimorphisms with male plants producing more and smaller flowers that excrete lower volumes of nectar with higher sugar concentrations as compared to females (Gehring et al., 2004; Delph et al., 2010). The quality of floral scent exhibits no clear sex-specific patterns, while male plants have been shown to emit higher or equal total amounts of VOC as compared to females in different studies (Dötterl & Jürgens 2005, Waelti et al. 2009)”.

Both male and female moths show pronounced behavioural responses to lilac aldehyde isomers and other VOC in the floral scent of S. latifolia (Dötterl et al., 2006). We therefore treated these VOC as typical floral scent compounds. We clarified this at p 7, ll 125-126: “A substantial fraction of floral VOC produced by S. latifolia triggers antennal and behavioural responses in male and female H. bicruris moths (Dötterl et al., 2006).” and p 9, ll 2010-218:” For targeted statistical analyses, we focused on those VOC that evidently mediate communication with H. bicruris according to Dötterl et al. (2006). We analysed the Shannon diversity per plant (calculated with R-package: vegan v.2.5-5, Oksanen et al. 2019) for 20 floral VOC in our data set that were shown to elicit electrophysiological responses in the antennae of H. bicruris (Supplementary File 1). Moreover, we analysed the intensities of three lilac aldehyde isomers, which trigger oriented flight and landing behaviour in both male and female H. bicruris most efficiently when compared to other VOC in the floral scent of S. latifolia. Furthermore, H. bicruris is able to detect the slightest differences in the concentration of these three compounds at very low dosages (Dötterl et al. 2006).”

We used biological pest control agents in a preventive manner because S. latifolia is often infested by thrips and aphids under greenhouse conditions. The writing in the previous manuscript version was not clear with this regard and we changed the text at p 8, ll 157-161: ” Plants received water and fertilisation (UniversolGelb 12-30-12, Everris-Headquarters, NL) when necessary for the entire experimental period and were prophylactically treated with biological pest control agents under greenhouse conditions to prevent thrips (agent Amblyseius barkeri and Amblyseius cucumeris) and aphid (agent Chrysoperla carnea) infestation (Katz Biotech GmbH, GE) .”

Indeed, flower size and scent emission can be correlated. Although the question whether differences in scent emission were based on a difference in flower size is an interesting one, it seemed less relevant to us because it is unlikely that our pollinators correct their perception of a scent for the size of a flower (see also p 19, 520-526). We were rather interested in whether scent emission differs between the plant treatments and thus pollinators may chemically perceive such differences. Moreover, we found it problematic to correct our models for flower size by including it as a covariate, which is the reason why we have not assessed this trait during scent collection. In this case, we would have corrected our scent responses for the effects of inbreeding, sex and population origin (i.e., the predictors we are interested in) because all of them determine the size of a flower (Figure 2 c,d). Hence, the inbreeding, sex and origin effects on flower scent would likely vanish. However, it is highly unlikely that the set of genes contributing to sex-, breeding treatment- and origin-based variation in flower size is exactly the same one that determines variation in scent emission per flower, which is basically the assumption underlying the model that includes flower size as a covariate. We critically mentioned the trade-off relationships and our reasoning to not correct for flower size at 9p ll 208-210: “The intensities of VOC were not corrected for flower size because we wanted to capture all variation in scent emission that is relevant for the receiver i.e., the pollinator.”

While the study is laser-focused on floral traits, as the authors are aware inbreeding affects the total phenotype of the plants including fitness and defense traits. For example, there are quite a few studies that have shown how inbreeding affects the plant defense phenotype. This could be addressed in the introduction and discussion.

We agree that this aspect is important and therefore addressed it in further detail in the introduction at p 4 ll 34-38: “While it is well established that inbreeding can increase a plant’s susceptibility to herbivores by diminishing morphological and chemical defences (Campbell et al., 2013; Kariyat et al., 2012; Kalske et al., 2014), its effects on plant-pollinator interactions are less well understood. Inbreeding may reduce a plant’s attractiveness to pollinating insects by compromising the complex set of floral traits involved in interspecific communication.” Since other referees suggested to rather tone down than increase the discussion based on floral scent results, we stick to the general feedback relationship among of herbivory and pollination, rather than relating it specifically to volatiles in the discussion at p 19, ll 535-544: “In summary, our research on S. latifolia suggests that in addition to inbreeding disrupting interactions with herbivores by changing plant leaf chemistry (Schrieber et al., 2018) it affects plant interactions with pollinators by altering flower chemistry. Our observations are in line with studies on other plant species (Ivey and Carr, 2005; Kariyat et al., 2012, 2021) and highlight that inbreeding has the potential to reset the equilibrium of species interactions by altering functional traits that have developed in a long history of co-evolution. These threats to antagonistic and symbiotic plant-insect interactions may mutually magnify in reducing plant individual fitness and altering the dynamics of natural plant populations under global change. As such, our study adds to a growing body of literature supporting the need to maintain or restore sufficient genetic diversity in plant populations during conservation programs.”

Reviewer #2 (Public Review):

A summary of what the authors were trying to achieve. This interesting and data-rich paper reports the results of several detailed experiments on the pollination biology of the dioceus plant Silene latfolia. The authors uses multiple accessions from several European (native range) and North American (introduced range) populations of S. latifolia to generate an experimental common garden. After one generation of within-population crosses, each cross included either two (half-)siblings or two unrelated individuals, they compared the effects of one-generation of inbreeding on multiple plant traits (height, floral size, floral scent, floral color), controlling for population origin. Thereby, they set out to test the hypothesis that inbreeding reduces plant attractiveness. Furthermore, they ask if the effect is more pronounced in female than male plants, which may be predicted from sexual selection and sex-chromosome-specific expression, and if the effect of inbreeding larger in native European populations than in North American populations, that may have already undergone genetic purging during the bottleneck that inbreeding reduces plant attractiveness. Finally, the authors evaluate to what extent the inbreeding-related trait changes affect floral attractiveness (measured as visitation rates) in field-based bioassays.

An account of the major strengths and weaknesses of the methods and results. The major strength of this paper is the ambitious and meticulous experimental setup and implementation that allows comparisons of the effect of multiple predictors (i.e. inbreeding treatment, plant origin, plant sex) on the intraspecific variation of floral traits. Previous work has shown direct effects of plant inbreeding on floral traits, but no previous study has taken this wholesale approach in a system where the pollination ecology is well known. In particular, very few studies, if any, has tested the effects of inbreeding on floral scent or color traits. Moreover, I particularly appreciate that the authors go the extra mile and evaluate the biological importance of the inbreeding-induced trait variation in a field bioassay. I also very much appreciate that the authors have taken into account the biological context by using a relevant vision model in the color analyses and by focusing on EAD-active compounds in the floral scent analyses.

The results are very interesting and shows that the effects of inbreeding on trait variation is both origin- and sex-dependent, but that the strongest effects were not always consistent with the hypothesis that North American plants would have undergone genetic purging during a bottleneck that would make these plants less susceptible to inbreeding effects. The authors made a large collection effort, securing seeds from eight populations from each continent, but then only used population origin and seed family origin as random factors in the models, when testing the overall effect of inbreeding on floral traits. It would have been very interesting with an analysis that partition the variance both in the actual traits under study and in the response to inbreeding to determine whether to what extent there is variation among populations within continents. Not the least, because it is increasingly clear that the ecological outcome of species interactions (mutualistic/antagonistic) in nursery pollination systems often vary among populations (cf. Thompson 2005, The geographic mosaic of coevolution), and some results suggest that this is the case also in Hadena-Silene interactions (e.g. Kephardt et al. 2006, New Phytologist). Furthermore, some plants involved in nursery pollination systems both show evidence of distinct canalization across populations of floral traits of importance for the interaction (e.g. Svensson et al. 2005), whereas others show unexpected and fine-grained variation in floral traits among populations (e.g. Suinyuy et al. 2015, Proceedings B, Thompson et al. 2017 Am. Nat., Friberg et al. 2019, PNAS). Hence, it is possible that the local population history and local variation in the interactions between the plants and their pollinators may be more important predictors for explaining variation in floral trait responses to inbreeding, than the larger-scale continental analyses. Not the least, because North American S. latifolia probably has multiple origins, with subsequent opportunity for admixture in secondary contact.

Yes, it is necessary to put populations from the same continent into one category, since native and invasive plant populations differ significantly in their evolutionary history (p 5, ll 74-81, http://onlinelibrary.wiley.com/doi/10.1111/j.1365-294X.2012.05751.x). Origin explained sufficient amounts of variation in several traits including flower number, corolla expansion, VOC diversity, lilac aldehyde A intensity, and pollinator visitation rates (see Figures 2-3; and Table 2) and some variation in in the magnitude of inbreeding effects (Figure 2e, f; Figure 3). Even if we would not be interested in differences among native and invasive populations, we would have to include origin as a fixed effect in our models because:

i) populations within a distribution range are no independent samples,

ii) origin explains sufficient variation in many responses,

iii) origin cannot be fitted as a random factor, since it has only two levels (the minimum number of levels for random effect is 4). We agree that it would be very interesting to specifically assess differences in the magnitude of breeding and sex effects among populations within origins. We now discuss this as important future research direction at p 18, ll 500-507: “As such, the precise mechanisms underlying variation in inbreeding effects on different scent traits across population origins of S. latifolia can only be explored based on comprehensive genomic resources, which are currently not available. Future studies should also incorporate field-data on the abundance of specialist pollinators and extend the focus from variation in the magnitude of inbreeding effects among geographic origins to variation among populations within geographic origins and individuals within populations. This would allow a detailed quantification of geographic variation in inbreeding effects and elaborating on the causes and ecological consequences of such variation (Thompson, 2005; Schrieber and Lachmuth, 2017; Thompson et al., 2017)”.

To empirically address within-origin variation of inbreeding effects with our data, we would have to i) fit correlated random intercepts and slopes for the interaction breeding-sex on the population random factor (models consume min. 22 DF); or ii) include population as a fixed effect in our models (models consume min. 67 DF). We have tried both of these approaches when preparing the revision, but unfortunately it turned out that our study is not designed to address this question. The models for both variants only partially converge (see R-script ll. 1568-1580), and even if they do this does not imply that one can draw solid inference from them. Approach i often results in multiple singular convergence warning messages implying that no variance is explained by population-specific reaction norms to the fixed effects specified in the random effects structure. Approach ii results in odd rank- deficient models (I was seriously worried about type I errors). We simply have too few replicates (5) per population-breeding treatment-sex combination for both approaches. For solid inference we would need 10approach i-40approach ii replicates = 640-2600 individuals. However, our experimental design is sufficient to address the hypothesis we have raised in the introduction as well as general differences in response variables among populations. We now provide information on variance partitioning for all models that include population as a random effect in S9. As you will see, population explains lower amounts of variation in our responses as the fixed effects in 9 out of 12 models. The random effects maternal and paternal genotype (mother&father) explain more variation than the random effect population in 6 of 12 cases. Thus, these data do not make a strong case for an extensive discussion of population-based differences in floral traits and this was also not a question or hypotheses we wanted to address with our study.

I see no major weaknesses in the study, and but in my detailed response, I have made a few questions and suggestions about the floral scent analyses. In short, the authors have used a technique that is not the standard method used for making quantitative floral scent analyses, and I am curious about how it was made sure that the results obtained from the static headspace sampling using PDMS adsorbents could be used as a quantitative measure. I would suggest the authors to validate the use of this method more thoroughly in the manuscript, and have detailed this comment in my response to the authors.

Also, and this may seem like a nit-picky comment, I am not convinced that the best way to describe the traits under study is "plant attractiveness", because in the experimental bioassays, most of the traits under study that are affected by the inbreeding treatment, did not result in a reduced pollinator visitation. Most (or all) of these traits may also be involved in other plant functions and important for other interactions, so I suggest potentially using a term like "floral traits" or "(putative) signalling traits".

We now avoid the term floral attractiveness throughout the manuscript and instead refer to “floral traits”.

An appraisal of whether the authors achieved their aims, and whether the results support their conclusions: By and large, the authors achieved the aims of this study, and drew conclusions based in these results. One interesting aspect of this work that I think could be discussed a bit deeper is the lack of congruence between the effects of inbreeding on floral traits and the variation in visitation pattern in the bioassay. In fact, the only large effect of inbreeding on a floral trait that may play a role as an explanatory factor is the reduction of emission of lilac aldehyde A in inbred female S. latifolia from North America, which correspond to a reduced visitation rate in this group in the pollinator visitation bioassay. I have made some specific suggestions in my comments to the authors.

We agree that this aspect required deeper discussion and revised the section at p 19, ll 520-526 accordingly. We believe that the limited spatial vision of H. bicruris in combination with our experimental setup for pollinator observations increased the relative importance of floral scent for pollinator visitation rates (suggested by referee #3).

A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community: I think that one important aspect of this work that may broaden the impact of this study further is the link between these experiment, and our expectations from the evolution of selfing. Selfing plant species most often conform to the selfing syndrome, presenting smaller, less scented flowers than outcrossing relatives. Traditionally, the selfing syndrome is explained by natural selection against individuals that invest energy into floral signalling, when attracting pollinators is no longer crucial for reproduction. Some studies (for example Andersson, 2012, Am. J. Bot), however, have shown that only one, or a few, generations of inbreeding may reduce floral size as much as quite strong selection for reduced signalling. Here, at least for some populations and sexes, similar results are obtained in this paper regarding several traits (including floral scent), and one way to put this paper in context is by discussing the results in the light of these previous papers.

We now address this issue at p 16, ll 417-420: “However, our findings highlight that even weak degrees of biparental inbreeding (i.e., one generation sib-mating) can result in a severe reduction of spatial flower trait and scent trait values that is detectable against the background of natural variation among multiple plant populations from a broad geographic region. This observation indirectly supports that the selfing syndrome (i.e., smaller, less scented flowers observed in selfing relative to outcrossing populations of hermaphroditic plant species) may not merely be a result of natural selection against resource investment into floral traits, but also a direct negative consequence of inbreeding (Andersson, 2012).”

Reviewer #3 (Public Review):

Schrieber et al. studied the effects of biparental inbreeding in the dioecious plant Silene latifolia, focusing specifically on traits important for floral attractiveness and pollinator attraction. These traits are especially important for dioecious species with separate sexes as they are obligate outcrossers. The authors find that inbreeding mostly decreases floral attractiveness, but that this effect tended to be stronger in the female flowers, which the authors suspect to result from the trade-off with larger investment in the sexual functions in the female plants. The authors then go on to couple the changes in visual and olfactory floral traits to pollinator attraction which allows them to conclude or at least speculate that differences in pollinator behavior are mostly driven by the changes in olfactory traits. The study is robust in its broad and well-balanced sampling of populations, rigorous and in large part meticulously documented experimental designs and linking of the effects on mechanisms to ecological function. The hypothesis are clearly stated and the study is able to address them mostly convincingly. However, some of the aspects of the decisions the authors made and possible caveats need to be addressed and elaborated on.

A major caveat, in my opinion, is that while the authors find stronger effects of inbreeding on pollinator visitation rates in the plants from the North American (Na) origin, these plants were tested in an environment that was foreign to them, which could have important consequences for the results of this study. This is specifically because the main pollinator Hadena bicruris moth is completely absent from the populations in Na, and yet, was the main pollinator observed in the pollinator attraction experiment. As this pollinator is also a seed predator, the Na populations are released from the selection pressure to avoid attracting the females of this species and thus risking the loss of seeds and fitness. In fact, some of the results suggest that the release from the specialist pollinator and seed predator in Na has led to increase in the attractiveness of the female flowers based on the higher number of flowers visited in the outcrossed females compared to outcrossed males in the plant from the Na origin and the similar, though not statistically significant, pattern in the olfactory cue. While ideally this pollinator attraction experiment should be repeated within the local range of the Na plants, this is of course is not feasible. Instead I suggest the problem should be addressed in the discussion explicitly and its consequences for the interpretation of the results should be considered.

Indeed, North American populations are tested in their “away”- habitat only and the observed plant performance and pollinator visitation rates can thus provide no direct implications for their “home”-habitat. We state this now more clearly at pp 11-12, ll 283-285. However, our design is appropriate for investigating inbreeding effects on plant-pollinator interactions in multiple plant populations in a common environment. Given the close taxonomic relationship of H. bicruris (main pollinator in Europe) and H. ectypa (main pollinator in North America), the behavioural responses of the former species to variation in the quality of its host plant was considered to overlap sufficiently with responses of the latter species as outlined at pp 11-12, ll 285-291.

The hypothesis that North American (NA) S. latifolia evolved higher attractiveness to female Hadena moths because H. ectypa is not able to oviposit on female plants in contrast to H. bicruris is indeed a highly interesting one. However, as you have outlined correctly, our study is not designed to elaborate on questions related to adaptive evolutionary differentiation among North American and European plants. Instead of addressing this hypothesis based on our data, we thus take reference to previous studies in the discussion p 17, ll 482-487: “As discussed in detail in previous studies, higher flower numbers in North American S. latifolia plants (Figure 1b) may result from changes in the selective regimes for numerous abiotic factors (Keller et al., 2009) or from the release of seed predation. As opposed to H. bicruris, H. ectypa pollinates North American S. latifolia without incurring costs for seed predation, which may result in the evolution of higher flower numbers, specifically in female plants (Elzinga and Bernasconi, 2009).”

The incorporation of the VOC data in the actual manuscript was quite limited and I found the reasoning for picking only the three lilac aldehydes (in addition to the Shannon diversity index) for the univariate statistical tests insufficient. How much more efficient was the effect of the lilac aldehydes compared to the other 17 compounds deemed important in the previous study? While the data on this one aldehyde matches the pollinator attraction results, having one compound out of 70 (or out of 20 if only considering the ones identified important for the main pollinator) seems, perhaps, fortuitous lest there is a good reason for focusing on these particular compounds.

We adapted the text to increase clarity but sticked to our previous choice for the analyses of VOC data.

i) We now explain our choice of analysing lilac aldehydes with more detail p9, ll 210-218: “For targeted statistical analyses, we focused on those VOC that evidently mediate communication with H. bicruris according to Dötterl et al. (2006). We analysed the Shannon diversity per plant (calculated with R-package: vegan v.2.5-5, Oksanen et al. 2019) for 20 floral VOC in our data set that were shown to elicit electrophysiological responses in the antennae of H. bicruris (Supplementary File 1). Moreover, we analysed the intensities of three lilac aldehyde isomers, which trigger oriented flight and landing behaviour in both male and female H. bicruris most efficiently when compared to other VOC in the floral scent of S. latifolia. Furthermore, H. bicruris is able to detect the slightest differences in the concentration of these three compounds at very low dosages (Dötterl et al. 2006).”

ii) If one analyses 20 compounds with zero-inflation models (actually two models in one) + 8 floral trait models + 2 pollinator visitation models (zi-models with two component models), one ends up with 52 models investigating complex fixed and random effect structures. To keep type-1 errors as low as possible (see also comment 2.12.b from Referee#2), we approached the more comprehensive VOC data sets with multivariate analyses or Shannon diversity.

iii) We tested the effect of sexoriginbreeding treatment on the Shannon diversity of 20 active VOC as well as in the random forest analyses with the 20 VOC and 70 VOC dataset and transparently reported the results from all of these analyses in the manuscript. Hence, the incorporation of VOC data was not limited. However, we agree that we have taken too little reference to these results and now changed the text accordingly. Results section p 13 ll 351-354: ”Multivariate statistical analyses of 20 H. bicruris active VOC and all 70 VOC detected in S. latifolia revealed no clear separation of floral headspace VOC patterns for any of the treatments (Figure 2-figure supplement 2). In summary, the combined effects of breeding treatment, sex and range on floral scent were rather week.”

Sampling time of VOCs is reported ambiguously. Was it from 21:00 to 17:00 the next day or in fact from 9pm to 5AM (instead of 5 pm as reported)? Please be more specific in the text as this is quite important. If sampling tubes were left in place during the daytime, some of the compounds could have evaporated due to heating of the tubes in the summer. It would also be important to mention whether all of the headspace VOCs were sampled on the same day and whether there could be variation in i.e. temperature.

Thank you very much for identifying this typo! It is from 9 pm to 5 am (p 9, l 186).

Considering the experimental setup for the pollinator attraction observations and the pooling of the data at the block level (which I think is the right choice) it seems possible the authors were more likely to get a result where pollinator behavior matches the long-distance cue, the VOCs. Short-distance cues such a subtle difference in flower size would perhaps not be distinguished with the current setup. I would be interested to know if the authors agree, and if so, mention this in the discussion.

Thank you very much for this excellent suggestion! We agree and discuss this aspect in detail at p 19, ll 520-526. Indeed, one would need two different experimental setups to assess the contributions of long and short distance cues. Our setup (large distances among plots) is optimal for long distance cues, while a setup for short distance cues should have all plants in close spatial proximity. However, the latter approach does then not allow to address long-distance cues and to exclude competition/facilitation for pollinators among plants from different treatment groups.

SciScore for 10.1101/2021.04.15.440004: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.04.14.439891: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 3. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.04.14.439793: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.04.13.439274: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.04.10.21255202: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

This study has some limitations due to the low number of volunteers per arm (N=15), and to the lack of subject randomization amongst study arms on the basis of GRAd neutralizing titer at baseline. Both these aspects could have had an impact in the poor vaccine dose effect observed. However, the explored dose range (4-fold between low and high dose) was limited, and no major dose effect was expected. Nevertheless, for phase 2/3 single-dose regimen we selected the 2×1011 vp (high) dose, due to higher and more consistent immunogenicity especially in the elderly cohort. As for the two-dose regimen, the intermediate dose of 1×1011 vp was selected which represents the best compromise between tolerability and immunogenicity. Finally, data beyond w4 post vaccination on persistence of humoral and cellular responses will be part of a future report once the study follow up will be completed.

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04791423</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Active, not recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Study of GRAd-COV2 for the Prevention of COVID-19 in Adults</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04528641</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Active, not recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">GRAd-COV2 Vaccine Against COVID-19</td></tr></table>

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a protocol registration statement.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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

Learn more at Review Commons

Reply to the reviewers

We thank the reviewers for their appreciation of our work and for their constructive feedback. We have addressed their comments in the point-by-point answers below. We provide a largely revised manuscript as well as the plan for new experiments, following requests from the reviewers.

Reviewer #1 (Evidence, reproducibility and clarity (Required)): In their manuscript, Ronchi P. et al. present a thorough and very well detailed workflow for 3D correlative-light and electron microscopy of whole cells in large tissues. Their approach of iterative block trimming and florescence imaging combined with laser branding allowed them explore previously inaccessible tissues and questions. They imaged mammary gland organoids, and resolved the organization of the cells in the organoid and mitotic events. They also specifically targeted tracheal terminal cells of a 3rd instar Drosophila larvae labeled with cytoplasmic DsRed to study their ultrastructure, and several Drosophila ovarian follicular cells (FC) where the cytoplasmic motor protein dynein was knocked down (KD) by RNAi. In the tracheal cells, they observed connected secretory vesicles, probably delivering extra-cellular matrix to the trachea tube. They also found that the overall shape of dynein KD FCs is distorted comparted to WT, and that the localization of multi-vesicular bodies/endosomes inside the FCs changed from an apical to basal membrane localization. Although the approach is not entirely new, the manuscript certainly paves the way for future studies to obtain ultrastructural information from large specimens and combine it with meaningful fluorescence information, it's also beautiful and polished.

\*Minor comments:**

1. The authors state that they (line 145) that they found the optimal concentration of UA and the best compromise between EM contrast and fluorescence preservation. However, no detail is provided as to how these parameters were experimentally determined. *

UA concentration can be optimized in a number of ways, including varying incubation temperature and time. We decided to modify the speed at which the temperature was increased after the freeze substitution step at -90°C. We have experimentally compared 3°C/h vs 5°/h (described in the original on-section CLEM protocol by Kukulski et al) and found a considerable difference for some of the samples we used. This is now described in the revision (lines 152-159). While other protocols might work for some samples, we found this protocol to provide good quality imaging with a large variety of samples we have worked with (including some that are not included in the current paper, e.g. gastrulating Drosophila embryos or C. elegans larvae).

• More detail as to how the block face was mounted and kept parallel to the glass bottom dish would be helpful. *

This is now described in lines 182-185.

Also, what was the optical slice of the confocal and what was the increment in Z?

The information is now included in lines 191-192.

• Have the authors tried fluorophores with shorter wavelength (like GFP)? And if so, have they estimated the penetration depth in resin? This would be informative because many GFP lines already exist in the Drosophila model.*

In the current version, we have limited our study to red fluorescent proteins because UA is autofluorescent in green. This could cause problems when imaging at shorter wavelengths. We have discussed this in lines 442-444. However, we agree that an analysis of the behavior of GFP in confocal imaging of the block could improve our work and increase the potential applicability of this method. We are therefore planning an experiment to compare the behavior of EGFP and mCherry during confocal imaging of the block. This experiment will be included in a future revised version.

• In figure 6 i, how did the authors identify the structures to be MBVs close to the basal surface in the mutant seeing as they do not look like the MVBs seen in WT cells?*

In both cases, we identified MVBs as vesicles with a clear lumen containing one or more vesicles of homogenous size. We have included a paragraph in the Material & Methods on “multivesicular body quantification” where this is specified (lines 597-599). The only difference between MVBs of WT and KD cells was their size (shown in Fig. 6j,k,l), and therefore the identification was unambigious.

Similarly, how were the structures identified as endosomes in figure 5f?

We thank the reviewer for pointing this out. We agree that it is impossible to discriminate between endocytic and exocytic vesicles in our static data. We have therefore rephrased this as “membrane trafficking” (line 355, line 358, line 946).

Can the authors quantify the total MVBs in the apical/basal membranes from both RNAi KD and WT?

We have now segmented all MVBs in 5 KD cells and 5 neighboring WT cells in 4 different oocytes. Representative images, as well as a quantitative analysis of the distribution of MVBs, are shown in Fig. 6m-o. When we segmented MVBs for this analysis, we realized that WT cells showed large MVBs in their apical side (~5-10% of total MVBs) while in KD cells this population was almost completely absent. This is consistent with a role of dynein in MVB fusion. The data are now included in Fig. 6p. We thank the reviewer for her/his suggestion to have a more rigorous analysis of the MVBs, which allowed us to make another interesting discovery.

• The motivation/question in the case of Drosophila samples was clear but not so obvious in the case of the mammary gland organoids. It would be nice if the authors could give a bit more information.*

We have included a justification for the use of organoids in lines 226-235.

• In the introduction (line 124). The dimensions are given in microns and millimeters, which can be a bit confusing. *

We have changed this (line 132).

• In the discussion (lines 427-431), "sample preparation protocols compatible with fluorescence preservation have proven satisfactory for FIB-SEM milling and imaging" have also been shown by others (Porrati et al., 2019).*

We agree with the reviewer and indeed Porrati et al., 2019 was cited in the introduction. We have not claimed that we have shown this for the first time. For completeness, we cite the paper again in the discussion (line 469).

• Figure 1:
  • It would be helpful if the cell referred to in g was highlighted.*

As suggested, we have indicated the cell with an arrowhead.

- Is the cell in (h) the one in g or in a as written?

We apologize for the mistake. It is indeed the one in g. We have corrected this (line 874).

- Is the image in (k) inverted compared to (i)?

The image in k is not inverted compared to i. We are showing raw images of the confocal and FIB-SEM datasets and therefore the two volumes are rotated 90º with respect to each other along the Y axis. As we have realized that this can be confusing for the readers, we have introduced a sentence in Materials and Methods to describe the different orientations between confocal and FIB-SEM datasets (lines 586-589).

*Figure 2:

• In panel d it seems that some numbers on the x axis were duplicated.*

We apologize for the mistake. We have corrected figure 2.

*Figure 5:

• How does the perfect overlap confirm the accuracy of targeting?*

We agree with the reviewer that the overlap is not a measure of accuracy. We have removed the sentence from the legend.

- In panel (e) it was not particularly easy to understand what is the basal lamina.

We have manually segmented the 2 basal membranes in different colors. We hope the reviewer will find this representation clearer.

- In panel (g) the fused vesicle is not as clear as the movie. I also found it open to interpretation whether this is in fact a fused vesicle.

We agree with the reviewer that a 3D object can be better appreciated in the stack image sequence rather than in a single 2D image. However, to help the visualization of the event in the figure, we have shown the 3 ortho-slices in a perspective view in Fig. 5g. This was the best representation we have found. The video with the stack will be available to the readers for a better inspection.

We also agree that it is formally impossible to be sure whether the vesicle is in fact releasing material in the apical space or taking it up. Therefore, we describe now the event as “putative site of fusion…” (line 947).

*Reviewer #1 (Significance (Required)):

The increasing demand for volume electron microscopy brings a lot of challenges to correlative light and volume electron microscopy workflows. Although the methods used by the authors are not new, their combination is original. The manuscript will certainly contribute to the field of correlative light and volume EM and provide a rather detailed protocol that can be reproduced by others. The workflow is also more efficient than what was previously achieved using x-ray instead of light microscopy(Bushong et al., 2015; Karreman et al., 2016).*

We thank the reviewer for the careful examination of our work and for the positive statement. We are aware that many of the methods used have already been described by others, but we believe that their combination is original and very powerful.

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

In this manuscript, Ronchi et al describe a workflow designed to facilitate the identification and downstream relocation of fluorescently tagged regions of interest within millimetre scale samples, ending with focused ion beam SEM acquisition of the target area. The work follows a logical progression, is well thought out, explained, and illustrated, with proof of concept experiments that are followed up by examples of systems where the potential for the application of the workflow in a 'real' biological question is demonstrated.

For me, the title reads better as ...targeting for FIB SEM acquisition... *

We have edited the title according to the reviewer’s suggestion

I have only minor suggestions for the revision of the manuscript from this initial version. The introduction, and introductory paragraphs for the two model systems would benefit from some revision to make them more concise however.

We have revised and shortened the introduction and introductory paragraphs for the model systems and we hope the reviewer will find it more concise.

\*Summary** Line 22 - omit large. *

Done (line 29)

Introduction Line 66 - It's probably clearer to discuss this concept as conductivity rather than grounding. We have changed this sentence (line 76)

Line 105 - Peddie and Collinson 2014 is not the correct reference for this statement. Presumably this is supposed to be Peddie et al 2014? ** We thank the reviewer for spotting this mistake. We have changed the citation (line 1110)

Line 124 - The external diameter of the carrier would give 7 mm2, but the internal diameter is smaller, so this size is slightly overstated.

We totally agree. The internal diameter of the carrier is 2mm and therefore the area 3.14 mm2. We have corrected the statement (line 132).

Results General comment - I find the use of NxNxN/N nm3 to be a confusing way of expressing the measurements, so would suggest splitting these up to express as: N nm3 or NxNxN nm.

To avoid confusion, we have now opted for: N nm x N nm x N nm.

Line 141 - no water was used in the FS mixture, and so wasn't needed for preservation of fluorescence? Dry/100% acetone? If no water is needed, this detail should be discussed. We added a clarification of this point (lines 148-150)

Line 142 - could the authors elaborate on the statement about timing and sample types, to give a better understanding of the context. The sentence referred to other possible applications (e.g. cell monolayers would require shorter FS time). However, as the method described here is aimed at large 3D samples, we find that longer FS times (72h) are always required. We have therefore removed the sentence (line 151).

Line 150 - on the choice of fluorophores, did the authors examine any shorter wavelengths, or was the decision to use red/far red based on any other evidence? Anecdotally, red and far red fluorophores may offer better preservation and less longevity in this context, but could the authors elaborate on their reasoning behind the choice shown here?

As replied to reviewer 1, point 3:

In the current version, we have limited our study to red fluorescent proteins because UA is autofluorescent in green. This could cause problems when imaging at shorter wavelengths. We have discussed this in lines 442-444.

However, we agree that an analysis of the behavior of GFP in confocal imaging of the block could improve our work and increase the potential applicability of this method. We are therefore planning an experiment to compare the behavior of EGFP and mCherry during confocal imaging of the block. This experiment will be included in a future revised version.

Line 168 - did immersion in water give rise to any distortion of the resin, or is HM20 sufficiently hydrophobic that this was not a concern? Mismatches in refractive indices (resin, water, glass, oil) could also presumably give rise to some small inaccuracies in depth prediction?

We observed a little distortion of the block face, due to hydration during the imaging step. However, as noticed during trimming at the microtome, this distortion was small and we could achieve a flat surface after removing 1-2 mm. Therefore this was not relevant for our measurements. We however now mention this in the discussion (lines 453-455). Mismatches of refractive indices also introduce inaccuracies, but these aberrations are reduced the closer the target is to the surface. Therefore, our predictions become more precise after each trimming step to approach the target.

Line 169 - was it possible to quantify the increase in signal? If the block is being hydrated, but the block is not absorbing water (re above point), then it must only be surface fluorophores that are hydrated

The quantified increase in fluorescence signal at the surface is now mentioned here (line 187) and can be observed in Fig. 2b. Indeed, only surface fluorophores are hydrated and we argue that this is an important player in the fluorescence intensity increase.

Line 179 - presumably this is a result of the surface of the block being hydrated (re above points). This is mentioned later, but could be explicitly stated here to make the point more strongly.

We now state this also in line 186.

Line 188 - Peddie et al 2014 contains some limited data for mCherry in sections that could be worth mentioning in support of the findings of reduced photobleaching rates

Thank you for pointing this out. We now cite Peddie et al 2014 (line 208-209)

Line 268 - It is not explicitly stated earlier, but multiple targets at similar depths would also be possible, presumably We have included a sentence to address this possibility (line 292-293)

Discussion Line 421 - sections cannot be repeatedly imaged without bleaching too much? Please elaborate on this statement to help strengthen the point as it isn't mentioned earlier in the results.

Our experience with in section fluorescence imaging is that fluorescent proteins are not very stable and bleach rather quickly. However, as we have not measured this with the same setup and with the same samples, we do not have a rigorous proof for this statement. As we believe the comparison with sections is not an important point here, we have removed the sentence (line 463)

Line 435 - FIB SEMs and 2Pi systems are not really so 'common' in the sense suggested, so this final statement should be reworded.

We have changed the sentence (lines 475-477)

M&Ms Line 540 - grooves, not groves

Changed (line 589)

Figure 3 legend Overall, it's a workflow comprising many methods, so it's best described as a schematic of the workflow.

Changed (line 900)

Confocal panel - target, not targets, and depth is misspelt.

We thank the reviewer for spotting these mistakes. We have corrected the figure*

*

Figure 4 legend Line 834 - as far as I can see, this is a different organoid that isn't shown in a and b, so this text should be removed.

The organoid is indeed a different one. We meant that the targeting was performed as shown in a and b. However, as the sentence could generate confusion, we have removed it (line 932).

Figure 5 legend Line 840 - was, not is

Changed (line 937)

Figure 6 a) It would help with clarity to also put e.g. white arrows on the WT epithelium

As we use arrows and arrowheads to indicate different events in the image, we have used green asterisks to label the nucleus of the WT cell and a red asterisk for the KD, as we have done in all the panels in figure 6, where both cell types are present in the same image.

f,g) It isn't really clear on first viewing what these images show, so they would benefit from some labels.

We have added labels to indicate all the cells represented in the images as well as the space in between (VM, vitelline material). Microvilli are now indicated with arrowheads. We have also explained in the figure legend that here we show in detail the structures indicated by black arrows in Fig. 6a, to help give a context to the high mag detail (lines 964-965).

\*Minor stylistic comments** There should be a space between numbers and units; this is inconsistent throughout. *

We have corrected this.

The use of black versus white text on the figures is inconsistent.

We have fixed this.

Table 1 - is it in the supplementary material or not? If it is, it should be referenced as such in the text. The formatting could use some refinement to match the standard of the other figures.

The table is supplementary material. We have now referenced it as such and we have reformatted it.

Capitalisation is inconsistent throughout.

We have revised the text.

The manuscript describes a workflow that connects several pre-existing methods to enable precision targeting of individual fluorescently tagged structures within a larger sample volume. The possibility for multi-modal imaging within a single embed specimen facilitates correlation of data for structure, with that of function. The work will be of interest to all scientists in the field of correlative microscopy

We thank the reviewer for her/his positive evaluation.

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

The manuscript is written very clearly overall. I would like to raise a number of issues that the authors might address. Most are at the level or proof-reading.

The workflow still depends on availability of a specialized confocal microscope with two-photon laser excitation for marking the region of interest. A tweak to the method might simply be to scratch or etch markings onto the planed surface near the edges. Provided a motorized stage is available on the light microscope, the region of interest could be located precisely with reference to those, and then relocated in the SEM. It would be enough to suggest this, or another similar method, for those who don't have access to the two-photon microscope.*

In our view, the 2pi branding is important to position the FIB-SEM acquisition with high precision, reliability and confidence. However, we agree with the reviewer that there are other approaches to accomplish this task, which we now mention in the text. One is to simply measure the distance from the edges or corners of the block (lines 256-259). Another, could be to manually introduce landmarks (lines 259-260).

The second is to clarify in the text that the top-down view of the confocal microscopy is orthogonal to that of the FIB. This appears as a note in the caption to Figure 1, but it is an important point to align the expectations of readers who are not closely familiar with the methods.

We agree with the reviewer that this is a point that requires further clarification. We have described this in Materials & Methods in the paragraph “Image processing, dataset registration, visualization and segmentation” (lines 586-589).

The legend labels in Figure 1 do not match the figure itself, as if it were recompiled from an earlier draft: g-j) refers next to a).

We apologize for the mistake. We have corrected it.

The decrease in fluorescence intensity with depth into the specimen remains a bit ambiguous. The significant part of the text is dedicated to the suggestion that inherent protein fluorescence is affected by water content in the resin. After cutting back from the surface, are the originally deeper layers still dim, or do they become brighter? In other words, is the effect chemical or optical?

As we wrote in the discussion, probably both optical effects and hydration play a role in the observed fluorescence drop. The hydration we describe probably only takes place on the block surface when dipping the block in water for imaging. Therefore, when we expose deeper layers after removing the resin on top, they do become brighter. However, we cannot completely disentangle the optical and hydration effect. To make this clearer, we have explained the point in more detail in the discussion (lines 452-455). At the same time, we are planning a new experiment to compare the fluorescence signal in the presence or absence of water in the dish, which will allow us to discriminate between the two effects.

• Loss of confocal intensity with depth would be expected on the basis of a refractive index mismatch to the design parameters of the objective, especially for high numerical aperture. The objective is specified as multi-immersion but no further details are given. *

Details of the lense we used are now given in Materials and Methods (lines 539-540)

Another easy test would be to embed fluorescent beads as intensity standards. There could also be absorption of the fluorescence emission by the resin and stain, but such a strong effect in a few tens of microns would suggest that the block is quite dark. That seems inconsistent with the images in supplementary figures. Personally I was not bothered by the dimming in depth, since the conclusions do not depend on quantitative fluorescence intensities.

We agree with the reviewer that, although the fluorescence intensity drop is an effect that is worth describing because it has an implication for the identification of fluorescent targets in the block, our method does not rely on quantitative imaging. In all cases, we were able to detect fluorescence signal even very deep from the block surface and this was enough to target those cells at the FIB-SEM.

In some cases pre-embedding correlative imaging can be quite successful, for example in studies of Jost Enninga (e.g., Mellouk et al, Cell Host & Microbe 2014) or Eric Jorgensen (Watanabe et al, Nature Methods 2011). Do the authors see a distinction between adherent

cell cultures and unsupported tissues or tissue sections?

We completely agree with the reviewer that pre-embedding CLEM can be extremely successful and it is a very valuable tool, especially for the study of dynamic event is cell cultures. However, while for adherent cells the targeting is essentially a 2D problem and is facilitated by the fact that cells can be identified on the surface of the block under the SEM beam, for larger 3D samples the situation is much more complicated. We often lack landmarks and surface references and an anisotropic deformation occurs during sample prep, making targeting and localization prediction extremely inaccurate.

Other investigators have insisted that FIB-SEM requires especially heavy labelling. What was done differently here to make the light labeling possible? Such clues may be very useful to ongoing developments in the literature. Also, the present protocol skips osmium staining entirely. The authors must have compared images with and without osmium. What visible features do we lose as a result?

We provide a detailed freeze substitution protocol in table S1, such that the method can be easily reproduced. Although FIB-SEM imaging of osmium-free samples is not very common, it has been shown by others before (Porrati et al., 2019), with a slightly different FS protocol.

We found that our sample preparation is good enough for the detection of all membranous organelles, but also microtubules, centrioles and other subcellular structures. We did not observe any big difference compared to the more standard protocols containing osmium (line 136).

Perhaps the greatest challenge to large volume electron microscopy is to deal with rare events. Correlative fluorescence light-electron microscopy effectively addresses the issue of finding the region of interest in a two-dimensional specimen such as a thin section or even a monolayer cell culture. For tissues the solutions are still at large. It is almost always impractical to image an entire organ at the resolution required to see macromolecules (work of Harald Hess being the exception that proves the rule). The issue is especially acute where the imaging is destructive, as in the case of serial block-face and FIB-SEM tomography. MicroCT has been used so far as the method of choice in the work-up to locate the region of interest within a large specimen, but the approach requires expensive equipment and time-consuming analysis. Furthermore, it can provide directional clues solely on the basis of morphology. Fluorescence would be a far simpler tool, and more informative when labeling is directed to specific molecular components. The manuscript of Ronchi et al provides a much-needed demonstration and detailed set of instructions for 3D CLEM en route to FIB-SEM volume imaging. The examples are presented are both convincing and esthetic. Success depended on integration of a number of factors, including changes to the specimen preparation, so the workflow will be very useful. In short, I recommend publication.

We thank the reviewer for the generous comments.

SciScore for 10.1101/2021.04.08.21254348: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

In addition to an impact on early classification of patients, current limitations in the availability of vaccine doses suggest a novel possible application for sensitive multi-antigen assays for SARS-CoV-2 seropositivity. It has been shown that the antibody response to the first vaccine dose in individuals with pre-existing immunity is comparable or greater to that observed in naïve individuals who have been immunized twice [17]. Screening of the unvaccinated population with an assay sufficiently sensitive to identify individuals previously infected despite waning of antibody titres over time, would allow these individuals to be given the vaccine as a single booster, sparing them from possible suffering and complications after a second dose, and freeing up many urgently needed vaccine doses to be given to individuals with no protection. The test described here would also provide comprehensive information to support selection of convalescent sera or plasma for therapeutic use. Our data indicate the importance of this multi-antigen, multi-isotype analysis to detect potential SARS-CoV-2 reinfections in vaccinated individuals and suggest a possible use in establishing alternative vaccine administration routes that may elicit more potent IgA responses. This multi-antigen and multi-Ig assay can be easily modified for detection of antibodies in other fluids as saliva and breast milk. It is also highly tunable to different research needs, including detection of different immunoglobul...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.04.08.439006: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

However, one of the major limitations in the use of these vectors for therapeutic purposes is the transient nature of the transgene expression and the systemic toxicity, which are both due to inflammatory and immune responses triggered by the residual expression of viral proteins40–42. To overcome these problems, HD-Ad, the third generation of Ad vectors, with all viral coding sequences deleted, was used in our study. HD-Ad minimizes the host immune response to the vector and allows long-term expression of the transgene in host tissues or organs, thereby providing a safer and more efficient way for gene delivery and transgene expression30–34. Consistently, we detected high level expression of the RBD in transfected cells and the majority of the protein was secreted, which ensures efficient antigen-dependent stimulation of the host immune system. Consequently, the HD-Ad_RBD vaccine induced extremely high levels of antigen-specific antibody response. For example, the IgG reciprocal GMT in sera reached 1,837,920 even though we used a moderate dose of the vaccine (5×109 viral particles) for vaccination. We also detected high levels of neutralizing antibody against the SARS-CoV-2 virus, and the reciprocal ID50 GMT was close to 1,000, which is in the same range of the neutralizing antibody induced by the two mRNA vaccines (mRNA-1273, BNT162b2) described above. The superior safety profile of HD-Ad allows us to deliver the vaccine by an intranasal route. Compared to intramuscular inj...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Graham, M. S., Sudre, C. H., May, A., Antonelli, M., Murray, B., Varsavsky, T., Kläser, K., Canas, L. S., Molteni, E., Modat, M., Drew, D. A., Nguyen, L. H., Polidori, L., Selvachandran, S., Hu, C., Capdevila, J., Koshy, C., Ash, A., Wise, E., … Ourselin, S. (2021). Changes in symptomatology, reinfection, and transmissibility associated with the SARS-CoV-2 variant B.1.1.7: An ecological study. The Lancet Public Health, 0(0). https://doi.org/10.1016/S2468-2667(21)00055-4

SciScore for 10.1101/2021.04.06.438540: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.04.06.438552: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 26. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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

Learn more at Review Commons

Reply to the reviewers

Response to Reviewers

Title: "Towards deciphering the Nt17 code: How the sequence and conformation of the first 17 amino acids in Huntingtin regulate the aggregation, cellular properties, and neurotoxicity of mutant Httex1".

Tracking #: RC-2021-00675 Authors: Vieweg et al.

MAJOR COMMENTS from Referees #1, #2, and #3

Referee #1

General comments

« The manuscript by Vieweg, Mahul-Mellier, Ruggeri et al., describes the role of the sequence and conformation of the extreme N-terminus of the Huntingtin protein in terms of aggregation and toxicity together with its relation to the polyglutamine length. The authors use some outstanding methods to ensure that the conclusions are based on good quality data. Overall, this is an excellent study.

We thank the referee for the very positive feedback and for recognizing the quality of our work and his/her appreciation of our systematic approach to dissect the role of the Nt17 domain in regulating the aggregation, cellular properties, and neurotoxicity of mutant Httex1.

“The manuscript is generally well written although it might benefit from reducing the length of the discussion section ».

We thank the referee for his/her valuable comment. We have reduced by 10% the discussion as per requested.

Major comments

1) “For their in vitro data, the authors do not go beyond 42 polyglutamines. Is there any particular reason for that? The authors see a clear difference between 36Q and 42Q, but although not critical, it would have been useful to use longer repeats. In my view, the authors should at least discuss the rationale for this, particularly as in cellular models they do use 72Q constructs.”

We thank the referee for raising this point.

Most HD patients have a polyQ repeat stretch of 40-45 glutamines (1-4).

In vitro, the use of Httex1 constructs consisting of 42 polyglutamine residues is sufficient to induce mutant Httex1 aggregation and fibril formation. Mutant Httex1 proteins with polyQ repeats of 72Q or higher are highly aggregation-prone and difficult to purify, handle, or disaggregates. This is why all of the in vitro aggregation studies are based on mutant Htt proteins with polyQ ranging from 23Q-53Q (5-14). We have reviewed the literature carefully and were unable to identify any in vitro studies with recombinant Htt proteins containing polyQ repeats of 72 or greater.

In cells, induction of mature Htt inclusions requires much longer polyQ repeats. This is clearly reflected by the fact that most cellular studies use mutant Htt with polyQ repeats above 64Q and up to 160Q to induce the formation of cellular aggregates (10, 15-30).

We have recently conducted a systematic study on the effect of the polyQ repeat length on Htt inclusion formation in cells https://www.biorxiv.org/content/10.1101/2020.07.29.226977v1 (21). Characterization of the inclusions by EM revealed that the polyQ tract length dramatically influences the ultrastructure properties and the architecture of the Httex1 inclusions in cells. The dark shell structure that delimitated the core from the periphery of the Httex1 72Q inclusions was absent in the Httex1 39Q inclusions. Also, the Httex1 39Q inclusions appeared less dense compared to that of the Httex1 72Q. Finally, no significant cell death was observed in HEK cells overexpressing 39Q constructs while overexpression of Httex1 72Q was toxic. For these reasons, we and others select to use Httex1 with polyQ repeat of 75 or higher.

2) « The role of the N-terminus 17 aminoacids of huntingtin (Nt17) is addressed by comparing peptides with and without the Nt17 and their relation to the adjacent polyglutamine tract. Using this approach, the peptide without the Nt17 is composed of pure polyglutamines in its N-terminus, followed by the rest of exon 1 in its C-terminus. This is clearly the key comparison to address the role of the Nt17 in the context of an exon1 containing polyQ. »

Yes. In fact, we did perform this experiment and assessed if the addition of the Nt17 would be sufficient to inhibit mutant Httex1 aggregation or make ΔNt17-Httex1 aggregate similar to Httex1. This data is included in the original version of the manuscript as Figure S5 in supporting material. We observed that that __the presence of the Nt17 peptide during the aggregation of ΔNt17-exon1 fibrils did not interfere with the aggregation kinetic of mutant Httex1 or alter the fibril morphology of ΔNt17-exon1,__ indicating that intramolecular interactions between the Nt17 domain and the adjacent polyQ tract are key determinants of mutant HTtex1 fibrillization and fibril morphology.

Referee #2

General comments

This article describes the results from studies into mechanisms of the aggregation and toxicity of Htt Exon1 protein. The authors investigated the role of N17, polyQ length, M9C mutation, and phosphorylation. Multiple approaches were used that included biochemical protein design, biophysical measurements, and cell biological experiments with cultured mammalian cells. The authors demonstrate the effects of protein context on aggregation. Furthermore, the authors were able to visualize the aggregates in mammalian cells and in neurons using multiple methods. These are interesting data.

We greatly appreciate the positive feedbacks on our data and our systematic and integrative approaches.

There are several major weaknesses in the study. First problem is that most of the results related to aggregation mechanisms and toxicity are not original and incremental when compared to many previously published articles.

We respectfully disagree with this assessment and suggestion that our studies are not original and represent only incremental advances.

Originality

Our study provides novel mechanistic insights into the role of not only the sequence but also the conformational properties of the Nt17 domain in regulating the dynamics of Httex1 fibrillization, the kinetic fibril growth, the structure and morphology of Httex1 fibrils. Besides, we also addressed for the first time how the Nt17 domain and phosphorylation at different residues within this domain regulate the cellular uptake, subcellular localization (phosphorylated proteins) and toxicity of extracellular monomeric and fibrillar forms of mutant Httex1 in primary neurons. We are not aware of previous reports that have conducted similar studies addressing these questions and using multiple methods. Also, some of our findings using native Httex1 sequences are not in agreement with previous reports using Httex1 proteins fused to peptide/protein tags, thus underscoring the limitations of previous studies.

1) We demonstrated that the Nt17 domain plays an important role in shaping the surface properties of mutant Httex1 fibrils and regulate their lateral association and cellular uptake. Our findings are not in agreement with previous findings published in eLife by Shen et al. __(10)__, where they reported that removal of the Nt17 domain has the opposite effect of what we observed in our study, i.e., ∆Nt17 promotes the formation of fibrils that exhibit a low tendency to laterally associated and form a “bundled” architecture (10). Careful examination of their constructs revealed that all the proteins they used contained a highly charged 15-mer peptide tag (S-tag: Lys-Glu-Thr-Ala-Ala-Ala-Lys-Phe-Glu-Arg-Gln-His-Met-Asp-Ser) at the C-terminus of Httex1, which we believe would strongly influence the aggregation properties of the mainly uncharged ∆Nt17-Httex1 and Httex1 protein, thus possibly explaining the discrepancy between our findings and those of Shen et al (10). In fact, a previous study has shown that adding short peptide tags such as the HA or the LUM tag to mutant Htt171 changed the toxicity dramatically. In addition, we show that the subcellular localization of Htt171 expressed in cells (e.g: expression of Htt171 carrying the LUM tag was more toxic than untagged Htt171 and induced the formation of nuclear aggregates rather than the classical cytoplasmic aggregates) (31). The reference to this paper is now included and discussed in the main manuscript (page 11).

Our observations __highlight__ the critical importance of using tag-free proteins to investigate the sequence and structural determinants of Httex1 aggregation and structure.

2) Our study is the first to demonstrate that the Nt17 domain influences the relationship between fibril length and polyQ repeat length. This correlation disappears when the Nt17 is removed. This aspect of our work was not explored by Shen et al. (10), who limited their in vitro aggregation study to Httex1 wild-type and mutants (∆Nt17 or ∆PRD for Polyn Rich Domain).

3) This is also the first study to assess the effect of modulating the helicity of Nt17 on fibrils growth and morphology and Httex1 cellular properties.

1. a) Using the helix and membrane-binding disrupting mutation (M8P), we showed that disrupting the Nt17 helix (M8P mutation) slows the aggregation propensity of Httex1 in vitro but does not alter the morphology of the fibrils. In contrast, removing the Nt17 domain leads to a strong lateral association of the fibrillar aggregates with ribbon-like morphology. This demonstrated that the __Nt17 sequence, but not its helical structure, is the key determinant of the quaternary packing of Httex1 fibrils. Shen et al. (10) did not investigate the effect of modulating the helicity of Nt17 on fibrils growth and morphology using M8P mutant__.
2. b) Our cellular studies comparing the membrane association and uptake of extracellularly added Httex1 43Q and M8P Httex1 43Q fibrils in primary rat striatal neurons showed that disrupting the Nt17 helix promotes the internalization of M8P Httex1 while Httex1 stays bound to the plasma membrane. These findings suggest that the Nt17 helical conformation persists in the fibrillar state or that the Nt17 domain regains its helical structure upon interaction with the plasma membrane resulting in the sequestration of Httex1 fibrils at the membrane and impeding their uptake. This aspect of our study has never been explored in previous studies.
3. c) Using the site-specific bona fide phosphorylation on T3, S13, SS16, and both S13/S16, this is the first study that shows that modulation of the overall helicity of Httex1 through site-specific phosphorylation of the Nt17 domain (pT3 stabilizes the alpha-helical conformation of Nt17 while pS13 and/or pS16 disrupts it (9)) enhance the rapid uptake of extracellular Httex1 monomeric species into neurons and their nuclear accumulation. Previous studies relied on phosphomimetic mutations (32), which we have shown do not reproduce the effect of phosphorylation at these residues on the structure of Nt17 (8, 9). Shen et al. __(10) did not investigate the effect of modulating the helicity of Nt17 on fibrils growth and morphology using site-specific __phosphorylation of the Nt17 domain. 4) Our overexpression model in HEK cells showed that removing the Nt17 domain or disrupting its helical structure (M8P mutation) was sufficient to prevent the cell death induced by Httex1 72Q overexpression and reduce the number of cells with inclusions drastically. Our data indicate that the cell death level correlates with the number of cells that contain inclusions or the number of inclusions formed in the cells or/and their subcellular localization. In contrast to our results, Shen et al.,__(10)__ demonstrated that the overexpression of ΔN17-Httex1 induced toxicity at a similar level as the full-length Httex1 in striatal-derived neurons or neurons from cortical rat brain slices culture, although ΔN17-Httex1 led to a significant reduction of punctate structures in these cells. The discrepancy between these studies and our Httex1 overexpression model in HEK cells may be due to the fact that in neurons, Httex1 lacking the Nt17 domain accumulates in the nucleus. In contrast, in HEK, it stayed mostly cytosolic. In line with this hypothesis, it has been recently shown that cytosolic inclusions (Httex1 200Q) and nuclear aggregates (Httex1 90Q) contribute – to various extents – to the onset and the progression of the disease in a transgenic HD mice model (33). Thus, the difference in cellular localization but also the cell type (HEK vs. neurons) could influence the toxic response of the cells to the overexpression of ∆Nt17-Httex1, with toxicity triggered only by the nuclear ∆Nt17-Httex1 species.

5) This is also the first study to investigate the role of the Nt17 domain and Nt17 PTMs in influencing the uptake, the subcellular localization, and the toxicity of extracellular Httex1 species (monomers and fibrils) in primary neurons. We showed that the helical propensity of Nt17 strongly influences the uptake of Httex1 fibrils into primary striatal neurons. At the same time, phosphorylation (at T3 or S13/S16) or removal of the Nt17 domain increased the uptake and accumulation of Httex1 fibrils into the nucleus and induced neuronal cell death. Our findings suggest that the Nt17 domain is exposed in the fibrillar state and is sufficiently dynamic to mediate fibril-membrane interactions and internalization.

Altogether our results, combined with previous findings from our groups and others demonstrating the role of Nt17 in regulating Htt degradation (34-36), suggest that this domain serves as one of the key master regulators of Htt aggregation subcellular localization of the pathological aggregates, and their toxicity. They further demonstrate that targeting Nt17 represents a viable strategy for developing disease-modifying therapies to treat HD.

Limitations of previous studies:

Although the effects of the Nt17 domain in regulating Httex1 aggregation and cellular properties have been studied and reported on by other groups, we would like to stress that most of the published studies had major limitations and used protein constructs that do not share the sequence of native Httex1 and exhibit biophysical and cellular properties that differ from those of native Httex1 sequences.

1) Many of the studies used Httex1-like model peptides (13, 37), which do not contain the complete sequence of Httex1 (e.g., Nt17 peptide (37)), contain additional solubilizing amino acids such as lysine residues(38-43) or are fused to large proteins (e.g., GST, YFP) (37).

2) Other studies relied on artificial fusion constructs whereby the polyQ domain (44-46) or Httex1 itself (12, 47-61) are fused to large solubilizing protein tags, such as glutathione-S-transferase (GST), maltose-binding protein (MBP) or thioredoxin (TRX) or C-terminal S-tag (10, 62, 63) or fluorescent proteins (e.g., GFP or YFP) (10, 15, 49, 64, 65) for the cellular studies.

One of the major limitations of using fusion constructs as precursors for the generation of Httex1 (12, 47-61) is the requirement to cleave the fusion protein in situ by adding a protease to release and initiate the aggregation of Httex1. Enzyme-mediated cleavage of Httex1-fusion proteins often results in the incorporation of additional amino acids at the N- or C-terminus of the protein. This could alter the biophysical and biochemical properties of Httex1 because of the important role of the Nt17 domain and the proline-rich domain in regulating the conformational and aggregation properties of the protein (38, 40, 43, 65, 66). Moreover, it has been shown that commonly used enzymes such as trypsin and thrombin may lead to cleavages within the Nt17 domain and result in the generation of undesired Httex1 fragments (7, 42, 60). The net effect of incomplete and/or unspecific enzymatic cleavage of Httex1 fusion proteins is the generation of heterogeneous protein mixtures, which precludes accurate interpretation and comparison of aggregation and structural data across different laboratories.

Moreover, several studies have shown that the fusion of small peptide tags or large fusion protein alters the aggregation of mutant Httex1 in vitro and in cells.

1. We have previously shown that the presence of such tags (e.g., GST) alters the ultrastructural and biochemical properties of Httex1 as well as its aggregation properties in vitro (11).

We have also recently completed a comprehensive assessment of the GFP tag's impact on the aggregation, inclusion formation, and cellular properties of Httex1 (preprint paper available in BioRxiv https://www.biorxiv.org/content/10.1101/2020.07.29.226977v1 (21)). In this paper, we show that inclusions produced by mutant Httex1 72Q-GFP exhibit striking differences in terms of organization, ultrastructural properties, composition, and their impact on mitochondria functions as compared to the inclusions formed by the tag-free mutant Httex1 72Q. These findings highlight the critical importance of developing new tools that minimize the impact of large fluorescent proteins and/or label-free imaging methods and monitoring Htt aggregation in inclusion formation in cells.

From Riguet et al., __(21)__. Influence of GFP on the ultrastructural properties of Httex1 cellular inclusions by Correlative light electron microscopy (CLEM). CLEM of Httex1 72Q (+/-GFP) transfected in HEK 293 cells after 48h. Confocal images of A. Httex1 72Q and. B Httex1 72Q GFP, 48h after transfection. Httex1 expression (red) was detected using a specific primary antibody against the N-terminal part of Htt (amino acids 50-64) and the nucleus was stained with DAPI (blue). Electron micrographs of C. Httex1 72Q and D. Httex1 72Q GFP inclusions corresponding to confocal images panel A, and B (white square), respectively. Add-in binary images generated from electron micrographs by median filtering and Otsu intensity threshold. E. **Schematic depictions and original electron micrographs of cytoplasmic inclusions formed by native (tag-free) mutant Huntington exon1 proteins (Httex1 72Q, left) and the corresponding GFP fusion protein (Httex1 72Q-GFP).

A recent study by Chongtham et al. (31) also supports our findings and shows that adding short peptide tags such as the HA or the LUM tag to mutant Htt171 changed dramatically the toxic properties of Htt171 as well as its subcellular localization and the compactness of the aggregates formed in cells (e.g.: expression of Htt171 carrying the LUM tag was more toxic than untagged Htt171 and induce the formation of nuclear aggregates rather than the classical cytoplasmic aggregates, See Figure 4).

Figure 4 from Chongtham et al., __(31)__. The influence of peptide modifications on HTT171 fragment behavior. (A) When expressed ubiquitously with da>Gal4, the HTT171-120Q fragment exhibits little or no lethality, but appending either an HA ( ... YPYDVPDYA∗)oraLUMtag ( ... GCCPGCCGG∗) to the C-terminus dramatically increases the toxicity of the fragment. (B) Surviving adult flies expressing an HA-tagged HTT171 transgene exhibit about half the life span of those expressing untagged 171. Flies expressingLUM-tagged HTT171 do not survive to adulthood. (C) Flies expressing HA- or LUM-tagged 171 in tracheal cells show only modest increases in lethality that do not rise to the level of significance (P=0.12; 0.09), but the inclusion of tags changes the subcellular behavior significantly. (D) In contrast, in the prothoracic gland, expression of LUM-tagged 171 shows a significant increase in toxicity compared to 171 alone, while the HA-tagged 171 borders on significance (P=0.051). (E) In trachea, pure 171 forms cytoplasmic aggregates, while the inclusion of HA causes some HTT to become nuclear diffuse, and inclusion of the LUM tag causes the bulk of the HTT to appear as diffuse nuclear material with some cytoplasmic aggregates remaining when expressed with btl>Gal4 at 29◦C. (F) In the prothoracic gland, addition of the LUM tag causes aggregated- **cytoplasmic HTT to become weakly staining diffuse-cytoplasmic material while HTT171HA remains as extensive aggregates in the cytoplasm with a haze of diffuse staining as well. Scale bars are 10 μm

Therefore, in this study, we aimed to investigate for the first time the role of the sequence and the conformational properties of the Nt17 domain in regulating the dynamics of Httex1 fibrillization, the structure and morphology of Httex1 fibrils using a tag-free Httex1 constructs. In our studies, we used multiple methods to examine the structural and cellular properties of these proteins under the same conditions and in the same cellular systems, thus making it possible to correlate the sequence, structural and cellular properties of the different Httex1 proteins (monomers and fibrils). We are happy to see that this was nicely recognized and appreciated by the referee.

Referee #1 “The authors use some outstanding methods to ensure that the conclusions are based on good quality data. Overall, this is an excellent study.” Referee #3 “Their findings provided the precise information for the role of tag-free Nt17. The paper advanced our knowledge of Nt17, especially in the Huntington disease field.”

Major comments

Referee #2 raised the following concerns:

1) « The main hypothesis of this study solely depends on the ability of N17 domain to enhance aggregation (Fig 1 and Fig 2). According to the method for the protein solubilization 1mM TCEP was added to ∆Htt-Ex1, but not to Htt-Ex1 proteins. It is necessary to rule out the potential effects of TCEP on aggregation assay. »

We thank the referee for raising this important point. Indeed, we were also concerned about the potential effect of TCEP and conducted experiments to address this point. Our data show that TCEP does not affect our aggregation assay. This new panel is now included in supporting information as Figure S2A-B and mentioned in the corresponding section of the Material and method (page 29).

2) « The author needs to provide biophysical data of the mutation and phosphorylated proteins with/without Tag. »

All the proteins used in this study have been extensively characterized in recent publications from our lab __(9, 11, 21)__. All these papers are cited throughout our manuscript as well as in the material and method section.

The expression, purification and characterization of native tag-free Httex1 with polyQ repeats ranging from 7 to 49Q has been fully described in Vieweg et al., 2016 __(11)__. In this paper, the aggregation properties of tag-free Httex1 and Httex1 fused to GST or MBP tags were compared by sedimentation assay, while the morphology and length of the resulting fibrils were compared by EM.

The semisynthesis, purification, and characterization of Httex1 42Q phosphorylated at Ser-13 and/or Ser-16 or at T3 was described respectively in Deguire et al., 2018 __(9) and Chiki et al., 2017 (8)__. These studies include kinetics of aggregation and morphological assessment (i.e: heights and lengths) by EM and AFM of the fibrils formed by phosphorylated or unphosphorylated mutants Httex1.

The Httex1 mutants carrying the GFP tag were not used in the in vitro studies but were studied in our overexpression-based cellular model. The direct comparison characterization of inclusion formation by tag-free and GFP-tagged mutant Httex1 and their impact on cellular homeostasis are fully described in a preprint paper available in BioRxiv https://www.biorxiv.org/content/10.1101/2020.07.29.226977v1 (21). In this paper, we show that inclusions produced by mutant Httex1 72Q-GFP exhibit striking differences in terms of organization, ultrastructural properties, composition, and their impact on mitochondria functions as compared to the inclusions formed by the tag-free mutant Httex1 72Q. These findings highlight the critical importance of developing new tools that minimize the impact of large fluorescent proteins and/or label-free imaging methods and monitoring Htt aggregation in inclusion formation in cells.

Referee #3

General comments

“Their findings provided the precise information for the role of tag-free Nt17.__ The paper advanced our knowledge of Nt17, especially in the Huntington disease field.”__

We thank referee #3 for the very positive feedback and for recognizing the quality, depth and significance of our work and its potential impact in the field of Huntingtin disease.

“However, the conceptual advance is limited.”

We respectfully disagree with this assessment that the conceptual advance of our study is limited.

Please see our detailed response to Referee #2 regarding our work's originality and novelty (pages 3-8, in our referees' letter).

Major comments

Referee #3 raised the following concerns:

1) Finding of lateral association (bundling) of __Δ__Nt17-Httex1 fibrils is interesting.

We agree and thank the referee for further highlighting this point.

However, pathological significance is not clear

We agree that the significance for delta 17 is not clear as we do not know whether this cleavage occurs in vivo or not. This is why we decided to extend our studies beyond the removal of Nt17 and investigated the effect of natural PTMs that are known to alter the sequence of Nt17 and modulate its helicity. One additional distinguishing feature of our work is that we used proteins (monomers and fibrils) that bear site-specific bona fide phosphorylation on T3, S13, SS16, and both S13/S16.

1. a) Does even non-truncated form also increase this kind of bundling when polyQ is expanded? We have addressed this specific question in a previous study (11) in which we have compared the morphology and length of fibrils formed from Httex1 with polyQ tract from 23Q to 43Q. The increased lateral association was not observed for the fibrils generated from Httex1 43Q or Httex1 23Q, 29Q, or 37Q (Figure 5F) (11). Besides, in this paper, we were the first to show an inverse correlation between the polyQ-length and fibril length, which suggests structural differences between Httex1 proteins with different polyQ repeat lengths. Others have investigated Httex1 with different polyQ repeat, but not using tag-free Httex1 proteins, and they did not observe this inverse relationship between polyQ lenth and fibril length, as we did here and in our previous studies (11).

2. b) When fibrils are added to striatal neurons like in Fig.5, is this structural feature preserved on the membrane or inside of the cells? We agree with the referee that this is an important point to address. However, deciphering the structural properties of the membrane-bound and internalized fibrils is not trivial, especially given the limited amount of unlabelled fibrils that are taken up by the cells. This would require extensive optimization of the CLEM technique or the use of an alternative approach such as tomography. Due to the resources and time required to address this important question, this part of the project will be included as part of future projects aimed at investigating the mechanisms of Htt seeding and propagation. We are not aware of any reports by other groups that monitor the structural changes of exogenous fibril after internalization into cells.

3. c) When Httex1 fibrils species are expressed, is this bundling also observed? In fact, we have recently completed a comprehensive analysis of the comparison of the inclusions formed by mutant Httex1-72Q and ΔNt17 Httex1.

In this study, we have shown that the expression of Httex1 72Q and the truncated form ΔNt17 Httex1 72Q form cytosolic inclusions of similar size and shape in HEK 293 (Figure 4). We have further characterized the architecture and organization of these inclusions at the ultrastructural level in the context of another project. Our findings are now available online (see Riguet et al., 2021, BioRxiv (21)).

Using correlative light electron microscopy, we showed that the inclusions formed by Httex1 full length or lacking the Nt17 domain exhibited similar architecture and a ring-like organization. Interestingly, we showed the inclusions are composed of highly organized fibrillar network at the core and periphery of the inclusions. In cells inclusion formation is a multiphasic process driven by different phases of polyQ dependent aggregation processes and complex interactions with lipids, proteins and organelles (ER).

Although CLEM approach in neurons provides very good contrast of cytosolic or nuclear inclusions, the resolution of this method is not sufficient to allow imaging at the level of individual fibrils and assessing their morphology. Differences between CLEM and EM resolution can be explained as the slices of the cellular objects are much thicker (~ 50 nm) than the fibrils prepared in vitro and directly deposited on the EM grids (the height of Httex1 pre-formed fibrils is between 5 and 7 nm). To improve the imaging and get a stronger contrast of de novo fibrils in our CLEM samples, we used a double-contrast method based on uranyl acetate and lead citrate stains. Nevertheless, the complex cellular environment and the presence of various cellular objects (e.g: organelles and proteins) surrounding the de novo fibrils might prevent the optimal stain penetration from allowing imaging at the level of individual fibrils. Finally, the preparation of the neuronal samples for CLEM imaging includes ethanol and detergents incubation and resin embedding. These steps can limit the ultrastructure detection of the de novo fibrils at the level of individual fibrils and therefore does not allow to determine their organization and their lateral association.

1. d) What function (cell death, membrane integrity or others) is most correlated with this structural feature? In our extracellular model, we have shown that the conformation and sequence properties of the Nt17 domain are key determinants of the internalization and the subcellular localization of Httex1 fibrils in primary striatal neurons. Httex1 43Q fibrils mostly accumulate at the outer side of the neuronal plasma membrane, Httex1-ΔNt17 43Q fibrils were detected primarily in the nucleus and the M8P-Httex1 43Q fibrils were equally distributed in the cytosol and nucleus.

Despite exhibiting completely different subcellular distribution and internalization levels, the 3 types of fibrils induced neuronal cell death with the highest toxicity observed for ∆Nt17-Httex1 (Figure 7).

Our data suggest that the neurotoxic response is primarily dependent on the subcellular localization of the Httex1 species: 1) accumulation of the Httex1 43Q fibrils on the plasma membrane is likely to induce loss of membrane integrity, based on previous observation with aSyn fibrils; 2) the nuclear accumulation of ∆Nt17-Httex1 aggregates has been previously shown to be highly toxic in several cellular and animal models (10, 67, 68).

Nevertheless, we could not rule out that the high toxicity of ΔNt17-Httex1 fibrils could also be due to their distinct biophysical and structural properties. ΔNt17-Httex1 forms broad fibrils characterized by lateral association, which could provide a surface for the sequestration of intracellular proteins.

2) The authors claimed, “we investigated for the first time, the role of the Nt17 sequence, PTMs and conformation in regulating the internalization and cell-to-cell propagation of monomeric and fibrillar forms of mutant Httex1”. However, so far this reviewer understands that the authors studied the internalization but not cell-to-cell propagation.

We agree and apologize for this mistake as we indeed only limited our study to the uptake, subcellular localization, and toxicity of extracellular Httex1 species in our primary neuronal model. The text has been amended, and cell-to-cell propagation has been removed from the abstract as well as in pages 2, 4, 12, and 17.

Minor points for Referees #1, #2 and #3

Referee #1

1. « On page 6, the data on how the Nt17 domain affects Httex1 aggregation, the information on which figure it is referring to is missing. Done. The information regarding the Figure related to this data has now been added page 6.

In Figure 1A, it is difficult to compare the data on Nt17 and DNt17, particularly for 36Q and 42Q, as the time axis are different. I understand that the kinetics are different, but particularly for the 42Q peptides (Nt17 and DNt17) as their kinetics are not that different, it may be useful to show them in the same panel. »

Done. The new panel that combined the data on Nt17 and DNt17 has now been added as Figure S1B.

Referee #2

1) “Fig 8 the color codes for PolyQ and PolyP need to be corrected. »

Done.

2) “It is a challenging technical problem to produce proteins which are rich in Pro and Gln content. But there is not enough experimental details provided in the methods. Please add detailed procedures for expression and purification of these proteins. »

We thank referee #2 for recognizing the technical challenges to express and produce Httex1 proteins and mutants. The expression, purification and characterization methods of all the proteins used in this manuscript have been extensively detailed in our previous studies (8, 9, 11, 69-71). We have now added the relevant references in the method section (page 29).

Referee #3

1) « Fig.3B arrowhead could not be seen. »

Done. Arrowheads are now added to Fig. 3B.

2) « Fig.4A: what do arrows mean? No scale bars? »

The arrows indicate the aggregates formed in HEK cells overexpressing Httex1 39Q and 72Q. This now added to the legend section of the Figure 4.

The scale bars are already present in both the main and the insets images.

3) « Fig.5A:no scale bars? »

Done. Scale bars were added in the 4 images where they were missing.

4) « Fig.S3. Height and length seem to be wrong. »

The measurement of height and length are performed as in literature (72), and are consistent with previous studies (8, 9, 11).

5) « Fig.S6C: hard to compare. D: What is Htt2-90? Also in Fig.S13. »

We thank the referee for bringing this to our attention and apologize for the lack of consistency in the names used for the proteins studied in Figures S6 and S13. We realized that in Figures S6 and S13 the names of the proteins have been either mislabelled due to the dash that was misplaced or the same proteins have been named in different ways. We agree that this makes it difficult to compare the data between the different panels. We have now corrected our mistakes and Figures S6B and S13 have been updated accordingly.

The name Htt2-90 corresponds to Httex1 expressed from amino acid 2 to amino acid 90, with the first N-terminal methionine removed.

6) « There are many abbreviations difficult to understand in supplement. » Fig.S1 Htt18-90(Q18C) etc.

His6-Intein Ssp stands for the Intein tagged with Histidine amino acid (6 units)

Htt18-90(Q18C) means Httex1 expressed from amino acid 18 to amino acid 90 with the Glutamine (Q) in position 18 mutated in a Cysteine (C).

Htt2-17 means Httex1 synthesized from amino acid 2 to amino acid 17, with the first N-terminal methionine removed.

Htt18-90(Q18A) corresponds to Httex1 expressed from amino acid 18 to amino acid 90 with the Q in position 18 mutated in an Alanine (A).

References

1. M. E. MacDonald, S. Gines, J. F. Gusella, V. C. Wheeler, Huntington’s Disease. NeuroMolecular Medicine 4, 7-20 (2003).
2. S. E. Andrew et al., The relationship between trinucleotide (CAG) repeat length and clinical features of Huntington's disease. Nature genetics 4, 398-403 (1993).
3. J. F. Gusella, M. E. MacDonald, Huntington's disease: the case for genetic modifiers. Genome Med 1, 80 (2009).
4. J. M. Andresen et al., The relationship between CAG repeat length and age of onset differs for Huntington's disease patients with juvenile onset or adult onset. Ann Hum Genet 71, 295-301 (2007).
5. A. S. Wagner et al., Self-assembly of Mutant Huntingtin Exon-1 Fragments into Large Complex Fibrillar Structures Involves Nucleated Branching. Journal of molecular biology 430, 1725-1744 (2018).
6. Y. Sun, A. Savanenin, P. H. Reddy, Y. F. Liu, Polyglutamine-expanded Huntingtin Promotes Sensitization of N-Methyl-D-aspartate Receptors via Post-synaptic Density 95. Journal of Biological Chemistry 276, 24713-24718 (2001).
7. A. Ansaloni et al., One-pot semisynthesis of exon 1 of the Huntingtin protein: new tools for elucidating the role of posttranslational modifications in the pathogenesis of Huntington's disease. Angewandte Chemie (International ed. in English) 53, 1928-1933 (2014).
8. A. Chiki et al., Mutant Exon1 Huntingtin Aggregation is Regulated by T3 Phosphorylation-Induced Structural Changes and Crosstalk between T3 Phosphorylation and Acetylation at K6. Angewandte Chemie International Edition 10.1002/anie.201611750, 1-6 (2017).
9. S. M. DeGuire et al., N-terminal Huntingtin (Htt) phosphorylation is a molecular switch regulating Htt aggregation, helical conformation, internalization, and nuclear targeting. Journal of Biological Chemistry 293, 18540-18558 (2018).
10. K. Shen et al., Control of the structural landscape and neuronal proteotoxicity of mutant Huntingtin by domains flanking the polyQ tract. eLife 5, 1-29 (2016).
11. S. Vieweg, A. Ansaloni, Z. M. Wang, J. B. Warner, H. A. Lashuel, An intein-based strategy for the production of tag-free huntingtin exon 1 proteins enables new insights into the polyglutamine dependence of Httex1 aggregation and fibril formation. Journal of Biological Chemistry 291, 12074-12086 (2016).
12. J. Legleiter et al., Mutant huntingtin fragments form oligomers in a polyglutamine length-dependent manner in Vitro and in Vivo. Journal of Biological Chemistry 285, 14777-14790 (2010).
13. B. Sahoo, D. Singer, R. Kodali, T. Zuchner, R. Wetzel, Aggregation behavior of chemically synthesized, full-length huntingtin exon1. Biochemistry 53, 3897-3907 (2014).
14. J. C. Boatz et al., Protofilament structure and supramolecular polymorphism of aggregated mutant huntingtin exon 1. Journal of molecular biology 10.1016/j.matdes.2019.108334 (2020).
15. F. J. B. Bäuerlein et al., In Situ Architecture and Cellular Interactions of PolyQ Inclusions. Cell 171, 179-187.e110 (2017).
16. A. Iwata, B. E. Riley, J. A. Johnston, R. R. Kopito, HDAC6 and microtubules are required for autophagic degradation of aggregated Huntingtin. Journal of Biological Chemistry 280, 40282-40292 (2005).
17. M. Kim et al., Mutant Huntingtin Expression in Clonal Striatal Cells : Dissociation of Inclusion Formation and Neuronal Survival by Caspase Inhibition. The Journal of neuroscience : the official journal of the Society for Neuroscience 19, 964-973 (1999).
18. Y. E. Kim et al., Soluble Oligomers of PolyQ-Expanded Huntingtin Target a Multiplicity of Key Cellular Factors. Molecular cell 63, 950-964 (2016).
19. H. Luo et al., Herp Promotes Degradation of Mutant Huntingtin: Involvement of the Proteasome and Molecular Chaperones. Molecular neurobiology 10.1007/s12035-018-0900-8 (2018).
20. T. R. Peskett et al., A Liquid to Solid Phase Transition Underlying Pathological Huntingtin Exon1 Aggregation. Molecular cell 10.1016/j.molcel.2018.04.007, 1-14 (2018).
21. N. Riguet et al., Disentangling the sequence, cellular and ultrastructural determinants of Huntingtin nuclear and cytoplasmic inclusion formation. bioRxiv 10.1101/2020.07.29.226977, 2020.2007.2029.226977 (2020).
22. K. Tagawa et al., Distinct aggregation and cell death patterns among different types of primary neurons induced by mutant huntingtin protein. Journal of Neurochemistry 89, 974-987 (2004).
23. Z. Zheng, A. Li, B. B. Holmes, J. C. Marasa, M. I. Diamond, An N-terminal nuclear export signal regulates trafficking and aggregation of huntingtin (Htt) protein exon 1. Journal of Biological Chemistry 288, 6063-6071 (2013).
24. S. W. Davies et al., Formation of Neuronal Intranuclear Inclusions Underlies the Neurological Dysfunction in Mice Transgenic for the HD Mutation. Cell 90, 537-548 (1997).
25. L. Mangiarini et al., Exon I of the HD gene with an expanded CAG repeat is sufficient to cause a progressive neurological phenotype in transgenic mice. Cell 87, 493-506 (1996).
26. D. Martindale et al., Length of huntingtin and its polyglutamine tract influences localization and frequency of intracellular aggregates. Nature genetics 18, 150-154 (1998).
27. J. Ochaba et al., PIAS1 Regulates Mutant Huntingtin Accumulation and Huntington's Disease-Associated Phenotypes In Vivo. Neuron 90, 507-520 (2016).
28. G. Schilling et al., Intranuclear inclusions and neuritic aggregates in transgenic mice expressing a mutant N-terminal fragment of huntingtin. Human Molecular Genetics 8, 397-407 (1999).
29. G. Matsumoto, S. Kim, R. I. Morimoto, Huntingtin and Mutant SOD1 Form Aggregate Structures with Distinct Huntingtin and Mutant SOD1 Form Aggregate Structures with Distinct Molecular Properties in Human Cells. The Journal of biological chemistry 281, 4477-4485 (2006).
30. S. Waelter et al., Accumulation of mutant huntingtin fragments in aggresome-like inclusion bodies as a result of insufficient protein degradation. Molecular biology of the cell 12, 1393-1407 (2001).
31. A. Chongtham et al., Effects of flanking sequences and cellular context on subcellular behavior and pathology of mutant HTT. Human molecular genetics 29, 674-688 (2020).
32. T. Maiuri, T. Woloshansky, J. Xia, R. Truant, The huntingtin N17 domain is a multifunctional CRM1 and ran-dependent nuclear and cilial export signal. Human Molecular Genetics 22, 1383-1394 (2013).
33. C. Landles et al., Subcellular Localization And Formation Of Huntingtin Aggregates Correlates With Symptom Onset And Progression In A Huntington’S Disease Model. Brain Communications 2 (2020).
34. C. Cariulo et al., Phosphorylation of huntingtin at residue T3 is decreased in Huntington’s disease and modulates mutant huntingtin protein conformation. Proceedings of the National Academy of Sciences 10.1073/pnas.1705372114, 201705372-201705372 (2017).
35. R. N. Hegde et al., TBK1 phosphorylates mutant Huntingtin and suppresses its aggregation and toxicity in Huntington's disease models. The EMBO journal 10.15252/embj.2020104671, e104671 (2020).
36. L. M. Thompson et al., IKK phosphorylates Huntingtin and targets it for degradation by the proteasome and lysosome. Journal of Cell Biology 187, 1083-1099 (2009).
37. R. S. Atwal et al., Kinase inhibitors modulate huntingtin cell localization and toxicity. Nature chemical biology 7, 453-460 (2011).
38. A. Bhattacharyya et al., Oligoproline effects on polyglutamine conformation and aggregation. Journal of molecular biology 355, 524-535 (2006).
39. S. Chen, V. Berthelier, W. Yang, R. Wetzel, Polyglutamine aggregation behavior in vitro supports a recruitment mechanism of cytotoxicity. Journal of molecular biology 311, 173-182 (2001).
40. S. L. Crick, K. M. Ruff, K. Garai, C. Frieden, R. V. Pappu, Unmasking the roles of N- and C-terminal flanking sequences from exon 1 of huntingtin as modulators of polyglutamine aggregation. Proceedings of the National Academy of Sciences 110, 20075-20080 (2013).
41. K. Kar, M. Jayaraman, B. Sahoo, R. Kodali, R. Wetzel, Critical nucleus size for disease-related polyglutamine aggregation is repeat-length dependent. Nature Structural and Molecular Biology 18, 328-336 (2011).
42. R. Mishra et al., Serine phosphorylation suppresses huntingtin amyloid accumulation by altering protein aggregation properties. Journal of molecular biology 424, 1-14 (2012).
43. A. K. Thakur et al., Polyglutamine disruption of the huntingtin exon1 N-terminus triggers a complex aggregation mechanism Ashwani. Nat Struct Mol Biol 16, 380-389 (2009).
44. D. Bulone, L. Masino, D. J. Thomas, P. L. San Biagio, A. Pastore, The interplay between PolyQ and protein context delays aggregation by forming a reservoir of protofibrils. PloS one 1, e111 (2006).
45. L. Masino, G. Kelly, K. Leonard, Y. Trottier, A. Pastore, Solution structure of polyglutamine tracts in GST-polyglutamine fusion proteins. FEBS Letters 513, 267-272 (2002).
46. Y. Nagai et al., A toxic monomeric conformer of the polyglutamine protein. Nature Structural and Molecular Biology 14, 332-340 (2007).
47. E. J. Bennett, N. F. Bence, R. Jayakumar, R. R. Kopito, Global Impairment of the Ubiquitin-Proteasome System by Nuclear or Cytoplasmic Protein Aggregates Precedes Inclusion Body Formation. Molecular cell 17, 351-365 (2005).
48. C. W. Bugg, J. M. Isas, T. Fischer, P. H. Patterson, R. Langen, Structural features and domain organization of huntingtin fibrils. Journal of Biological Chemistry 287, 31739-31746 (2012).
49. P. R. Dahlgren et al., Atomic force microscopy analysis of the Huntington protein nanofibril formation. Nanomedicine: Nanotechnology, Biology, and Medicine 1, 52-57 (2005).
50. W. C. Duim, Y. Jiang, K. Shen, J. Frydman, W. E. Moerner, Super-resolution fluorescence of huntingtin reveals growth of globular species into short fibers and coexistence of distinct aggregates. ACS Chemical Biology 9, 2767-2778 (2014).
51. J. M. Isas, R. Langen, A. B. Siemer, Solid-State Nuclear Magnetic Resonance on the Static and Dynamic Domains of Huntingtin Exon-1 Fibrils. Biochemistry 54, 3942-3949 (2015).
52. J. Legleiter et al., Monoclonal antibodies recognize distinct conformational epitopes formed by polyglutamine in a mutant huntingtin fragment. Journal of Biological Chemistry 284, 21647-21658 (2009).
53. E. Monsellier, V. Redeker, G. Ruiz-Arlandis, L. Bousset, R. Melki, Molecular interaction between the chaperone Hsc70 and the N-terminal flank of huntingtin exon 1 modulates aggregation. Journal of Biological Chemistry 290, 2560-2576 (2015).
54. P. J. Muchowski et al., Hsp70 and Hsp40 chaperones can inhibit self-assembly of polyglutamine proteins into amyloid-like fibrils. Proceedings of the National Academy of Sciences 97, 7841-7846 (2000).
55. Y. Nekooki-machida et al., Distinct conformations of in vitro and in vivo amyloids of huntingtin-exon1 show different cytotoxicity. Proceedings of the National Academy of Sciences 106, 9679-9684 (2009).
56. L. G. Nucifora et al., Identification of novel potentially toxic oligomers formed in vitro from mammalian-derived expanded huntingtin exon-1 protein. Journal of Biological Chemistry 287, 16017-16028 (2012).
57. L. Pieri, K. Madiona, L. Bousset, R. Melki, Fibrillar α-synuclein and huntingtin exon 1 assemblies are toxic to the cells. Biophysical Journal 102, 2894-2905 (2012).
58. M. A. Poirier et al., Huntingtin spheroids and protofibrils as precursors in polyglutamine fibrilization. Journal of Biological Chemistry 277, 41032-41037 (2002).
59. E. Scherzinger et al., Huntingtin encoded polyglutamine expansions form amyloid-like protein aggregates in vitro and in vivo. Cell 90, 549-558 (1997).
60. E. Scherzinger et al., Self-assembly of polyglutamine-containing huntingtin fragments into amyloid-like fibrils: implications for Huntington's disease pathology. Proceedings of the National Academy of Sciences of the United States of America 96, 4604-4609 (1999).
61. J. L. Wacker, M. H. Zareie, H. Fong, M. Sarikaya, P. J. Muchowski, Hsp70 and Hsp40 attenuate formation of spherical and annular polyglutamine oligomers by partitioning monomer. Nature Structural and Molecular Biology 11, 1215-1222 (2004).
62. M. Jayaraman et al., Kinetically competing huntingtin aggregation pathways control amyloid polymorphism and properties. Biochemistry 51, 2706-2716 (2012).
63. A. K. Thakur, W. Yang, R. Wetzel, Inhibition of polyglutamine aggregate cytotoxicity by a structure-based elongation inhibitor. FASEB J 18, 923-925 (2004).
64. D. W. Colby et al., Potent inhibition of huntingtin aggregation and cytotoxicity by a disulfide bond-free single-domain intracellular antibody. Proceedings of the National Academy of Sciences of the United States of America 101, 17616-17621 (2004).
65. S. Tam, R. Geller, C. Spiess, J. Frydman, The chaperonin TRiC controls polyglutamine aggregation and toxicity through subunit-specific interactions. Nature Cell Biology 8, 1155-1162 (2006).
66. R. S. Atwal et al., Huntingtin has a membrane association signal that can modulate huntingtin aggregation, nuclear entry and toxicity. Human Molecular Genetics 16, 2600-2615 (2007).
67. X. Gu et al., N17 Modifies Mutant Huntingtin Nuclear Pathogenesis and Severity of Disease in HD BAC Transgenic Mice. Neuron 85, 726-741 (2015).
68. M. B. Veldman et al., The N17 domain mitigates nuclear toxicity in a novel zebrafish Huntington's disease model. Molecular neurodegeneration 10, 67 (2015).
69. M. s. Anass Chiki et al., One -pot Semi synthesis Of Exon1 Of The Mutant Huntingtin Protein : An Important Advance Towards Elucidating The Molecular And Structural Determinants Of Huntingtin's. Health and Biomedical, 1047-1047 (2014).
70. A. Chiki et al., Site-specific phosphorylation of Huntingtin exon 1 recombinant proteins enabled by the discovery of novel kinases. Chembiochem : a European journal of chemical biology 10.1002/cbic.202000508 (2020).
71. A. Reif, A. Chiki, J. Ricci, H. A. Lashuel, Generation of native, untagged huntingtin Exon1 monomer and fibrils using a SUMO fusion strategy. Journal of Visualized Experiments 2018, 1-9 (2018).
72. F. S. Ruggeri, T. Šneideris, M. Vendruscolo, T. P. J. Knowles, Atomic force microscopy for single molecule characterisation of protein aggregation. Arch Biochem Biophys 664, 134-148 (2019).

SciScore for 10.1101/2021.03.30.21254624: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

This study, however, is subject to some limitations. Although the examined single-center study population was large, our cohort might not be considered representative of the general population due to young age and disproportionate gender distribution. The data on symptoms and their duration are only reliable to a limited extent, since they were retrospectively analyzed from mostly self-reported symptoms entered into a web tool. Furthermore, instead of a nasal swab, we used an oro-and nasopharyngeal swab to investigate RADT performance, which impedes the feasibility for the general public. In combating overdispersed SARS-CoV-2 transmission, rapid detection and isolation of highly infectious individuals is a primary goal (8, 9, 36–40). In our investigation the Standard Q RADT was able to reliably detect high viral load as well as all culture positive samples. Therefore, this test could be used as a fast surrogate marker for viral cultivation in order to identify and prevent SARS-CoV-2 transmissions by highly infectious individuals. Although less sensitive than RT-qPCR, RADT could compensate for this disadvantage through easy and feasible mass screenings (8, 41). Furthermore, one might suspect that RT-qPCR positive, but RADT negative individuals do not pose a high risk of transmissions, since all samples remained culture negative in our experimental setup. However, individual results must be interpreted with caution as SARS-CoV-2 infection could remain undetected in early stages...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a protocol registration statement.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.04.05.21254712: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your code and data.

This study has several limitations. PCR test results that were chosen as a golden standard, were carried out in different laboratories. Although official test certificates that are registered in the national database were provided, false-positive results cannot be ruled out [28]. But as mentioned above, PCR false-positivity is likely to underestimate antibody test sensitivity in this study. Neutralization test results should be considered with caution as well [29]. Such tests can be considered as a surrogate marker of protection from reinfection, but this association needs to be explored in population-based epidemiological studies [15]. In conclusion, this validation study provides a reference that can be used in further seroprevalence reports to correct the results based on test sensitivity and specificity. Choice of the test for longitudinal surveillance is critical for making conclusions about the spread of SARS-CoV-2 and the durability of the immune response. Local tests should be rigorously evaluated for seroprevalence studies because the benefits of using properly validated tests are not only financial or related to matters of convenience. They may provide more accurate and unbiased assessments for the course of the pandemic.

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04406038</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Active, not recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Study of the Spread of COVID-19 in Saint Petersburg, Russia</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">ISRCTN11060415</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NA</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NA</td></tr></table>

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.04.06.438630: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.04.04.438404: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.04.03.438258: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 31. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• No funding statement was detected.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Author Response:

Reviewer #1 (Public Review):

Redmond et al. use single-cell and single-nucleus RNA-sequencing to reveal the molecular heterogeneity that underlies regional differences in neural stem cells in the adult mouse V-SVZ. The authors generated two datasets: one which was whole cell RNA-seq of whole V-SVZ and one which consisted of nuclear RNA-seq of V-SVZ microdissected into anterior-posterior and dorsal-ventral quadrants. The authors first identified distinct subtypes of B cells and showed that these B cell subtypes correspond to dorsal and ventral identities. Then, they identified distinct subtypes of A cells and classified them into dorsal and ventral identities. Finally, the authors identified a handful of genes that they conclude constitute a conserved molecular signature for dorsal or ventral lineages. The text of the manuscript is well written and clear, and the figures are organized and polished. The datasets generated in this manuscript will be a great resource for the field of adult neurogenesis. However, the arguments and supporting data used to assign dorsal/ventral identities to B cells and A cells could be strengthened, and more rigorous data analysis could result in new biological insights into stem and progenitor cell heterogeneity in the V-SVZ.

We thank the Reviewer for their feedback on our manuscript. As suggested by Reviewer #1, we are performing additional analyses in the following areas:

1) Performing additional analyses to further strengthen the dorsal/ventral scRNA-Seq B cell marker analysis and its relationship to our sNucRNA-Seq B data.

2) Performing additional analyses to identify potential novel biological insights into stem & progenitor cell heterogeneity and text edits to discuss how differentially-expressed sets of genes among B cells and A cells are related to biological processes and/or signaling pathways.

Reviewer #2 (Public Review):

The paper is well written, and the data are well analyzed and presented. My concerns centre on terminology and alternative explanations of some of the data, which the authors might deal with in the introduction or discussion.

We thank Reviewer #2 for their positive reception of our manuscript and the data, and for the constructive suggestions, which we have addressed by changes to the manuscript and in our responses below:

1) I am slightly confused about some of the data shown in Figure 1. If B cells are defined as GFAP expressing cells, then why do only 25% of the B cells in the plot in Figure 1C express GFAP? I may be missing something here, as other readers may as well. Similarly in the same panel, only 25% of astrocytes seem to be expressing GFAP or GFP driven by a GFAP promotor.

Importantly, among all cells captured in our scRNA-Seq, only B cells (51.86%), a subpopulation of parenchymal astrocytes (25%) and a small subpopulation of ependymal cells (E cells) had GFAP expression. This is consistent with immunocytochemical staining (Ponti et al. 2013) and other studies of scRNA-Seq expression (Xie et al. 2020). Similarly, Gfp (under the control of hGFAP promoter) is not expected to be expressed in all B cells (here 31.08% of B cells are Gfp+).

Note that previous work has shown that B cells express different levels of GFAP protein, and some B1 cells were negative (Ponti et al. 2013). This supports the notion that this intermediate filament is a good marker of the V-SVZ primary progenitors, but also present in a subpopulation of parenchymal astrocytes and ependymal cells. However, a negative signal for GFAP does not imply that a cell is not a B cell. This highlights the importance of our clustering analysis revealing additional genes associated with B cells. Our analysis suggests that a combination of Gfap, Thrombospondin 4, Slc1a3 (GLAST) and S100a6 provide a better marker combination to identify B cells.

The reason for the variability among B cells in the expression of GFAP remains unknown. It could be associated with the normal regulation of intermediate filaments as B cells transit the cell cycle or different stages of their activation or quiescence. It could also be linked to technical aspects of scRNA-Seq analysis: e.g gene dropout; detection limits; sequencing saturation. Since on our dot plot the actual proportion is only graphically shown, to clarify this issue in the text we have added the specific percentages and the following sentences:

“A fraction of both populations expressed GFAP: 51.85% of B cells (clusters 5,13,14 & 22), 24.37% of parenchymal astrocytes (clusters 21, 26 & 29). This is consistent with previous reports (Chai et al. 2017; Xie et al. 2020; Ponti et al. 2013). Note that across all cells captured in our scRNAseq analysis, only B cells, parenchymal astrocytes or ependymal cells expressed GFAP. Among these three cell types, B cells had the highest average expression of GFAP (4.41 for B cells, 1.00 for astrocytes, 0.29767 for Ependymal cells, values in Pearson residuals). Other markers, like S100a6 (Kjell et al. 2020) (88.9% of B cells; 54% of parenchymal astrocytes and 80% of ependymal cells) and Thbs4 (Zywitza et al. 2018) (45% of B cells; 28.77% in parenchymal astrocytes, 2.88 % in ependymal cells) are also expressed preferentially in B cells and parenchymal astrocytes, but they alone do not distinguish these two cell populations.”

2) The authors term the germinal zone of the adult mouse brain - the ventricular-subventricular zone. They should discuss the evidence that the adult germinal zone is made up of cells from both the ventricular zone and the sub ventricular zone in the late embryo, where those zones are described clearly on the basis of morphology. Many of the early embryonic neural stem cells are present in the ventricular zone before the sub ventricular zone has developed and continue to be present into the adult. If there is not clear mouse evidence that the progeny of embryonic sub ventricular cells are present in the adult germinal zone independent of embryonic ventricular zone progeny, then the authors might consider calling the zone - the adult ventricular zone, or alternatively terming the neurogenic area around the lateral ventricle the adult germinal zone or by a more straightforward descriptive term - the adult subependymal zone or the adult periventricular zone. Also, I think the first word in line 6 on page 3 should be neural rather than neuronal.

We agree that the terminology in the field is confusing and multiple names have been used to describe the same region. In order to clarify that we are referring to the same adult periventricular germinal region, we have added a short sentence in the introduction to indicate that the V-SVZ is also referred by other authors as the SVZ, the subependyma or subependymal zone: We have added in the text: “This neurogenic region has also been referred to as the SVZ or the subependymal zone (Kazanis et al. 2017; Morshead et al. 1994)”.

This reviewer argues that the adult V-SVZ should only be called V-SVZ if a lineage relationship could be established with the embryonic SVZ. To our knowledge there is no need to link the adult SVZ to the embryo, as this structure, like the embryonic SVZ, anatomically sits beneath the VZ (the area next to the ventricle). However, a lineage relationship does exist between the adult V-SVZ and the embryonic VZ, established in previous studies showing that PreB1 cells around E15.5 became quiescent and give rise to adult B cells in the V-SVZ (Fuentealba et al., 2015; Furutachi et al., 2015). In addition, developmental studies show a continuum in the gradual transformation of the embryonic periventricular germinal layers, including the SVZ. Importantly, B1 cells are derived from VZ radial glia (RG), maintain RG markers and retain RG-like interkinetic behavior establishing that functionally and anatomically a VZ is retained in the adult (Merkle et al., 2004; Mirzadeh et al., 2008). Therefore the adult periventricular epithelium is not made of a pure layer of ependymal cells with progenitor cells underneath, as previously thought. Moreover, recent work indicates that just like in the embryo, the more basal adult SVZ progenitors (B2 cells) can be derived from adult VZ progenitors (B1 cells) (Obernier et al. 2018). This transformation of apical to basal cells begins to occur in embryonic stages further suggesting equivalences between the adult and the embryonic progenitor cells. For all the above reasons we prefer to use the term V-SVZ.

In line 6, page 3, We have changed neuronal cell types to “neural cell types”, as suggested.

3) The authors refer to their molecularly described B cells as stem cells. Certainly, their lab and others have shown that adult olfactory bulb neurons are the progeny of those B cells, however the classic definition of stem cells (in the blood or intestine for example) require demonstration that single stem cells can make all of the differentiated cells in that tissue. Is their evidence that a single adult B1 cell can make astrocytes, neurons and oligodendrocytes? Indeed, what percentage of the single adult B cells characterized here on the bases of RNA expression can be shown to be multipoint for both macroglial and neuron lineages in vivo or in vitro? Perhaps progenitor or precursor cells might be a better term for a B cells that appears to give rise to neurons primarily.

This is also an issue of definitions. We modified the text to refer to the primary progenitors in the V-SVZ as adult neural stem cells, or progenitor cells “NSPCs”. We agree that this needs to be clarified and in the introduction we modified one paragraph to indicate:

“From the initial interpretation that adult NSPCs are multipotent and able to generate a wide range of neural cell types (Reynolds and Weiss 1992; van der Kooy and Weiss 2000; Morshead et al. 1994), more recent work suggest that the adult NSPCs in vivo are heterogeneous and specialized, depending on their location, for the generation of specific types of neurons, and possibly glia (Merkle et al. 2014; Fiorelli et al. 2015; Chaker, Codega, and Doetsch 2016; Merkle, Mirzadeh, and Alvarez-Buylla 2007; Tsai et al. 2012; Delgado et al. 2020).”

Under normal in vivo conditions, a primitive state for NSCs capable of generating all neuronal and glial cell types of the CNS may only exist at very early stages of development and even their regional specification seems to occur very early (as early as E10.5; Fuentealba et al. 2015). Note that recent work in the hematopoietic system suggests that stem cells there also become restricted embryonically (Carrelha et al., 2018) and in adults their potential to generate lymphoid or myeloid lineages changes dramatically with age, yet at all these ages they are referred as HSCs. We are well aware of the work from the van der Kooy lab, suggesting the existence in the V-SVZ of rare “primitive” Oct4+/GFAP- cells that may be pluripotent and earlier in the lineage from B cells (Reeve et al., 2017). However, as indicated above lineage tracing from the embryo indicates that adult NSPC are specified in the embryo and are already in place and regionally specified between E11.5 and E15. We have investigated whether we could detect Oct4+/Gfap- cells in our datasets. However, we did not detect Oct4 expression in B cells or other cell types. We now indicate in the discussion:

“It has been suggested that in the adult V-SVZ a more primitive population of Oct4+/GFAP- NSCs may be present and that these cells may be earlier in the lineage from the “definitive” GFAP+ B cells (Reeve et al. 2017). However, regionally specified NSPCs can be lineage traced to the embryo (Fuentealba et al. 2015; Furutachi et al. 2015), and we could not detect a population of Oct4+ cells in our datasets. We, however, cannot exclude that rare primitive OCT4+ NSPCs were not captured in our analysis for technical reasons.” ……. “This underscores the early embryonic regional specification of adult V-SVZ NSPCs and how these primary progenitors maintain a memory of their regions of origin.”

4) This may be more than a semantic issue, as the rare clonal neurophere forming neural stem cells that do make all three neural cell types in vitro, and also maintain their AP and DV positional identity through clonal passaging in vitro (Hitoshi et al, 2002). However, Emx1 expressing cortical neural stem cells can be lineage traced as they migrate from the embryonic cortical germinal zone to the striata germinal zone in the perinatal period (Willaime-Morawek et al, 2006). Surprisingly, in their new striatal home the Emx1 lineage cortical neural stem cells will turn down Emx1 expression and turn up Dlx2 striatal germinal zone expression. The switch in positional identities of clonal neural stem cells can be seen also in vitro when the stem cells are co-cultured with an excess of cells from a different region and then regrown as clonal neural stem cells. This may suggested that Emx1 expressing neural stem cells (the clonal neurosphere forming cells), may switch their positional identities in vivo as they migrate into the striatal germinal zone, but the downstream neuron producing precursor B cells studied in this paper may maintain their Emx1 expression into the adult germinal zone. This raises an interesting issue concerning which cells in the neural stem cell lineage can be regionally re-specified.

The interesting question about plasticity and respecification is not addressed by our current manuscript that focuses on the gene expression profile of unmanipulated cells from adult samples. However, regional re-specification is controversial. While work from van der Kooy lab suggests that striatal Emx1+ NSPCs originate in the pallium and migrate into the striatum in the perinatal brain (Willaime-Morawek et la., 2006), other studies suggest that rare Emx1 cells are already present in the developing LGE from embryonic stages as early as E12.5 (Gorski et al. 2002). In addition, we have labeled neonatal radial glial cells in the pallium, when this migration has been suggested to occur, and do not see migration of cells ventrally into the striatal wall. We have also transplanted dorsal NSPCs into ventral locations -- and vice versa -- and do not observe evidence of regional re-specification (Merkle, Mirzadeh, and Alvarez-Buylla 2007; Delgado et al. 2020).

5) The authors nicely show dorsal versus ventral germinal zone lineages are marked by some of the same positional genes from B cells to A cells, suggesting complete dorsal versus ventral neurogenic lineages giving rise to different types of olfactory bulb neurons. Indeed, they nicely test this idea with dissection of the dorsal versus ventral germinal zones, followed by nuclear RNA sequencing. However, they don't discuss the broader issues concerning the embryological origins of the dorsal versus ventral germinal zones. Emx1 is one of the genes the authors use to mark dorsal lineages. The authors reference papers (Young et al, 2007; Willaime-Morawek et al, 2006;2008) that use Emx1 lineage tracing to show that certain classes of olfactory bulb neurons originate from embryonic cortical neural stem cells that migrate perinatally from the cortical germinal zone into the dorsal subcortical germinal zone. Could cortical versus subcortical embryonic origins of the dorsal versus ventral adult germinal zone explain the origin of different sets of adult olfactory bulb neurons? Further, the authors report that one of the GO terms for their dorsal lineages in cortical regionalization.

This is a very interesting question that unfortunately we cannot answer. The dorsal domain includes both pallial and subpallial components, but the specific origin of B cells in this dorsal domain and the contribution of the pallium and subpallium remains unresolved.

We went back to our data to try to find evidence of pallial vs. subpallial components in the dorsal B clusters (5 & 22). Indeed, there are some hints that cluster 22 may be more pallial and 5 more dorsal subpallial. However, when we try to confirm differential distribution of markers associated with these two dorsal subdomains anatomically, it is not possible to determine segregation, likely due to the intermixing of cells as the wedge is formed. We also looked for Dbx1, a relatively specific marker of the border region between pallium and subpallium that has been termed ventral pallium, but unfortunately our scRNA-Seq dataset did not capture this marker. Further, targeted lineage tracing of this region is required to determine the subdivisions of the dorsal V-SVZ. We have added as requested a short discussion on this issue:

“The dorsal V-SVZ domain is likely further subdivided into multiple subdomains. In the current analysis we pooled together clusters B(5) and B(22) as dorsal. However, largely pallial marker Emx1 and dorsal lateral ganglionic eminence marker Gsx2 were differentially enriched in clusters B(22) and B(5), respectively, suggesting that these two clusters may also represent different sets of regionally specified B cells with distinct embryonic origins. These regions become blurred by cells intermixing in the formation of the wedge region in the postnatal V-SVZ making it difficult to confirm their origin based on expression patterns. In addition to pallial and dorsal subpallial markers, this wedge region likely also includes what has been termed the ventral pallium (Puelles et al. 2016), which is characterized in the embryo by the expression of Dbx1. Unfortunately, our scRNA-Seq analysis did not detect this marker. Further lineage tracing experiments will help determine the precise embryonic origin and nature of the dorsal V-SVZ, including the wedge region.”

6) The percentages of dividing cells based on gene expression is given for some clusters of cells but not others. It might be useful to have a chart showing the percentages of cells in cycle (ki67 expression) for each cluster. This might be especially useful in characterizing some fo the differences between various subclusters of B, A and C cells. On page 9 it is suggested that the heterogeneity amongst C cell clusters was driven by cell cycle genes. However, it is possible to remove the cell cycle genes from the data analysis to see if this then produces clearer dorsal versus ventral positional identities. This may be an important issue as the dorsal versus ventral positional identity genes appear to be expressed more in less dividing A and B cells, than in the more dividing C cells. This leads to a potentially alternative conclusion - that dorsal/ventral regional identity genes are primarily expressed in the non-dividing post mitotic cells in their resident dorsal or ventral region, and not in precursor cells in the lineage.This could be easiy tested by removing the cell cycle genes from the analysis of highly dividing clusters to see if they then break down into doral versus ventral clusters.

We now provide a table indicating the fraction of proliferating cells (defined as in S phase or G2-M phase) for all scRNA-Seq clusters.

Concerning whether dorsal and ventral identities are maintained during the period of proliferation we have analyzed our data looking at dorsal and ventral signature levels over pseudotime (Figure 6-Supplement 1F). Here we do not observe a reduction in either dorsal or ventral score at the proliferative cell stages (pseudotime ~0.75, Figure 2L). This is in contrast to gene signatures that show clear up- or down-regulation over pseudotime, such as Gfap, Egfr & Dcx (Figure 2M). To understand how cell clustering is affected in the absence of proliferative gene influence, and whether clearer dorsal/ventral signatures are observed in proliferating cells, we are performing additional analyses using our scRNA-Seq dataset that is clustered after cell-cycle gene regression.

References Cited:

Chaker, Zayna, Paolo Codega, and Fiona Doetsch. 2016. “A Mosaic World: Puzzles Revealed by Adult Neural Stem Cell Heterogeneity.” Wiley Interdisciplinary Reviews. Developmental Biology 5 (6): 640–58.

Delgado, Ryan N., Benjamin Mansky, Sajad Hamid Ahanger, Changqing Lu, Rebecca E. Andersen, Yali Dou, Arturo Alvarez-Buylla, and Daniel A. Lim. 2020. “Maintenance of Neural Stem Cell Positional Identity by.” Science 368 (6486): 48–53.

Fiorelli, Roberto, Kasum Azim, Bruno Fischer, and Olivier Raineteau. 2015. “Adding a Spatial Dimension to Postnatal Ventricular-Subventricular Zone Neurogenesis.” Development 142 (12): 2109–20.

Fuentealba, Luis C., Santiago B. Rompani, Jose I. Parraguez, Kirsten Obernier, Ricardo Romero, Constance L. Cepko, and Arturo Alvarez-Buylla. 2015. “Embryonic Origin of Postnatal Neural Stem Cells.” Cell 161 (7): 1644–55.

Furutachi, Shohei, Hiroaki Miya, Tomoyuki Watanabe, Hiroki Kawai, Norihiko Yamasaki, Yujin Harada, Itaru Imayoshi, et al. 2015. “Slowly Dividing Neural Progenitors Are an Embryonic Origin of Adult Neural Stem Cells.” Nature Neuroscience 18 (5): 657–65.

Gorski, Jessica A., Tiffany Talley, Mengsheng Qiu, Luis Puelles, John L. R. Rubenstein, and Kevin R. Jones. 2002. “Cortical Excitatory Neurons and Glia, but Not GABAergic Neurons, Are Produced in the Emx1-Expressing Lineage.” The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 22 (15): 6309–14.

Kazanis, Ilias, Kimberley A. Evans, Evangelia Andreopoulou, Christina Dimitriou, Christos Koutsakis, Ragnhildur Thora Karadottir, and Robin J. M. Franklin. 2017. “Subependymal Zone-Derived Oligodendroblasts Respond to Focal Demyelination but Fail to Generate Myelin in Young and Aged Mice.” Stem Cell Reports 8 (3): 685–700.

Kooy, D. van der, and S. Weiss. 2000. “Why Stem Cells?” Science 287 (5457): 1439–41.

Merkle, Florian T., Luis C. Fuentealba, Timothy A. Sanders, Lorenza Magno, Nicoletta Kessaris, and Arturo Alvarez-Buylla. 2014. “Adult Neural Stem Cells in Distinct Microdomains Generate Previously Unknown Interneuron Types.” Nature Neuroscience 17 (2): 207–14.

Merkle, Florian T., Zaman Mirzadeh, and Arturo Alvarez-Buylla. 2007. “Mosaic Organization of Neural Stem Cells in the Adult Brain.” Science 317 (5836): 381–84.

Morshead, C. M., B. A. Reynolds, C. G. Craig, M. W. McBurney, W. A. Staines, D. Morassutti, S. Weiss, and D. van der Kooy. 1994. “Neural Stem Cells in the Adult Mammalian Forebrain: A Relatively Quiescent Subpopulation of Subependymal Cells.” Neuron 13 (5): 1071–82.

Ponti, Giovanna, Kirsten Obernier, Cristina Guinto, Lingu Jose, Luca Bonfanti, and Arturo Alvarez-Buylla. 2013. “Cell Cycle and Lineage Progression of Neural Progenitors in the Ventricular-Subventricular Zones of Adult Mice.” Proceedings of the National Academy of Sciences of the United States of America 110 (11): E1045–54.

Puelles, Luis, Loreta Medina, Ugo Borello, Isabel Legaz, Anne Teissier, Alessandra Pierani, and John L. R. Rubenstein. 2016. “Radial Derivatives of the Mouse Ventral Pallium Traced with Dbx1-LacZ Reporters.” Journal of Chemical Neuroanatomy 75 (Pt A): 2–19.

Reeve, Rachel L., Samantha Z. Yammine, Cindi M. Morshead, and Derek van der Kooy. 2017. “Quiescent Oct4 Neural Stem Cells (NSCs) Repopulate Ablated Glial Fibrillary Acidic Protein NSCs in the Adult Mouse Brain.” Stem Cells 35 (9): 2071–82.

Reynolds, B. A., and S. Weiss. 1992. “Generation of Neurons and Astrocytes from Isolated Cells of the Adult Mammalian Central Nervous System.” Science 255 (5052): 1707–10.

Tsai, Hui-Hsin, Huiliang Li, Luis C. Fuentealba, Anna V. Molofsky, Raquel Taveira-Marques, Helin Zhuang, April Tenney, et al. 2012. “Regional Astrocyte Allocation Regulates CNS Synaptogenesis and Repair.” Science 337 (6092): 358–62.

Xie, Xuanhua P., Dan R. Laks, Daochun Sun, Asaf Poran, Ashley M. Laughney, Zilai Wang, Jessica Sam, et al. 2020. “High Resolution Mouse Subventricular Zone Stem Cell Niche Transcriptome Reveals Features of Lineage, Anatomy, and Aging.”Cold Spring Harbor Laboratory. https://doi.org/10.1101/2020.07.27.223602.

Funny, this is the paper we read earlier this semester.

Gusman, M. S., Grimm, K. J., Cohen, A. B., & Doane, L. D. (2021). Stress and Sleep Across the Onset of the COVID-19 Pandemic: Impact of Distance Learning on U.S. College Students’ Health Trajectories. PsyArXiv. https://doi.org/10.31234/osf.io/m5zv9

goes in starred section

i love. its complicated. make it a 'starred subsection' or something.

SciScore for 10.1101/2021.04.02.438182: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.04.02.438204: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04303299</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Fight COVID-19 Trial</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04392427</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Not yet recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">New Antiviral Drugs for Treatment of COVID-19</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04260594</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Not yet recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Clinical Study of Arbidol Hydrochloride Tablets in the Treat…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04355026</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Use of Bromhexine and Hydroxychloroquine for Treatment of CO…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04276688</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Completed</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Lopinavir/ Ritonavir, Ribavirin and IFN-beta Combination for…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04445272</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Completed</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Clinical Trial to Evaluate the Effectiveness and Safety of T…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04433078</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">RepurpoSing Old Drugs TO SuppRess a Modern Threat: COVID-19 …</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04358614</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Completed</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Baricitinib Therapy in COVID-19</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04338958</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Ruxolitinib in Covid-19 Patients With Defined Hyperinflammat…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04280705</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Completed</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Adaptive COVID-19 Treatment Trial (ACTT)</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04315948</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Active, not recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Trial of Treatments for COVID-19 in Hospitalized Adults</td></tr></table>

Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Sadly no reference to which book or portion in which this segment appears.

SciScore for 10.1101/2021.04.01.438087: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.03.31.437907: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 20. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.04.01.438120: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.03.30.437622: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.03.29.437516: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

A limitation of our study is the infection of participants primarily in the third trimester, because these samples were collected during the initial wave of the SARS-CoV-2 pandemic in Boston. Whether maternal SARS-CoV-2 infection in the first and second trimester alters ISG and Fc receptor expression, and how such altered expression might durably impact placental immune function, is a question that remains to be answered in future studies. In addition, although we found no significant association between disease severity and placental gene expression or antibody transfer, such examinations were limited by the relatively small number of women with severe or critical illness. Finally, while our regression models did not find time from infection to delivery to be a significant contributor to the antibody transfer ratios, we cannot entirely rule out any contribution of timing of maternal infection to the reduced antibody transfer noted in males. However, our robust sexually dimorphic gene and protein expression results, with significant upregulation of both placental ISGs and Fc receptors in males, demonstrate placental factors are a stronger driver of antibody transfer than any time-from-infection effect. In conclusion, our comprehensive evaluation of the impact of fetal sex on placental gene expression and transplacental antibody transfer in maternal SARS-CoV-2 infection provides insight into sexually dimorphic or sex-specific placental innate and adaptive immune responses to m...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.03.29.436639: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• No funding statement was detected.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.03.28.437363: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 25 and 27. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.03.25.21254215: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.03.24.21254277: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Reviewer #2 (Public Review):

In this manuscript, the authors set out to provide a comprehensive meta-analysis of associations between masculinized phenotypes and fitness-relevant outcomes (mating, reproduction, and offspring viability), so as to assess the current state of evidence for hypotheses of sexual selection on human males across high- and low-fertility populations. I enjoyed reading this manuscript, which is well organized and very clearly written. I also appreciated the depth of the analyses reported by the authors. Overall, I am pleased with this research and think it will make a valuable contribution to the literature on human sexual selection and masculinity more generally.

I do not have any major concerns regarding the methods and results. However, I think the paper would greatly benefit from introducing greater nuance into the theoretical framework and conclusions, which I believe will meaningfully change some of the takeaways presented in the discussion. I have provided references throughout to aid the authors in this effort during revision, though they should certainly not feel compelled to cite each reference provided. I would also appreciate that the authors provide some estimates of (a priori) statistical power when they make claims regarding statistical power in the interpretation of results.

Major comments:

The authors have done a very nice job of efficiently introducing the reader to mainstream hypotheses regarding sexual selection on human male phenotypes, particularly those emphasized within evolutionary psychology. I recognize that the authors' primary contribution is empirical and that they have in large part followed the typical presentation of these hypotheses in previous literature. However, given that this paper may be an important point of reference for future research in this area, I would like to encourage the authors to address some important nuances in greater detail that are frequently overlooked.

(i) The authors argue that "Sexual selection is commonly argued to have acted more strongly on male traits as a consequence of greater variance in males' reproductive output (3) and male-biased operational sex ratio, i.e. a surplus of reproductively available males relative to fertile females (e.g. 4)". This argument then leads to a discussion of why formidability as indexed by strength and other potential indicators of physical dominance are expected to be under selection in males. However, recent work in sexual selection theory has begun to emphasize the importance of the co-evolution of male offspring care and reproductive competition, leading in many cases to opposite predictions compared to classical models of OSR. In particular, more recent models predict that males should often increase rather than decrease offspring care relative to mating effort when men are in relative abundance. These predictions have received support in recent empirical studies in human populations, and help to explain otherwise puzzling patterns such as e.g. the association between male-biased sex ratios and monogamy + low reproductive skew across many taxa. Please see

Kokko, H., & Jennions, M. D. (2008). Parental investment, sexual selection and sex ratios. Journal of evolutionary biology, 21(4), 919-948. Schacht, R., Rauch, K. L., & Mulder, M. B. (2014). Too many men: the violence problem?. Trends in Ecology & Evolution, 29(4), 214-222. Schacht, R., & Borgerhoff Mulder, M. (2015). Sex ratio effects on reproductive strategies in humans. Royal Society open science, 2(1), 140402.

Considering these models, one might expect that a variety of behavioral and psychological phenotypes would be under male-specific sexual selection that are simply not considered in the present study. One might also expect that appropriate proxies of male fitness will also vary across populations, independently of the presence/absence of contraception. The authors argue that they selected mating-based proxies of reproductive behaviors and attitudes under the assumption that "preferences for casual sex, number of sexual partners, and age at first sexual intercourse (earlier sexual activity allows for a greater lifetime number of sexual partners)... correlated with reproductive success in men under ancestral conditions". Yet, in large-scale industrialized societies that have undergone a demographic transition, high status males are often observed to invest more in offspring care and the production of intergenerationally transferable wealth at the expense of greater fertility, which may be an adaptive response to shifting demands in relation to competition for status.

Shenk, M. K., Kaplan, H. S., & Hooper, P. L. (2016). Status competition, inequality, and fertility: implications for the demographic transition. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1692), 20150150.

In general, long-run fitness may often not map so simply onto promiscuous sexual behavior in such a straightforward way. Measures such as age at first intercourse may also be confounded with environmental heterogeneity among participants, which could instead indicate environmentally induced plasticity within individuals' lifetimes toward a faster pace of life.

(ii) Related to this point, the authors discussion of the relationship between testosterone and male phenotypes is somewhat over-simplified, although again in keeping with much of the previous literature in evolutionary psychology. While it was long emphasized that testosterone is a mechanism of aggression per se, recent work has shown that testosterone is better understood as a mechanism for increasing status-seeking, competitive behavior, which can greatly vary in form across socioecological contexts.

Eisenegger, C., Haushofer, J., & Fehr, E. (2011). The role of testosterone in social interaction. Trends in cognitive sciences, 15(6), 263-271.

Unfortunately, most of the fWHR and 2D:4D literature has ignored these findings and continues to focus solely on aggression even in WEIRD student samples, where we can be certain that aggression is generally not a viable strategy for attaining and maintaining social status. To my knowledge, only a few studies have explicitly tested this more nuanced hypothesis regarding associations between masculinized phenotypes and differing forms of status-seeking behavior, both of which have found support for ecologically contingent effects in regards to fWHR. Martin et al. (2019) predicted and found support in bonobos for higher fWHR predicting higher scores on an affiliative measure of social rank among both males and females, consistent with the importance of relationship strength and social network centrality for competitive advantage among bonobos. Similarly, Hahn et al. (2017) found that fWHR in human males consistently predicts prosocial behavior and leadership in large-scale institutions. This is consistent with the fact that leadership traits, rather than aggression and formidability per se, are often important predictors of status in human societies (and in contexts of relatively higher SES within those societies).

Hahn, T., Winter, N. R., Anderl, C., Notebaert, K., Wuttke, A. M., Clément, C. C., & Windmann, S. (2017). Facial width-to-height ratio differs by social rank across organizations, countries, and value systems. PLoS One, 12(11), e0187957. Martin, J. S., Staes, N., Weiss, A., Stevens, J. M. G., & Jaeggi, A. V. (2019). Facial width-to-height ratio is associated with agonistic and affiliative dominance in bonobos (Pan paniscus). Biology Letters, 15(8), 20190232.

In regard to the male-male competition hypothesis, as noted in the previous comment, we might therefore expect sexual selection to occur on a variety of male traits other than formidability related measures, as well as to be highly population-specific-rather than there being some universal optimum for "masculine" traits-given that what constitutes an adaptive male phenotype likely varies across populations in regard to both male-male competition and female choice. Finally, it should be noted that testosterone is by no means the only sex hormone relevant to considering patterns of human sexual dimorphism. Please see Dunsworth (2020) for a discussion of the centrality of estrogen in proximally explaining sexual dimorphism in body size

Dunsworth, H. M. (2020). Expanding the evolutionary explanations for sex differences in the human skeleton. Evolutionary Anthropology, 29, 108-116.

(iii) The authors should provide more references to (and brief discussion of) mixed results regarding the degree of sexual dimorphism in facial and digit ratio metrics. While they cite a few studies in the introduction, one might leave the text with the impression that there is clear enough evidence for 2D:4D being influenced by (pre-natal) sex hormones and being a sexually dimorphic phenotype. However, these results have been strongly challenged, not only be ref 14 and 20 in the main text, but also various other studies e.g.

Barrett, E., Thurston, S. W., Harrington, D., Bush, N. R., Sathyanarayana, S., Nguyen, R., ... & Swan, S. (2020). Digit ratio, a proposed marker of the prenatal hormone environment, is not associated with prenatal sex steroids, anogenital distance, or gender-typed play behavior in preschool age children. Journal of Developmental Origins of Health and Disease, 1-10. Richards, G. (2017). What is the evidence for a link between digit ratio (2D: 4D) and direct measures of prenatal sex hormones?. Early Human Development. Richards, G., Browne, W. V., Aydin, E., Constantinescu, M., Nave, G., Kim, M. S., & Watson, S. J. (2020). Digit ratio (2D: 4D) and congenital adrenal hyperplasia (CAH): Systematic literature review and meta-analysis. Hormones and Behavior, 126, 104867. Richards, G., Browne, W. V., & Constantinescu, M. (2021). Digit ratio (2D: 4D) and amniotic testosterone and estradiol: An attempted replication of Lutchmaya et al.(2004). Journal of Developmental Origins of Health and Disease.

Similarly, not all metrics of facial masculinity are equally valid given current empirical evidence. In a recent longitudinal study, only cheekbone prominence was found to show consistent evidence of sexual dimorphism across age groups.

Robertson, J. M., Kingsley, B. E., & Ford, G. C. (2017). Sexually dimorphic faciometrics in humans from early adulthood to late middle age: Dynamic, declining, and differentiated. Evolutionary Psychology, 15(3), 1474704917730640.

Overall, I found the authors' discussion of how they selected the specific facial metrics lumped together in their analyses to be underspecified. Please note in the discussion as well that BMI is a well-known confound in studies of facial masculinity and may be a cause of null results in the present study (unless I happened to miss this in the regard to the moderation results - if so, my apologies!).

Geniole, S. N., Denson, T. F., Dixson, B. J., Carré, J. M., & McCormick, C. M. (2015). Evidence from meta-analyses of the facial width-to-height ratio as an evolved cue of threat. PloS one, 10(7), e0132726.

(iv) Finally, please provide reference to and potentially brief discussion of the current state of the literature as regards "good genes" hypotheses of female choice, which is relevant for determining how useful previous studies are for directly addressing this hypothesis. Please see:

Achorn, A. M., & Rosenthal, G. G. (2020). It's not about him: Mismeasuring 'good genes' in sexual selection. Trends in Ecology & Evolution, 35, 206-219.

SciScore for 10.1101/2021.03.26.437180: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.03.26.21254427: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

There are several limitations associated with this study. Among the greatest limitations is the lack of standardization between assays, even among the same manufacturers. This was previously noted with the neutralization assay, though the same is true among commercial assays. Since several of the assays have been designated as qualitative (i.e. the Roche, Abbott, and Ortho Clinical assays), there is limited evidence that semi-quantitative results are comparable between different instruments by the same manufacturer above the cutoff. For example, since there is no material to verify linearity at higher concentrations, a result of 15 S/C at one institution using the Ortho Clinical assay may vary from the Ortho Clinical assay at another institution. This may underlie the differences between the established cutoff and FDA cutoff for the Roche assay. In general, this problem will continue to plague the field until quantitative assays are universally adopted and standardized to SARS-CoV-2 antibody reference material, such as that recently released by the World Health Organization (27). This is further complicated by unclear direction as to how to report a qualitative assay result as quantitative under an EUA, which does not permit modification of the manufacturer’s Instructions for Use. Another limitation of the current study is that a limited number of assays were evaluated, limiting the generalizability of results. It is also important to note that these specimens were obtained e...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.03.26.21254337: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Strengths and limitations: This is the first qualitative study examining views and experiences of students and staff of regular asymptomatic SARS-CoV-2 testing in a university setting using LFTs. It highlights a number of key issues related to acceptability and feasibility of regular testing as well as its behavioural implications. We note some limitations. The mean number of tests conducted by each interview and survey participant was higher than the mean number of tests in non-interviewed participants, so our sample may over-represent those who continued to test regularly. Additionally, the FACTS participants were university student and staff volunteers, whose motivation to participate and perceived benefits may be different from those in the wider university population, and other non-university settings. The majority of interview participants were also staff. We adopted rapid qualitative analysis to aid identification of key issues but full transcription of qualitative data could have minimised the potential for errors of interpretation. However, we discussed interpretation of data on a regular basis with other members of the team and extensive notes have been made after each interview. Implications for policy and practice: Our study indicates that messages highlighting the benefits for family, friends and society in identifying asymptomatic cases and contributing to fighting the pandemic and ultimately lifting lockdowns might be beneficial for encouraging regular use of L...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.03.21.21254072: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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

Learn more at Review Commons

Reply to the reviewers

Response to all reviewers

We thank all the reviewers for carefully considering our manuscript and providing useful comments and suggestions. We agree with the general comment that testing our key findings in breast cancer cells is important. We will therefore carry out this work over the coming months and include this data in the revision. The other specific comments we address individually in the point-by-point responses below, which provides an outline of the other new experiments we plan to carry out prior to revision.

In addition to this, we would like to just highlight one general point that we only picked up when considering these responses. It is important to highlight this to all reviewers now, since we believe it adds clinical weight to our conclusions. This relates to the issue of P53, which our manuscript shows drives resistance to CDK4/6 inhibition in cells by inhibiting long-term cell cycle withdrawal following genotoxic damage.

P53 loss has been implicated in abemaciclib resistance in breast cancer patients (P53 mutation was detected in 2/18 responsive patients and 10/13 non-responsive patents (Patnaik et al., 2016)). This was recently corroborated in a larger scale study in breast cancer: the first whole exome sequencing study aimed at characterising intrinsic and acquired resistance to CDK4/6 inhibitors (Wander et al., 2020). In this recent study, P53 loss/mutation was identified in 0/18 sensitive tumours, 14/28 intrinsically resistant tumours, and 9/13 tumour with acquired resistance**. This was the most frequent single genetic change associated with resistance (58.5%), although 8 other genetic changes were also associated with resistance to differing degrees (7-27%).

Most of these other resistance events occurred in pathways known previously to help drive G1/S progression following CDK4/6 inhibition: i.e. fully predictable resistance mechanism (RB loss, CCNE2 amplification, ER loss, RAS/AKT1 activation, FGFR2/ERB22 mutation/amplification). Importantly, when the authors attempted to recapitulate these resistance event in breast cancer cell lines, they could demonstrate the expected increase in proliferation following CDK4/6 inhibition in all situation tested, except for P53 loss. This caused the authors to conclude that “loss of P53 function is not sufficient to drive CDK4/6i resistance”. This would appear to us to be an unsatisfactory explanation given the clinical data. However, the authors speculated further that: “Enrichment of TP53 mutation in resistant specimens may result from heavier pre-treatment (including chemotherapies), may be permissive for the development of other resistance-promoting alterations, or may cooperate with secondary alterations to drive CDK4/6i resistance in vivo.”

We believe that our data provide a crucial alternative explanation for these clinical findings. P53 does not affect the efficiency of a G1 arrest (fig.2), but rather it prevents the resulting genotoxic damage from inducing long-term cell cycle withdrawal (figs.2,3). Therefore, this could explain why it drives resistance in clinical disease but not in the in vitro cell growth assays employed by Wander et al. This highlights a crucial general point of our paper – important effects like this can be missed or misinterpreted until the true nature of long-term cell cycle withdrawal is appreciated.

As part of our breast cancer work at revision we will analyse this closely by comparing the effect of p53 loss on long-term cell cycle withdrawal. If the current RPE1 data holds true in breast cancer, then we believe that out study would provide a crucial explanation for these clinical findings, and in turn, these clinical data would throw weight behind our conclusion that genotoxic damage and p53 loss is a clinically important consequence of CDK4/6 inhibition in patients.

---

Reviewer #1 (Evidence, reproducibility and clarity (Required)): Comments on 'CDK4/6 inhibitors induce replication stress to cause long-term cell cycle withdrawal' The rationale for this work is to understand the mechanism by which Cdk4/6 inhibitors inhibit tumour cell growth, specifically via senescence which seems to be a frequent outcome of Cdk4/6 inhibition. Although several mechanisms by which Cdk4/6 inhibition induce senescence have been proposed these have varied with the cancer cell model studied. To examine the mechanism for the cytostatic effect of cdk4/6i in therapy without potential confounding effects of different cancer cell line backgrounds, Crozier et al tackle this question in the non-transformed, immortalised diploid human cell line, RPE1. They use live cell imaging and colony formation to track the impact of G1 arrests of different lengths induced by a range of clinically relevant cdk4/6 inhibitors. They also use CRISPR-mediated removal of p53 to examine the role of p53 in the observed cell cycle responses. After noting that G1 arrest of over 2 days leads to a pronounced failure in continued cell cycle and proliferation that is associated with features of replication stress, they perform a proteomics analysis to determine the factors responsible for this. They discover that MCM complex components and some other replicative proteins are downregulated and overall suggest a mechanism whereby downregulation of these essential replication components during a prolonged G1 induce replication stress and ultimate failure of proliferation. They show the impact of cdk4/6 inhibition can be increased by combining with either aneuploidy induction (to indirectly elevate replication stress), aphidicolin (to directly elevate replication stress) or chemotherapy agents that damage DNA. Overall this is a well written and presented manuscript. Data are extremely clearly presented and described clearly within the text. Most appropriate controls were included and the work is performed to a high standard. I have a few comments about the proteomic analysis, and the link between MCM component deregulation and the induction of replication stress:

- We thank the reviewer for this careful, detailed review, and for their kind comments about our work.

**Major points:**

1. Relevance to cancer. I appreciate that examining the mechanism in a diploid line is a sensible place to start. However it remains a bit unclear precisely which aspects of this mechanism might be conserved in cancer. It could be helpful to provide evidence (if it exists) of the impact of cdk4/6 inhibition in tumour cells. For example, are catastrophic mitosis, senescence, etc observed? And is there anything further known about the relationship between tumour mutations such as p53 and clinical response to Cdk4/6i?

- It is important to point out that senescence is a common outcome of CDK4/6 inhibition in tumour cells, but exactly why tumour cells become senescent is still unclear. There have been many possible explanations proposed (see introduction), but so far, none of these implicate DNA damage. This is surprising for us, considering that DNA damage remains the best-known inducer of senescence and this is how most other broad-spectrum anti-cancer drugs induce permanent cell cycle exit. P53 loss has been associated with CDK4/6i resistance in the clinic, but this has also not previously been linked to genotoxic stress or senescence following CDK4/6 inhibition (see detailed description of this in comment to all reviewers above).** Therefore, our data could help to explain both of these key findings. However, we appreciate the importance of testing these results in breast cancer cells, therefore we will perform these experiments and include the data after revision.

Also - many of the phenotypes followed in this manuscript vary considerably with the length of G1 and the length of release. Which of these scenarios might mimic in vivo conditions?

- We see that a prolonged arrest (> 2 days) is necessary to see genotoxic effects in RPE cells. Clinically, palbociclib is administered in 3-week on/1-week off cycles, therefore this is consistent with the possibility that replication stress is induced during the off periods to cause genotoxic damage and cell cycle withdrawal.

Relating to the downregulation of MCM complex members, and the potential impact on origin licensing, how would this mechanism be manifest in cancer cells that have already deregulated gene transcription programs, and are already experiencing replication stress?

- We hypothesise that cancer cells with ongoing replication stress maybe more sensitive to the MCM downregulation caused by CDK4/6 inhibition. The rationale is that a reduction in licenced origins would impair the ability of dormant origins to fire in response to replication problems, therefore making elevated levels of replication stress less tolerable. This is consistent with the enhanced effect of CDK4/6 inhibition seen when replication stress is elevated in RPE cells. Moreover, others have shown that experimentally reducing MCM protein levels induces hypersensitivity to replication stress in transformed cell lines such as U2OS and HeLa (Ge et al., 2007; Ibarra et al., 2008). Thus, low MCM levels and reduced origin licensing can contribute to replication failure in cancer cells.

1. MCM protein levels and proposed impact on chromatin loading and origin licensing. Several MCM components are clearly reduced at the protein level. A chromatin assay (assaying fluorescence of signal remaining after pre-extraction of cytosolic proteins) suggests that MCM loading on chromatin is reduced, and this is taken to suggest a reduction in origin licensing. This is quite an indirect method - and it is difficult to conclude that the reduced chromatin bound fraction really represents a meaningful reduction in origin licensing. It would be more convincing if either positive and negative controls for this assay were included. Moreover it is not clear if this MCM reduction and proposed reduction in licensed origins would actually impact replication in an otherwise unperturbed state? Many more origins are licensed than actually fire during a normal S-phase, so it is not entirely clear that MCM levels could lead directly to replication stress here.

- Quantifying the non-extractable MCM proteins is in truth the most direct assay for origin licensing (not origin firing) available in human cells. To our knowledge, there are no reports of MCM loading by this or similar assays that are not strongly correlated with origin licensing per se. The reviewer is correct that modest reductions in MCM loading are well-tolerated in the absence of other perturbations. Specifically, Ge et al found no proliferation effects after 50% MCM loading reduction, but any further reduction introduced a proliferation delay (Ge et al., 2007). Of note, the U2OS cells used in that study also have a functional p53 response.

- Another important point that is worth emphasizing, is that many of the differentially downregulated proteins only function at replication forks (fig.4c). Therefore, we believe that the replication stress is a combined result of poor licencing and reduced levels of replication fork proteins that are needed after the origins fire. We will clarify this point in the revised manuscript.

1. Loss of MCM protein levels and chromatin loading occurs after 1 day, not 4 days, of Cdk4/6 inhibition. The current proposal (based on evidence from the live cell imaging, and the induction of hallmarks of replication stress in figures 1-3) seems to be that something occurs between 2 and 7 days of cdk4/6i to prevent cells from resuming a normal cell cycle. Thus the proteomics was performed between 2 and 7 days, and MCM proteins identified as major changed proteins between those times. However, according to Western blots and FACS profiles in Figure 4, the major reduction in MCM protein levels, and chromatin loading occurs already at 1 day of of cdk4/6i (Figure 4d,e,f). However, replication stress is not observed after this timepoint (Figure 3) - so this seems to decouple the timings of MCM reduction from induction of replication stress. How can this be reconciled?

- We agree that some of the observed changes to replisome components are quite considerable after just 1 day of arrest (some of these downregulations such as Cdc6 or phospho-Rb can be attributed to the cell cycle arrest itself - Cdc6 is unstable in G1 - but others, such MCM proteins, are not typically lost during G1). We were initially surprised by this too, considering that the phenotype clearly appears later than 1 day of arrest. It is important to state though, that the levels of almost all replisome components continue to decline as the duration of arrest is extended, eventually falling to considerably lower levels than seen after just 1 day. This is observed for MCM2, MCM3 and PCNA by western (fig.4e,e) and a large number of other replisome components by proteomics (fig.4c, 2 vs 7 days). Even MCM loading, which is 58% reduced after just 1-day arrest, is still reduced even further to just 20% of controls after 7 days (p- Our interpretation of the phenotypic data in light of this, is that replication problems become apparent when the number of licensed origins and the function of the replisome is compromised below a certain threshold; which most likely depends on cell type and, in particular, the levels of endogenous replication stress. So, in RPE cells, 1-day treatment is clearly tolerable, perhaps because there are still enough origins to complete DNA replication successfully. But, importantly, if replication stress is enhanced in these cells then 1-day of palbociclib arrest now starts to cause observable defects. This is evident in Figure 5h, where 1-day palbociclib treatment causes minimal effect on long-term growth on its own, but growth is reduced considerably when replication stress is elevated with genotoxic drugs. We interpret this to mean that the reduction in licenced origins and replisome components observed after 1 day of arrest, starts to become problematic in situations when replication stress is elevated.*

- This is actually an important point that we will highlight this at revision, because one prediction is that other cells with elevated replication stress (e.g. tumour cells with oncogene-induced replication stress) may begin to see defects after as little as 1-day palbociclib arrest.

**Minor points:**

1. All the live cell tracking figures would be even more informative if a quantification of key features (such as a cumulative frequency of S-phase entry, or a mean+SD of time in G1, S and G2) were also presented.

- We agree this will be useful, and we will include this information after revision.

1. In Figure 2D the cells released from palbociclib seem to delay longer in G1 until they start to enter S phase, compared to cells co-treated with STLC (Figure 2B). Why would this be? It is difficult to tell if other subtle effects might be present in between the +STCL and -STLC conditions, so additional graphs such as those suggested above might be informative here in particular.

- Fig.2d shows a representative experiment (50 cells) because it is difficult to interpret these individual cell cycle profiles when more than 50 cells are presented. However, we have all the data from 3 experiments (150 cells), therefore we will also calculate timings as suggested and present this information after revision.

1. Figure 4f It would be helpful to see the FACS plot for at least one of the conditions quantified in the graph as a comparison.

- These plots will be included after revision

1. MCM2 protein is not down in p53 wt, but is reduced in p53 KO cells - why is this? And why is MCM2 not impacted when the other MCM complex members are?

- We think perhaps there has been a mistake in interpreting these graphs. MCM2 is actually slightly lower in WT than KO cells at 1 days, and similar at 4 and 7 days (Fig.4d,e). MCM2 is also reduced slightly more than MCM3 (fig.4d,e) and MCM2, 3, 4, and 5 are all reduced by similar extents between 2 and 7 days palbociclib arrest (30-40% reductions; Fig.4c).

Inducing aneuploidy with reversine to elevate replication stress may result in additional aneuploidy-related stresses that confound this interpretation. For example, aneuploidy per se is known to elevate p21 and p53 levels, and chromosome mis-segregation could elevate DNA damage. For these reasons these experiments are not as compelling as the direct elevation of replication stress using aphidicolin.

- We agree that the aneuploidy experiment could have many different interpretations, and only one of these relates specifically to replication stress. This was also commented on by reviewer 3, so we feel it is best to remove this data and just keep the data on drugs that affect replication stress or DNA damage directly. We will address the effects of aneuploidy more extensively in a separate study.

**Interesting points to follow up/add more mechanism**

1. What is mechanism of protein downregulation of MCM etc? Was gene transcription impacted, or is this a question of protein stability? Depletion of one subunit can destabilise the complex leading to protein loss of the other MCM subunits, so perhaps this effect could be due to downregulation of a single MCM complex member.
2. Are these findings specific to Cdk4/6 inhibitors, or would another means or arresting cells in G1 have the same impact?

Both of these points are interesting questions and they are actually the focus of an entirely separate study that is ongoing. In particular, we are working on the mechanism(s) of MCM and replisome downregulation.

Reviewer #1 (Significance (Required)): The central question of the paper is an important one so this work would be of interest to many in the clinical and preclinical fields, and also to the cell cycle and replication stress fields.

- We thank the reviewer for this, and we agree that linking CDK4/6 inhibitors to genotoxic stress is important both for our understanding of cell cycle control and for cancer treatment. We are actually amazed that these drugs have not previously been linked to genotoxic stress, given that they appear to have broad pan-cancer activity and all other broad-spectrum anti-cancer drug work by causing genotoxic stress.

Reviewer #2 (Evidence, reproducibility and clarity (Required)): In this paper, Saurin and colleagues investigate the effects of CDK4/6 inhibitors on cell cycle arrest and re-entry. The authors report that long-term G1 arrest induced by CDK4/6i interferes with DNA replication during the next cell cycle, leading to DNA damage and mitotic catastrophe. Additionally, this compromised replication state sensitizes cells to chemotherapeutics that enhance replication stress. The major claims advanced in this paper are well-supported by the presented evidence. Well I have several questions regarding the significance (see below), I have only a few minor points regarding the methodology. 1) Regarding the down-regulation of MCM components induced by long-term palbo treatment shown in Figure 4: MCM levels are tightly regulated by cell cycle phase. I could imagine that this gene expression change may be a consequence of, for instance, 2 days CDK4/6i treatment arresting 95% of cells in G1 while 7 days of CDK4/6i treatment causes a 99.9% G1 arrest. The data in Figure 1B seems to argue against this hypothesis, but how was that data generated? Can the authors rule out a subtle change in S-phase % over 7 days in palbo? Alternately, is the down-regulation of MCM genes a consequence of cells entering senescence?

- We have performed extensive long-term movies with these cells, and we never see cells dividing or exiting G1 after the first day of palbociclib treatment. This is illustrated in fig.1b which demonstrates that 100% of FUCCI cells are in G1 (Red) at each of the timepoints. This will be clarified in the legend. In addition, MCM protein levels do not actually oscillate with cell cycle phase (Matson et al., 2017; Méndez and Stillman, 2000), although their mRNA levels certainly do (Leone et al., 1998; Whitfield et al., 2002). Furthermore, RPE and mammalian fibroblasts retain MCM proteins after 2 days of growth factor withdrawal despite transcriptional repression of their respective genes **(Cook et al., 2002; Matson et al., 2019)

- We see significant changes in MCM levels at a time when cells are still permissive to enter the cell cycle following drug release. Therefore, MCM reduction is not a consequence of senescence. Rather, we believe that it is one of the causes of cell cycle withdrawal following the subsequent S-phase.

2) For the drug studies presented in figure 5, it is important that the authors perform the appropriate statistical comparisons and analyses to demonstrate true synergy. The authors show that combining palbo and certain chemotherapies causes a greater decrease in clonogenicity than palbo alone. This may or may not be surprising (see below) - but this by itself is insufficient to support the claim that palbo "sensitizes" cells to genotoxins. If you treat cells with two poisons, in 9 out of 10 cases, you'll kill more cells than if you treat cells with one poison alone. But that could be due to totally independent effects - see, for instance, Palmer and Sorger Cell 2017. There are several well-established statistical methods for investigating drug synergy - like Loewe Additivity or Bliss Independence - and one of these methods should be used to analyze the drug-combination studies presented in Figure 5.

- This analysis will be performed at revision

Reviewer #2 (Significance (Required)): While this study is a comprehensive analysis of the effects of CDK4/6i in RPE1 cells in 2d culture, I am not convinced of its broader significance. 1) So far as I can tell, the authors do not cite any studies establishing that CDK4/6i results in a significant increase in G1-arrested cells in treated patients. What evidence is there for this claim? I am aware that this has been demonstrated in xenografts and in mouse models, but I could not find evidence for this from actual clinical studies. Here, I am reminded of the very interesting work from Beth Weaver's group on paclitaxel - Zasadil STM 2014. While it had been widely assumed that paclitaxel causes a mitotic arrest, they actually show that this drug kills tumor cells by promoting mitotic catastrophe without inducing a complete mitotic arrest. Similarly, in the absence of existing clinical data, the underlying assumption regarding the effects of CDK4/6i that motivates this paper may not be accurate. For instance, if CDK4/6i acts through the immune system (as suggested by Jean Zhao and others), then this G1 arrest phenotype could be entirely secondary to the drug's actual mechanism-of-action.

- We are very surprised by the suggestion that CDK4/6 inhibitors may not need to cause a G1 arrest in patient tumours. We appreciate that that these inhibitors effect the immune system in many different ways to combat tumourigenesis, but there is also an overwhelming amount of evidence that a G1 arrest in patient tumours is critical for the overall response. Perhaps the most striking evidence is the fact that RB loss in tumours is one of the best-characterised mechanism of resistance in breast cancer patients (Condorelli et al., 2018; Costa et al., 2020; Li et al., 2018; O'Leary et al., 2018; Wander et al., 2020). In addition, tumours types that typically achieve a poor CDK4/6i-induced G1 arrest in preclinical models, such as TNBCs, also exhibit a poor response to CDK4/6i therapy in patients. Recently a luminal androgen receptor subtype of TNBCs has been identified that responds to CDK4/6 inhibition, due to low CDK2 activity which can otherwise drive G1 progression independently of CDK4/6 in basal-like TNBCs (Asghar et al., 2017; Liu et al., 2017). This rationalises combination therapies that converge to inhibit G1 more effectively in this subtype (e.g. AR antagonist + CDK4/6 inhibition (Christenson et al., 2021)), which is akin to the oestrogen receptor and CDK4/6 combinations that have proven so successful at treating HR+ breast cancer. Many other combinations are also currently in trials based on the same premise that inhibiting upstream G1/S regulators can enhancing the response by inducing a more efficient G1 arrest (MEK, PI3K, AKT, mTOR) (Klein et al., 2018).

- In response to the specific question about clinical G1 arrest in patients, tumour samples from breast cancer patients shows a decrease in S-phase specific markers pRB and TopoIIa following abemaciclib treatment (Patnaik et al., 2016) and there is extensive evidence of a profound cell cycle arrest following CDK4/6 inhibition as judged by staining with the mitotic marker Ki67 (Hurvitz et al., 2020; Johnston et al., 2019; Ma et al., 2017; Prat et al., 2020). Whilst this does not formally prove a G1-arrest is specifical responsible for this overall cell cycle arrest, that is the implicit assumption given the known mechanism of action of CDK4/6 inhibitors in cells.

2) How relevant are RPE1 cells? Clinically, CDK4/6 inhibitors are combined with fulvestrant (which would not have an effect in RPE1), and the activity that they exhibit in breast cancer has not been matched in any other cancer types. The underlying biology of HR+ breast cancer (particularly regarding the regulation of CCND1 expression and the G1/S transition by estrogen) may not be recapitulated by other cell types. Moreover, the artificial media used in cell culture experiments may alter the regulation of the G1/S transition. I do not believe that these experiments conducted in RPE1 cells in 2d cell culture are generalizable.

- Fulvestrant/tamoxifen are effective because they enhance the efficiency of a CDK4/6i arrest by reducing Cyclin D expression to enhance Cyclin D-CDK4/6 inhibition. That convergence onto the G1/S transition is why ER antagonists enhance the CDK4/6 response. i.e. CDK activity is inhibited and CycD transcription is reduced, therefore this double hit allows breast cancer cells to arrest in G1 more efficiently than healthy tissue which is not oestrogen-responsive (this provides yet more evidence the G1 arrest in tumours is crucial for the clinical response). It is true that RPE1 cells do not respond to the oestrogen treatment, but that is not really relevant here in our opinion. We are not testing the efficiency of a G1 arrest beyond the initial characterisation in figure 1. We are mainly examining how cells respond to that G1 arrest afterwards. It could be that components of the cell culture media affect that downstream response in unanticipated ways, but we feel that is very unlikely.

- Having said that, we agree that the general point on the relevance of RPE cells is a valid one, and we will repeat key experiment in breast cancer cells. We suspect that the reason replisome components become widely downregulated during a G1 arrest will not be a specific phenomenon that is characteristic of one particular cell type. Nevertheless, it is important to validate that assumption.

3) I am confused about the effects of CDK4/6i on genotoxin sensitivity. Replogle and Amon PNAS 2020 and several citations contained therein report that CDK4/6i protects cells from DNA damage. Moreover, trilaciclib has recently received FDA approval for its ability to protect the bone marrow from cytotoxic chemotherapy! Is this a question of dose timing/intensity? The FDA approval of trilaciclib for this indication should certainly be discussed. This underscores my concern that certain findings in this paper are RPE1/tissue culture artifacts, with limited generalizability.

- The studies the reviewer refers to demonstrate that halting cell cycle progression can protect cells from genotoxic drugs that cause DNA damage during S-phase. However, we can only think that the reviewer must have missed the critical point here: The genotoxic agents in figure 5 were added after washout from CDK4/6 inhibition (we will highlight this more clearly in the revised manuscript). After drug removal, cells enter S-phase with replication competence problems (as a result of the CDK4/6 arrest) and they then experience additional problems during S-phase (as a result of the genotoxic agents included following washout). These effects synergise to enhance replication stress, a key conclusion of figure 5.

- This does is in no way support that notion that “findings in this paper are RPE1/tissue culture artefacts with limited generalizability”. Experiments in 2D tissue culture have furnished some of the most important fundamental discoveries in cancer research. It remains to be seen whether our study will cause a paradigm shift in our thinking about how CDK4/6 inhibitors work, but we believe that it may do. We appreciate that this will not become clear until our findings are followed up and validated in preclinical models and human disease, but that does not, in our opinion, make them any less valid at this stage. As stated earlier, we will confirm this is not a RPE1 cell phenomenon, but if this holds up in breast cancer cells then we believe our data will have an important impact on future preclinical and clinical work in this area.

**Referees cross-commenting** I think that we largely agree that RPE1 is not a great model for this study, and repeating certain key experiments in an ER+ BC line like MCF7 may be warranted.

- We agree that it would add value to examine our findings in BC cells, therefore we will address this point at revision by repeating key experiments in BC cells.

Additionally, I wanted to draw attention to the fact that, to my knowledge, the evidence for palbociclib inducing a G1 arrest in patients is incredibly spotty. For early-stage breast tumors where palbo is most effective, nearly all tumor cells are in G1 anyway. I think that it makes the most sense that palbo is actually working through immune modulation or through some secondary mechanism, rather than enforcing a G1 arrest. So I'm not sure about the premise of this study.

- As discussed above, there is extensive evidence that proliferation is reduced in response to CDK4/6 inhibition in patients (Hurvitz et al., 2020; Johnston et al., 2019; Ma et al., 2017; Patnaik et al., 2016; Prat et al., 2020). We agree that proliferation in patient tumours can be slower than observed in preclinical models, and there can be many reasons for this, especially within solid tumour where hypoxia is a major factor that limits proliferation. However, we do not agree that this implies that drugs that target these tumours do not act on proliferating cells. In fact, most other broad-spectrum non-targeted chemotherapies used to treat cancer also work by targeting dividing cells, and many of these are also more effective in early stage breast cancer. In addition, and as discussed extensively above, there are many studies supporting the interpretation that a G1 arrest is critical for CDK4/6i response in breast cancer patients. Considering all of these points, we strongly believe that the premise of our study – to characterise why a G1 arrest becomes irreversible – is valid and important. This point Is also made in numerous recent reviews which also highlight that this key mechanistic information is currently lacking (Goel et al., 2018; Klein et al., 2018; Knudsen and Witkiewicz, 2017; Wagner and Gil, 2020).

- We do not disagree that the immune effects are important in patients – indeed, we cited and discussed these studies in our manuscript. However, we would argue that this works together with a G1 arrest in tumour cells. The G1 arrest most likely induces a senescent response that stimulates immune engagement and tumour clearance. These multifactorial effect of CDK4/6 inhibition, on both the tumour and the immune system, are discussed at length in these reviews: (Goel et al., 2018; Klein et al., 2018; Wagner and Gil, 2020).

Reviewer #3 (Evidence, reproducibility and clarity (Required)): The authors clearly demonstrate, with appropriate techniques, that cells treated with clinically relevant CDK4/6 inhibitors lead to a cell cycle arrest, that is only partly reversible. The authors also demonstrate clearly that release from a cdk4/6i arrest leads to two phenomena: the inability to initiate S-phase, and a cell cycle exit in G2. The inability to initiate S-phase is partly dependent on p53, the cell cycle exit is fully dependent on p53. In the absence of p53, cells that are released from a CDK4/6i block frequently enter mitosis with unrepaired DNA lesions. The authors clearly demonstrate that cdk4/6 inhibition leads to down regulation of key replication genes. Combined treatment with genotoxic agents further exaggerates the phenotype of cell cycle exit upon cdk4/6 inhibition. **Specific comments:** Figure 1B: the loss of reversibility remains at approximately 50%. Does the phenotype of replication protein depletion not happen in the 50% of cells that do restart the cell cycle? it would be good if the authors could experimentally address the heterogeneity that is observed.

- This is actually a result of the fixed analysis use in fig.1B. The irreversibility is much higher than 50% after long durations of arrest, but at the 24h timepoint used in this fixed assay many cells have exited G1 but not yet had a chance to revert back into G1 from S/G2 phase. We will reinforce this point in the legend. This highlights the value of our extensive live cell assays that can fully capture cell cycle profiles, and accurately determine when cell do/don’t enter or withdraw from different stages of the cell cycle. We believe that an overreliance of fixed endpoints in previous studies may have contributed to the genotoxic effects in S-phase being missed previously: many studies show senescence after drug washout, but the cause of that senescence only becomes apparent when you observe that cells withdraw with defects after the first S-phase.

Figure 1C: the G1 state after S-phase. The read-out here is loss of the Fucci reporter geminin. Does observation reflect p53-dependent activation of the APC/C-Cdh1 prematerely? this is a known effect of persistent DNA damage in G2 cells.

- Yes, we expect that APC/C-Cdh1 activation causes geminin and cyclin degradation when cells permanently withdraw from the cell cycle from G2. This is likely caused by p53-dependent p21 activation in response to DNA replication defects, as has been shown previously in direct response to DNA damage.

Figure 2: there seem to be two distinct phenotypes when comparing p53-wt and p53-KO: the ability to initiate S-phase after CDK4/6i removal (which is largely gone in p53 KO, only slight number after 7d treatment). And cell cycle-drop-out after S-phase (this seems to be fully p53 dependent). I am not sure if a single mechanisms explains both.

- We agree that there are p53-dependent effects on speed/extent of S-phase entry and on the resulting withdrawal from G2. It may not be a single mechanism that connected these effects, although they may be related. Our manuscript mainly focusses on the DNA replication defects and cell cycle withdrawal, but in the future, it will be important to also characterise what causes the delay in cell cycle re-entry following CDK4/6 inhibition. We suspect that this could reflect differing depths of quiescence, potentially caused by p21, which would explain the p53-dependence.

Figure 3a: related to the proviso point. it is unclear if the p21 up regulation happens in G1 or G2 cells, and related to the inability of cells to initiate S-phase, or the cell cycle exit in G2.

- This is a good point, and as discussed above, we suspect both maybe related to p21. We will examine p21 levels during a G1 arrest to compare to the levels seen following release, and we will include this data after revision.

It is stated that a combined action of the p53 pathways and ATR signaling prevent mitotic entry in RPE-wt cells. However, ATR should also be able to do this in p53-KO cells. Does cdk4/6i inhibiton also down-regulation of ATR pathway components?

- We do not detect downregulation of any ATRi components in the mass spec data comparing 2 and 7 day palbociclib arrest.

Following the observation that CDK4/6i leads to replication stress, I would hypothesise that these cells would be very sensitive to agents that inhibit the response to replication stress (inhibitors of Wee1, ATR or Chk1). Yet, these agents work preferentially in p53-deficient cells, and require cell cycle progression. Sequential treatment with CDK4/6 inhibition followed by cell cycle checkpoint inhibition may help in uncovering the phenotype.

- This is a good point and we will perform experiments with ATR inhibitors after release from CDK4/6 inhibition to examine if this enhances the phenotype.

The authors increase the amount of replication stress using chemotherapeutic approaches or MPS1 inhibitors. The chemotherapeutic approaches are relevant clinically, but mechanistically it don't understand this beyond adding up treatments that lead to replication defects.

- We agree that the main value of these experiments is not to provide mechanistic insight, but rather to demonstrate that CDK4/6 inhibition can enhance the effect of current genotoxic drugs. Considering CDK4/6 inhibitors are well-tolerated, this could represent an effective way to enhance the tumour-selectivity of current genotoxic therapeutics. This has been suggested previously in a pancreatic cancer study (Salvador-Barbero et al., 2020), but the reasons given for synergy were different (DNA damage repair) and the order of drugs exposure was reversed (genotoxic before CDK4/6i). This underscores the potential importance of our new data.

- From a mechanistic point of view, these data do still suggest that CDK4/6i and genotoxic drugs converge onto the same replication stress phenotype, thereby supporting our overall conclusions. One interpretation is that a reduction in replisome levels and licenced replication origins impairs the ability of cells to overcome replication problems induced by chemotherapy drugs. Conceptualising how these drugs may synergize in this way will be important in designing new studies and trials to address this synergy more broadly.

The aneuploidy treatment is a bit weird, because it may trigger a p53 response, before the cells are released from a cdk4.6i arrest. besides, mps1 inhibition does more than just cause replication stress and is not very clinically relevant in this context.

- We agree that the aneuploidy experiment could have many different interpretations, and only one of these relates specifically to replication stress. This was also commented on by reviewer 1, so we feel it is best to remove this data and just keep the data on drugs that affect replication stress or DNA damage directly. We will address the effects of aneuploidy more extensively in a separate study.

Reviewer #3 (Significance (Required)): In their manuscript entitled: Crozier and co-workers studied the effects of CDK4/6 inhibition on cell growth. CDK4/6 inhibitors are currently used in the treatment for hormone-positive breast cancers, but their cell biological effects on tumor cells remain incompletely clear, which may hamper the further clinical development of these drugs for breast cancer or other cancers. Inhibition of CDK4/6 is known to trigger a cell cycle arrest, and it is currently unclear how this could lead to long-term tumor control. This manuscript addresses the question why cdk4/6 inhibitors cause long-term cell cycle exit.

- We thank the reviewer for this simple description of our work, which we think pitches the significance very clearly. There are currently 15 different CDK4/6 inhibitors in clinical trials, and more than 100 further trials using the 3 currently licenced inhibitors in a wide variety of tumour types and drug combinations. Although the clinical work on these drugs is huge, it is unclear how they cause long-term cell cycle arrest and we now link this to genotoxic stress for the first time. This explains clearly why this work is potentially very significant. We agree, however, that the main caveat is the need to demonstrate our findings are also applicable to breast cancer cells. But, if this is the case, we believe this would represent a paradigm shift in our understanding of how these drugs work, especially considering that genomic damage is an universal route to prolonged cell cycle exit in response to almost all other broad-spectrum anti-cancer drugs.

There are two issues that affect the significance of the findings: the authors start their manuscript with a strong translational/clinical issue, but solely use RPE1 cell lines to address this issue2. it remains unclear if their observations hold true in breast cancer models. it would be advised to repeat key findings in a hormone receptor-positive breast cancer model.

- We will examine the applicability of our findings in breast cancer cells and include this work at revision.

the effects of CDK4/6 inhibitors are observed in clinically relevant doses. however, the effects are observed upon switch-like wash out. this does not per se reflect the pharmacodynamics of more gradual increase and decrease of drug concentrations in tuner cells. by washing out the CDK4/6 inhibitors. the significant of this work would be greater if cell cycle exit with replication stress would be observed either in clinical samples or in vivo treated cancer cells.

- We agree that the significance of this work will ultimately only become fully apparent if replication stress is confirmed in clinical samples or in vivo. We envisage that our study will stimulate exactly this type of analysis in future. However, we would also add that the gradual increase/decrease in drug concentrations seen in patients is still likely to lead to switch like cell cycle re-entry given the switch-like nature of cell cycle controls at the G1/S transition. So, the timing may be different, but we would not predict that the downstream response in S-phase would be. However, whether replication stress is seen during drug-free washout periods in patients is clearly a critical future question, as we highlight in the discussion.

References

Asghar, U.S., Barr, A.R., Cutts, R., Beaney, M., Babina, I., Sampath, D., Giltnane, J., Lacap, J.A., Crocker, L., Young, A., et al. (2017). Single-Cell Dynamics Determines Response to CDK4/6 Inhibition in Triple-Negative Breast Cancer. Clin Cancer Res 23, 5561-5572.

Christenson, J.L., O'Neill, K.I., Williams, M.M., Spoelstra, N.S., Jones, K.L., Trahan, G.D., Reese, J., Van Patten, E.T., Elias, A., Eisner, J.R., et al. (2021). Activity of combined androgen receptor antagonism and cell cycle inhibition in androgen receptor-positive triple-negative breast cancer. Mol Cancer Ther.

Condorelli, R., Spring, L., O'Shaughnessy, J., Lacroix, L., Bailleux, C., Scott, V., Dubois, J., Nagy, R.J., Lanman, R.B., Iafrate, A.J., et al. (2018). Polyclonal RB1 mutations and acquired resistance to CDK 4/6 inhibitors in patients with metastatic breast cancer. Annals of oncology : official journal of the European Society for Medical Oncology 29, 640-645.

Cook, J.G., Park, C.H., Burke, T.W., Leone, G., DeGregori, J., Engel, A., and Nevins, J.R. (2002). Analysis of Cdc6 function in the assembly of mammalian prereplication complexes. Proceedings of the National Academy of Sciences of the United States of America 99, 1347-1352.

Costa, C., Wang, Y., Ly, A., Hosono, Y., Murchie, E., Walmsley, C.S., Huynh, T., Healy, C., Peterson, R., Yanase, S., et al. (2020). PTEN Loss Mediates Clinical Cross-Resistance to CDK4/6 and PI3Kα Inhibitors in Breast Cancer. Cancer Discov 10, 72-85.

Ge, X.Q., Jackson, D.A., and Blow, J.J. (2007). Dormant origins licensed by excess Mcm2-7 are required for human cells to survive replicative stress. Genes Dev 21, 3331-3341.

Goel, S., DeCristo, M.J., McAllister, S.S., and Zhao, J.J. (2018). CDK4/6 Inhibition in Cancer: Beyond Cell Cycle Arrest. Trends Cell Biol 28, 911-925.

Hurvitz, S.A., Martin, M., Press, M.F., Chan, D., Fernandez-Abad, M., Petru, E., Rostorfer, R., Guarneri, V., Huang, C.S., Barriga, S., et al. (2020). Potent Cell-Cycle Inhibition and Upregulation of Immune Response with Abemaciclib and Anastrozole in neoMONARCH, Phase II Neoadjuvant Study in HR(+)/HER2(-) Breast Cancer. Clin Cancer Res 26, 566-580.

Ibarra, A., Schwob, E., and Méndez, J. (2008). Excess MCM proteins protect human cells from replicative stress by licensing backup origins of replication. Proceedings of the National Academy of Sciences of the United States of America 105, 8956-8961.

Johnston, S., Puhalla, S., Wheatley, D., Ring, A., Barry, P., Holcombe, C., Boileau, J.F., Provencher, L., Robidoux, A., Rimawi, M., et al. (2019). Randomized Phase II Study Evaluating Palbociclib in Addition to Letrozole as Neoadjuvant Therapy in Estrogen Receptor-Positive Early Breast Cancer: PALLET Trial. J Clin Oncol 37, 178-189.

Klein, M.E., Kovatcheva, M., Davis, L.E., Tap, W.D., and Koff, A. (2018). CDK4/6 Inhibitors: The Mechanism of Action May Not Be as Simple as Once Thought. Cancer Cell 34, 9-20.

Knudsen, E.S., and Witkiewicz, A.K. (2017). The Strange Case of CDK4/6 Inhibitors: Mechanisms, Resistance, and Combination Strategies. Trends in cancer 3, 39-55.

Leone, G., DeGregori, J., Yan, Z., Jakoi, L., Ishida, S., Williams, R.S., and Nevins, J.R. (1998). E2F3 activity is regulated during the cell cycle and is required for the induction of S phase. Genes Dev 12, 2120-2130.

Li, Z., Razavi, P., Li, Q., Toy, W., Liu, B., Ping, C., Hsieh, W., Sanchez-Vega, F., Brown, D.N., Da Cruz Paula, A.F., et al. (2018). Loss of the FAT1 Tumor Suppressor Promotes Resistance to CDK4/6 Inhibitors via the Hippo Pathway. Cancer Cell 34, 893-905.e898.

Liu, C.Y., Lau, K.Y., Hsu, C.C., Chen, J.L., Lee, C.H., Huang, T.T., Chen, Y.T., Huang, C.T., Lin, P.H., and Tseng, L.M. (2017). Combination of palbociclib with enzalutamide shows in vitro activity in RB proficient and androgen receptor positive triple negative breast cancer cells. PloS one 12, e0189007.

Ma, C.X., Gao, F., Luo, J., Northfelt, D.W., Goetz, M., Forero, A., Hoog, J., Naughton, M., Ademuyiwa, F., Suresh, R., et al. (2017). NeoPalAna: Neoadjuvant Palbociclib, a Cyclin-Dependent Kinase 4/6 Inhibitor, and Anastrozole for Clinical Stage 2 or 3 Estrogen Receptor-Positive Breast Cancer. Clin Cancer Res 23, 4055-4065.

Matson, J.P., Dumitru, R., Coryell, P., Baxley, R.M., Chen, W., Twaroski, K., Webber, B.R., Tolar, J., Bielinsky, A.K., Purvis, J.E., et al. (2017). Rapid DNA replication origin licensing protects stem cell pluripotency. eLife 6.

Matson, J.P., House, A.M., Grant, G.D., Wu, H., Perez, J., and Cook, J.G. (2019). Intrinsic checkpoint deficiency during cell cycle re-entry from quiescence. J Cell Biol 218, 2169-2184.

Méndez, J., and Stillman, B. (2000). Chromatin association of human origin recognition complex, cdc6, and minichromosome maintenance proteins during the cell cycle: assembly of prereplication complexes in late mitosis. Mol Cell Biol 20, 8602-8612.

O'Leary, B., Cutts, R.J., Liu, Y., Hrebien, S., Huang, X., Fenwick, K., André, F., Loibl, S., Loi, S., Garcia-Murillas, I., et al. (2018). The Genetic Landscape and Clonal Evolution of Breast Cancer Resistance to Palbociclib plus Fulvestrant in the PALOMA-3 Trial. Cancer Discov 8, 1390-1403.

Patnaik, A., Rosen, L.S., Tolaney, S.M., Tolcher, A.W., Goldman, J.W., Gandhi, L., Papadopoulos, K.P., Beeram, M., Rasco, D.W., Hilton, J.F., et al. (2016). Efficacy and Safety of Abemaciclib, an Inhibitor of CDK4 and CDK6, for Patients with Breast Cancer, Non-Small Cell Lung Cancer, and Other Solid Tumors. Cancer Discov 6, 740-753.

Prat, A., Saura, C., Pascual, T., Hernando, C., Muñoz, M., Paré, L., González Farré, B., Fernández, P.L., Galván, P., Chic, N., et al. (2020). Ribociclib plus letrozole versus chemotherapy for postmenopausal women with hormone receptor-positive, HER2-negative, luminal B breast cancer (CORALLEEN): an open-label, multicentre, randomised, phase 2 trial. Lancet Oncol 21, 33-43.

Salvador-Barbero, B., Álvarez-Fernández, M., Zapatero-Solana, E., El Bakkali, A., Menéndez, M.D.C., López-Casas, P.P., Di Domenico, T., Xie, T., VanArsdale, T., Shields, D.J., et al. (2020). CDK4/6 Inhibitors Impair Recovery from Cytotoxic Chemotherapy in Pancreatic Adenocarcinoma. Cancer Cell 37, 340-353.e346.

Wagner, V., and Gil, J. (2020). Senescence as a therapeutically relevant response to CDK4/6 inhibitors. Oncogene.

Wander, S.A., Cohen, O., Gong, X., Johnson, G.N., Buendia-Buendia, J.E., Lloyd, M.R., Kim, D., Luo, F., Mao, P., Helvie, K., et al. (2020). The genomic landscape of intrinsic and acquired resistance to cyclin-dependent kinase 4/6 inhibitors in patients with hormone receptor positive metastatic breast cancer. Cancer Discov.

Whitfield, M.L., Sherlock, G., Saldanha, A.J., Murray, J.I., Ball, C.A., Alexander, K.E., Matese, J.C., Perou, C.M., Hurt, M.M., Brown, P.O., et al. (2002). Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol Biol Cell 13, 1977-2000.

SciScore for 10.1101/2021.03.24.21253992: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Limitations of Study: As with all case studies, a limitation on the generalizability of our findings to wider patient populations is present. Also, while the lack of immunological responsiveness to vaccination or acute infection in immunosuppressed and solid organ transplant populations is well documented, there may be additional mechanisms contributing to these defects beyond those discussed in this manuscript - particularly with regards to SARS-CoV-2 infection. Our analysis of the immunophenotype of the patient was limited to surveys of circulating immune dynamics; however, numerous studies have also described perturbations in immunity at tissue sites not easily amenable to direct interrogation. We also did not directly analyze antigen specific T-cell responses during either infection, which may reveal additional dysfunction not discussed within this manuscript. Lastly, we also did not fully address every potential avenue of viral immune evasion to immune responses and accordingly suggest that a greater understanding of the virus-intrinsic and host-intrinsic features determining susceptibility to SARS-CoV-2 reinfections is required. Future studies should investigate not only the circulating and systemic adaptive immune responses during SARS-CoV-2 reinfections, but also the possibility that local defects in immune responsiveness among barrier tissue sites may also enable recurrent SARS-CoV-2 infection. Quantification and Statistical Analysis: All statistical analysis was per...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a protocol registration statement.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.03.23.21254060: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.03.24.436864: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 27. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

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

Learn more at Review Commons

Reply to the reviewers

We are grateful to the editors at Review Commons and to the reviewers for their thoughtful attention to our manuscript. Our work presents data showing that deletion of the apoptosis regulator Mcl-1 in CNS stem cells that give rise to neurons and glia resulted in specific degeneration of the white matter, beginning after postnatal day 7 (P7). Cellular analysis shows that oligodendrocytes were depleted while astrocytes persisted. Co-deletion of apoptosis effectors Bax or Bak rescued different aspects of the Mcl-1 deletion phenotype, confirming the role of apoptosis. Based on these observations, we conclude that oligodendrocytes require MCL-1 to prevent spontaneous apoptosis, and that MCL-1 depletion results in leukodystrophy, which resembles severe cases of the human disorder Vanishing White Matter Disease (VWMD). We further suggest that MCL-1 deficiency, caused by the eIF2B mutations of VWMD, may play a critical role in VWMD pathogenesis.

The reviewers questioned the similarity of the Mcl-1 deletion phenotype to VWMD and were not convinced that MCL-1 deficiency is integral to VWMD. Based on reviewer feedback, we concede that a firm link to VWMD is not supported by the available data. We consider, however, that our findings that MCL-1 is required for oligodendrocyte survival and white matter stability remain highly significant. Accordingly, we propose to revise the work as suggested by Reviewer 1 to highlight the insight our data provide as to apoptosis regulation in glia and its importance for brain development, and to revise the title, as suggested by Reviewer 3, to remove the specific reference to VWMD.

In the revision, we will make clear that the comparison to specific leukodystrophies is hypothetical and will require extensive follow-up experiments that are suggested by the findings of this work, as described in the reviews. Revising our work by removing the assertion that our data strongly implicate MCL-1 in VWMD pathogenesis will address the main reviewer concern, strengthen the logical flow, and highlight the potential for MCL-1 to be broadly relevant to white matter pathology. The significance of our findings that oligodendrocytes depend on MCL-1 protein to prevent their spontaneous apoptosis, and that MCL-1 deficiency produces white matter degeneration, will not be altered by these changes. Our data will continue to show that MCL-1 dependence is a physiologic vulnerability of oligodendrocytes that sets them apart from astrocytes and neurons and that this vulnerability is sufficient to cause white matter-specific brain degeneration when MCL-1 expression is blocked.

The other issues raised by the reviewers are all tractable and can be addressed with new experiments that we can complete in a short time-frame, such as studies of retinal pathology and addition immunohistochemistry studies, or with changes to the text. We consider that with these revisions, the manuscript will be an important contribution to understanding glial biology and the pathogenesis of white matter-specific disorders. We describe in detail below our responses to reviewer feedback and planned changes to the manuscript.

Reviewer comments are in italics and our responses are in plain text.

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

**Comments** *

While we acknowledge many important points in this review, this first point is based on a premise that is inaccurate. Based on published data, we respectfully disagree with the statement that “Depletion of MCL-1 in any tissue would promote apoptosis in cells of this tissue”. Most cells do not require an anti-apoptotic protein to prevent spontaneous apoptosis; cells that depend on anti-apoptotic proteins are specifically referred to as “primed for apoptosis” (1-5). Our conditional deletion genotypes ablated Mcl-1 in neurons of the forebrain and cerebellum and in all subtypes of glial cells. The loss of oligodendrocytes in our Mcl-1-deleted mice shows that a specific subset of white matter cells in the postnatal brain require MCL-1. Together with the increase in apoptosis and the rescues by co-deletion of Bax or Bak, these data demonstrate that cells within the oligodendrocyte lineage are primed for apoptosis in a manner that is restricted by MCL-1. In contrast, we have shown in published data that we cite in this manuscript that conditional deletion of Mcl-1 cerebellar granule neurons, the largest neuronal population in the brain, does not cause apoptosis (6); these data provide direct evidence that large populations of cells in the brain do not depend on MCL-1. We therefore disagree with the characterization of the brain-specific Mcl-1 deletion phenotype as “non-specific”.

• The white matter disease is interpreted as similar to VWM; VWM is specifically investigated and MCL-1 is found to be decreased in VWM brain tissue. The decrease is most likely nonspecific. Decrease in MCL-1 is most likely part of a general mechanism of degeneration of brain tissue or white matter. That is a different but also important conclusion. It is essential that other progressive leukodystrophies and acquired brain diseases with tissue degeneration, such as encephalitis, are investigated as well to see whether MCL-1 is also decreased in these disorders. If so, the MCL-1 decrease in white matter disease and other brain degenerative disease should be described as a final common pathway rather than specifically applicable to VWM.*

We agree that MCL-1 is likely to be a final common point in multiple disease processes that affect white matter. As described in our response to point 3 below, we are persuaded by the reviewers that the proposed similarity of the Mcl-1 deletion phenotype and VWMD is not sufficiently supported by the available evidence. We will revise the text to make clear that we consider that impaired MCL-1 is “likely part of a general mechanism of degeneration of… white matter”.

• Adding to point 2 is the fact that the pathology of the brain-specific MCL-1 knock-out mouse does not resemble the pathology of VWM at all. The central features of VWM are abnormal astrocyte morphology with astrocytes having a few stunted processes, lack of reactive astrogliosis, lack of microgliosis, increase in number of oligodendrocytes and presence of foamy oligodendrocytes. The increase in oligodendrocytes in VWM may be such that the high cellularity leads to diffusion restriction on MRI. Bergmann glia are typically ectopic, but not reduced in number. By contrast, the brain-specific MCL-1 knock-out mouse is characterized by decreased numbers of oligodendrocytes, increased numbers of microglia, reactive astrogliosis, decreased numbers of Bergmann glia and ectopic granule cells. No morphological abnormalities of oligodendrocytes and astrocytes are observed. So, histopathologically the only shared feature is preferential involvement of the brain white matter.*

We are persuaded by the reviewers that our assertion of a high degree of similarity between the Mcl-1 deletion phenotype and VWMD was not adequately supported by our available data. In the revision, we will state that a role for MCL-1 deficiency in VWMD pathogenesis is hypothetical, and that additional studies beyond the scope of this project will be needed to test this hypothesis. However, we reassert that the white matter specificity of the Mcl-1-deletion phenotype is important.

The reviewer accurately characterizes the pathology of the Mcl-1 deletion phenotype and notes “the preferential involvement of the white matter”. We consider that the preferential involvement of white matter, and of oligodendrocytes within the white matter are highly significant. We will revise the work to focus on the Mcl-1 deletion phenotype, the white matter specificity, and the potential relevance to diverse white matter-specific disease.

While we concede that more data would be needed to firmly connect MCL-1 to VWMD, we do not agree that the Mcl-1 phenotype “does not resemble the pathology of VWM at all”. There is a diversity of published observations of pathology in VWMD and not all published reports support the descriptions in the reviewer comment. This diversity of findings is highly relevant to our work. For example, while autopsy studies of humans with end stage VWMD show lack of microgliosis (7), studies of mice with a mutation known to cause VWMD in humans, that clearly recapitulate VWMD, show robust microgliosis earlier in the disease process (8). These different observations raise the possibility that microgliosis occurs during the period of active neurodegeneration or at least that in murine brain, the VWMD process activates a microglial reaction. Either interpretation would support a likeness between Mcl-1-deleted mice and VWMD mouse models. Another study of cerebellar pathology in twin human fetuses with characteristic VWMD mutations showed complete absence of Bergmann glia (9). We propose in the revision to address the reviewer’s concerns by presenting the diversity of perspectives on microglial reaction and Bergmann glial changes in VWMD, including all of the citations above.

• The clarity of the work would benefit from a different approach to introduce the study. It would help the reader to know that (1) gray matter cell specific Mcl-1 deletion in mice did not cause apoptosis and (2) apoptosis may have different effector proteins. This important information is now in the discussion. The switch to another cell type in the brain (hGFAP+ cells) would be logical and the significance of the work may improve. When approaching the topic from the field of leukodystrophies one would not necessarily think of deleting the Mcl-1 gene, especially as this gene is not associated with any known leukodystrophy and tends to associate with preneoplastic and neoplastic disease.*

We appreciate these suggestions, which we agree will enhance the logical flow and the significance, in line with our response to point 3. We will revise the Introduction as suggested.

• The authors claim that the ISR is activated in VWM, which means that eIF2α phosphorylation levels are increased, general protein synthesis is decreased and a transcription pathway is regulated by ATF4 and other factors. However, this is not what is seen in VWM. Increased eIF2α phosphorylation and reduced general protein synthesis are not observed in VWM; strikingly, the level of eIF2α phosphorylation is reduced, general protein synthesis appears at a normal rate, and only the ATF4-regulated transcriptome is continuously expressed in VWM astrocytes. *

This point is not well-settled, as published studies show that the ISR is activated in VWMD despite decreased eIF2α phosphorylation (10, 11). Published scRNA-seq studies of mice with VWMD mutations moreover, show that the ISR transcriptome is activated in oligodendrocytes, as well as neurons, endothelial cells and microglia (8). We will address this concern in the revision by citing these published reports that show both decreased eIF2α phosphorylation and lines of evidence that support ISR activation.

Fritsh et al. show that MCL-1 protein synthesis is reduced by increased eIF2α phosphorylation due to reduced translation rates at the Mcl-1 mRNA and not due to differences in Mcl-1 mRNA levels.

We agree with this interpretation of Fritsh et al, which is fully compatible with our proposed mechanism. We suggest that ISR activation in VWMD decreases translation of Mcl-1 mRNA, leading to reduced MCL-1 protein expression. MCL-1 protein is rapidly degraded and may therefore be a more sensitive detector of impaired translation than other readouts. We currently cite published work documenting altered translation in VWMD in the manuscript and in the revision will add the reference Moon et al, which is directly on point (11).

One would a priori not expect to find altered MCL-1 synthesis rates in the mildly affected VWM mouse model Eif2B5R132H/R132H.

The model does not show reduced global translation under normal conditions, but rather hypo-activity of eIF2B affects the translation of specific mRNAs (12). We will make this point clear in the revision.

Actually, ISR deregulation has not been reported in the Eif2B5R132H/R132H VWM mouse model. The authors need to rephrase this part of their study taking this information into account, when explaining their experiments and interpreting their results.

Consistent with the data that the ISR is activated in VWMD, mice show ATF4 up-regulation and other evidence of ISR activation (13) and impaired responses to physiologic stress (14, 15). In the revision, we will add these citations. To address the reviewer concerns, we will state in the revision that ISR activation is one of many potential mechanisms of reduced MCL-1 expression.

The authors now imply that their study adds mechanistic insight into the VWM field and that is not the case.

As we describe in response to point 3, we will acknowledge in the revision that the assertion that MCL-1 deficiency causes VWMD is hypothetical.

In addition, Figure 7C shows differences in actin signal rather than MCL-1 signal, suggesting that transfer of the actin protein from the gel to the blot was not optimal for the middle lanes. MCL-1 protein may thus not be reduced in these samples from Eif2B5R132H/R132H VWM mice.

We stand by our Western blot data that show that MCL-1 levels are lower in the Eif2B5R132H/R132H VWM mouse model, coincident with the onset of symptoms. The Western blot shown is a representative image that includes 3 biological replicates for each condition and of a total of 12 mice. The quantification demonstrates the reproducibility of the finding.

• Can the authors show in which cell type was apoptosis found (lines 315-316)? Their study uses the hGFAP - Cre mouse model to generate conditional Mcl-1 knock-out mice. The original paper by Zhuo et al. describing the hGFAP - promoter mouse model suggests that Mcl-1 expression is also affected in neurons and ependymal cells. The authors can investigate this further to assess which cell types (1) are sensitive to apoptosis by Mcl-1 deletion and (2) depend on Bax and Bak.*

Apoptosis may occur at different times in different cell populations, and asynchronous apoptosis can be difficult to detect at any point in time, which can complicate the suggested studies. Despite significant effort, we have not been able to co-localize any markers with dying cells in our model.

To address the question of neuronal involvement, the revised manuscript will refer to prior published studies (16-18) which show that Mcl-1 deletion affects forebrain neural progenitors. In this context, we will discuss that our Mcl-1 deletion studies show that significant neural progenitor populations survive prenatal Mcl-1 deletion and generate appropriate cortical and hippocampal architecture in Mcl-1-deleted mice at P7, prior to the onset of white matter degeneration.

To identify involved glial cells, we quantified the cells that were depleted or persisted in the Mcl-1 deleted brain. These studies identified oligodendrocytes and Bergmann glial as cell types depleted during P7-P15, when postnatal degeneration occurs in Mcl-1 deleted mice. In contrast, astrocytes persisted, indicating that astrocytes are not MCL-1-dependent. In the review, we will add new data quantifiying the immature, PDGFRA-expression subset of oligodendrocytes, which will increase the specification of which cells are depleted by Mcl-1 deletion.

We share the reviewer’s interest in the question of which subsets of Mcl-1 dependent cells are rescued by co-deletion of Bax or Bak. As known markers may not be sufficient to distinguish these subsets, we consider that scRNA-seq studies are an ideal approach to identify these subsets and their specific gene expression patterns. However, these studies are outside the scope of the present work, which establishes that specific white matter cells depend on Mcl-1.

• Heterozygous deletion of Bak greatly reduces the number of Bak-expressing cells (Fig. 3C, line, 331-333). Authors need to explain this remarkable finding. *

As we state in the text, the reduced Bak expression in the heterozygous Bak +/- mice is consistent with a gene dosage effect, which has been observed for other genes.

Please provide raw IHC data.

Our IHC data is “raw” in the sense of unaltered. We are happy to include a supplementary figure with additional low power and high-power images of BAK staining.

Co-staining with neuronal, astrocytic or oligodendrocytic markers would be insightful.

To address this point, we have successfully performed double labeling with antibodies to BAK and with antibodies to the oligodendrocyte marker SOX10 and the astrocyte marker GFAP. We will add these images to the revision. These images show that BAK+ cells include oligodendrocytes and astrocytes. The position and morphology of the BAK+ cells show that they are not neurons.

In addition, what does the Western blot signal for the BAX protein represent in Bax homozygous knock out mice (Fig. 3C)?

We will add text stating that the small residual BAX protein detected in the conditional Bax-deleted mice can be attributed to BAX expression in cells outside the Gfap lineage, including endothelial cells, vascular fibroblasts, and microglia.

Can the percentage of BAX+ cells in Mcl-1/BaxdKO corpus callosum be determined, similarly as was done for BAK? Co-staining with neuronal, astrocytic or oligodendrocytic markers would be insightful here as well. The legend of Fig. 3D does not state what staining is shown (H&E?).

We were not able to label BAX protein in individual cells using immunohistochemistry. In contrast, BAK immunohistochemistry worked well, allowing us to analyze the cellular distribution of BAK protein. We will revise the legend in 3D to state the staining is H&E.

• What explains the strong GFAP expression in processes of Mcl-1 KO astrocytes? Are these cells refractory to apoptosis or to hGFAP-driven Cre expression and recombination? Do they lack BAK or BAX or other apopotic-regulating protein? Or do specific factors compensate for the loss of MCL-1?*

As we discuss in our response to point 1 above, not all cells require MCL-1 to prevent spontaneous apoptosis. The persistence of GFAP+ astrocytes in Mcl-1-deleted mice shows that astrocytes do not require MCL-1 to maintain their survival. These data do not mean that these astrocytes are refractory to apoptosis, but rather they are not primed for apoptosis in a way that is critically restricted by MCL-1. We will add a discussion of these implications to the revision.

• Which developing symptoms do the authors refer to in line 468? Please specify and introduce appropriate references.*

We will add a description of symptoms to the revision.

• The definition of leukodystrophies given in the paper is outdated. Leukodystrophies are not invariably progressive and fatal disorders. For more recent definition of leukodystrophies see Vanderver et al., Case definition and classification of leukodystrophies and leukoencephalopathies, Mol Genet Metab 2015, and van der Knaap et al., Leukodystrophies a proposed classification system based on pathology, Acta Neuropathol 2017.*

We appreciate this advice. We will revise the Introduction accordingly and cite the recommended work.

• It is not correct that there is no specific targeted therapy clinically implemented to arrest progression of the disease in any leukodystrophy. Perhaps hematopoietic stem cell transplantation is not specific targeted, although curative if applied in time in adrenoleukodystrophy and metachromatic leukodystrophy, but certainly genetically engineered autologous hematopoietic stem cells would qualify the definition. In any case, the suggestion that no leukodystrophy is treatable is not correct.*

We appreciate this correction. We will revise the text to provide a more detailed description of treatment options while underscoring the need for mechanistic insight.

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

In this manuscript, the authors characterize the phenotype associated with brain-specific deletion of the mcl-1 gene in mice as a model for vanishing white matter-like disease in humans. Unfortunately, the gfap gene is expressed in many cell types during development which are outside of the intended cell type for this study, so functional data presented from the mutant mice is open to interpretation. The authors have not ruled out other interpretations of their results. The authors need to address major shortcomings in their data interpretation by addressing the following issues.

We appreciate that concerns related to vision and hearing in the Mcl-1 deleted mice, and address these concerns as described below.

On line 57, the authors indicate that seizures are common in leukodystrophy. This is controversial. Patients may have attacks that look like seizures, but without EEG recordings there is no way to distinguish these events from myoclonus. The authors should note this ambiguity.

We will note this ambiguity in the revision. On line 58, the authors indicate the absence of treatments for leukodystrophies. The authors should review the following articles: PMID: 7582569, 15452666 and 27882623, and moderate the text.

We will cite these papers and moderate the text as recommended

The methods section is lacking in details in several areas. For example beginning line 136, there is virtually no indication of the MRI details without going to secondary literature. The authors should provide a brief description including magnet strength, type of imaging and the general sequence, software used to collect and analyze the images.

We will include these details in the revision.

Were the brains actually harvested fresh, where mechanical stresses easily deform brain structure, prior to immersion fixation for 48h? This could be troubling despite the method being previously published.

Brains were harvested fresh and drop fixed. We have extensive experience over more than ten years in handling brain tissue from neonatal mice and subsequently analyzing MRI images and sections. These methods have allowed us to make quantitative volumetric comparisons of the 3-dimensional architecture of the developing brain using MRI in prior studies, that detected genotypic differences in brain growth without confounding fixation artefact (19). We can confirm that no mechanical stress of handling can reproduce the white matter specific changes that we see in the Mcl-1-deleted brain. We did not detect any abnormalities in control brains subjected to the same handling techniques. Beginning on line126, the authors could at least indicate the fixative details and whether the mice were perfused or tissue was immersion fixed. Compare this lack of detail with the description of lysis buffer beginning on line 158.

We will add fixation details to the revision.

Behavioral testing at young ages is rather problematic regarding data interpretation. For example, open field testing (Fig. 2B) at postnatal day 7, which relies on visual cues, is rather dubious when mice do not open their eyes until 12-13 days after birth. How would the pups know if they were in the middle of an open field and exhibit thigmotaxis, even if they were capable of the behavior at such a young age? Thus, the P7 data likely cannot be interpreted in terms of the knockouts being normal.

We fully agree with the reviewer on the challenges with behavioral analysis of such young mice. The rationale for the open field test was that, at P7, mouse pups are gaining greater control of hind limb function, which can be observed as a transition from pivoting in one place to forward locomotion. Thus, we measured the number of pivots and distance traveled in the open field as indicators for maturation of motor function. Center time was presented to show that, at P7, both WT and knockout mice stayed in the middle (i.e., the groups were at the same stage of limited mobility). We consider that these measures, together with geotaxis and latency to righting (Table 1), provide a developmentally-appropriate neurologic assessment for an age when behaviors are very limited. We will make clear in the revision that these specific tests must be considered together in order to be informative.

By P14, when the mutants exhibit a phenotype, they are already significantly underweight, which can lead to non-specific phenotypes such as retinal dysfunction or degeneration. Did the authors look for pathological changes in the retina?

Further, GFAP is expressed in retina of many vertebrate species (PMID 1283834) which would inactivate mcl1 in that tissue and possibly lead to blindness. Indeed, the table at the following link provides a list of tissues in which the gfap-cre transgene is expressed during development. The authors need to address this major issue. http://www.informatics.jax.org/allele/MGI:2179048?recomRibbon=open

We appreciate this suggestion and we will look for pathology in the retina and optic nerve. Such pathology, if we find it, is likely to be specific, as the optic nerve is myelinated and we have already noted extensive myelination abnormalities in the Mcl-1-deleted mice. If we find retinal or optic nerve abnormalities, we will note the potential for these abnormalities to impact on open field testing.

For the startle response, which relies on normal hearing, did the authors check to determine if the mutants are deaf? This is very difficult at such a young age, especially prior to tight junction assembly in the lateral wall at around P14. Again, GFAP is expressed in the cochlea at an early age (see PMID 20817025) and may have caused degenerative pathology in this tissue. The authors need to address this major issue.

The reviewer brings up the potential issue of deafness as a confounding factor for acoustic startle testing. Our results showed that startle responses in the mutant mice were increased at P14, which clearly indicates the mice were able to hear the acoustic stimuli. Further, at P14 and P21, both WT and knockout mice had orderly patterns of prepulse inhibition, providing confirmation of good hearing ability at each timepoint. We will make these points clear in the revision.

*Reviewer #2 (Significance (Required)):

Unknown.*

The reviewer has not raised specific issues with the significance. We consider the significance of our work to be the finding that oligodendrocyte-lineage glial cells depend on MCL-1 and thus are primed for apoptosis, such that disrupting MCL-1 expression results in catastrophic degeneration of the cerebral white matter. Addressing the reviewer’s concerns described in the section on Evidence, reproducibility and clarity will support this significance.*

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

Cleveland et al. tried to argue that brain-specific depletion of apoptosis regulator MCL-1 reproduces Vanishing White Matter Disease (VWMD) in mice. The authors show that brain-specific MCL-1 deficiency leads to brain atrophy, increased brain cell apoptosis, decreased oligodendrocytes, decreased MBP immunoreactivity, and activation of astrocytes and microglia. It is known that VWMD is a hypomyelinating disorder caused by mutation of eIF2B subunits, which displays severe myelin loss but minimal oligodendrocyte apoptosis or loss in the CNS white matter. In fact, a number of studies show increased oligodendrocyte numbers in the CNS white matter.*

Published reports show decreased normal oligodendrocytes and increased immature oligodendrocyte populations (20)**.

The characteristic oligodendrocyte pathology is foamy oligodendrocytes (Wong et al., 2000), rather than apoptosis.

Foamy oligodendrocyte pathology and increased oligodendrocyte apoptosis are not mutually exclusive. The above referenced paper, Wong et al, in addition to foamy oligodendrocytes, also describes a “decrease in numbers of cells with oligodendroglial phenotype, both normal and abnormal” (21); this decrease is compatible with increased apoptosis. Moreover, published reports specifically describe apoptotic oligodendrocytes in human brains with VWMD (22). To address this point, we propose to include both of these citations in the revision to reference foamy oligodendrocyte pathology in VWMD and to state that this pathologic finding does exclude a role for apoptosis in VWMD pathogenesis.

Since the CNS pathology of brain-specific MCL-1 deficient mice is drive by brain cell apoptosis, the relevance of this mouse model to VWMD is very limited.

Whether apoptosis plays a mechanistic role in VWMD is less clear than this comment suggests, as described in multiple publications (22, 23).

The title of this manuscript is misleading, and should be changed.

We accept that our statement that Mcl-1-deletion recapitulates VWMD is premature and not adequately supported by the available data. We will revise the title, introduction and discussion accordingly, to focus on the white matter specificity of the Mcl-1-deletion phenotype.

*Moreover, there are a number of major concerns.**

1. Figure 1 clearly shows severe atrophy of neocortex in Mcl-1 cKO mice; however, the white matter appears largely normal in the cerebellum and brain stem. Mcl-1 cKO mice also display ventricular dilation and possible atrophy of corpus callosum. The authors should discuss severe atrophy of neocortex in Mcl-1 cKO mice and the possibility that ventricular dilation and corpus callosum atrophy result from severe atrophy of neocortex?*

The cortical atrophy that the reviewer notes begins after P7 and is minimal at P14 when white matter loss is already pronounced. At P21, when there is clear cortical thinning, the white matter loss is extreme. Based on the time course, we consider that the white matter loss is the primary pathology, and the cortical thinning is secondary. Importantly, glial cells populate the cortex as well as the white matter and our cellular data show that oligodendrocytes are reduced in the cortex as well as in the white matter structures. Based on these lines of evidence, we consider that the primary cell type affected is the oligodendroglial population of the glia. We will add a discussion along these lines to the revision.

We agree that the brain stem is preserved. Our data show that the hGFAP-Cre promoter is least efficient in the brain stem and midbrain regions (Sup Fig.1). We will note this differential efficiency in the revision.

• The motor and sensory tests in Figure 2 are potential interesting, but their relevance to myelin abnormalities is limited. The authors should perform the behaviors tests that are highly relevant to myelin abnormalities.*

The tests presented show progressive neurologic impairment, correlating with the onset of neuropathology. In the revision we will note that ataxia and tremor are common features of leukodystrophies and the Mcl-1-deleted mice show both ataxia and tremor.

• It is well expected that there are increased apoptotic cells in the brain of Mcl-1 cKO mice. The authors should perform double labeling to demonstrate which cell types undergo apoptosis: neurons, oligodendrocytes, or other cell types? On the other hand, Figure 3A shows that there are substantial apoptotic cells in the cerebral cortex, which is consistent with severe cerebral cortex atrophy in Mcl-1 cKO mice, suggesting neuron apoptosis in the cerebral cortex. Neuron apoptosis would further rule out the relevance of Mcl-1 cKO mice to VWMD.*

These studies would be of interest, but we have not been able to co-label apoptotic cells in the Mcl-1-deleted mice with any marker. In the advanced state of apoptosis when dying cells are detectable by TUNEL staining, the relevant marker proteins have been degraded beyond recognition by IHC. In contrast, the apoptotic marker cleaved caspase-3, which is positive earlier in the apoptotic process and might allow marker co-labeling, was not detectably elevated in the Mcl-1-deleted mice. We attribute the lack of cleaved caspase-3+ cells to the asynchronous nature of the increased cell death, and to the short duration in which dying cells are cleaved caspase-3+. While double label studies of dying cells have been problematic, our studies quantifying each cell type provide information to address the reviewer’s question. Our cell counts show clearly that oligodendrocytes are the primary cell type reduced in number in the Mcl-1 deleted mice.

• Figure1, 4 the authors use H&E staining to demonstrate white matter loss. H&E staining is good to show general CNS morphology; however, it is impossible to use H&E staining to quantify the integrity of the white matter. The authors should perform specific staining to quantify white matter loss in the mouse models.*

Our MBP stains later in the paper are used to quantify white matter loss.

• Figure 5, MBP IHC is good to show general myelin staining, but is not a reliable assay to quantify myelin integrity in the CNS. The authors should perform electron microscopy analysis to quantify myelin integrity in the CNS in the mouse models.*

Our studies of MBP staining show that the myelinated area in cross sections is significantly reduced in the Mcl-1-deleted mice. Electron microscopy studies cannot show whether the myelinated area is reduced and studies of myelin integrity are not needed to prove that reduced oligodendrocytes correlate with reduced myelination.

• Figure 6, SOX10 is a marker of oligodendrocytes and OPCs. The authors should quantify the number of oligodendrocytes (using oligodendrocyte markers, such as CC1) and the number of OPCs (using OPC markers, such as NG2). Does deletion of BAK or BAX reduce oligodendrocyte apoptosis in the CNS of Mcl-1 cKO mice?*

We agree that this is an important question, and we are working to quantify OPCs in the Mcl-1-deleted mice by counting cells labelled with the OPC marker PDGFRA. We will add these data to the revision and discuss their significance when we know what they show.

• The authors show that the level of MCL-1 is comparable in brain lysates of wildtype and eIF2B5 R132H/R132H mice at the age of 7 months, and moderately decreased in eIF2B5 R132H/R132H mice at the age of 10 months. VWMDis a developmental disorder. Similarly, brain-specific MCL-1 deficiency causes developmental abnormalities in the CNS. The normal level of MCL-1 in 7-month-old eIF2B5 R132H/R132H mice strongly suggests that MCL-1 is not a major player involved in the pathogenesis of VWMD. Does brain-specific MCL-1 deficiency starting at the age of 10 months (using CreERT mice) cause CNS abnormalities in adult mice?*

We agree that Mcl-1 deletion in our model disrupts postnatal brain development. Our studies show that in early life, oligodendrocytes depend on MCL-1 to prevent spontaneous apoptosis. It is an interesting, but separate question whether Mcl-1 deletion induced in the adult would also cause a similar phenotype. The suggested studies would take over a year to conduct, and while they are of interest, they are not required to prove our main point, which is that developmental leukodystrophies may result from the dependence of oligodendrocytes on MCL-1. In the revision, we will state that our comparison on the Mcl-1-deletion phenotype to VWMD is hypothetical, and that additional studies are needed to test this hypothesis.

• Does MCL-1 deletion exacerbate the pathology in eIF2B5 R132H/R132H mice? Moreover, does MCL-1 overexpression rescue the pathology in eIF2B5 R132H/R132H mice? These two experiments are necessary to demonstrate the involvement of MCL-1 in VWMDpathogenesis.*

We agree that these are interesting and important studies; however, these studies will require years to complete and extensive resources. These studies are not needed to show that Mcl-1 deletion produces early onset white matter degeneration, which is our main point. As in our response to point 7 above, we will state in the revision that our comparison on the Mcl-1-deletion phenotype to VWMD is hypothetical, and list these experiments as follow up studies that are needed to test this hypothesis.

*Reviewer #3 (Significance (Required)):

The study will not significantly advance the understanding of VWMD pathogenesis.*

We recognize that our assertion of a direct relevance to VWMD was premature, and that additional studies, beyond the scope to this project, are needed to determine if MCL-1 deficiency contributes to VWMD pathology. We agree that the available data do not yet inform VWMD pathogenesis, but these data may become relevant to VWMD as follow-up studies are conducted. The data remain highly relevant to the broad group of leukodystrophies as they demonstrate a physiologic vulnerability of oligodendrocytes that sets them apart from astrocytes and neurons, and thus may play a role in disorders in which oligodendrocyte pathology is central.

Neuroscientists may be interested in the reported findings.

We appreciate the reviewer noting the significance for neuroscience.

My field of expertise: oligodendrocyte, myelin, neurodegeneration, ER stress

References cited:

1. K. A. Sarosiek, C. Fraser, N. Muthalagu, P. D. Bhola, W. Chang, S. K. McBrayer, A. Cantlon, S. Fisch, G. Golomb-Mello, J. A. Ryan, J. Deng, B. Jian, C. Corbett, M. Goldenberg, J. R. Madsen, R. Liao, D. Walsh, J. Sedivy, D. J. Murphy, D. R. Carrasco, S. Robinson, J. Moslehi, A. Letai, Developmental Regulation of Mitochondrial Apoptosis by c-Myc Governs Age- and Tissue-Specific Sensitivity to Cancer Therapeutics. Cancer Cell 31, 142-156 (2017).
2. R. Dumitru, V. Gama, B. M. Fagan, J. J. Bower, V. Swahari, L. H. Pevny, M. Deshmukh, Human Embryonic Stem Cells Have Constitutively Active Bax at the Golgi and Are Primed to Undergo Rapid Apoptosis. Mol Cell 46, 573-583 (2012).
3. T. Ni Chonghaile, K. A. Sarosiek, T. T. Vo, J. A. Ryan, A. Tammareddi, G. Moore Vdel, J. Deng, K. C. Anderson, P. Richardson, Y. T. Tai, C. S. Mitsiades, U. A. Matulonis, R. Drapkin, R. Stone, D. J. Deangelo, D. J. McConkey, S. E. Sallan, L. Silverman, M. S. Hirsch, D. R. Carrasco, A. Letai, Pretreatment mitochondrial priming correlates with clinical response to cytotoxic chemotherapy. Science 334, 1129-1133 (2011).
4. J. A. Ryan, J. K. Brunelle, A. Letai, Heightened mitochondrial priming is the basis for apoptotic hypersensitivity of CD4+ CD8+ thymocytes. Proc Natl Acad Sci U S A 107, 12895-12900 (2010).
5. M. Certo, V. D. G. Moore, M. Nishino, G. Wei, S. Korsmeyer, S. A. Armstrong, A. Letai, Mitochondria primed by death signals determine cellular addiction to antiapoptotic BCL-2 family members. Cancer Cell 9, 351-365 (2006).
6. A. J. Crowther, V. Gama, A. Bevilacqua, S. X. Chang, H. Yuan, M. Deshmukh, T. R. Gershon, Tonic activation of Bax primes neural progenitors for rapid apoptosis through a mechanism preserved in medulloblastoma. The Journal of neuroscience : the official journal of the Society for Neuroscience 33, 18098-18108 (2013).
7. D. Rodriguez, A. Gelot, B. della Gaspera, O. Robain, G. Ponsot, L. L. Sarlieve, S. Ghandour, A. Pompidou, A. Dautigny, P. Aubourg, D. Pham-Dinh, Increased density of oligodendrocytes in childhood ataxia with diffuse central hypomyelination (CACH) syndrome: neuropathological and biochemical study of two cases. Acta Neuropathol 97, 469-480 (1999).
8. Y. L. Wong, L. LeBon, A. M. Basso, K. L. Kohlhaas, A. L. Nikkel, H. M. Robb, D. L. Donnelly-Roberts, J. Prakash, A. M. Swensen, N. D. Rubinstein, S. Krishnan, F. E. McAllister, N. V. Haste, J. J. O'Brien, M. Roy, A. Ireland, J. M. Frost, L. Shi, S. Riedmaier, K. Martin, M. J. Dart, C. Sidrauski, eIF2B activator prevents neurological defects caused by a chronic integrated stress response. Elife 8, (2019).
9. A. Trimouille, F. Marguet, F. Sauvestre, E. Lasseaux, F. Pelluard, M. L. Martin-Negrier, C. Plaisant, C. Rooryck, D. Lacombe, B. Arveiler, O. Boespflug-Tanguy, S. Naudion, A. Laquerriere, Foetal onset of EIF2B related disorder in two siblings: cerebellar hypoplasia with absent Bergmann glia and severe hypomyelination. Acta Neuropathol Commun 8, 48 (2020).
10. T. E. M. Abbink, L. E. Wisse, E. Jaku, M. J. Thiecke, D. Voltolini-Gonzalez, H. Fritsen, S. Bobeldijk, T. J. Ter Braak, E. Polder, N. L. Postma, M. Bugiani, E. A. Struijs, M. Verheijen, N. Straat, S. van der Sluis, A. A. M. Thomas, D. Molenaar, M. S. van der Knaap, Vanishing white matter: deregulated integrated stress response as therapy target. Ann Clin Transl Neurol 6, 1407-1422 (2019).
11. S. L. Moon, R. Parker, EIF2B2 mutations in vanishing white matter disease hypersuppress translation and delay recovery during the integrated stress response. RNA 24, 841-852 (2018).
12. G. Raini, R. Sharet, M. Herrero, A. Atzmon, A. Shenoy, T. Geiger, O. Elroy-Stein, Mutant eIF2B leads to impaired mitochondrial oxidative phosphorylation in vanishing white matter disease. J Neurochem 141, 694-707 (2017).
13. L. Kantor, D. Pinchasi, M. Mintz, Y. Hathout, A. Vanderver, O. Elroy-Stein, A point mutation in translation initiation factor 2B leads to a continuous hyper stress state in oligodendroglial-derived cells. PLoS One 3, e3783 (2008).
14. Y. Cabilly, M. Barbi, M. Geva, L. Marom, D. Chetrit, M. Ehrlich, O. Elroy-Stein, Poor cerebral inflammatory response in eIF2B knock-in mice: implications for the aetiology of vanishing white matter disease. PLoS One 7, e46715 (2012).
15. L. Marom, I. Ulitsky, Y. Cabilly, R. Shamir, O. Elroy-Stein, A point mutation in translation initiation factor eIF2B leads to function--and time-specific changes in brain gene expression. PLoS One 6, e26992 (2011).
16. L. C. Fogarty, R. T. Flemmer, B. A. Geizer, M. Licursi, A. Karunanithy, J. T. Opferman, K. Hirasawa, J. L. Vanderluit, Mcl-1 and Bcl-xL are essential for survival of the developing nervous system. Cell Death Differ 26, 1501-1515 (2019).
17. S. M. Hasan, A. D. Sheen, A. M. Power, L. M. Langevin, J. Xiong, M. Furlong, K. Day, C. Schuurmans, J. T. Opferman, J. L. Vanderluit, Mcl1 regulates the terminal mitosis of neural precursor cells in the mammalian brain through p27Kip1. Development 140, 3118-3127 (2013).
18. C. D. Malone, S. M. Hasan, R. B. Roome, J. Xiong, M. Furlong, J. T. Opferman, J. L. Vanderluit, Mcl-1 regulates the survival of adult neural precursor cells. Mol Cell Neurosci 49, 439-447 (2012).
19. S. E. Williams, I. Garcia, A. J. Crowther, S. Li, A. Stewart, H. Liu, K. J. Lough, S. O'Neill, K. Veleta, E. A. Oyarzabal, J. R. Merrill, Y. I. Shih, T. R. Gershon, Aspm sustains postnatal cerebellar neurogenesis and medulloblastoma growth. Development, (2015).
20. M. Bugiani, I. Boor, B. van Kollenburg, N. Postma, E. Polder, C. van Berkel, R. E. van Kesteren, M. S. Windrem, E. M. Hol, G. C. Scheper, S. A. Goldman, M. S. van der Knaap, Defective glial maturation in vanishing white matter disease. J Neuropathol Exp Neurol 70, 69-82 (2011).
21. K. Wong, R. C. Armstrong, K. A. Gyure, A. L. Morrison, D. Rodriguez, R. Matalon, A. B. Johnson, R. Wollmann, E. Gilbert, T. Q. Le, C. A. Bradley, K. Crutchfield, R. Schiffmann, Foamy cells with oligodendroglial phenotype in childhood ataxia with diffuse central nervous system hypomyelination syndrome. Acta Neuropathol 100, 635-646 (2000).
22. K. Van Haren, J. P. van der Voorn, D. R. Peterson, M. S. van der Knaap, J. M. Powers, The life and death of oligodendrocytes in vanishing white matter disease. J Neuropathol Exp Neurol 63, 618-630 (2004).
23. M. Bugiani, I. Boor, J. M. Powers, G. C. Scheper, M. S. van der Knaap, Leukoencephalopathy with vanishing white matter: a review. J Neuropathol Exp Neurol 69, 987-996 (2010).

SciScore for 10.1101/2021.03.17.21253349: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

No key resources detected.

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Strengths and limitations: We use nation-wide register-based data on both notified COVID-19 and occupation. Because we include all residents of Norway aged 20 to 70 years and utilize routinely registered data, important strengths of our study are limited bias due to selection and misclassification. Important limitations are, first, we were not able to adjust for socioeconomic factors such as income or education, living conditions, urban living, health literacy or risky behavior. We acknowledge that all these factors are relevant for risk of infection and may be associated with persons’ occupation. Second, there may be differences within occupational groups in working contracts, working conditions and tasks. We hypothesize that workers on temporary contracts work more disadvantaged hours and perform tasks with higher risk of infection than others, as well as experience a stronger pressure to come to work even with symptoms of infection. Our data did not include information on whether participants had temporary or permanent position, but we suspect that immigrants more often than others work on temporary contracts. Third, other characteristics of occupations, such as distance to other people and possibility to work from home may (10) be just as relevant as occupations per se. A relatively large proportion of the population, about one in four, was not registered with any work, and excluding these from the reference group did not lead to substantial changes in results. A fourth l...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.03.22.436379: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.03.23.436593: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Consonant Pronunciation

b - boy / ban

c - chat / ceeb

d - dog / dara

f - fire / fas

g - good / agsi

j - job / jox

k - keep / kaani

l - land / lakk

m - mark / naam

n - nice / àdduna

ñ - genius / gaañ

p - paint / puso

r - rat / raxas

s - soup / kawas

t - take / tubaab

w - wait / kéwél

x - (see note below) / xaalis

y - yes / yaa

There is no English equivalent for this sound, it is a slightly guttural sound that is between x and k. It may also be pronounced merely as h, especially among non-natives.

mb - mbaa

mp - dàmp

nd - ndey

ng - ngan

nj - njaay

ŋ - masoŋ

Prenasalized consonants don't exist in English the way they do in Wolof. ng and ŋ sound almost the same if not the same. Words like sing, bring, king, mango, etc. are the closest to this sound.

Vowel Pronunciation

a - absorb / sant

aa - far / laaj

e - get / dem

ee - where / seet

é - say / bés

ée - sane / wéer

ë - bird / kër

i - pit / fit

ii - meet / siis

o - moment / fomp

ó - no / jóg

oo - door / loos

óo - phone / fóon

u - cook / ubbi

uu - moon / juuyoo

SciScore for 10.1101/2021.03.22.436337: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 65, 66, 67, 68, 62 and 63. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.03.15.435423: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Limitations: We acknowledge several limitations. First, our human protein-protein interactome was built using high-quality data from multiple sources; yet it is still incomplete. The PPIs in our interactome is undirected. However, it has been shown that incorporating directionality of the human PPI does not change network proximity results [114]. Therefore, the network associations could be either positive or negative, and require further investigation. In addition, as our network proximity analysis relies on disease-associated genes, literature bias could affect the results because more highly-studied genes are more likely to appear in the dataset. Second, we analyzed expression levels of the key SARS-CoV-2 entry factors and found low expression levels for ACE2 and TMPRSS2. However, we cannot rule out the possibility of SARS-CoV-2 directly targeting the brain via as-yet unidentified mechanisms. Third, possible pathways of neuroinflammation and microvascular injury were tested using data of either individuals with AD or COVID-19, but not both. Future studies using genetics and multi-omics data from individuals with both AD and COVID-19 will be needed to confirm and extend these network-based findings. The significance of our findings in the context of the general population of COVID-19 frequently suffering from “brain fog” without a formal diagnosis of AD needs further investigation.

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04320277</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Not yet recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Baricitinib in Symptomatic Patients Infected by COVID-19: an…</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04321993</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Treatment of Moderate to Severe Coronavirus Disease (COVID-1…</td></tr></table>

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Ha! Was just searching for those three texts. Resnick's is on Archive.org (1hr loan).

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

Learn more at Review Commons

Reply to the reviewers

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

The authors aimed to understand the control and the elimination of disseminated tumor cells by NK cells within the lung, their main question being how pulmonary NK cells are able to prevent tumor cells from colonization in the lung.

To dissect this question, Hiroshi Ichise and colleagues took advantage of the ultra-sensitive bioluminescence whole body imaging system combined with intravital two-photon microscopy technology involving genetically-encoded biosensors tumor or NK cells to explore the behavior and functional competences of NK cells in an experimental lung metastasis model.

First, the authors have monitored the fate of intravenously injected B16-Akaluc cells from 5 min to 10 days and observe that tumor cells decrease rapidly within the first 12-24 hours. In parallel, they performed asialoGM1+ and NK1.1+ cells depletion by injection of depleting anti-aGM1 and anti-NK1.1 antibodies in order to see the involvement of these populations on the elimination of the disseminated tumor cells. They conclude that a rapid decrease of the tumor cells is mediated by NK cells. Consisting with this first data, the authors observe also the same early NK cells mediated impact on two other syngenic mouse tumor cell lines : the BRAFV600E melanoma and the colon adenocarcinoma MC-38.

In a second part, the authors dissected NK cell dynamic behaviors in the pulmonary capillaries by taking advantage of the NKp46iCRExrosa26dtTomato mice where NKp46+ cells are fluorescents and performed 2P intravital imaging to follow the in situ the NKp46+ cells behavior. They could nicely observe that NK cells arrive from the capillaries and patrol on the lung epithelial cells in a stall-crawl-jump manner. Moreover, they also show that the attachment to the pulmonary capillaries is mediated by LFA-1. In the presence of B16F10 tumor model, they observe that NK cells stay longer in the capillaries and increase their duration time of crawling indicating that NK cells stay in contact longer with tumor cells.

The authors then explored the NK-mediated tumor killing in the lung by measuring tumor cell apoptosis using B16F10-SCAT3 cells (which leads to visualize caspase 3 activation) and Ca2+ influx in tumor cells expressing two Ca2+ sensors, GCaMP6s and R-GECO. They could observe casp3 activation but also Ca+ influx on tumor cells within few minutes after encountering NK cells. They also observe that evasion of NK cell surveillance is mediated by Nectin-5 and Nectin-2 expressed on tumor cells.

Then, they focus on NK cell activation by looking at ERK activation. To do so, they have isolated NK cells from Tg mice expressing a FRET-based ERK biosensor and performed in vitro killing assay against B16-R-GECO tumor cells but also in vivo experiments. For the in vivo experiments, they have developed reporter mice whose NK cells express the FRET biosensor for ERK. They observe that ERK-dependent NK cell activation contributes to the elimination of disseminated tumor cells within the first few hours but not after 24hours. Indeed, theu observe that B16F10-Akaluc tumor cells are equally eliminated when injected 24h after a first injection of B16F10 or PBS in mice. The authors concluded that tumor cell acquire the capacity to evade NK cell surveillance after 24h rather than a hypothesis toward NK cells loose tumoricidal activity over time.

Finally, the authors have explored their last result on the potential tumor cell evasion of the NK cell surveillance. They show that this NK cell evasion is mediated by the shedding of cell surface Necl-5. They next show that clivage of extracellular domain of Necl-5 was mediated by thrombin in vitro and that anti-coagulation factors such as Warfarin, Edoxaban or Dabigatran Etexilate promote tumor elimination as observed by the bioluminescence experiments. This loss prevents the NK cell signaling needed for effective killing of tumor targets.

However, most of the results remain correlations and have not been formally demonstrated or miss controls.

B16F10 is a well known and characterized NK cell target in a in vivo model so the first part is not really knew except the in situ behavior of NK cells within the lung capillaries. The new mecanism of thrombin-mediated shedding of Necl-5 causing evasion from NK Cell surveillance is really concentrated on the last figure (Fig N{degree sign}6) and some supplemental experiments are mandatory and needed to really confirm this affirmation.

Response: We deeply appreciate the reviewer’s effort to evaluate our work. The reviewer criticizes that the mechanism is well known except “the in situ behavior of NK cells within the lung capillaries.” Indeed, this is what we wish to emphasize in our work. Nobody has ever seen how NK cells kill metastatic tumor cells in the lung. There is a big GAP between in vitro tissue culture experiments and in vivo macroscopic counting of metastatic nodules. Most researchers do not even know when and where in the lung NK cells kill metastatic tumor cells. Live imaging is a powerful approach to address such questions.

Reviewer #1 (Significance (Required)):

There are several points to address to improve the significance of these data.

\*Major points***

1) A global point : 3 mice/group is to small to analyse and interprete data because of the heterogeneity of the mice. Mean +/- SEM have to represented instead of SD.

Response: For the sake of animal welfare, researchers are asked to use minimal number of mice. Moreover, only one mouse can be observed in each imaging session, which takes several hours. In most experiments we performed two independent experiments with three mice each. We believe, the number is appropriate for this type of experiment. In the case of small number of samples, we think SD is better than SEM.

2) The authors used the well known polyclonal anti-asialoGM1 Ab to deplete NK cells. AsialoGM1 is also expressed by ILC1, T, NKT and gd+T cells but also basophils (Trambley J et al., Asialo GM1(+) CD8(+) T cells play a critical role in costimulation blockade-resistant allograft rejection. JCI, 1999). The authors checked the involvement only for the basophils. They have to check the depletion of each of these populations specifically in the lung to assume that the depletion impact only the NK cells or they must change their conclusion on the entire manuscrit and say that not only NK cells is responsible and involved in the control of the disseminated tumor cells but maybe also ILC1, NKT and or gd+T cells.

Response: We obtained similar observations by using BALB/c nu/nu mice, which lack T cells. Therefore, we can exclude the contribution of T cells at least in the acute phase (*3) Lines 133 to 136 : The authors say that they « did not observe any significant difference in the relative increase of the bioluminescence signal between the control and αAGM1-treated mice, implying that NK cells eliminate disseminated melanoma cells primarily in the acute phase (Response: After 24 hrs, the slope of increment of bioluminescence intensity (BLI) did not change significantly betweenαAGM1-treated mice and control mice. In both mice, the doubling times of melanoma cells are approximately one day.

4) Fig S3A-B : The authors say that basophils express aGM1 so they performed basophils involvement on the elimination of B16F10 tumor cells with depleting aCD200R3 mab. They also checked the involvement of neutrophils and monocytes. They observed that basophils, neutrophils and monocytes are not involved on the B16F10 elimination. But what is the hypohesis to assess the role of neutrophils and monocytes ? Moreover, they did not explore Basophil roles in the other models including MC-38, BRAFV600E and 4T1 tumor cells.

Response: We depleted neutrophils and monocytes because antibody-mediated removal of leukocytes could have non-specifically increased the survival of tumor cells. As for expanding the number of experiments with different cell lines, we are afraid but it is too much burden, considering the period required for the experiments and animal welfare.

5a) Fig 1D : Missing control : the author must add the WT Balbc + a-AGM1 as control.

Response: We have this data, which will be included in the revised paper.

5b) Lines 154 to 156 : the authors say that « T cell immunity does not contribute to tumor cell reduction » because tumor cells are eliminated in the nu/nu mice as efficiently as in the WT Balbc mice. This is not correct because they are looking in a window that correspond to innate immunity activation (up to 24h) so they cannot talk about T cell immunity, the adpative response will come more later around 8 days after.

Response: Yes, we are focusing on the early phase of the rejection of metastatic tumor cells. We will rephrase the sentences.

6) Line 159 : (refer to point #2) To affirm that NK cells is critical and involved in the elimination of the disseminated tumor, authors have to perform experiment in a model of NK cell deficiency. The most relevant nowaday is the NKp46ICRExrosa26DTA mice that are deficients in NK cells but also ILC1 cells. Indeed, the authors have used the NKp46iCre mice model for other questions.

Response: As the reviewer stated, the contribution of NK cells in the rejection of metastatic tumors is very well known. We do not think we need to repeat the experiments by using other genetically modified mouse lines, which will take at least one year. We wish to emphasize again that the new findings of our paper are in the in vivo imaging.

7a) Fig 2F : IC missing

Response: According to the reviewer's suggestion, we will perform control experiments with an isotype control.

7b) Lines 181-182 : Authors conclude that the effect of anti-LFA-1 on NK cells adhesion to the pulmonary endothelial cells is mediated primarily by LFA-1. It is not totally true because it is partially mediated as observed in the fig 2F. So authors should change their conclusion and precise that the involvement is partially mediated by LFA-1.

Response: We will rephrase the result section in the revised paper.

8) Fig S5B-C-D and S7: The authors talk about tumor cell death. But they are analyzing Ca2+ influx in vitro so it is a little bit different from the cell death. I'm wondering how the cell death is mesured espacially in the fig S5D and S7?

Response: Under microscopes, apoptosis can be easily recognized by the appearance of blebs. We will include videos in the revised paper.

9) Fig 4H and lines 232-233 : the authors conclude that « damage to tumor cells is dependent on the engagement of DNAM-1 on NK cells ». There is any experiment performed to affirm this point so the authors cannot maintain this conclusion. First, the authors only analyzed Ca2+ influx at a specific time point. So this result only show that Nectin-5 and/or Nectin-2 expressed by B16F10 is involved in the Ca2+ influx following NK cell contact but there is any data on DNAM-1 contribution. So, the role on the NK cells and specifically DNAM-1+ NK cells have not been adressed here. To answer to that question, the author have to perform in vivo model of engrafted WT vs Necl-5/2 ko B16F10 in a WT vs DNAM1 deficient NK cells mouse model to ascertain the contribution of Necl-5/2-DNAM-1 on NK cells. Moreover, survival curve and bioluminescent experiments would be very appreciated.

Response: We have shown the data with Necl-5/Nectin-2-deficientB16F10 cells in Fig. S7. I understand the importance of the experiment with the DNAM-1-deficient mice. But the introduction of another knockout mouse line cannot be performed easily. Instead, we will tone down the conclusion on the requirement of signaling from Necl-5/Nectin-2 to DNAM-1.

10) Lines 253-254 : the authors talk about tumor apoptosis but they are looking at Ca2+ influx. So, they should change their conclusion or show killing experiment.

Response: In Figure S7, we have shown that the sustained Ca2+ influx is a useful surrogate marker for apoptosis. We will include this information explicitly in the revised paper.

11) Fig 6 : the authors conclude that the trombin dependent shedding of Necl-5 causes evasion of NK cells surveillance. Moreover, all experiments are correlations and do not implicate in the same experiment Necl-5, DNAM-1+ NK cells and trombin or anti-coagulation factors. So, as in the comment #9, to adress this point, the authors should inject WT vs Necl-5 deficient B16F10-Akaluc into WT vs NK cell depleted mice and monitor the bioluminescence of the tumor cells within 24h following injection of anti coagulation factors as in the fig 6H. Moreover, the monitoring of the survival curve and the number of the lung metastasis would be also very important and informative to really answer to this point.

Response: We will try the requested experiments during revision.

\*Minor points***

1) Fig 2E: The authors assess the involvement of LFA-1 and MAC-1 on the NK cells attachement to the the pulmonary endothelial cells. But there is other adhesion molecules that are known to be expressed by NK cells as for example CR4 (CD11c/CD18). So, the attachement of NK cells could be also due to this molecule.

Response: We agree. The text will be modified to suggest the involvement of other adhesion molecules.

2) Lines 190 to 197 : Authors should put this methodology part in the « material and method » in order to be more clear on the message they want to deliver.

Response: We will modify the text according to the suggestion.

3) line 228 : There is any hypothesis or explanation regarding the use of Necl5/Necl2 deficient B16F10. Why authors decided to go and explore this pathway ? Authors could add some transition sentence and explanation to help readers.

Response: We will refer to previous papers suggesting the role of DNAM-1 and its ligands, Necl-5 and nectin-2.

4) The author could performed the same experiment as in Fig S7D and assessed ERK activation of DNAM+ vs - NK cells against WT vs Necl-5/Necl-2KO R-GEKO B16F10 cells.

Response: We will try the suggested experiments.

5) Line 283 : Thanks to reformulate the sentence. Check the firgures associated with the text.

Response: We will correct this error. The figures will be Fig. 5E and 5F.

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

The authors use in vivo imaging techniques to investigate the killing of lung metastasis by NK cells. They demonstrate that the cleavage of CD155 may result in resistance of killing by NK cells and suggest that this could be an immune evasion mechanism of metastatic tumor cells.

Overall, the subject is highly relevant, and the in vivo imaging is an interesting and highly relevant technique. However, the message, that tumor cells escape the killing by NK cells by cleavage of CD155 is interesting, but not yet fully supported by the data.

\*Major comments:***

1. • Figure 6: To support their main claim the authors would need to transfect the tumor cells with a CD155 mutant, which cannot be cleaved by Thrombin and show that these tumor cells can no longer escape NK cell-mediated killing. This experiment is straight forward and feasible. Another important experiment along this line would be the use the CD155/CD112 deficient tumor cells (Which the authors use in figure 4) in the experiments shown in figure 1. One would expect that tumor control by NK cells within the first 24h is absent when using these tumor cells.* Response: We previously made five CD155 mutants, which could be resistant to thrombin-mediated cleavage, and re-expressed in CD155/CD112 deficient tumor cells. However, none of the mutants was not killed by NK cells both in vivo and in vitro. It appears that the potential thrombin-cleavage site(s) reside in the recognition site by DNAM-1. We will include this observation in the discussion.

• Figure 5: The demonstration that ERK is activated in this in vivo setting is novel. However, ERK activation is not DNAM-1 specific and the ERK inhibitor is significantly less effective that the depletion of NK cells. Therefore, the relevance of these data to the main message of the manuscript is unclear and the figure could be omitted.*

Response: We agree that the modest effect of MEKi implies that ERK activation is dispensable for NK activation. However, ERK activation is a useful marker of NK cell activation. The data shown here vividly show the timing of NK cell activation and following tumor cell killing. Because the in vivo dynamics of NK cell activation and tumor cell killing is the most important message of this work, we wish to show this data.

• In general, the issue of NK cell exhaustion should be addressed in more detail. The experiments do not address serial killing activity of NK cells and more data is needed to show that it is not an exhaustion of NK cells but the cleavage of CD155 from the tumor cells that prevents further killing.*

Response: We believe, Fig. 5G clearly shows that NK cells are not exhausted 24 hours after tumor cell injection.

**Minor comments:**

• Figure 1C: The relevance of this experiment needs to be better explained.*

Response: We will rephrase the result section in the revised paper.

• Figure 3A: What does SHG stand for?*

Response: It is shown in line 625, M&M section. We will show the statement that SHG stands for second harmonic generation channel in the figure legend.

• Figure 3: Please add a statistical analysis for these experiments.*

Response: We will include P values in the revised paper.

• Figure 4: The use of the caspase-3 and the calcium sensors may detect different cytotoxic mechanisms used by the NK cells. While caspase-3 can be activated by death receptor and perforin/granzyme B mediated killing, the calcium sensor may report mostly on perforin mediated membrane damage. These killing mechanisms have different kinetics and are differentially used during serial killing by NK cells. This should be addressed (at least in the discussion).*

Response: We thank this invaluable comment. We will include this discussion.

Reviewer #2 (Significance (Required)):

Investigating the in vivo cytotoxicity of NK cells against tumor cells by using live imaging technologies is highly relevant for the understanding of the dynamic relationship between tumor and killer cells. Therefore, the subject of this manuscript and the technologies used are very relevant, as in vivo killing activities do not always translate to the in vivo setting.

Response: We thank the reviewer for the favorable comment.

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

\*Summary***

Ichise et al., present a solid work describing the modality and time frame of action of NK control over seeding metastatic cells within the lung vasculature. Th authors use a variety of technique able to dissect how NK patrol lung vasculature, that they interact with cancer cells as they interact with the endothelial cells and they activate a ERK dependent activation leading to calcium influx in cancer cells leading to their death. The data support the notion that this NK control occur over an early time frame, 4h after cancer cells arrival and is mediated by Necl expression on cancer cells. After this time point cancer cells show a thrombin dependent loss of Necl expression on their surface and therefore become resistant to NK control.

\*Comments:***

The data presented are supporting the conclusions. This work utilizes a variety of elegant strategy combining reporter strategy with in vivo imaging to assess the phenomenon of interaction, ERK activation, Calcium Inflax and Apoptosis activation directly in the lung.

In term of experiments, I found the work thorough and complete.

The data a presented well overall and the statistics seems adequate.

I only have few suggestions:

Supplementary Figure S3, show the use of antiLy6G to deplete neutrophils in the lungs of C57BL/6 mice injected with melanoma B16F10 cells. It was recently shown that this antibody is not efficient in depleting neutrophils in this background, but only lead neutrophils to internalise the Ly6G so they cannot be detected by FACS. As shown in Boivin et al 2020 http://doi.org/10.1038/s41467-020-16596-9) neutrophils depletion in C57BL/6 mice can be achieved by using antiGr1 antibody. Therefore, if the authors aim to show this additional control, which I also agree is really good to have, I suggest performing the experiment accordingly to the best-known practice.

Response: We will perform the suggested experiment.

Figure 1E: in the text the experiment is described as 4T1 Akaluc cells were inoculated into the foot pad of BALB/c mice with either control antibody or αAGM1, but the legend states that mice subcutaneously injected with B16 Akaluc cells into footpad.

As B16 melanoma cells are not in BALB/c background, I assume the legend needs to be corrected as the cells should be 4T1, however I wonder if injecting 4T1 breast cancer cells in the footpad could have let to the substantial growth required for lung metastasis without impairing the animal mobility. Could it be that cells where actually injected in the fat pad of the mice and this is just a misspelling in the text?

In this case, the different in the tissue residence NK cells could also potentially explain why 4T1 are not cleared in the fat pad like the B6 cells are in the footpad.

The authors should comment on the difference in the in clearance of the cells at the injection site in Figure 1C VS Figure 1E.

Response: We apology the erratum in the legend.

Figure 1C was performed to examine whether NK cells in the lung could be exhausted or inert 14 days after the inoculation of B16F10 cells. In this experiment, Akaluc-expressing B16F10 cells were inoculated to monitor the bioluminescence for 24 hrs.

In figure 1E, we used Akaluc-expressing 4T1 breast cancer cells because 4T1 cells inoculated into footpad can be spontaneously metastasized to the lung (Kamioka et al., 2017). We observed the bioluminescence of 4T1 cells in the lung for up to 20 days.

Ref: Kamioka, Y., Takakura, K., Sumiyama, K., and Matsuda, M. (2017). Intravital FRET imaging reveals osteopontin-mediated polymorphonuclear leukocyte activation by tumor cell emboli. Cancer Sci 108, 226-235.

Reviewer #3 (Significance (Required)):

The present work is highly relevant to the field of cancer metastasis. While it is known that NK are responsible for the first line of defence against metastatic seeding, most of the studies focuses on how they are suppressed or influenced by other immune cells. The present study provides a very accurate description of their mechanism of action, how they depend in the interaction with the endothelial cells and highlight the novel aspect of thrombin in inducing cancer cells NK resistance. What cause thrombin activation is the next relevant question, by in my opinion this study is complete and important.

My field of expertise is cancer metastasis and their interaction with the immune system and I personally enjoy very much reading this work.

Response: We thank the reviewer for favorable comments and appreciate the effort to evaluate our work.

SciScore for 10.1101/2021.03.15.435440: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.